Sample records for time required learning

  1. Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.

    PubMed

    Saito, Priscila T M; Nakamura, Rodrigo Y M; Amorim, Willian P; Papa, João P; de Rezende, Pedro J; Falcão, Alexandre X

    2015-01-01

    Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.

  2. Student Perceptions of a Flipped Pharmacotherapy Course

    PubMed Central

    Khanova, Julia; McLaughlin, Jacqueline E.; Rhoney, Denise H.; Roth, Mary T.

    2015-01-01

    Objective. To evaluate student perception of the flipped classroom redesign of a required pharmacotherapy course. Design. Key foundational content was packaged into interactive, text-based online modules for self-paced learning prior to class. Class time was used for active and applied—but primarily case-based—learning. Assessment. For students with a strong preference for traditional lecture learning, the perception of the learning experience was negatively affected by the flipped course design. Module length and time required to complete preclass preparation were the most frequently cited impediments to learning. Students desired instructor-directed reinforcement of independently acquired knowledge to connect foundational knowledge and its application. Conclusion. This study illustrates the challenges and highlights the importance of designing courses to effectively balance time requirements and connect preclass and in-class learning activities. It underscores the crucial role of the instructor in bridging the gap between material learned as independent study and its application. PMID:26839429

  3. Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

    ERIC Educational Resources Information Center

    Warhurst, Russell

    2013-01-01

    Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

  4. Lifelong Learning Organisers: Requirements for Tools for Supporting Episodic and Semantic Learning

    ERIC Educational Resources Information Center

    Vavoula, Giasemi; Sharples, Mike

    2009-01-01

    We propose Lifelong Learning Organisers (LLOs) as tools to support the capturing, organisation and retrieval of personal learning experiences, resources and notes, over a range of learning topics, at different times and places. The paper discusses general requirements for the design of LLOs based on findings from a diary-based study of everyday…

  5. An Empirical Study of Factors Driving the Adoption of Mobile Learning in Omani Higher Education

    ERIC Educational Resources Information Center

    Sarrab, Mohamed; Al Shibli, Ibtisam; Badursha, Nabeela

    2016-01-01

    Mobile learning (M-learning) provides a new learning channel in which learners can access content and just in time information as required irrespective of the time and location. Even though M-learning is fast evolving in many regions of the world, research addressing the driving factors of M-learning adoption is in short supply. This article…

  6. Implementation of a team-based learning course: Work required and perceptions of the teaching team.

    PubMed

    Morris, Jenny

    2016-11-01

    Team-based learning was selected as a strategy to help engage pre-registration undergraduate nursing students in a second-year evidence-informed decision making course. To detail the preparatory work required to deliver a team-based learning course; and to explore the perceptions of the teaching team of their first experience using team-based learning. Descriptive evaluation. Information was extracted from a checklist and process document developed by the course leader to document the work required prior to and during implementation. Members of the teaching team were interviewed by a research assistant at the end of the course using a structured interview schedule to explore perceptions of first time implementation. There were nine months between the time the decision was made to use team-based learning and the first day of the course. Approximately 60days were needed to reconfigure the course for team-based learning delivery, develop the knowledge and expertise of the teaching team, and develop and review the resources required for the students and the teaching team. This reduced to around 12days for the subsequent delivery. Interview data indicated that the teaching team were positive about team-based learning, felt prepared for the course delivery and did not identify any major problems during this first implementation. Implementation of team-based learning required time and effort to prepare the course materials and the teaching team. The teaching team felt well prepared, were positive about using team-based learning and did not identify any major difficulties. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

  7. Integration of Temporal and Ordinal Information During Serial Interception Sequence Learning

    PubMed Central

    Gobel, Eric W.; Sanchez, Daniel J.; Reber, Paul J.

    2011-01-01

    The expression of expert motor skills typically involves learning to perform a precisely timed sequence of movements (e.g., language production, music performance, athletic skills). Research examining incidental sequence learning has previously relied on a perceptually-cued task that gives participants exposure to repeating motor sequences but does not require timing of responses for accuracy. Using a novel perceptual-motor sequence learning task, learning a precisely timed cued sequence of motor actions is shown to occur without explicit instruction. Participants learned a repeating sequence through practice and showed sequence-specific knowledge via a performance decrement when switched to an unfamiliar sequence. In a second experiment, the integration of representation of action order and timing sequence knowledge was examined. When either action order or timing sequence information was selectively disrupted, performance was reduced to levels similar to completely novel sequences. Unlike prior sequence-learning research that has found timing information to be secondary to learning action sequences, when the task demands require accurate action and timing information, an integrated representation of these types of information is acquired. These results provide the first evidence for incidental learning of fully integrated action and timing sequence information in the absence of an independent representation of action order, and suggest that this integrative mechanism may play a material role in the acquisition of complex motor skills. PMID:21417511

  8. Increased Learning Time under Stimulus-Funded School Improvement Grants: High Hopes, Varied Implementation

    ERIC Educational Resources Information Center

    McMurrer, Jennifer

    2012-01-01

    Research has long suggested that significantly increasing quality time in school for teaching and learning can have a positive impact on student achievement. Recognizing this connection, federal guidance requires low-performing schools to increase student learning time if they are implementing two popular reform models using school improvement…

  9. Active learning reduces annotation time for clinical concept extraction.

    PubMed

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Learning Science by Constructing Models: Can Dragoon Increase Learning without Increasing the Time Required?

    ERIC Educational Resources Information Center

    VanLehn, Kurt; Chung, Greg; Grover, Sachin; Madni, Ayesha; Wetzel, Jon

    2016-01-01

    A common hypothesis is that students will more deeply understand dynamic systems and other complex phenomena if they construct computational models of them. Attempts to demonstrate the advantages of model construction have been stymied by the long time required for students to acquire skill in model construction. In order to make model…

  11. Improving Learning with the Critical Thinking Paradigm: MIBOLC Modules A and B

    DTIC Science & Technology

    2009-02-06

    Model encourages more active learning by requiring much of the learning material to be read prior to classroom instruction, and allotting more time to...for mental interaction with content Rote memorization Multiple Choice exams/quizzes Lower level of intensity in course work Active ... Learning Engaged Lecture Requires mental interaction with content Close reading to understand essential ideas Exams/Quizzes reflective of

  12. Evaluation of the Massachusetts Expanded Learning Time (ELT) Initiative. Year Five Final Report: 2010-2011. Executive Summary

    ERIC Educational Resources Information Center

    Checkoway, Amy; Gamse, Beth; Velez, Melissa; Caven, Meghan; de la Cruz, Rodolfo; Donoghue, Nathaniel; Kliorys, Kristina; Linkow, Tamara; Luck, Rachel; Sahni, Sarah; Woodford, Michelle

    2012-01-01

    The Massachusetts Expanded Learning Time (ELT) initiative was established in 2005 with planning grants that allowed a limited number of schools to explore a redesign of their respective schedules and add time to their day or year. Participating schools are required to expand learning time by at least 300 hours per academic year to improve student…

  13. Evaluation of the Massachusetts Expanded Learning Time (ELT) Initiative. Year Five Final Report: 2010-2011. Volume I

    ERIC Educational Resources Information Center

    Checkoway, Amy; Gamse, Beth; Velez, Melissa; Caven, Meghan; de la Cruz, Rodolfo; Donoghue, Nathaniel; Kliorys, Kristina; Linkow, Tamara; Luck, Rachel; Sahni, Sarah; Woodford, Michelle

    2012-01-01

    The Massachusetts Expanded Learning Time (ELT) initiative was established in 2005 with planning grants that allowed a limited number of schools to explore a redesign of their respective schedules and add time to their day or year. Participating schools are required to expand learning time by at least 300 hours per academic year to improve student…

  14. The Lie of Online Learning.

    ERIC Educational Resources Information Center

    Zielinski, Dave

    2000-01-01

    Managers look at online training as an activity that should be done "off time" whereas employees still think of it as something to be done during working hours. No valid study has shown that online delivery reduces learning time. A better understanding of learning needs must be considered before requiring online training. (JOW)

  15. The effectiveness of snow cube throwing learning model based on exploration

    NASA Astrophysics Data System (ADS)

    Sari, Nenden Mutiara

    2017-08-01

    This study aimed to know the effectiveness of Snow Cube Throwing (SCT) and Cooperative Model in Exploration-Based Math Learning in terms of the time required to complete the teaching materials and student engagement. This study was quasi-experimental research was conducted at SMPN 5 Cimahi, Indonesia. All student in grade VIII SMPN 5 Cimahi which consists of 382 students is used as population. The sample consists of two classes which had been chosen randomly with purposive sampling. First experiment class consists of 38 students and the second experiment class consists of 38 students. Observation sheet was used to observe the time required to complete the teaching materials and record the number of students involved in each meeting. The data obtained was analyzed by independent sample-t test and used the chart. The results of this study: SCT learning model based on exploration are more effective than cooperative learning models based on exploration in terms of the time required to complete teaching materials based on exploration and student engagement.

  16. Listening as a Method of Learning a Foreign Language at the Non-Language Faculty of the University

    ERIC Educational Resources Information Center

    Kondrateva, Irina G.; Safina, Minnisa S.; Valeev, Agzam A.

    2016-01-01

    Learning a foreign language is becoming an increasingly important with Russia's integration into the world community. In this regard, increased requirements for the educational process and the development of new innovative teaching methods meet the requirements of the time. One of the important aspects of learning a foreign language is listening…

  17. A requirement for memory retrieval during and after long-term extinction learning

    PubMed Central

    Ouyang, Ming; Thomas, Steven A.

    2005-01-01

    Current learning theories are based on the idea that learning is driven by the difference between expectations and experience (the delta rule). In extinction, one learns that certain expectations no longer apply. Here, we test the potential validity of the delta rule by manipulating memory retrieval (and thus expectations) during extinction learning. Adrenergic signaling is critical for the time-limited retrieval (but not acquisition or consolidation) of contextual fear. Using genetic and pharmacologic approaches to manipulate adrenergic signaling, we find that long-term extinction requires memory retrieval but not conditioned responding. Identical manipulations of the adrenergic system that do not affect memory retrieval do not alter extinction. The results provide substantial support for the delta rule of learning theory. In addition, the timing over which extinction is sensitive to adrenergic manipulation suggests a model whereby memory retrieval occurs during, and several hours after, extinction learning to consolidate long-term extinction memory. PMID:15947076

  18. Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials.

    PubMed

    Wilmes, Katharina Anna; Schleimer, Jan-Hendrik; Schreiber, Susanne

    2017-04-01

    Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the required temporal precision. In contrast to classical spike-timing dependent plasticity of excitatory synapses, the proposed inhibitory learning mechanism does not necessarily require the definition of an upper bound of synaptic weights because of its tendency to self-terminate once annihilation of bAPs has been reached. Our study provides a functional context in which one of the many time-dependent learning rules that have been observed experimentally - specifically, a learning rule with anti-Hebbian shape - is assigned a relevant role for inhibitory synapses. Moreover, the described mechanism is compatible with an upregulation of excitatory plasticity by disinhibition. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  19. Mobile Authoring of Open Educational Resources as Reusable Learning Objects

    ERIC Educational Resources Information Center

    Kinshuk; Jesse, Ryan

    2013-01-01

    E-learning technologies have allowed authoring and playback of standardized reusable learning objects (RLO) for several years. Effective mobile learning requires similar functionality at both design time and runtime. Mobile devices can play RLO using applications like SMILE, mobile access to a learning management system (LMS), or other systems…

  20. Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

    PubMed

    Edwards, Ann L; Dawson, Michael R; Hebert, Jacqueline S; Sherstan, Craig; Sutton, Richard S; Chan, K Ming; Pilarski, Patrick M

    2016-10-01

    Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Case series study. We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses. © The International Society for Prosthetics and Orthotics 2015.

  1. Effective Informal Learning: Considerations for the Workplace.

    ERIC Educational Resources Information Center

    Yannie, Mark

    2002-01-01

    Offers practical advice for learning more effectively on the job. Highlights include types of communication, including written and verbal; informal learning; a work environment that is conducive to informal learning, including organizational culture, job responsibilities, performance requirements, time and scheduling factors, and career stage;…

  2. Promoting the School Learning Processes: Principals as Learning Boundary Spanners

    ERIC Educational Resources Information Center

    Benoliel, Pascale; Schechter, Chen

    2017-01-01

    Purpose: The ongoing challenge to sustain school learning and improvement requires schools to explore new ways, and at the same time exploit previous experience. The purpose of this paper is to attempt to expand the knowledge of mechanisms that can facilitate school learning processes by proposing boundary activities and learning mechanisms in…

  3. E-Learning System Overview Based on Semantic Web

    ERIC Educational Resources Information Center

    Alsultanny, Yas A.

    2006-01-01

    The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. e-Learning is efficient task relevant and just-in-time learning grown from the learning requirements of the new dynamically changing, distributed business world. In this paper we design an e-Learning system…

  4. The basal ganglia is necessary for learning spectral, but not temporal features of birdsong

    PubMed Central

    Ali, Farhan; Fantana, Antoniu L.; Burak, Yoram; Ölveczky, Bence P.

    2013-01-01

    Executing a motor skill requires the brain to control which muscles to activate at what times. How these aspects of control - motor implementation and timing - are acquired, and whether the learning processes underlying them differ, is not well understood. To address this we used a reinforcement learning paradigm to independently manipulate both spectral and temporal features of birdsong, a complex learned motor sequence, while recording and perturbing activity in underlying circuits. Our results uncovered a striking dissociation in how neural circuits underlie learning in the two domains. The basal ganglia was required for modifying spectral, but not temporal structure. This functional dissociation extended to the descending motor pathway, where recordings from a premotor cortex analogue nucleus reflected changes to temporal, but not spectral structure. Our results reveal a strategy in which the nervous system employs different and largely independent circuits to learn distinct aspects of a motor skill. PMID:24075977

  5. Intellectual Innovation: A Paradigm Shift in Workforce Development

    DTIC Science & Technology

    2016-08-01

    varying learning abilities and disabilities , and require vary­ ing lengths of time to learn and Although experienced employees need less training...training courses or objectives, organizations should develop a tailored plan that focuses on what each employee needs to learn . Time and effort are... learns in a different way, which can include the use of visual and/or audible as well as the hands­on method of instruc­ tion. Employees also have

  6. Why Service-Learning?

    ERIC Educational Resources Information Center

    Speck, Bruce W.

    2001-01-01

    Describes two significant theoretical approaches to service learning (philanthropic and civil) so that professors are aware of two different impulses that inform service learning. In addition, addresses three critical concerns about service learning: it takes too much time and too many resources, it should not be required, and it should be…

  7. A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

    ERIC Educational Resources Information Center

    Tatkovic, Nevenka; Ruzic, Maja

    2004-01-01

    The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

  8. Learning Analytics Platform, towards an Open Scalable Streaming Solution for Education

    ERIC Educational Resources Information Center

    Lewkow, Nicholas; Zimmerman, Neil; Riedesel, Mark; Essa, Alfred

    2015-01-01

    Next generation digital learning environments require delivering "just-in-time feedback" to learners and those who support them. Unlike traditional business intelligence environments, streaming data requires resilient infrastructure that can move data at scale from heterogeneous data sources, process the data quickly for use across…

  9. Time and Effort Required by Persons with Spinal Cord Injury to Learn to Use a Powered Exoskeleton for Assisted Walking

    PubMed Central

    Bryce, Thomas N.; Dijkers, Marcel P.

    2015-01-01

    Background: Powered exoskeletons have been demonstrated as being safe for persons with spinal cord injury (SCI), but little is known about how users learn to manage these devices. Objective: To quantify the time and effort required by persons with SCI to learn to use an exoskeleton for assisted walking. Methods: A convenience sample was enrolled to learn to use the first-generation Ekso powered exoskeleton to walk. Participants were given up to 24 weekly sessions of instruction. Data were collected on assistance level, walking distance and speed, heart rate, perceived exertion, and adverse events. Time and effort was quantified by the number of sessions required for participants to stand up, walk for 30 minutes, and sit down, initially with minimal and subsequently with contact guard assistance. Results: Of 22 enrolled participants, 9 screen-failed, and 7 had complete data. All of these 7 were men; 2 had tetraplegia and 5 had motor-complete injuries. Of these, 5 participants could stand, walk, and sit with contact guard or close supervision assistance, and 2 required minimal to moderate assistance. Walk times ranged from 28 to 94 minutes with average speeds ranging from 0.11 to 0.21 m/s. For all participants, heart rate changes and reported perceived exertion were consistent with light to moderate exercise. Conclusion: This study provides preliminary evidence that persons with neurological weakness due to SCI can learn to walk with little or no assistance and light to somewhat hard perceived exertion using a powered exoskeleton. Persons with different severities of injury, including those with motor complete C7 tetraplegia and motor incomplete C4 tetraplegia, may be able to learn to use this device. PMID:26364280

  10. Time and Effort Required by Persons with Spinal Cord Injury to Learn to Use a Powered Exoskeleton for Assisted Walking.

    PubMed

    Kozlowski, Allan J; Bryce, Thomas N; Dijkers, Marcel P

    2015-01-01

    Powered exoskeletons have been demonstrated as being safe for persons with spinal cord injury (SCI), but little is known about how users learn to manage these devices. To quantify the time and effort required by persons with SCI to learn to use an exoskeleton for assisted walking. A convenience sample was enrolled to learn to use the first-generation Ekso powered exoskeleton to walk. Participants were given up to 24 weekly sessions of instruction. Data were collected on assistance level, walking distance and speed, heart rate, perceived exertion, and adverse events. Time and effort was quantified by the number of sessions required for participants to stand up, walk for 30 minutes, and sit down, initially with minimal and subsequently with contact guard assistance. Of 22 enrolled participants, 9 screen-failed, and 7 had complete data. All of these 7 were men; 2 had tetraplegia and 5 had motor-complete injuries. Of these, 5 participants could stand, walk, and sit with contact guard or close supervision assistance, and 2 required minimal to moderate assistance. Walk times ranged from 28 to 94 minutes with average speeds ranging from 0.11 to 0.21 m/s. For all participants, heart rate changes and reported perceived exertion were consistent with light to moderate exercise. This study provides preliminary evidence that persons with neurological weakness due to SCI can learn to walk with little or no assistance and light to somewhat hard perceived exertion using a powered exoskeleton. Persons with different severities of injury, including those with motor complete C7 tetraplegia and motor incomplete C4 tetraplegia, may be able to learn to use this device.

  11. Digging Deeper: Professional Learning Can Go beyond the Basics to Reach Underserved Students

    ERIC Educational Resources Information Center

    Gleason, Sonia Caus

    2010-01-01

    Consistent, excellent teaching is the single greatest factor in improving student achievement over time. School leadership is the second. Excellent teaching and strong leadership require deliberate, ongoing professional learning. In working with high-poverty school systems over time, the following basics emerge: (1) time; (2) content; (3)…

  12. Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

    PubMed Central

    Jie, Biao; Liu, Mingxia; Liu, Jun

    2016-01-01

    Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313

  13. Learner-Focused Evaluation Cycles: Facilitating Learning Using Feedforward, Concurrent and Feedback Evaluation

    ERIC Educational Resources Information Center

    Cathcart, Abby; Greer, Dominique; Neale, Larry

    2014-01-01

    There is a growing trend to offer students learning opportunities that are flexible, innovative and engaging. As educators embrace student-centred agile teaching and learning methodologies, which require continuous reflection and adaptation, the need to evaluate students' learning in a timely manner has become more pressing. Conventional…

  14. Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    PubMed

    Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

    2015-02-01

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

  15. Learning from Transitioning to New Technology That Supports Online and Blended Learning: A Case Study

    ERIC Educational Resources Information Center

    Lock, Jennifer; Johnson, Carol

    2017-01-01

    Transitioning from one technology to another within educational institutions is complex and multi-faceted, and requires time. Such a transition involves more than making the new technology available for use. It requires knowing the people involved, designing differentiated support structures, and integrating various resources to meet their…

  16. Blending Communities and Team-Based Learning in a Programming Course

    ERIC Educational Resources Information Center

    Cabrera, Ignacio; Villalon, Jorge; Chavez, Jorge

    2017-01-01

    In recent years, engineering education teachers have needed to incorporate technology-supported collaboration to enhance learning. Implementing these activities requires course redesign, which must be meticulous for their full potential to be reached. This can require a lot of work for first time users, which can be a barrier to implementation.…

  17. It Takes Time and Experience to Learn How to Interpret Gaze in Mentalistic Terms

    ERIC Educational Resources Information Center

    Leavens, David A.

    2006-01-01

    What capabilities are required for an organism to evince an "explicit" understanding of gaze as a mentalistic phenomenon? One possibility is that mentalistic interpretations of gaze, like concepts of unseen, supernatural beings, are culturally-specific concepts, acquired through cultural learning. These abstract concepts may either require a…

  18. Critical Issues for E-Learning Delivery: What May Seem Obvious Is Not Always Put into Practice

    ERIC Educational Resources Information Center

    McPherson, M.A.; Nunes, J.M.

    2008-01-01

    The successful adoption of information and communication technology to enhance learning can be very challenging, requiring a complex blend of technological, pedagogical and organizational components, which may at times require the resolution of contradictory demands and conflicting needs. The research reported in this paper investigated and…

  19. The flip side of traditional nursing education: A literature review.

    PubMed

    Ward, Maria; Knowlton, Mary C; Laney, Candice W

    2018-03-01

    The flipped classroom (FC) andragogy purports an improvement of critical thinking and problem-solving skills in students. This literature review explores fourteen research studies and discusses outcome measures reported on the effectiveness of using this teaching modality. Students described the learning activities during the classroom meeting times as valuable and indicated the interaction and engagement were beneficial to their learning. Many students opined an increased comprehension of the subject matter. Overall, the FC required more work on the part of the students and the faculty, and the majority of students preferred the traditional classroom (TC) passive method of learning over the FC active learning andragogy as a result of the substantial time commitment required for preparation necessitated by the FC. Five of the fourteen studies evaluated student learning outcome measures; four studies showed an improvement in the FC environment compared to the TC and one reported the FC was at least as effective as the TC. Further studies with quantifiable outcome measures are required to determine the effectiveness of a FC on critical thinking and problem-solving skills of nursing students. Copyright © 2018. Published by Elsevier Ltd.

  20. Implementing Small-Group Instruction: Insights from Successful Practitioners.

    ERIC Educational Resources Information Center

    Cooper, James L.; MacGregor, Jean; Smith, Karl A.; Robinson, Pamela

    2000-01-01

    College faculty who have successfully implemented small-group instruction address common concerns such as: reduced content coverage, reduced amount of learning, need for prerequisite learning, importance of solitary learning, colleagues' concerns, student resistance, logistics, evaluation, use of teaching assistants, and time requirements. (DB)

  1. Adding Learning to Knowledge-Based Systems: Taking the "Artificial" Out of AI

    Treesearch

    Daniel L. Schmoldt

    1997-01-01

    Both, knowledge-based systems (KBS) development and maintenance require time-consuming analysis of domain knowledge. Where example cases exist, KBS can be built, and later updated, by incorporating learning capabilities into their architecture. This applies to both supervised and unsupervised learning scenarios. In this paper, the important issues for learning systems-...

  2. Sensorimotor Distractions When Learning with Mobile Phones On-the-Move

    ERIC Educational Resources Information Center

    Castellano, Soledad; Arnedillo-Sánchez, Inmaculada

    2016-01-01

    This paper presents a discussion on potential conflicts originated by sensorimotor distractions when learning with mobile phones on-the-move. While research in mobile learning points to the possibility of everywhere, all the time learning; research in the area suggests that tasks performed while on-the-move predominantly require low cognitive…

  3. A Model for an Integrated Learning Community.

    ERIC Educational Resources Information Center

    Van Sickle, Shaila; Mehs, Doreen

    Fort Lewis College (Colorado) developed a 17 credit, multidisciplinary learning program for first-time freshmen. The Integrated Learning Program (ILP) meets several of the college's general education requirements, is issue-oriented, and is taught by a team of five faculty members. The goals of the program include getting students to learn how to…

  4. Robotic partial nephrectomy shortens warm ischemia time, reducing suturing time kinetics even for an experienced laparoscopic surgeon: a comparative analysis.

    PubMed

    Faria, Eliney F; Caputo, Peter A; Wood, Christopher G; Karam, Jose A; Nogueras-González, Graciela M; Matin, Surena F

    2014-02-01

    Laparoscopic and robotic partial nephrectomy (LPN and RPN) are strongly related to influence of tumor complexity and learning curve. We analyzed a consecutive experience between RPN and LPN to discern if warm ischemia time (WIT) is in fact improved while accounting for these two confounding variables and if so by which particular aspect of WIT. This is a retrospective analysis of consecutive procedures performed by a single surgeon between 2002-2008 (LPN) and 2008-2012 (RPN). Specifically, individual steps, including tumor excision, suturing of intrarenal defect, and parenchyma, were recorded at the time of surgery. Multivariate and univariate analyzes were used to evaluate influence of learning curve, tumor complexity, and time kinetics of individual steps during WIT, to determine their influence in WIT. Additionally, we considered the effect of RPN on the learning curve. A total of 146 LPNs and 137 RPNs were included. Considering renal function, WIT, suturing time, renorrhaphy time were found statistically significant differences in favor of RPN (p < 0.05). In the univariate analysis, surgical procedure, learning curve, clinical tumor size, and RENAL nephrometry score were statistically significant predictors for WIT (p < 0.05). RPN decreased the WIT on average by approximately 7 min compared to LPN even when adjusting for learning curve, tumor complexity, and both together (p < 0.001). We found RPN was associated with a shorter WIT when controlling for influence of the learning curve and tumor complexity. The time required for tumor excision was not shortened but the time required for suturing steps was significantly shortened.

  5. ACHP | ACHP Native Youth Program

    Science.gov Websites

    distance learning. RESOURCES Native Youth in Historic Preservation Newsletter Historic Preservation ?" in the E-Learning Course Catalogue section: a one-time registration is required for this FREE

  6. Development of Novel Magnetic Metal Oxide Films and Carbon Nanotube Materials for Magnetic Device Applications

    DTIC Science & Technology

    2015-01-23

    From these studies we learned that nano wires of Fe grown in the lumens of multi-walled carbon nanotubes ( MWCNTs ) required four times higher 35...studies we learned that nano wires of Fe grown in the lumens of multi-walled carbon nanotubes ( MWCNTs ) required four times higher magnetic field...properties of nano-metric Fe thin films on 325 MgO(100) and nano wires of Fe prepared in the lumens of MWCNTs using magnetron DC-sputtering were studied

  7. The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task.

    PubMed

    Du, Yue; Clark, Jane E

    2018-05-03

    This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

  8. Active learning based segmentation of Crohns disease from abdominal MRI.

    PubMed

    Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

    2016-05-01

    This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. [CMACPAR an modified parallel neuro-controller for control processes].

    PubMed

    Ramos, E; Surós, R

    1999-01-01

    CMACPAR is a Parallel Neurocontroller oriented to real time systems as for example Control Processes. Its characteristics are mainly a fast learning algorithm, a reduced number of calculations, great generalization capacity, local learning and intrinsic parallelism. This type of neurocontroller is used in real time applications required by refineries, hydroelectric centers, factories, etc. In this work we present the analysis and the parallel implementation of a modified scheme of the Cerebellar Model CMAC for the n-dimensional space projection using a mean granularity parallel neurocontroller. The proposed memory management allows for a significant memory reduction in training time and required memory size.

  10. Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression.

    PubMed

    Gijsberts, Arjan; Metta, Giorgio

    2013-05-01

    Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context. Rather than developing a novel algorithm from the ground up, the method is based on the thoroughly studied Gaussian Process Regression algorithm, therefore ensuring a solid theoretical foundation. Non-linearity and a bounded update complexity are achieved simultaneously by means of a finite dimensional random feature mapping that approximates a kernel function. As a result, the computational cost for each update remains constant over time. Finally, algorithmic simplicity and support for automated hyperparameter optimization ensures convenience when employed in practice. Empirical validation on a number of synthetic and real-life learning problems confirms that the performance of Incremental Sparse Spectrum Gaussian Process Regression is superior with respect to the popular Locally Weighted Projection Regression, while computational requirements are found to be significantly lower. The method is therefore particularly suited for learning with real-time constraints or when computational resources are limited. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Totally laparoscopic aortic surgery: comparison of the apron and retrocolic techniques in a porcine model.

    PubMed

    Huynh, Hai; Elkouri, Stephane; Beaudoin, Nathalie; Bruneau, Luc; Guimond, Cathie; Daniel, Véronique; Blair, Jean-François

    2007-01-01

    This study evaluated the learning curve for a second-year general surgery resident and compared 2 totally laparoscopic aortic surgery techniques in 10 pigs: the transretroperitoneal apron approach and the transperitoneal retrocolic approach. Five end points were compared: success rate, percentage of conversion, time required, laparoscopic anastomosis quality, and learning curve. The first 3 interventions required an open conversion. The last 7 were done without complications. Mean dissection time was significantly higher with the apron approach compared with the retrocolic approach. The total times for operation, clamping, and arteriotomy time were similar. All laparoscopic anastomoses were patent and without stenosis. The initial learning curve for laparoscopic anastomosis was relatively short for a second-year surgery resident. Both techniques resulted in satisfactory exposure of the aorta and similar mean operative and clamping time. Training on an ex vivo laparoscopic box trainer and on an animal model seems to be complementary to decrease laparoscopic anastomosis completion time.

  12. An E-Portfolio to Enhance Sustainable Vocabulary Learning in English

    ERIC Educational Resources Information Center

    Tanaka, Hiroya; Yonesaka, Suzanne M.; Ueno, Yukie

    2015-01-01

    Vocabulary is an area that requires foreign language learners to work independently and continuously both in and out of class. In the Japanese EFL setting, for example, more than 97% of the population experiences approximately six years of English education at secondary school during which time they are required to learn approximately 3,000 words…

  13. A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

    PubMed

    Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

    2017-01-01

    The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

  14. Designing Collaborative E-Learning Environments Based upon Semantic Wiki: From Design Models to Application Scenarios

    ERIC Educational Resources Information Center

    Li, Yanyan; Dong, Mingkai; Huang, Ronghuai

    2011-01-01

    The knowledge society requires life-long learning and flexible learning environment that enables fast, just-in-time and relevant learning, aiding the development of communities of knowledge, linking learners and practitioners with experts. Based upon semantic wiki, a combination of wiki and Semantic Web technology, this paper designs and develops…

  15. Learner Self-Regulation and Web 2.0 Tools Management in Personal Learning Environment

    ERIC Educational Resources Information Center

    Yen, Cherng-Jyh; Tu, Chih-Hsiung; Sujo-Montes, Laura E.; Armfield, Shadow W. J.; Chan, Junn-Yih

    2013-01-01

    Web 2.0 technology integration requires a higher level of self-regulated learning skills to create a Personal Learning Environment (PLE). This study examined each of the four aspects of learner self-regulation in online learning (i.e., environment structuring, goal setting, time management, & task strategies) as the predictor for level of…

  16. Leadership collaboration during health reform: an action learning approach with an interagency group of executives in Tasmania.

    PubMed

    Harpur, Siobhan

    2012-05-01

    To use an action learning approach to encourage a group of executive leaders, responsible for the implementation of a state health reform agenda, to consider the leadership required to drive improvement in healthcare services. Based on an assertion that knowledge is co-produced and that deliberative and structured conversation can be a mechanism to drive change, an action learning approach was used to facilitate an interagency group of executive leaders, responsible for the implementation of a state health reform agenda, who were encouraged to consider the leadership required to drive improvement in healthcare services. It was difficult to assert how the group contributed specifically to the implementation of the health reform agenda but individuals gained insights and there was informal resolution of institutional tensions and differences. The method may provide new knowledge to the reform process over time. Getting the participants together was challenging, which may reflect the reality of time-poor executives, or a low commitment to giving time to structured and deliberative informal dialogue. Further work is required to test this thesis and the action learning approach with other parts of healthcare workforce.

  17. Mastery learning for health professionals using technology-enhanced simulation: a systematic review and meta-analysis.

    PubMed

    Cook, David A; Brydges, Ryan; Zendejas, Benjamin; Hamstra, Stanley J; Hatala, Rose

    2013-08-01

    Competency-based education requires individualization of instruction. Mastery learning, an instructional approach requiring learners to achieve a defined proficiency before proceeding to the next instructional objective, offers one approach to individualization. The authors sought to summarize the quantitative outcomes of mastery learning simulation-based medical education (SBME) in comparison with no intervention and nonmastery instruction, and to determine what features of mastery SBME make it effective. The authors searched MEDLINE, EMBASE, CINAHL, ERIC, PsycINFO, Scopus, key journals, and previous review bibliographies through May 2011. They included original research in any language evaluating mastery SBME, in comparison with any intervention or no intervention, for practicing and student physicians, nurses, and other health professionals. Working in duplicate, they abstracted information on trainees, instructional design (interactivity, feedback, repetitions, and learning time), study design, and outcomes. They identified 82 studies evaluating mastery SBME. In comparison with no intervention, mastery SBME was associated with large effects on skills (41 studies; effect size [ES] 1.29 [95% confidence interval, 1.08-1.50]) and moderate effects on patient outcomes (11 studies; ES 0.73 [95% CI, 0.36-1.10]). In comparison with nonmastery SBME instruction, mastery learning was associated with large benefit in skills (3 studies; effect size 1.17 [95% CI, 0.29-2.05]) but required more time. Pretraining and additional practice improved outcomes but, again, took longer. Studies exploring enhanced feedback and self-regulated learning in the mastery model showed mixed results. Limited evidence suggests that mastery learning SBME is superior to nonmastery instruction but takes more time.

  18. A learning controller for nonrepetitive robotic operation

    NASA Technical Reports Server (NTRS)

    Miller, W. T., III

    1987-01-01

    A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

  19. Temporal and Region-Specific Requirements of αCaMKII in Spatial and Contextual Learning

    PubMed Central

    Achterberg, Katharina G.; Buitendijk, Gabriëlle H.S.; Kool, Martijn J.; Goorden, Susanna M.I.; Post, Laura; Slump, Denise E.; Silva, Alcino J.; van Woerden, Geeske M.

    2014-01-01

    The α isoform of the calcium/calmodulin-dependent protein kinase II (αCaMKII) has been implicated extensively in molecular and cellular mechanisms underlying spatial and contextual learning in a wide variety of species. Germline deletion of Camk2a leads to severe deficits in spatial and contextual learning in mice. However, the temporal and region-specific requirements for αCaMKII have remained largely unexplored. Here, we generated conditional Camk2a mutants to examine the influence of spatially restricted and temporally controlled expression of αCaMKII. Forebrain-specific deletion of the Camk2a gene resulted in severe deficits in water maze and contextual fear learning, whereas mice with deletion restricted to the cerebellum learned normally. Furthermore, we found that temporally controlled deletion of the Camk2a gene in adult mice is as detrimental as germline deletion for learning and synaptic plasticity. Together, we confirm the requirement for αCaMKII in the forebrain, but not the cerebellum, in spatial and contextual learning. Moreover, we highlight the absolute requirement for intact αCaMKII expression at the time of learning. PMID:25143599

  20. Building an Expanded Learning Time and Opportunities School: Principals' Perspectives

    ERIC Educational Resources Information Center

    Malone, Helen Janc

    2011-01-01

    Expanded learning time and opportunities (ELTO) requires a committed school leader who is willing to partner with community-based organizations in order to provide strong academic and enrichment daily experiences for his or her students. This article examines four such leaders and the diverse approaches they took to implement ELTO in their…

  1. Teachers' Learning in School-Based Development

    ERIC Educational Resources Information Center

    Postholm, May Britt; Waege, Kjersti

    2016-01-01

    Background and purpose: Many researchers agree that teachers' learning processes are social and that teachers need to be brought together to learn from each other. Researchers have also stated that intellectual and pedagogical change requires professional development activities that take place over a period of time in school. The purpose of the…

  2. The Domains for the Multi-Criteria Decisions about E-Learning Systems

    ERIC Educational Resources Information Center

    Uysal, Murat Pasa

    2012-01-01

    Developments in computer and information technologies continue to give opportunities for designing advanced E-learning systems while entailing objective and technical evaluation methodologies. Design and development of E-learning systems require time-consuming and labor-intensive processes; therefore any decision about these systems and their…

  3. Flipping Quantitative Classes: A Triple Win

    ERIC Educational Resources Information Center

    Swart, William; Wuensch, Karl L.

    2016-01-01

    In the "flipped" class, students use online materials to learn what is traditionally learned by attending lectures, and class time is used for interactive group learning. A required quantitative business class was taught as a flipped classroom in an attempt to improve student satisfaction in the course and reduce the "transactional…

  4. Time to rethink the neural mechanisms of learning and memory

    PubMed Central

    Gallistel, Charles R.; Balsam, Peter D

    2014-01-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing – dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity which has never been precisely defined. These points are well illustrated by studies showing that temporal relationships between events are rapidly learned-even over long delays- and this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. PMID:24309167

  5. Student Produced Television Programs: The Relationship of Play Theory, Flow Experiences, and Experiential Learning.

    ERIC Educational Resources Information Center

    Koehn, Stephen C.; Lowry, David N.

    Television production is a complicated task. It requires advanced technical skills and abilities, as well as tremendous creative input. It requires an outlying of time by an individual to learn the skills and implement the creative ideas he or she might have for a television show. A study examined the perceptions of 30 students who were highly…

  6. Circadian time-place (or time-route) learning in rats with hippocampal lesions.

    PubMed

    Cole, Emily; Mistlberger, Ralph E; Merza, Devon; Trigiani, Lianne J; Madularu, Dan; Simundic, Amanda; Mumby, Dave G

    2016-12-01

    Circadian time-place learning (TPL) is the ability to remember both the place and biological time of day that a significant event occurred (e.g., food availability). This ability requires that a circadian clock provide phase information (a time tag) to cognitive systems involved in linking representations of an event with spatial reference memory. To date, it is unclear which neuronal substrates are critical in this process, but one candidate structure is the hippocampus (HPC). The HPC is essential for normal performance on tasks that require allocentric spatial memory and exhibits circadian rhythms of gene expression that are sensitive to meal timing. Using a novel TPL training procedure and enriched, multidimensional environment, we trained rats to locate a food reward that varied between two locations relative to time of day. After rats acquired the task, they received either HPC or SHAM lesions and were re-tested. Rats with HPC lesions were initially impaired on the task relative to SHAM rats, but re-attained high scores with continued testing. Probe tests revealed that the rats were not using an alternation strategy or relying on light-dark transitions to locate the food reward. We hypothesize that transient disruption and recovery reflect a switch from HPC-dependent allocentric navigation (learning places) to dorsal striatum-dependent egocentric spatial navigation (learning routes to a location). Whatever the navigation strategy, these results demonstrate that the HPC is not required for rats to find food in different locations using circadian phase as a discriminative cue. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Distance learning perspectives.

    PubMed

    Pandza, Haris; Masic, Izet

    2013-01-01

    The development of modern technology and the Internet has enabled the explosive growth of distance learning. distance learning is a process that is increasingly present in the world. This is the field of education focused on educating students who are not physically present in the traditional classrooms or student's campus. described as a process where the source of information is separated from the students in space and time. If there are situations that require the physical presence of students, such as when a student is required to physically attend the exam, this is called a hybrid form of distance learning. This technology is increasingly used worldwide. The Internet has become the main communication channel for the development of distance learning.

  8. Learning behaviour and preferences of family medicine residents under a flexible academic curriculum

    PubMed Central

    Sy, Alice; Wong, Eric; Boisvert, Leslie

    2014-01-01

    Abstract Objective To determine family medicine residents’ learning behaviour and preferences outside of clinical settings in order to help guide the development of an effective academic program that can maximize their learning. Design Retrospective descriptive analysis of academic learning logs submitted by residents as part of their academic training requirements between 2008 and 2011. Setting London, Ont. Participants All family medicine residents at Western University who had completed their academic program requirements (N = 72) by submitting 300 or more credits (1 credit = 1 hour). Main outcome measures Amount of time spent on various learning modalities, location where the learning took place, resources used for self-study, and the objective of the learning activity. Results A total of 72 residents completed their academic requirements during the study period and logged a total of 25 068 hours of academic learning. Residents chose to spend most of their academic time engaging in self-study (44%), attending staff physicians’ teaching sessions (20%), and participating in conferences, courses, or workshops (12%) and in postgraduate medical education sessions (12%). Textbooks (26%), medical journals (20%), and point-of-care resources (12%) were the 3 most common resources used for self-study. The hospital (32%), residents’ homes (32%), and family medicine clinics (14%) were the most frequently cited locations where academic learning occurred. While all physicians used a variety of educational activities, most residents (67%) chose self-study as their primary method of learning. The topic for academic learning appeared to have some influence on the learning modalities used by residents. Conclusion Residents used a variety of learning modalities and chose self-study over other more traditional modalities (eg, lectures) for most of their academic learning. A successful academic program must take into account residents’ various learning preferences and habits while providing guidance and training in the use of more effective learning methods and resources to maximize educational outcomes. PMID:25551133

  9. Stakeholders' views of shared learning models in general practice: a national survey.

    PubMed

    van de Mortel, Thea; Silberberg, Peter; Ahern, Christine; Pit, Sabrina

    2014-09-01

    The number of learners requiring general practice placements creates supervisory capacity constraints. This research examined how a shared learning model may affect training capacity. The number of learners requiring general practice placements creates supervisory capacity constraints. This research examined how a shared learning model may affect training capacity. A total of 1122 surveys were completed: 75% of learners had participated in shared learning; 25% of multi-level learner practices were not using shared learning. Learners were positive about shared learning (4.3-4.4/5), considering it an effective way to learn that created training capacity (4.1-4.2/5). 79-88% of learners preferred a mixture of one-to-one teaching and shared learning. Supervisors thought shared learning was more cost- and time-efficient, and created training capacity (4.3-4.4/5). Shared learning models have the potential to increase GP training capacity. Many practices are not utilising shared learning, representing capacity loss. Regional training providers should emphasise positive aspects of shared learning to facilitate uptake.

  10. Routines Are the Foundation of Classroom Management

    ERIC Educational Resources Information Center

    Lester, Robin Rawlings; Allanson, Patricia Bolton; Notar, Charles E.

    2017-01-01

    Classroom management is the key to learning. Routines are the foundation of classroom management. Students require structure in their lives. Routines provide that in all of their life from the time they awake until the time they go to bed. Routines in a school and in the classroom provide the environment for learning to take place. The paper is…

  11. EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes

    ERIC Educational Resources Information Center

    Wagh, Aditi; Wilensky, Uri

    2018-01-01

    Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience…

  12. Tips for Reading Tutors = Consejos para los Tutores en Lectura.

    ERIC Educational Resources Information Center

    Department of Education, Washington, DC.

    Reading is the basis for learning and school success. While reading is learned primarily in the classroom, many students need extra time and help. Research shows that tutoring is a great way for individuals and groups outside school to support learning, but effective tutoring requires appropriate training and careful planning. This brochure,…

  13. Student Learning in an Electric Circuit Theory Course: Critical Aspects and Task Design

    ERIC Educational Resources Information Center

    Carstensen, Anna-Karin; Bernhard, Jonte

    2009-01-01

    Understanding time-dependent responses, such as transients, is important in electric circuit theory and other branches of engineering. However, transient response is considered difficult to learn since familiarity with advanced mathematical tools such as Laplace transforms is required. Here, we analyse and describe a novel learning environment…

  14. Everything I Know about Teaching I Learned from Jazz

    ERIC Educational Resources Information Center

    Luquet, Wade

    2015-01-01

    The instant availability of information has changed the paradigm of teaching. Whereas at one time teaching and learning was information being passed, memorized, and repeated, students can now find their own knowledge. Learning now consists of using information in creative ways and requires a shift in how students are taught. This is quite similar…

  15. Maximizing Experiential Learning for Student Success

    ERIC Educational Resources Information Center

    Coker, Jeffrey Scott; Porter, Desiree Jasmine

    2015-01-01

    Several years ago, Elon University set out to better understand experiential learning on campus. At the time, there was a pragmatic need to collect data that would inform revisions to the core curriculum, including an experiential-learning requirement (ELR) that had been in place since 1994. The question was whether it made sense to raise the…

  16. A Glance at Institutional Support for Faculty Teaching in an Online Learning Environment

    ERIC Educational Resources Information Center

    Lion, Robert W.; Stark, Gary

    2010-01-01

    With continued advances in web-based learning, colleges and universities strive to meet the needs and interests of students, faculty, and staff. New instructional technologies have at least one thing in common: the learning curve associated with users becoming adept. Mastery requires significant time and attention. Providing the best quality…

  17. Learning Hebrew by Writing in English

    ERIC Educational Resources Information Center

    Jacobson, Rolf A.

    2011-01-01

    This essay explores a midrange teaching and learning issue regarding the teaching of biblical languages and one strategy for addressing the issue. Seminary students do not yield a great enough return in exchange for the investment they are required to make in learning biblical languages. Students invest great time and money, but they do not learn…

  18. The New Economy, Technology, and Learning Outcomes Assessment

    ERIC Educational Resources Information Center

    Moore, Anne H.

    2007-01-01

    Many observers describe the 21st century as a complex age with new demands for education and new requirements for accountability in teaching and learning to meet society's needs in a new, global economy. At the same time, innovations in teaching and learning and proposals for measuring them often seem disconnected from public and political…

  19. Understanding Presence, Affordance and the Time/Space Dimensions for Language Learning in Virtual Worlds

    ERIC Educational Resources Information Center

    Nocchi, Susanna; Blin, Françoise

    2013-01-01

    Notwithstanding their potential for novel approaches to language teaching and learning, Virtual Worlds (VWs) present numerous technological and pedagogical challenges that require new paradigms if the language learning experience and outcomes are to be successful. In this presentation, we argue that the notions of presence and affordance, together…

  20. Key Words in Instruction. Online Learning and Virtual Schools

    ERIC Educational Resources Information Center

    Lamb, Annette; Callison, Daniel

    2005-01-01

    Online learning and virtual schools allow students to take classes any time and anywhere. These emerging learning environments require school library media specialists to expand their thinking about their resources and services. Creation of a virtual library can provide access to remote materials that enhance the experience of online learners.…

  1. Should Attendance Be Required in Lecture Classrooms in Dental Education? Two Viewpoints: Viewpoint 1: Attendance in the Lecture Classroom Should Be Required and Viewpoint 2: Attendance Should Not Be Required in the Lecture Classroom.

    PubMed

    Cutler, Christopher W; Parise, Mary; Seminario, Ana Lucia; Mendez, Maria Jose Cervantes; Piskorowski, Wilhelm; Silva, Renato

    2016-12-01

    This Point/Counterpoint discusses the long-argued debate over whether lecture attendance in dental school at the predoctoral level should be required. Current educational practice relies heavily on the delivery of content in a traditional lecture style. Viewpoint 1 asserts that attendance should be required for many reasons, including the positive impact that direct contact of students with faculty members and with each other has on learning outcomes. In lectures, students can more easily focus on subject matter that is often difficult to understand. A counter viewpoint argues that required attendance is not necessary and that student engagement is more important than physical classroom attendance. This viewpoint notes that recent technologies support active learning strategies that better engage student participation, fostering independent learning that is not supported in the traditional large lecture classroom and argues that dental education requires assimilation of complex concepts and applying them to patient care, which passing a test does not ensure. The two positions agree that attendance does not guarantee learning and that, with the surge of information technologies, it is more important than ever to teach students how to learn. At this time, research does not show conclusively if attendance in any type of setting equals improved learning or ability to apply knowledge.

  2. Learning curves in health professions education.

    PubMed

    Pusic, Martin V; Boutis, Kathy; Hatala, Rose; Cook, David A

    2015-08-01

    Learning curves, which graphically show the relationship between learning effort and achievement, are common in published education research but are not often used in day-to-day educational activities. The purpose of this article is to describe the generation and analysis of learning curves and their applicability to health professions education. The authors argue that the time is right for a closer look at using learning curves-given their desirable properties-to inform both self-directed instruction by individuals and education management by instructors.A typical learning curve is made up of a measure of learning (y-axis), a measure of effort (x-axis), and a mathematical linking function. At the individual level, learning curves make manifest a single person's progress towards competence including his/her rate of learning, the inflection point where learning becomes more effortful, and the remaining distance to mastery attainment. At the group level, overlaid learning curves show the full variation of a group of learners' paths through a given learning domain. Specifically, they make overt the difference between time-based and competency-based approaches to instruction. Additionally, instructors can use learning curve information to more accurately target educational resources to those who most require them.The learning curve approach requires a fine-grained collection of data that will not be possible in all educational settings; however, the increased use of an assessment paradigm that explicitly includes effort and its link to individual achievement could result in increased learner engagement and more effective instructional design.

  3. An "education for life" requirement to promote lifelong learning in an internal medicine residency program.

    PubMed

    Panda, Mukta; Desbiens, Norman A

    2010-12-01

    Lifelong learning is an integral component of practice-based learning and improvement. Physicians need to be lifelong learners to provide timely, efficient, and state-of-the-art patient care in an environment where knowledge, technology, and social requirements are rapidly changing. To assess graduates' self-reported perception of the usefulness of a residency program requirement to submit a narrative report describing their planned educational modalities for their future continued medical learning ("Education for Life" requirement), and to compare the modalities residents intended to use with their reported educational activities. Data was compiled from the Education for Life reports submitted by internal medicine residents at the University of Tennessee College of Medicine Chattanooga from 1998 to 2000, and from a survey sent to the same 27 graduates 2 to 4 years later from 2000 to 2004. Twenty-four surveys (89%) were returned. Of the responding graduates, 58% (14/24) found the Education for Life requirement useful for their future continued medical learning. Graduates intended to keep up with a mean of 3.4 educational modalities, and they reported keeping up with 4.2. In a multivariable analysis, the number of modalities graduates used was significantly associated with the number they had planned to use before graduation (P  =  .04) but not with their career choice of subspecialization. The majority of residents found the Education for Life requirement useful for their future continued medical learning. Graduates, regardless of specialty, reported using more modalities for continuing their medical education than they thought they would as residents. Considering lifelong learning early in training and then requiring residents to identify ways to practice lifelong learning as a requirement for graduation may be dispositive.

  4. An “Education for Life” Requirement to Promote Lifelong Learning in an Internal Medicine Residency Program

    PubMed Central

    Panda, Mukta; Desbiens, Norman A.

    2010-01-01

    Background Lifelong learning is an integral component of practice-based learning and improvement. Physicians need to be lifelong learners to provide timely, efficient, and state-of-the-art patient care in an environment where knowledge, technology, and social requirements are rapidly changing. Objectives To assess graduates' self-reported perception of the usefulness of a residency program requirement to submit a narrative report describing their planned educational modalities for their future continued medical learning (“Education for Life” requirement), and to compare the modalities residents intended to use with their reported educational activities. Materials and Methods Data was compiled from the Education for Life reports submitted by internal medicine residents at the University of Tennessee College of Medicine Chattanooga from 1998 to 2000, and from a survey sent to the same 27 graduates 2 to 4 years later from 2000 to 2004. Results Twenty-four surveys (89%) were returned. Of the responding graduates, 58% (14/24) found the Education for Life requirement useful for their future continued medical learning. Graduates intended to keep up with a mean of 3.4 educational modalities, and they reported keeping up with 4.2. In a multivariable analysis, the number of modalities graduates used was significantly associated with the number they had planned to use before graduation (P  =  .04) but not with their career choice of subspecialization. Conclusion The majority of residents found the Education for Life requirement useful for their future continued medical learning. Graduates, regardless of specialty, reported using more modalities for continuing their medical education than they thought they would as residents. Considering lifelong learning early in training and then requiring residents to identify ways to practice lifelong learning as a requirement for graduation may be dispositive. PMID:22132278

  5. Learning Through New Approaches to Forest Governance: Evidence from Harrop-Procter Community Forest, Canada.

    PubMed

    Egunyu, Felicitas; Reed, Maureen G; Sinclair, John A

    2016-04-01

    Collaborative forest governance arrangements have been viewed as promising for sustainable forestry because they allow local communities to participate directly in management and benefit from resource use or protection. Such arrangements are strengthened through social learning during management activities that can enhance capacity to solve complex problems. Despite significant research on social learning in collaborative environmental governance, it is not clear how social learning evolves over time, who influences social learning, and whether learning influences management effectiveness. This study investigates how social learning outcomes change over time, using an in-depth study of a community forest in Canada. Personal interviews, focus group meetings, and participant observation revealed that most participants started engaging in community forestry with limited knowledge and learned as they participated in management activities. However, as the community forest organization became effective at complying with forestry legislation, learning opportunities and outcomes became more restricted. Our results run contrary to the prevalent view that opportunities for and outcomes of social learning become enlarged over time. In our case, learning how to meet governmental requirements increased professionalism and reduced opportunities for involvement and learning to a smaller group. Our findings suggest the need to further test propositions about social learning and collaborative governance, particularly to determine how relationships evolve over time.

  6. Learning Through New Approaches to Forest Governance: Evidence from Harrop-Procter Community Forest, Canada

    NASA Astrophysics Data System (ADS)

    Egunyu, Felicitas; Reed, Maureen G.; Sinclair, John A.

    2016-04-01

    Collaborative forest governance arrangements have been viewed as promising for sustainable forestry because they allow local communities to participate directly in management and benefit from resource use or protection. Such arrangements are strengthened through social learning during management activities that can enhance capacity to solve complex problems. Despite significant research on social learning in collaborative environmental governance, it is not clear how social learning evolves over time, who influences social learning, and whether learning influences management effectiveness. This study investigates how social learning outcomes change over time, using an in-depth study of a community forest in Canada. Personal interviews, focus group meetings, and participant observation revealed that most participants started engaging in community forestry with limited knowledge and learned as they participated in management activities. However, as the community forest organization became effective at complying with forestry legislation, learning opportunities and outcomes became more restricted. Our results run contrary to the prevalent view that opportunities for and outcomes of social learning become enlarged over time. In our case, learning how to meet governmental requirements increased professionalism and reduced opportunities for involvement and learning to a smaller group. Our findings suggest the need to further test propositions about social learning and collaborative governance, particularly to determine how relationships evolve over time.

  7. Cognitive learning: a machine learning approach for automatic process characterization from design

    NASA Astrophysics Data System (ADS)

    Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

    2018-03-01

    Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

  8. Designing health care risk management on-line:meeting regulators' concerns for fixed-hour curriculum.

    PubMed

    Hyer, Kathryn; Taylor, Heidi H; Nanni, Kennith

    2004-01-01

    This paper describes the experience of creating a continuing professional education on-line risk management program that is designed to meet Florida's educational requirements for licensure as a risk manager in health-care settings and details the challenges faced when the in-class didactic program of 15 eight-hour sessions is reformatted as an on-line program. Structuring instructor/learner interactivity remains a challenge, especially if the program allows learner control and is a key feature in marketing the program. The article presents the dilemmas for state regulators as they work to determine if the on-line program meets legislative intent and statutory requirements because the learning platform does not have a clock function that accumulates time for each learner. While some details reflect the uniqueness of the 120-hour educational requirements for risk managers in Florida, the experience of the authors provides insight into the development of continuing professional education distance learning programs that are multidisciplinary and move primarily from a time-based format into a curriculum that uses time as only one dimension of the evaluation of learning.

  9. Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

    NASA Astrophysics Data System (ADS)

    Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

    2013-04-01

    SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

  10. The Relationship between the Amount of Learning and Time (The Example of Equations)

    ERIC Educational Resources Information Center

    Kesan, Cenk; Kaya, Deniz; Ok, Gokce; Erkus, Yusuf

    2016-01-01

    The main purpose of this study is to determine the amount of time-dependent learning of "solving problems that require establishing of single variable equations of the first order" of the seventh grade students. The study, adopting the screening model, consisted of a total of 84 students, including 42 female and 42 male students at the…

  11. The Effect of Time on Difficulty of Learning (The Case of Problem Solving with Natural Numbers)

    ERIC Educational Resources Information Center

    Kaya, Deniz; Kesan, Cenk

    2017-01-01

    The main purpose of this study is to determine the time-dependent learning difficulty of "solving problems that require making four operations with natural numbers" of the sixth grade students. The study, adopting the scanning model, consisted of a total of 140 students, including 69 female and 71 male students at the sixth grade. Data…

  12. Faculty Identification, Recruitment, Selection, Assignment and Development at the Marine Corps University

    DTIC Science & Technology

    1993-04-30

    in the " active learning " process and require less preparation time. Maneuver warfare philosophy asks the officer to think "two levels up" the chain...34passive" to " active " learning . 47 25 Until recently, the Marine Corps Schools and most other miiltary educational institutions used the standard approach...future. This conclusion drove the trend to adult education or active learning . Basically, passive learning or traditional, pedagogical learning is

  13. Incremental Support Vector Machine Framework for Visual Sensor Networks

    NASA Astrophysics Data System (ADS)

    Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

    2006-12-01

    Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

  14. Real Time Data System (RTDS)

    NASA Technical Reports Server (NTRS)

    Muratore, John F.

    1991-01-01

    Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

  15. Bidirectional extreme learning machine for regression problem and its learning effectiveness.

    PubMed

    Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

    2012-09-01

    It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

  16. Coral Reefs, Convicts, Cadavers, Coffee Shops and Couture: Customizing Experiential Learning to Increase Comfort and Engagement

    ERIC Educational Resources Information Center

    Grabowsky, Gail L.; Hargis, Jace; Davidson, Janet; Paynter, Allison; Suh, Junghwa; Wright, Claire

    2017-01-01

    Experiential learning (EL) can offer a high impact educational opportunity that benefits students from diverse backgrounds, creating an inclusive learning environment. Barriers to the generalization of EL can include a lack of institutional support, risk avoidance, time, and faculty instructional ability. As well EL require additional efforts from…

  17. Word Learning in 6-Month-Olds: Fast Encoding-Weak Retention

    ERIC Educational Resources Information Center

    Friedrich, Manuela; Friederici, Angela D.

    2011-01-01

    There has been general consensus that initial word learning during early infancy is a slow and time-consuming process that requires very frequent exposure, whereas later in development, infants are able to quickly learn a novel word for a novel meaning. From the perspective of memory maturation, this shift in behavioral development might represent…

  18. Time to rethink the neural mechanisms of learning and memory.

    PubMed

    Gallistel, Charles R; Balsam, Peter D

    2014-02-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Effects of team-based learning on self-regulated online learning.

    PubMed

    Whittaker, Alice A

    2015-04-10

    Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p < 0.001) of self-regulated learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

  20. Work-based learning in health care environments.

    PubMed

    Spouse, J

    2001-03-01

    In reviewing contemporary literature and theories about work-based learning, this paper explores recent trends promoting life-long learning. In the process the paper reviews and discusses some implications of implementing recent policies and fostering le arning in health care practice settings. Recent Government policies designed to provide quality health care services and to improve staffing levels in the nursing workforce, have emphasized the importance of life-long learning whilst learning-on-the-job and the need to recognize and credit experiential learning. Such calls include negotiation of personal development plans tailored to individual educational need and context-sensitive learning activities. To be implemented effectively, this policy cann ot be seen as a cheap option but requires considerable financial resourcing for preparation of staff and the conduct of such activities. Successful work-based learning requires investment in staff at all levels as well as changes to staffing structures in organizations and trusts; changes designed to free people up to work and learn collaboratively. Creating an organizational environment where learning is prized depends upon a climate of trust; a climate where investigation and speculation are fostered and where time is protected for engaging in discussions about practice. Such a change may be radical for many health care organizations and may require a review of current policies and practices ensuring that they include education at all levels. The nature of such education also requires reconceptualizing. In the past, learning in practice settings was seen as formal lecturing or demonstration, and relied upon behaviourist principles of learning. Contemporary thinking suggests effective learning in work-settings is multi-faceted and draws on previously acquired formal knowledge, contextualizes it and moulds it according to situations at hand. Thinking about work-based learning in this way raises questions about how such learning can be supported and facilitated.

  1. A machine learning model with human cognitive biases capable of learning from small and biased datasets.

    PubMed

    Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

    2018-05-09

    Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

  2. Investigating the Retention and Time-Course of Phonotactic Constraint Learning From Production Experience

    PubMed Central

    Warker, Jill A.

    2013-01-01

    Adults can rapidly learn artificial phonotactic constraints such as /f/ only occurs at the beginning of syllables by producing syllables that contain those constraints. This implicit learning is then reflected in their speech errors. However, second-order constraints in which the placement of a phoneme depends on another characteristic of the syllable (e.g., if the vowel is /æ/, /f/ occurs at the beginning of syllables and /s/ occurs at the end of syllables but if the vowel is /I/, the reverse is true) require a longer learning period. Two experiments question the transience of second-order learning and whether consolidation plays a role in learning phonological dependencies. Using speech errors as a measure of learning, Experiment 1 investigated the durability of learning, and Experiment 2 investigated the time-course of learning. Experiment 1 found that learning is still present in speech errors a week later. Experiment 2 looked at whether more time in the form of a consolidation period or more experience in the form of more trials was necessary for learning to be revealed in speech errors. Both consolidation and more trials led to learning; however, consolidation provided a more substantial benefit. PMID:22686839

  3. Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

    PubMed

    Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

    2015-01-01

    A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

  4. Making time for learning-oriented leadership in multidisciplinary hospital management groups.

    PubMed

    Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

    2015-01-01

    Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

  5. The learning curve of robot-assisted laparoscopic fundoplication in children: a prospective evaluation and CUSUM analysis.

    PubMed

    Cundy, Thomas P; Rowland, Simon P; Gattas, Nicholas E; White, Alan D; Najmaldin, Azad S

    2015-06-01

    Fundoplication is a leading application of robotic surgery in children, yet the learning curve for this procedure (RF) remains ill-defined. This study aims to identify various learning curve transition points, using cumulative summation (CUSUM) analysis. A prospective database was examined to identify RF cases undertaken during 2006-2014. Time-based surgical process outcomes were evaluated, as well as clinical outcomes. A total of 57 RF cases were included. Statistically significant transitions beyond the learning phase were observed at cases 42, 34 and 37 for docking, console and total operating room times, respectively. A steep early learning phase for docking time was overcome after 12 cases. There were three Clavien-Dindo grade ≥ 3 complications, with two patients requiring redo fundoplication. We identified numerous well-defined learning curve trends to affirm that experience confers significant temporal improvements. Our findings highlight the value of the CUSUM method for learning curve evaluation. Copyright © 2014 John Wiley & Sons, Ltd.

  6. Ground Rules in Team Projects: Findings from a Prototype System to Support Students

    ERIC Educational Resources Information Center

    Whatley, Janice

    2009-01-01

    Student team project work in higher education is one of the best ways to develop team working skills at the same time as learning about the subject matter. As today's students require the freedom to learn at times and places that better match their lifestyles, there is a need for any support for team project work to be also available online. Team…

  7. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning

    PubMed Central

    Raza, Meher; Ivry, Richard B.

    2016-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. NEW & NOTEWORTHY We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the alternating serial reaction time task, exhibited good test-retest reliability in measures of learning and performance. However, the learning measures did not correlate between the two tasks, arguing against a shared process for implicit motor learning. PMID:27832611

  8. Simultaneous acquisition of multiple auditory-motor transformations in speech

    PubMed Central

    Rochet-Capellan, Amelie; Ostry, David J.

    2011-01-01

    The brain easily generates the movement that is needed in a given situation. Yet surprisingly, the results of experimental studies suggest that it is difficult to acquire more than one skill at a time. To do so, it has generally been necessary to link the required movement to arbitrary cues. In the present study, we show that speech motor learning provides an informative model for the acquisition of multiple sensorimotor skills. During training, subjects are required to repeat aloud individual words in random order while auditory feedback is altered in real-time in different ways for the different words. We find that subjects can quite readily and simultaneously modify their speech movements to correct for these different auditory transformations. This multiple learning occurs effortlessly without explicit cues and without any apparent awareness of the perturbation. The ability to simultaneously learn several different auditory-motor transformations is consistent with the idea that in speech motor learning, the brain acquires instance specific memories. The results support the hypothesis that speech motor learning is fundamentally local. PMID:21325534

  9. QUICR-learning for Multi-Agent Coordination

    NASA Technical Reports Server (NTRS)

    Agogino, Adrian K.; Tumer, Kagan

    2006-01-01

    Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.

  10. Efficient Grammar Induction Algorithm with Parse Forests from Real Corpora

    NASA Astrophysics Data System (ADS)

    Kurihara, Kenichi; Kameya, Yoshitaka; Sato, Taisuke

    The task of inducing grammar structures has received a great deal of attention. The reasons why researchers have studied are different; to use grammar induction as the first stage in building large treebanks or to make up better language models. However, grammar induction has inherent computational complexity. To overcome it, some grammar induction algorithms add new production rules incrementally. They refine the grammar while keeping their computational complexity low. In this paper, we propose a new efficient grammar induction algorithm. Although our algorithm is similar to algorithms which learn a grammar incrementally, our algorithm uses the graphical EM algorithm instead of the Inside-Outside algorithm. We report results of learning experiments in terms of learning speeds. The results show that our algorithm learns a grammar in constant time regardless of the size of the grammar. Since our algorithm decreases syntactic ambiguities in each step, our algorithm reduces required time for learning. This constant-time learning considerably affects learning time for larger grammars. We also reports results of evaluation of criteria to choose nonterminals. Our algorithm refines a grammar based on a nonterminal in each step. Since there can be several criteria to decide which nonterminal is the best, we evaluate them by learning experiments.

  11. Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns

    PubMed Central

    Matsubara, Takashi

    2017-01-01

    Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning. PMID:29209191

  12. Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

    PubMed

    Matsubara, Takashi

    2017-01-01

    Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

  13. A Personalized Study Method for Learning University Physics

    ERIC Educational Resources Information Center

    Aravind, Vasudeva Rao; Croyle, Kevin

    2017-01-01

    Students learn scientific concepts and mathematical calculations relating to scientific principles by repetition and reinforcement. Teachers and instructors cannot practically spend the long time required during tutorials to patiently teach students the calculations. Usually, teachers assign homework to provide practice to students, hoping that…

  14. Research-based recommendations for implementing international service-learning.

    PubMed

    Amerson, Roxanne

    2014-01-01

    An increasing number of schools of nursing are incorporating international service-learning and/or immersion experiences into their curriculum to promote cultural competence. The purpose of this paper is to identify research-based recommendations for implementing an international service-learning program. A review of literature was conducted in the Cumulative Index of Nursing and Allied Health Literature database using the keywords international, immersion, cultural competence, nursing, and international service-learning. Additional references were located from the reference lists of related articles. Planning of international or immersion experiences requires consideration of the type of country, the length of time, and design of the program; the use of a service-learning framework; opportunities that require the student to live and work in the community, provide hands-on care, participate in unstructured activities, and make home visits; and a method of reflection. Increasing cultural competence does not require foreign travel, but it does necessitate that students are challenged to move outside their comfort zone and work directly with diverse populations. These research-based recommendations may be used either internationally or locally to promote the most effective service-learning opportunities for nursing students. © 2014.

  15. Autonomous reinforcement learning with experience replay.

    PubMed

    Wawrzyński, Paweł; Tanwani, Ajay Kumar

    2013-05-01

    This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

    NASA Astrophysics Data System (ADS)

    Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

    2000-06-01

    When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

  17. The key to success in elite athletes? Explicit and implicit motor learning in youth elite and non-elite soccer players.

    PubMed

    Verburgh, L; Scherder, E J A; van Lange, P A M; Oosterlaan, J

    2016-09-01

    In sports, fast and accurate execution of movements is required. It has been shown that implicitly learned movements might be less vulnerable than explicitly learned movements to stressful and fast changing circumstances that exist at the elite sports level. The present study provides insight in explicit and implicit motor learning in youth soccer players with different expertise levels. Twenty-seven youth elite soccer players and 25 non-elite soccer players (aged 10-12) performed a serial reaction time task (SRTT). In the SRTT, one of the sequences must be learned explicitly, the other was implicitly learned. No main effect of group was found for implicit and explicit learning on mean reaction time (MRT) and accuracy. However, for MRT, an interaction was found between learning condition, learning phase and group. Analyses showed no group effects for the explicit learning condition, but youth elite soccer players showed better learning in the implicit learning condition. In particular, during implicit motor learning youth elite soccer showed faster MRTs in the early learning phase and earlier reached asymptote performance in terms of MRT. Present findings may be important for sports because children with superior implicit learning abilities in early learning phases may be able to learn more (durable) motor skills in a shorter time period as compared to other children.

  18. Machine learning for real time remote detection

    NASA Astrophysics Data System (ADS)

    Labbé, Benjamin; Fournier, Jérôme; Henaff, Gilles; Bascle, Bénédicte; Canu, Stéphane

    2010-10-01

    Infrared systems are key to providing enhanced capability to military forces such as automatic control of threats and prevention from air, naval and ground attacks. Key requirements for such a system to produce operational benefits are real-time processing as well as high efficiency in terms of detection and false alarm rate. These are serious issues since the system must deal with a large number of objects and categories to be recognized (small vehicles, armored vehicles, planes, buildings, etc.). Statistical learning based algorithms are promising candidates to meet these requirements when using selected discriminant features and real-time implementation. This paper proposes a new decision architecture benefiting from recent advances in machine learning by using an effective method for level set estimation. While building decision function, the proposed approach performs variable selection based on a discriminative criterion. Moreover, the use of level set makes it possible to manage rejection of unknown or ambiguous objects thus preserving the false alarm rate. Experimental evidences reported on real world infrared images demonstrate the validity of our approach.

  19. Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation.

    PubMed

    Tighe, Patrick J; Lucas, Stephen D; Edwards, David A; Boezaart, André P; Aytug, Haldun; Bihorac, Azra

    2012-10-01

      The purpose of this project was to determine whether machine-learning classifiers could predict which patients would require a preoperative acute pain service (APS) consultation.   Retrospective cohort.   University teaching hospital.   The records of 9,860 surgical patients posted between January 1 and June 30, 2010 were reviewed.   Request for APS consultation. A cohort of machine-learning classifiers was compared according to its ability or inability to classify surgical cases as requiring a request for a preoperative APS consultation. Classifiers were then optimized utilizing ensemble techniques. Computational efficiency was measured with the central processing unit processing times required for model training. Classifiers were tested using the full feature set, as well as the reduced feature set that was optimized using a merit-based dimensional reduction strategy.   Machine-learning classifiers correctly predicted preoperative requests for APS consultations in 92.3% (95% confidence intervals [CI], 91.8-92.8) of all surgical cases. Bayesian methods yielded the highest area under the receiver operating curve (0.87, 95% CI 0.84-0.89) and lowest training times (0.0018 seconds, 95% CI, 0.0017-0.0019 for the NaiveBayesUpdateable algorithm). An ensemble of high-performing machine-learning classifiers did not yield a higher area under the receiver operating curve than its component classifiers. Dimensional reduction decreased the computational requirements for multiple classifiers, but did not adversely affect classification performance.   Using historical data, machine-learning classifiers can predict which surgical cases should prompt a preoperative request for an APS consultation. Dimensional reduction improved computational efficiency and preserved predictive performance. Wiley Periodicals, Inc.

  20. Functional requirements for reward-modulated spike-timing-dependent plasticity.

    PubMed

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2010-10-06

    Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

  1. Effects of Real-Time Captioning and Sign Language Interpreting on the Learning of College Students Who Are Deaf or Hard of Hearing

    ERIC Educational Resources Information Center

    Smith-Pethybridge, Valorie

    2009-01-01

    College personnel are required to provide accommodations for students who are deaf and hard of hearing (D/HoH), but few empirical studies have been conducted on D/HoH students as they learn under the various accommodation conditions (sign language interpreting, SLI, real-time captioning, RTC, and both). Guided by the experiences of students who…

  2. Delivering Training Assessments in a Soldier Centered Learning Environment: Year One

    DTIC Science & Technology

    2014-09-01

    virtual classroom in comparison to the mobile training. Social cognitive theory (see Bandura , 1986) would support the idea that creating a social ...R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355-365. Bandura , A. (1986). Social foundations of thought...architecture that would allow timely feedback with customizable levels of specificity necessitates a time investment and requires expertise in learning theory

  3. Value-Based Requirements Traceability: Lessons Learned

    NASA Astrophysics Data System (ADS)

    Egyed, Alexander; Grünbacher, Paul; Heindl, Matthias; Biffl, Stefan

    Traceability from requirements to code is mandated by numerous software development standards. These standards, however, are not explicit about the appropriate level of quality of trace links. From a technical perspective, trace quality should meet the needs of the intended trace utilizations. Unfortunately, long-term trace utilizations are typically unknown at the time of trace acquisition which represents a dilemma for many companies. This chapter suggests ways to balance the cost and benefits of requirements traceability. We present data from three case studies demonstrating that trace acquisition requires broad coverage but can tolerate imprecision. With this trade-off our lessons learned suggest a traceability strategy that (1) provides trace links more quickly, (2) refines trace links according to user-defined value considerations, and (3) supports the later refinement of trace links in case the initial value consideration has changed over time. The scope of our work considers the entire life cycle of traceability instead of just the creation of trace links.

  4. Time and learning efficiency in Internet-based learning: a systematic review and meta-analysis.

    PubMed

    Cook, David A; Levinson, Anthony J; Garside, Sarah

    2010-12-01

    Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based instruction are associated with improved learning efficiency. we searched databases including MEDLINE, CINAHL, EMBASE, and ERIC from 1990 through November 2008. STUDY SELECTION AND DATA ABSTRACTION we included all studies quantifying learning time for Internet-based instruction for health professionals, compared with other instruction. Reviewers worked independently, in duplicate, to abstract information on interventions, outcomes, and study design. we identified 20 eligible studies. Random effects meta-analysis of 8 studies comparing Internet-based with non-Internet instruction (positive numbers indicating Internet longer) revealed pooled effect size (ES) for time -0.10 (p = 0.63). Among comparisons of two Internet-based interventions, providing feedback adds time (ES 0.67, p =0.003, two studies), and greater interactivity generally takes longer (ES 0.25, p = 0.089, five studies). One study demonstrated that adapting to learner prior knowledge saves time without significantly affecting knowledge scores. Other studies revealed that audio narration, video clips, interactive models, and animations increase learning time but also facilitate higher knowledge and/or satisfaction. Across all studies, time correlated positively with knowledge outcomes (r = 0.53, p = 0.021). on average, Internet-based instruction and non-computer instruction require similar time. Instructional strategies to enhance feedback and interactivity typically prolong learning time, but in many cases also enhance learning outcomes. Isolated examples suggest potential for improving efficiency in Internet-based instruction.

  5. Learning about the Human Body. Superific Science Book IV. A Good Apple Science Activity Book for Grades 5-8+.

    ERIC Educational Resources Information Center

    Conway, Lorraine

    Designed to supplement a basic life science or biology program, this document provides teachers with experiential learning activities dealing with the human body. The learning activities vary in the length of time needed for their completion, and require a minimum of equipment and materials. The activities focus on: (1) the human skeleton; (2)…

  6. The impact of blended learning on student performance in a cardiovascular pharmacotherapy course.

    PubMed

    McLaughlin, Jacqueline E; Gharkholonarehe, Nastaran; Khanova, Julia; Deyo, Zach M; Rodgers, Jo E

    2015-03-25

    To examine student engagement with, perception of, and performance resulting from blended learning for venous thromboembolism in a required cardiovascular pharmacotherapy course for second-year students. In 2013, key foundational content was packaged into an interactive online module for students to access prior to coming to class; class time was dedicated to active-learning exercises. Students who accessed all online module segments participated in more in class clicker questions (p=0.043) and performed better on the examination (p=0.023). There was no difference in clicker participation or examination performance based on time of module access (prior to or after class). The majority of participants agreed or strongly agreed that foundational content learned prior to class, applied activities during class, and content-related questions in the online module greatly enhanced learning. This study highlights the importance of integrating online modules with classroom learning and the role of blended learning in improving academic performance.

  7. MHEG Based Distance Learning System on Information Superhighway.

    ERIC Educational Resources Information Center

    Lee, SeiHoon; Yoon, KyungSeob; Wang, ChangJong

    As the need for distance education grows, requirements for the development of high-speed network-based real-time distance learning systems increases. MHEG-5 is the fifth part of the MHEG (Multimedia and Hypermedia information coding Experts Group) standard, and it defines a final-form representation for application interchange. This paper…

  8. Lessons Learned from Client Projects in an Undergraduate Project Management Course

    ERIC Educational Resources Information Center

    Pollard, Carol E.

    2012-01-01

    This work proposes that a subtle combination of three learning methods offering "just in time" project management knowledge, coupled with hands-on project management experience can be particularly effective in producing project management students with employable skills. Students were required to apply formal project management knowledge to gain…

  9. Summary of Michigan multispectral investigations program

    NASA Technical Reports Server (NTRS)

    Legault, R. R.

    1970-01-01

    The development of techniques to extend spectral signatures in space and time is reported. Signatures that were valid for 30 miles have been extended for 129 miles using transformation and sun sensor data so that a complicated multispectral recognition problem that required 219 learning sets can now be done with 13 learning sets.

  10. Transferable Skills for Online Peer Learning

    ERIC Educational Resources Information Center

    McLuckie, J.; Topping, K. J.

    2004-01-01

    Efforts to enhance learning through peer interaction in an electronic forum are now commonplace. However, facilitation and moderation of such a forum by academic staff can be of limited effectiveness and very time-consuming. The skills required by peer learners to effectively manage such distributed discourse for themselves have rarely been…

  11. More Time for Learning

    ERIC Educational Resources Information Center

    Magaña, Alex; Saab, Michelle; Svoboda, Valerie

    2017-01-01

    In 2012, things were not looking good for Grant Beacon Middle School in Denver, Colorado. Enrollment numbers were declining, students were not reaching required academic levels, and the Denver Public Schools district designated the school as on watch. By implementing a plan that emphasized expanded learning opportunities--adding five hours to the…

  12. The Learning Hippocampus: Education and Experience-Dependent Plasticity

    ERIC Educational Resources Information Center

    Wenger, Elisabeth; Lövdén, Martin

    2016-01-01

    The hippocampal formation of the brain plays a crucial role in declarative learning and memory while at the same time being particularly susceptible to environmental influences. Education requires a well-functioning hippocampus, but may also influence the development of this brain structure. Understanding these bidirectional influences may have…

  13. Sustainability for Innovative Education--The Case of Mobile Learning

    ERIC Educational Resources Information Center

    Bachmair, Ben; Pachler, Norbert

    2015-01-01

    The successful introduction of mobile learning into education is arguably premised on sustainability in the sense of an ability to maintain innovation over time and to become embedded into mainstream practice. This paper argues that such an endeavour requires a discursive approach, decoupling sustainability from the notion of unambiguity…

  14. Learning from Simple Ebooks, Online Cases or Classroom Teaching When Acquiring Complex Knowledge. A Randomized Controlled Trial in Respiratory Physiology and Pulmonology

    PubMed Central

    Worm, Bjarne Skjødt

    2013-01-01

    Background and Aims E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. Methods 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. Results For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). Conclusions E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method. PMID:24039917

  15. Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

    PubMed

    Worm, Bjarne Skjødt

    2013-01-01

    E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

  16. Role of computer-based learning in tooth carving in dentistry: An Indian perspective.

    PubMed

    Juneja, Saurabh; Juneja, Manjushree

    2016-01-01

    Tooth carving is an important practical preclinical exercise in the curriculum in Indian dental education setup. It forms the basis of introduction to tooth anatomy, morphology and occlusion of primary and permanent teeth through practical approach. It requires enormous time and manpower to master the skill. Therefore, there is an imminent necessity to incorporate computer-based learning of the art of tooth carving for effective teaching and efficient student learning. This will ensure quality time to be spent on other academic and research activities by students and faculty in addition to adding value as a teaching aid.

  17. Learning In networks

    NASA Technical Reports Server (NTRS)

    Buntine, Wray L.

    1995-01-01

    Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

  18. Mirror reversal and visual rotation are learned and consolidated via separate mechanisms: recalibrating or learning de novo?

    PubMed

    Telgen, Sebastian; Parvin, Darius; Diedrichsen, Jörn

    2014-10-08

    Motor learning tasks are often classified into adaptation tasks, which involve the recalibration of an existing control policy (the mapping that determines both feedforward and feedback commands), and skill-learning tasks, requiring the acquisition of new control policies. We show here that this distinction also applies to two different visuomotor transformations during reaching in humans: Mirror-reversal (left-right reversal over a mid-sagittal axis) of visual feedback versus rotation of visual feedback around the movement origin. During mirror-reversal learning, correct movement initiation (feedforward commands) and online corrections (feedback responses) were only generated at longer latencies. The earliest responses were directed into a nonmirrored direction, even after two training sessions. In contrast, for visual rotation learning, no dependency of directional error on reaction time emerged, and fast feedback responses to visual displacements of the cursor were immediately adapted. These results suggest that the motor system acquires a new control policy for mirror reversal, which initially requires extra processing time, while it recalibrates an existing control policy for visual rotations, exploiting established fast computational processes. Importantly, memory for visual rotation decayed between sessions, whereas memory for mirror reversals showed offline gains, leading to better performance at the beginning of the second session than in the end of the first. With shifts in time-accuracy tradeoff and offline gains, mirror-reversal learning shares common features with other skill-learning tasks. We suggest that different neuronal mechanisms underlie the recalibration of an existing versus acquisition of a new control policy and that offline gains between sessions are a characteristic of latter. Copyright © 2014 the authors 0270-6474/14/3413768-12$15.00/0.

  19. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

    PubMed

    Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

    2017-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the alternating serial reaction time task, exhibited good test-retest reliability in measures of learning and performance. However, the learning measures did not correlate between the two tasks, arguing against a shared process for implicit motor learning. Copyright © 2017 the American Physiological Society.

  20. Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

    PubMed Central

    Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

    2016-01-01

    The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

  1. Computer-based learning: interleaving whole and sectional representation of neuroanatomy.

    PubMed

    Pani, John R; Chariker, Julia H; Naaz, Farah

    2013-01-01

    The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). Copyright © 2012 American Association of Anatomists.

  2. Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

    PubMed Central

    Pani, John R.; Chariker, Julia H.; Naaz, Farah

    2015-01-01

    The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). PMID:22761001

  3. Effect of Cognitive Style on Learning and Retrieval of Navigational Environments.

    PubMed

    Boccia, Maddalena; Vecchione, Francesca; Piccardi, Laura; Guariglia, Cecilia

    2017-01-01

    Field independence (FI) has been found to correlate with a wide range of cognitive processes requiring cognitive restructuring. Cognitive restructuring, that is going beyond the information given by the setting, is pivotal in creating stable mental representations of the environment, the so-called "cognitive maps," and it affects visuo-spatial abilities underpinning environmental navigation. Here we evaluated whether FI, by fostering cognitive restructuring of environmental cues on the basis of an internal frame of reference, affects the learning and retrieval of a novel environment. Fifty-four participants were submitted to the Embedded Figure Test (EFT) for assessing their Cognitive Style (CS) and to the Perspective Taking/Spatial Orientation Test (PTSOT) and the Santa Barbara Sense of Direction Scale (SBSOD) for assessing their spatial perspective taking and orientation skills. They were also required to learn a path in a novel, real environment (route learning, RL), to recognize landmarks of this path among distracters (landmark recognition, LR), to order them (landmark ordering, LO) and to draw the learned path on a map (map drawing, MD). Retrieval tasks were performed both immediately after learning (immediate-retrieval) and the day after (24 h-retrieval). Performances on EFT significantly correlated with the time needed to learn the path, with MD (both in the immediate- and in the 24 h- retrievals), results on LR (in 24-retrieval) and performances on PTSOT. Interestingly, we found that gender interacted with CS on RL (time of learning) and MD. Females performed significantly worse than males only if they were classified as FD, but did not differ from males if they were classified as FI. These results suggest that CS affects learning and retrieval of navigational environment, especially when a map-like representation is required. We propose that CS may be pivotal in forming the cognitive map of the environment, likely due to the higher ability of FI individuals in restructuring environmental cues in a global and flexible long-term representation of the environment.

  4. 18th Annual NDIA SOLIC Symposium and Exhibition - Warfare in the Seams: Defense and Industry Partnering to Win the Long War. Volume 1. Presentations

    DTIC Science & Technology

    2007-02-28

    Program •Services executed Defense HUMINT Activities •DIA ran attaché system •Over time , deferred the Secretary’s Authorities •Post-1995 (Perry and White...ornl.gov orbucma@doe.ic.gov 26 February, 2007 TT L SENSO RS COMMS time trust Intelligence …the power of change… hameleon ORNL Cognitive Radio Program...and internal states in real- time to meet user requirements and goals • Learns: uses statistical signal processing and machine learning to reflect

  5. [Information technology in learning sign language].

    PubMed

    Hernández, Cesar; Pulido, Jose L; Arias, Jorge E

    2015-01-01

    To develop a technological tool that improves the initial learning of sign language in hearing impaired children. The development of this research was conducted in three phases: the lifting of requirements, design and development of the proposed device, and validation and evaluation device. Through the use of information technology and with the advice of special education professionals, we were able to develop an electronic device that facilitates the learning of sign language in deaf children. This is formed mainly by a graphic touch screen, a voice synthesizer, and a voice recognition system. Validation was performed with the deaf children in the Filadelfia School of the city of Bogotá. A learning methodology was established that improves learning times through a small, portable, lightweight, and educational technological prototype. Tests showed the effectiveness of this prototype, achieving a 32 % reduction in the initial learning time for sign language in deaf children.

  6. Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task.

    PubMed

    Gobel, Eric W; Parrish, Todd B; Reber, Paul J

    2011-10-15

    Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of the frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Neural Correlates of Skill Acquisition: Decreased Cortical Activity During a Serial Interception Sequence Learning Task

    PubMed Central

    Gobel, Eric W.; Parrish, Todd B.; Reber, Paul J.

    2011-01-01

    Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. PMID:21771663

  8. Validating the Use of Deep Learning Neural Networks for Correction of Large Hydrometric Datasets

    NASA Astrophysics Data System (ADS)

    Frazier, N.; Ogden, F. L.; Regina, J. A.; Cheng, Y.

    2017-12-01

    Collection and validation of Earth systems data can be time consuming and labor intensive. In particular, high resolution hydrometric data, including rainfall and streamflow measurements, are difficult to obtain due to a multitude of complicating factors. Measurement equipment is subject to clogs, environmental disturbances, and sensor drift. Manual intervention is typically required to identify, correct, and validate these data. Weirs can become clogged and the pressure transducer may float or drift over time. We typically employ a graphical tool called Time Series Editor to manually remove clogs and sensor drift from the data. However, this process is highly subjective and requires hydrological expertise. Two different people may produce two different data sets. To use this data for scientific discovery and model validation, a more consistent method is needed to processes this field data. Deep learning neural networks have proved to be excellent mechanisms for recognizing patterns in data. We explore the use of Recurrent Neural Networks (RNN) to capture the patterns in the data over time using various gating mechanisms (LSTM and GRU), network architectures, and hyper-parameters to build an automated data correction model. We also explore the required amount of manually corrected training data required to train the network for reasonable accuracy. The benefits of this approach are that the time to process a data set is significantly reduced, and the results are 100% reproducible after training is complete. Additionally, we train the RNN and calibrate a physically-based hydrological model against the same portion of data. Both the RNN and the model are applied to the remaining data using a split-sample methodology. Performance of the machine learning is evaluated for plausibility by comparing with the output of the hydrological model, and this analysis identifies potential periods where additional investigation is warranted.

  9. When Less Is More in Cognitive Diagnosis: A Rapid Online Method for Diagnosing Learner Task-Specific Expertise

    ERIC Educational Resources Information Center

    Kalyuga, Slava

    2008-01-01

    Rapid cognitive diagnosis allows measuring current levels of learner domain-specific knowledge in online learning environments. Such measures are required for individualizing instructional support in real time, as students progress through a learning session. This article describes 2 experiments designed to validate a rapid online diagnostic…

  10. A Rubric to Track the Development of Secondary Pre-Service and Novice Teachers' Summative Assessment Literacy

    ERIC Educational Resources Information Center

    Edwards, Frances

    2017-01-01

    Teachers require specialised assessment knowledge and skills in order to effectively assess student learning. These knowledge and skills develop over time through ongoing teacher learning and experiences. The first part of this paper presents a Summative Assessment Literacy Rubric (SALRubric) constructed to track the development of secondary…

  11. Teacher-Led Reforms Have a Big Advantage--Teachers

    ERIC Educational Resources Information Center

    Stanulis, Randi N.; Cooper, Kristy S.; Dear, Benita; Johnston, Amanda M.; Richard-Todd, Rhonda R.

    2016-01-01

    Becoming a better teacher by learning and implementing new ways of teaching requires time, effort, persistence, and a belief that new strategies will enhance student learning. But when educational leaders try to improve teachers and teaching from the outside, by bringing in reformers to transform how teachers engage in the core business of…

  12. Overcoming Barriers between Volunteer Professionals Advising Project-Based Learning Teams with Regulation Tools

    ERIC Educational Resources Information Center

    Rees Lewis, Daniel G.; Easterday, Matthew W.; Harburg, Emily; Gerber, Elizabeth M.; Riesbeck, Christopher K.

    2018-01-01

    To provide the substantial support required for project-based learning (PBL), educators can incorporate professional experts as "design coaches." However, previous work shows barriers incorporating design coaches who can rarely meet face-to-face: (1) communication online is time-consuming, (2) updating coaches online is not perceived as…

  13. A Proposed Blueprint Model towards the Evaluation of Educational System in Iran

    ERIC Educational Resources Information Center

    Mehrafsha, S. Jahangir

    2011-01-01

    The pursuit of quality gave rise to the concept of Iran Universities as learning organizations. Iran Universities must have the capacity to learn if they are to survive the demands and requirements of the emerging times. This includes liberating traditional methodologies that are anchored on positivism and seemingly dependent on technical…

  14. How Cognitive Requirement of Prompt and Time in Course Are Correlated with Intersubjectivity within Threaded Discussions

    ERIC Educational Resources Information Center

    Hall, Barbara M.

    2011-01-01

    Threaded discussions represent conversational turn-taking in asynchronous, online learning environments. Given the crucial role that discussions play in the construction of knowledge within an online course, the quality of the interaction that occurs within threaded discussions is important to achieving the learning objectives of the designed…

  15. Transforming Traditional Lectures into Problem-Based Blended Learning: Challenges and Experiences

    ERIC Educational Resources Information Center

    Dalsgaard, Christian; Godsk, Mikkel

    2007-01-01

    This paper presents our experiences and the challenges identified in transforming traditional lecture-based modules at a university into problem-based blended learning within a social constructivist approach. Our experiment was, among other factors, motivated by an urgent need to meet new curriculum requirements by reducing the lecturing time in a…

  16. 20 CFR 404.1565 - Your work experience as a vocational factor.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    .... 404.1565 Section 404.1565 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL OLD-AGE... for you to learn to do it, and was substantial gainful activity. We do not usually consider that work... requires little or no judgment and can be learned in a short period of time. (b) Information about your...

  17. 20 CFR 404.1565 - Your work experience as a vocational factor.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    .... 404.1565 Section 404.1565 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL OLD-AGE... for you to learn to do it, and was substantial gainful activity. We do not usually consider that work... requires little or no judgment and can be learned in a short period of time. (b) Information about your...

  18. Community College Pathways: A Descriptive Report of Summative Assessments and Student Learning

    ERIC Educational Resources Information Center

    Strother, Scott; Sowers, Nicole

    2014-01-01

    Carnegie's Community College Pathways (CCP) offers two pathways, Statway® and Quantway®, that reduce the amount of time required to complete developmental mathematics and earn college-level mathematics credit. The Pathways aim to improve student success in mathematics while maintaining rigorous content, pedagogy, and learning outcomes. It is…

  19. Learn Street Skateboarding through 3D Simulations of Angle Rotations

    ERIC Educational Resources Information Center

    Adi, Erwin; Aditya, I Gde Made Krisna; Citrawati, Meriyana

    2010-01-01

    Learning physical activities such as sports and games is expensive and time-consuming. A common advice is "repetition makes perfection," which implies that wrong actions must soon be noticed and avoided. A knowledgeable tutor is often required to provide good feedback for that purpose. However, this facility is available only for those…

  20. "Happy Professional Development at an Unhappy Time": Learning to Teach for Historical Thinking in a High-Pressure Accountability Context

    ERIC Educational Resources Information Center

    Meuwissen, Kevin W.

    2017-01-01

    Adolescent learners stand to benefit when history teachers center their practice on investigating open-ended questions, interrogating evidence, and constructing persuasive arguments. Taking up this kind of teaching requires professional development (PD) experiences that are sustained, subject-specific, learning-focused, and collaborative and that…

  1. Learning and Teaching Together: Weaving Indigenous Ways of Knowing into Education

    ERIC Educational Resources Information Center

    Tanaka, Michele T. D.

    2016-01-01

    Across Canada, new curriculum initiatives require teachers to introduce students to Aboriginal content. In response, many teachers unfamiliar with Aboriginal approaches to learning and teaching are seeking ways to respectfully weave this material into their lessons. At the same time, many teachers are also grappling with how to foster inclusive…

  2. ICT-Aided Engineering Courses: A Multi-Campus Course Management

    ERIC Educational Resources Information Center

    Dana-Picard, Thierry; Kidron, Ivy; Komar, Meir; Steiner, Joseph

    2006-01-01

    Jerusalem College of Technology (JCT) is a multi-campus institution with identical syllabi for courses in every campus. Moreover, learning at JCT requires at the same time synchronous and asynchronous learning and teaching. For some introductory courses in Mathematics for Engineering students, websites have been built and now upgraded in order to…

  3. MEF and MEB Red Teams: Required Conditions and Placement Options

    DTIC Science & Technology

    2013-04-16

    aids decision making and influences how the organization views the problems it is facing. Discussion : The 2010 Commandant’s Planning Guidance...the Regional Command (South West) Red Team. Throughout this time, ‘discovery learning ’ and trial and error were the means in which I gained an...who I had the pleasure of working with and learning from during my time as a red teamer. Most importantly, I would like to thank my family for their

  4. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

    PubMed Central

    Miconi, Thomas

    2017-01-01

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

  5. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    PubMed

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  6. Theories of willpower affect sustained learning.

    PubMed

    Miller, Eric M; Walton, Gregory M; Dweck, Carol S; Job, Veronika; Trzesniewski, Kali H; McClure, Samuel M

    2012-01-01

    Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower-whether willpower is viewed as a limited or non-limited resource-impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people's ability to recruit their cognitive resources to sustain learning over time.

  7. Theories of Willpower Affect Sustained Learning

    PubMed Central

    Miller, Eric M.; Walton, Gregory M.; Dweck, Carol S.; Job, Veronika; Trzesniewski, Kali H.; McClure, Samuel M.

    2012-01-01

    Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower–whether willpower is viewed as a limited or non-limited resource–impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people’s ability to recruit their cognitive resources to sustain learning over time. PMID:22745675

  8. PredicT-ML: a tool for automating machine learning model building with big clinical data.

    PubMed

    Luo, Gang

    2016-01-01

    Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

  9. Just a Matter of Time.

    ERIC Educational Resources Information Center

    Smith, Frank

    2001-01-01

    Struggling students are often victimized by time constraints--arbitrarily imposed timetables for mastering material and meeting standards. People learn best from experience, not by information acquisition, skill development, rote memorization, or assessment. Reading, writing, arithmetic, scientific understanding, and civics require student…

  10. Assessing students' readiness towards e-learning

    NASA Astrophysics Data System (ADS)

    Rahim, Nasrudin Md; Yusoff, Siti Hawa Mohd; Latif, Shahida Abd

    2014-07-01

    The usage of e-Learning methodology has become a new attraction for potential students as shown by some higher learning institutions in Malaysia. As such, Universiti Selangor (Unisel) should be ready to embark on e-Learning teaching and learning in the near future. The purpose of the study is to gauge the readiness of Unisel's students in e-Learning environment. A sample of 110 students was chosen to participate in this study which was conducted in January 2013. This sample consisted of students from various levels of study that are foundation, diploma and degree program. Using a structured questionnaire, respondents were assessed on their basic Internet skills, access to technology required for e-Learning and their attitude towards characteristics of successful e-Learning student based on study habits, abilities, motivation and time management behaviour. The result showed that respondents did have access to technology that are required for e-Learning environment, and respondents were knowledgeable regarding the basic Internet skills. The finding also showed that respondents' attitude did meet all characteristics of successful e-Learning student. Further analysis showed that there is no significant relationshipeither among gender, level of study or faculty with those characteristics. As a conclusion, the study shows that current Unisel's students are ready to participate in e-Learning environment if the institution decided to embark on e-Learning methodology.

  11. Simulation-based medical education: time for a pedagogical shift.

    PubMed

    Kalaniti, Kaarthigeyan; Campbell, Douglas M

    2015-01-01

    The purpose of medical education at all levels is to prepare physicians with the knowledge and comprehensive skills, required to deliver safe and effective patient care. The traditional 'apprentice' learning model in medical education is undergoing a pedagogical shift to a 'simulation-based' learning model. Experiential learning, deliberate practice and the ability to provide immediate feedback are the primary advantages of simulation-based medical education. It is an effective way to develop new skills, identify knowledge gaps, reduce medical errors, and maintain infrequently used clinical skills even among experienced clinical teams, with the overall goal of improving patient care. Although simulation cannot replace clinical exposure as a form of experiential learning, it promotes learning without compromising patient safety. This new paradigm shift is revolutionizing medical education in the Western world. It is time that the developing countries embrace this new pedagogical shift.

  12. Learning the opportunity cost of time in a patch-foraging task

    PubMed Central

    Constantino, Sara; Daw, Nathaniel D.

    2015-01-01

    Although most decision research concerns choice between simultaneously presented options, in many situations options are encountered serially and the decision is whether to exploit an option or search for a better one. Such problems have a rich history in animal foraging but we know little about the psychological processes involved. In particular, it is unknown whether learning in these problems is supported by the well studied neurocomputational mechanisms involved in more conventional tasks. We investigated how humans learn in a foraging task, which requires deciding whether to harvest a depleting resource or switch to a replenished one. The optimal choice (given by the Marginal Value Theorem; MVT) requires comparing the immediate return from harvesting to the opportunity cost of time, which is given by the long-run average reward. In two experiments, we varied opportunity cost across blocks. Subjects adjusted their behavior to blockwise changes in environmental characteristics. We examined how subjects learned their choice strategies by comparing choice adjustments to a learning rule suggested by the MVT (where the opportunity cost threshold is estimated as an average over previous rewards) and to the predominant incremental learning theory in neuroscience, temporal-difference learning (TD). Trial-by-trial decisions were better explained by the MVT threshold learning rule. These findings expand on the foraging literature, which has focused on steady-state behavior, by elucidating a computational mechanism for learning in switching tasks that is distinct from those used in traditional tasks, and suggest connections to research on average reward rates in other domains of neuroscience. PMID:25917000

  13. Specificity and transfer effects in time production skill: examining the role of attention.

    PubMed

    Wohldmann, Erica L; Healy, Alice F; Bourne, Lyle E

    2012-05-01

    Two experiments examined transfer of a prospective, time production skill under conditions involving changes in concurrent task requirements. Positive transfer of the time production skill might be expected only when the attentional demands of the concurrent task were held constant from training to test. However, some positive transfer was found even when the concurrent task at retraining was made either easier or more difficult than the concurrent task learned during training. The amount and direction of transfer depended more on the pacing of the stimuli in the secondary task than on the difficulty of the secondary task, even though difficulty affects attentional demands more. These findings are consistent with the procedural reinstatement principle of skill learning, by which transfer from one task to another depends on an overlap in procedures required by the two skills.

  14. Towards understanding and managing the learning process in mail sorting.

    PubMed

    Berglund, M; Karltun, A

    2012-01-01

    This paper was based on case study research at the Swedish Mail Service Division and it addresses learning time to sort mail at new districts and means to support the learning process on an individual as well as organizational level. The study population consisted of 46 postmen and one team leader in the Swedish Mail Service Division. Data were collected through measurements of time for mail sorting, interviews and a focus group. The study showed that learning to sort mail was a much more complex process and took more time than expected by management. Means to support the learning process included clarification of the relationship between sorting and the topology of the district, a good work environment, increased support from colleagues and management, and a thorough introduction for new postmen. The identified means to support the learning process require an integration of human, technological and organizational aspects. The study further showed that increased operations flexibility cannot be reinforced without a systems perspective and thorough knowledge about real work activities and that ergonomists can aid businesses to acquire this knowledge.

  15. Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

    NASA Technical Reports Server (NTRS)

    Ferreira, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2016-01-01

    Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

  16. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules.

    PubMed

    Frémaux, Nicolas; Gerstner, Wulfram

    2015-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide "when" to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  17. Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

    NASA Technical Reports Server (NTRS)

    Ferreira, Paulo; Paffenroth, Randy; Wyglinski, Alexander; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale John

    2016-01-01

    Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

  18. The Impact of Blended Learning on Student Performance in a Cardiovascular Pharmacotherapy Course

    PubMed Central

    McLaughlin, Jacqueline E.; Gharkholonarehe, Nastaran; Khanova, Julia; Deyo, Zach M.

    2015-01-01

    Objective. To examine student engagement with, perception of, and performance resulting from blended learning for venous thromboembolism in a required cardiovascular pharmacotherapy course for second-year students. Design. In 2013, key foundational content was packaged into an interactive online module for students to access prior to coming to class; class time was dedicated to active-learning exercises. Assessment. Students who accessed all online module segments participated in more in class clicker questions (p=0.043) and performed better on the examination (p=0.023). There was no difference in clicker participation or examination performance based on time of module access (prior to or after class). The majority of participants agreed or strongly agreed that foundational content learned prior to class, applied activities during class, and content-related questions in the online module greatly enhanced learning. Conclusion. This study highlights the importance of integrating online modules with classroom learning and the role of blended learning in improving academic performance. PMID:25861105

  19. The Requirements Generation System: A tool for managing mission requirements

    NASA Technical Reports Server (NTRS)

    Sheppard, Sylvia B.

    1994-01-01

    Historically, NASA's cost for developing mission requirements has been a significant part of a mission's budget. Large amounts of time have been allocated in mission schedules for the development and review of requirements by the many groups who are associated with a mission. Additionally, tracing requirements from a current document to a parent document has been time-consuming and costly. The Requirements Generation System (RGS) is a computer-supported cooperative-work tool that assists mission developers in the online creation, review, editing, tracing, and approval of mission requirements as well as in the production of requirements documents. This paper describes the RGS and discusses some lessons learned during its development.

  20. Learning curves for single incision and conventional laparoscopic right hemicolectomy: a multidimensional analysis.

    PubMed

    Park, Yoonah; Yong, Yuen Geng; Yun, Seong Hyeon; Jung, Kyung Uk; Huh, Jung Wook; Cho, Yong Beom; Kim, Hee Cheol; Lee, Woo Yong; Chun, Ho-Kyung

    2015-05-01

    This study aimed to compare the learning curves and early postoperative outcomes for conventional laparoscopic (CL) and single incision laparoscopic (SIL) right hemicolectomy (RHC). This retrospective study included the initial 35 cases in each group. Learning curves were evaluated by the moving average of operative time, mean operative time of every five consecutive cases, and cumulative sum (CUSUM) analysis. The learning phase was considered overcome when the moving average of operative times reached a plateau, and when the mean operative time of every five consecutive cases reached a low point and subsequently did not vary by more than 30 minutes. Six patients with missing data in the CL RHC group were excluded from the analyses. According to the mean operative time of every five consecutive cases, learning phase of SIL and CL RHC was completed between 26 and 30 cases, and 16 and 20 cases, respectively. Moving average analysis revealed that approximately 31 (SIL) and 25 (CL) cases were needed to complete the learning phase, respectively. CUSUM analysis demonstrated that 10 (SIL) and two (CL) cases were required to reach a steady state of complication-free performance, respectively. Postoperative complications rate was higher in SIL than in CL group, but the difference was not statistically significant (17.1% vs. 3.4%). The learning phase of SIL RHC is longer than that of CL RHC. Early oncological outcomes of both techniques were comparable. However, SIL RHC had a statistically insignificant higher complication rate than CL RHC during the learning phase.

  1. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    PubMed

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  2. Time pressure in scenario-based online construction safety quizzes and its effect on students' performance

    NASA Astrophysics Data System (ADS)

    Jaeger, Martin; Adair, Desmond

    2017-05-01

    Online quizzes have been shown to be effective learning and assessment approaches. However, if scenario-based online construction safety quizzes do not include time pressure similar to real-world situations, they reflect situations too ideally. The purpose of this paper is to compare engineering students' performance when carrying out an online construction safety quiz with time pressure versus an online construction safety quiz without time pressure. Two versions of an online construction safety quiz are developed and administered to randomly assigned engineering students based on a quasi-experimental post-test design. The findings contribute to scenario-based learning and assessment of construction safety in four ways. First, the results confirm earlier findings that 'intrinsic stress' does not seem to impair students' performance. Second, students who carry out the online construction safety quiz with time pressure are less likely to 'learn by trial and error'. Third, students exposed to time pressure appreciate that they become better prepared for real life. Finally, preparing students to work under time pressure is an important industry requirement. The results of this study should encourage engineering educators to explore and implement ways to include time pressure in scenario-based online quizzes and learning.

  3. Speeding up the learning of robot kinematics through function decomposition.

    PubMed

    Ruiz de Angulo, Vicente; Torras, Carme

    2005-11-01

    The main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self-organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.

  4. A new vision for distance learning and continuing medical education.

    PubMed

    Harden, Ronald M

    2005-01-01

    Increasing demands on continuing medical education (CME) are taking place at a time of significant developments in educational thinking and new learning technologies. Such developments allow today's CME providers to better meet the CRISIS criteria for effective continuing education: convenience, relevance, individualization, self-assessment, independent learning, and a systematic approach. The International Virtual Medical School (IVIMEDS) provides a case study that illustrates how rapid growth of the Internet and e-learning can alter undergraduate education and has the potential to alter the nature of CME. Key components are a bank of reusable learning objects, a virtual practice with virtual patients, a learning-outcomes framework, and self-assessment instruments. Learning is facilitated by a curriculum map, guided-learning resources, "ask-the-expert" opportunities, and collaborative or peer-to-peer learning. The educational philosophy is "just-for-you" learning (learning customized to the content, educational strategy, and distribution needs of the individual physician) and "just-in-time" learning (learning resources available to physicians when they are required). Implications of the new learning technologies are profound. E-learning provides a bridge between the cutting edge of education and training and outdated procedures embedded in institutions and professional organizations. There are important implications, too, for globalization in medical education, for multiprofessional education, and for the continuum of education from undergraduate to postgraduate and continuing education.

  5. The effect of modeled absolute timing variability and relative timing variability on observational learning.

    PubMed

    Grierson, Lawrence E M; Roberts, James W; Welsher, Arthur M

    2017-05-01

    There is much evidence to suggest that skill learning is enhanced by skill observation. Recent research on this phenomenon indicates a benefit of observing variable/erred demonstrations. In this study, we explore whether it is variability within the relative organization or absolute parameterization of a movement that facilitates skill learning through observation. To do so, participants were randomly allocated into groups that observed a model with no variability, absolute timing variability, relative timing variability, or variability in both absolute and relative timing. All participants performed a four-segment movement pattern with specific absolute and relative timing goals prior to and following the observational intervention, as well as in a 24h retention test and transfers tests that featured new relative and absolute timing goals. Absolute timing error indicated that all groups initially acquired the absolute timing, maintained their performance at 24h retention, and exhibited performance deterioration in both transfer tests. Relative timing error revealed that the observation of no variability and relative timing variability produced greater performance at the post-test, 24h retention and relative timing transfer tests, but for the no variability group, deteriorated at absolute timing transfer test. The results suggest that the learning of absolute timing following observation unfolds irrespective of model variability. However, the learning of relative timing benefits from holding the absolute features constant, while the observation of no variability partially fails in transfer. We suggest learning by observing no variability and variable/erred models unfolds via similar neural mechanisms, although the latter benefits from the additional coding of information pertaining to movements that require a correction. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. The Place of Rock and Mineral Identification in Geoscience Programs

    NASA Astrophysics Data System (ADS)

    Nicholls, J.

    2011-12-01

    Geoscience programs traditionally required a significant amount of class and laboratory time for students to learn to identify Earth materials: minerals, rocks, soils, and fossils. Two decades ago, courses devoted to the mineral sciences, mineralogy and petrology, constituted approximately 20% of a geoscience program. Today, they make up between 5% and 10% of the courses in such a program. Two decades ago students spent their laboratory time learning to identify Earth materials. Today, they do the same thing, even though the time set aside for students to achieve proficiency is limited. A typical learning objective for a geoscience program reads: Identify common Earth materials and interpret their composition, origin and uses. The three underlined words convey the essence of the objective: We ask students to identify and interpret common Earth materials, which begs the questions: Do the common Earth materials provide adequate information for interpreting the composition, origin, and use of Earth materials? Do modern curricula contain enough laboratory time for students to learn to identify Earth materials? Do all geoscientists need to be able to identify Earth materials? The assemblage kyanite plus sillimanite is crucial for interpreting metamorphic history yet they are not common minerals. The IUGS classification contains 179 rock names yet we expect students to identify only a handful of them. The upper mantle is dominated by peridotite yet do geophysicists need to be able to identify peridotite in order to study the upper mantle? All geoscientists should be able to interpret Earth materials, at least at some level, and deduce the information Earth materials provide about Earth history and processes. Only a subset of geoscientists needs to learn how to identify them. Identification skills can be learned in upper level courses designed for those who will become mineral scientists. Many of the interpretations derived from Earth materials can be learned in the lower level courses required of all geoscience students.

  7. Synchronization of Chaotic Systems without Direct Connections Using Reinforcement Learning

    NASA Astrophysics Data System (ADS)

    Sato, Norihisa; Adachi, Masaharu

    In this paper, we propose a control method for the synchronization of chaotic systems that does not require the systems to be connected, unlike existing methods such as that proposed by Pecora and Carroll in 1990. The method is based on the reinforcement learning algorithm. We apply our method to two discrete-time chaotic systems with mismatched parameters and achieve M step delay synchronization. Moreover, we extend the proposed method to the synchronization of continuous-time chaotic systems.

  8. Workplace learning through collaboration in primary healthcare: A BEME realist review of what works, for whom and in what circumstances: BEME Guide No. 46.

    PubMed

    Mertens, Fien; de Groot, Esther; Meijer, Loes; Wens, Johan; Gemma Cherry, Mary; Deveugele, Myriam; Damoiseaux, Roger; Stes, Ann; Pype, Peter

    2018-02-01

    Changes in healthcare practice toward more proactive clinical, organizational and interprofessional working require primary healthcare professionals to learn continuously from each other through collaboration. This systematic review uses realist methodology to consolidate knowledge on the characteristics of workplace learning (WPL) through collaboration by primary healthcare professionals. Following several scoping searches, five electronic bibliographic databases were searched from January 1990 to December 2015 for relevant gray and published literature written in English, French, German and Dutch. Reviewers worked in pairs to identify relevant articles. A set of statements, based on the findings of our scoping searches, was used as a coding tree to analyze the papers. Interpretation of the results was done in alternating pairs, discussed within the author group and triangulated with stakeholders' views. Out of 6930 references, we included 42 publications that elucidated who, when, how and what primary healthcare professionals learn through collaboration. Papers were both qualitative and quantitative in design, and focused largely on WPL of collaborating general practitioners and nurses. No striking differences between different professionals within primary healthcare were noted. Professionals were often unaware of the learning that occurs through collaboration. WPL happened predominantly through informal discussions about patient cases and modeling for other professionals. Any professionals could both learn and facilitate others' learning. Outcomes were diverse, but contextualized knowledge seemed to be important. Primary care professionals' WPL is multifaceted. Existing social constructivist and social cognitivist learning theories form a framework from which to interpret these findings. Primary care policy makers and managers should ensure that professionals have access to protected time, earmarked for learning. Time is required for reflection, to learn new ways of interaction and to develop new habits within clinical practice.

  9. Why internet-based education?

    PubMed Central

    Gernsbacher, Morton Ann

    2015-01-01

    This essay illustrates five ways that Internet-based higher education can capitalize on fundamental principles of learning. Internet-based education can enable better mastery through distributed (shorter, more frequent) practice rather than massed (longer, less frequent) practice; it can optimize performance because it allows students to learn at their peak time of their day; it can deepen memory because it requires cheat-proof assignments and tests; it can promote critical thinking because it necessitates intellectual winnowing and sifting; and it can enhance writing skills by requiring students to write frequently and for a broad audience. PMID:25653625

  10. Why internet-based education?

    PubMed

    Gernsbacher, Morton Ann

    2014-01-01

    This essay illustrates five ways that Internet-based higher education can capitalize on fundamental principles of learning. Internet-based education can enable better mastery through distributed (shorter, more frequent) practice rather than massed (longer, less frequent) practice; it can optimize performance because it allows students to learn at their peak time of their day; it can deepen memory because it requires cheat-proof assignments and tests; it can promote critical thinking because it necessitates intellectual winnowing and sifting; and it can enhance writing skills by requiring students to write frequently and for a broad audience.

  11. 20 CFR 220.133 - Skill requirements.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... needs little or no judgment to do simple duties that can be learned on the job in a short period of time... claimant can usually learn to do the job in 30 days, and little job training and judgment are needed. The... machines which are automatic or operated by others); or (4) Machine tending. (c) Semi-skilled work. Semi...

  12. 20 CFR 404.1568 - Skill requirements.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... are handling, feeding and offbearing (that is, placing or removing materials from machines which are automatic or operated by others), or machine tending, and a person can usually learn to do the job in 30... judgment to do simple duties that can be learned on the job in a short period of time. The job may or may...

  13. 20 CFR 404.1568 - Skill requirements.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... are handling, feeding and offbearing (that is, placing or removing materials from machines which are automatic or operated by others), or machine tending, and a person can usually learn to do the job in 30... judgment to do simple duties that can be learned on the job in a short period of time. The job may or may...

  14. 20 CFR 220.133 - Skill requirements.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... needs little or no judgment to do simple duties that can be learned on the job in a short period of time... claimant can usually learn to do the job in 30 days, and little job training and judgment are needed. The... machines which are automatic or operated by others); or (4) Machine tending. (c) Semi-skilled work. Semi...

  15. Elaboration and Organization Strategies Used by Prospective Class Teachers While Studying Social Studies Education Textbooks

    ERIC Educational Resources Information Center

    Tay, Bayram

    2013-01-01

    Problem Statement: Students spend a considerable amount of their time studying from textbooks, which play an important role in their learning activities. The strategies students use to learn work as guides, requiring them to mentally process, make sense of and internalize information offered to them during the instructional process. Of these,…

  16. A Study of Postsecondary Competency-Based Education Practices in the Context of Disruptive Innovation Theory

    ERIC Educational Resources Information Center

    Mallett, Christopher

    2016-01-01

    The American public's interests are well-served by a strong, effective postsecondary education system. And yet the industry's predominant learning and service paradigm, one that credentials learning by measuring student's time on task and that treats all learners largely the same from a pacing and a requirements perspective is inconsistent with…

  17. The Power Dynamics and Politics of Survey Design: Measuring Workload Associated with Teaching, Administering and Supporting Work-Integrated Learning Courses

    ERIC Educational Resources Information Center

    Clark, Lindie; Rowe, Anna; Cantori, Alex; Bilgin, Ayse; Mukuria, Valentine

    2016-01-01

    Work-integrated learning (WIL) courses can be more time consuming and resource intensive to design, teach, administer and support than classroom-based courses, as they generally require different curricula and pedagogical approaches as well as additional administrative and pastoral responsibilities. Workload and resourcing issues are reported as…

  18. Handwriting Instruction for a High-Tech Society: Will Handwriting Be Necessary?

    ERIC Educational Resources Information Center

    Furner, Beatrice A.

    Assuming that some handwriting will be necessary in the computer age, questions remain as to the instructional techniques that facilitate learning in handwriting, whether the cost and time required to teach two forms of writing can be justified, and which form is learned more easily and is better suited for use in a technological age. Effective…

  19. From Homemaking to Solidarity: Global Engagement as Common Good in an Age of Global Populism

    ERIC Educational Resources Information Center

    Toms, Cynthia

    2018-01-01

    The challenging and rapidly evolving times in which we live require that students understand, analyze, and address the complex realities facing their nation and world. However, efforts in global learning have primarily focused on expansion of programs rather than student learning and meaningful community engagement. Building on Bouma-Prediger and…

  20. Influencing Factors for Adopting Technology Enhanced Learning in the Medical Schools of Punjab, Pakistan

    ERIC Educational Resources Information Center

    Iqbal, Shazia; Ahmad, Shahzad; Willis, Ian

    2017-01-01

    As the successful establishment of technology supported educational systems requires wide investment in terms of finances and faculty time, this study explores the influencing factors in the adoption of Technology Enhanced Learning (TEL) and the main barriers encountered during the use of TEL in Punjab, Pakistan. Semi-structured interviews were…

  1. English Digital Dictionaries as Valuable Blended Learning Tools for Palestinian College Students

    ERIC Educational Resources Information Center

    Dwaik, Raghad A. A.

    2015-01-01

    Digital technology has become an indispensable aspect of foreign language learning around the globe especially in the case of college students who are often required to finish extensive reading assignments within a limited time period. Such pressure calls for the use of efficient tools such as digital dictionaries to help them achieve their…

  2. 20 CFR 404.1568 - Skill requirements.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... are handling, feeding and offbearing (that is, placing or removing materials from machines which are automatic or operated by others), or machine tending, and a person can usually learn to do the job in 30... judgment to do simple duties that can be learned on the job in a short period of time. The job may or may...

  3. 20 CFR 220.133 - Skill requirements.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... needs little or no judgment to do simple duties that can be learned on the job in a short period of time... claimant can usually learn to do the job in 30 days, and little job training and judgment are needed. The... machines which are automatic or operated by others); or (4) Machine tending. (c) Semi-skilled work. Semi...

  4. 20 CFR 404.1568 - Skill requirements.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... are handling, feeding and offbearing (that is, placing or removing materials from machines which are automatic or operated by others), or machine tending, and a person can usually learn to do the job in 30... judgment to do simple duties that can be learned on the job in a short period of time. The job may or may...

  5. 20 CFR 220.133 - Skill requirements.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... needs little or no judgment to do simple duties that can be learned on the job in a short period of time... claimant can usually learn to do the job in 30 days, and little job training and judgment are needed. The... machines which are automatic or operated by others); or (4) Machine tending. (c) Semi-skilled work. Semi...

  6. 20 CFR 220.133 - Skill requirements.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... needs little or no judgment to do simple duties that can be learned on the job in a short period of time... claimant can usually learn to do the job in 30 days, and little job training and judgment are needed. The... machines which are automatic or operated by others); or (4) Machine tending. (c) Semi-skilled work. Semi...

  7. 20 CFR 404.1568 - Skill requirements.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... are handling, feeding and offbearing (that is, placing or removing materials from machines which are automatic or operated by others), or machine tending, and a person can usually learn to do the job in 30... judgment to do simple duties that can be learned on the job in a short period of time. The job may or may...

  8. Teacher Teams That Get Results: 61 Strategies for Sustaining and Renewing Professional Learning Communities

    ERIC Educational Resources Information Center

    Gregory, Gayle H.; Kuzmich, Lin

    2007-01-01

    Sustaining results-oriented team efforts is hard work, and achieving diversified solutions to complex issues over time requires commitment an creativity. To support the momentum of learning communities, this book provides an illustrated collection of ready-to-use tools and examples of plans in action for results-oriented faculty and staff.…

  9. Development and Evaluation of ALEC Micro-Wand IIIe (tradename) Training

    DTIC Science & Technology

    1994-08-01

    requires a learning environment in which trainees are active participants in the planning, delivery, and evaluation of instruct on. Both the procedural...is useful outside of the training environ - ,ment, it must take place in contexts that resemble the situations in which the know- ledge and skills will...that knowledge at later times. i2 USACERL TR TA-94/04 Transfer of Learning. Transfer of learning from the training environment to the job is a

  10. Reducing the computational footprint for real-time BCPNN learning

    PubMed Central

    Vogginger, Bernhard; Schüffny, René; Lansner, Anders; Cederström, Love; Partzsch, Johannes; Höppner, Sebastian

    2015-01-01

    The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware. PMID:25657618

  11. Reducing the computational footprint for real-time BCPNN learning.

    PubMed

    Vogginger, Bernhard; Schüffny, René; Lansner, Anders; Cederström, Love; Partzsch, Johannes; Höppner, Sebastian

    2015-01-01

    The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware.

  12. Multi Car Elevator Control by using Learning Automaton

    NASA Astrophysics Data System (ADS)

    Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

    We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

  13. [Providing successful education and further training: 10 tips].

    PubMed

    Brand, Paul L P; Boendermaker, Peter M; Venekamp, Ruud M

    2014-01-01

    Almost all physicians teach or provide postgraduate medical education from time to time. Although many people assume that there are 'born teachers' and 'those who will never learn to teach', teaching is an ability. The knowledge and skills required to teach well can be learned and practised. In this review article, we present 10 tips that will help the busy clinician to teach effectively. The 10 tips, which are based on the principles of adult learning, are: prepare your teaching session, involve the learners actively, connect to the learners' level of competence, define learning objectives, make the subject of your teaching relevant to the learners, use questions, be a good role model, vary your teaching methods, practise your teaching, and limit the amount of material you are teaching in each session.

  14. Active controllers and the time duration to learn a task

    NASA Technical Reports Server (NTRS)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  15. Learning classifier systems for single and multiple mobile robots in unstructured environments

    NASA Astrophysics Data System (ADS)

    Bay, John S.

    1995-12-01

    The learning classifier system (LCS) is a learning production system that generates behavioral rules via an underlying discovery mechanism. The LCS architecture operates similarly to a blackboard architecture; i.e., by posted-message communications. But in the LCS, the message board is wiped clean at every time interval, thereby requiring no persistent shared resource. In this paper, we adapt the LCS to the problem of mobile robot navigation in completely unstructured environments. We consider the model of the robot itself, including its sensor and actuator structures, to be part of this environment, in addition to the world-model that includes a goal and obstacles at unknown locations. This requires a robot to learn its own I/O characteristics in addition to solving its navigation problem, but results in a learning controller that is equally applicable, unaltered, in robots with a wide variety of kinematic structures and sensing capabilities. We show the effectiveness of this LCS-based controller through both simulation and experimental trials with a small robot. We then propose a new architecture, the Distributed Learning Classifier System (DLCS), which generalizes the message-passing behavior of the LCS from internal messages within a single agent to broadcast massages among multiple agents. This communications mode requires little bandwidth and is easily implemented with inexpensive, off-the-shelf hardware. The DLCS is shown to have potential application as a learning controller for multiple intelligent agents.

  16. Learning Abstract Physical Concepts from Experience: Design and Use of an RC Circuit

    NASA Astrophysics Data System (ADS)

    Parra, Alfredo; Ordenes, Jorge; de la Fuente, Milton

    2018-05-01

    Science learning for undergraduate students requires grasping a great number of theoretical concepts in a rather short time. In our experience, this is especially difficult when students are required to simultaneously use abstract concepts, mathematical reasoning, and graphical analysis, such as occurs when learning about RC circuits. We present a simple experimental model in this work that allows students to easily design, build, and analyze RC circuits, thus providing an opportunity to test personal ideas, build graphical descriptions, and explore the meaning of the respective mathematical models, ultimately gaining a better grasp of the concepts involved. The result suggests that the simple setup indeed helps untrained students to visualize the essential points of this kind of circuit.

  17. Learning in fully recurrent neural networks by approaching tangent planes to constraint surfaces.

    PubMed

    May, P; Zhou, E; Lee, C W

    2012-10-01

    In this paper we present a new variant of the online real time recurrent learning algorithm proposed by Williams and Zipser (1989). Whilst the original algorithm utilises gradient information to guide the search towards the minimum training error, it is very slow in most applications and often gets stuck in local minima of the search space. It is also sensitive to the choice of learning rate and requires careful tuning. The new variant adjusts weights by moving to the tangent planes to constraint surfaces. It is simple to implement and requires no parameters to be set manually. Experimental results show that this new algorithm gives significantly faster convergence whilst avoiding problems like local minima. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Impact of problem-based, active learning on graduation rates for 10 generations of Dutch medical students.

    PubMed

    Schmidt, Henk G; Cohen-Schotanus, Janke; Arends, Lidia R

    2009-03-01

    We aimed to study the effects of active-learning curricula on graduation rates of students and on the length of time needed to graduate. Graduation rates for 10 generations of students enrolling in the eight Dutch medical schools between 1989 and 1998 were analysed. In addition, time needed to graduate was recorded. Three of the eight schools had curricula emphasising active learning, small-group instruction and limited numbers of lectures; the other five had conventional curricula to varying degrees. Overall, the active-learning curricula graduated on average 8% more students per year, and these students graduated on average 5 months earlier than their colleagues from conventional curricula. Four hypotheses potentially explaining the effect of active learning on graduation rate and study duration were considered: (i) active-learning curricula promote the social and academic integration of students; (ii) active-learning curricula attract brighter students; (iii) active-learning curricula retain more poor students, and (iv) the active engagement of students with their study required by active-learning curricula induces better academic performance and, hence, lower dropout rates. The first three hypotheses had to be rejected. It was concluded that the better-learning hypothesis provides the most parsimonious account for the data.

  19. Hospitals as learning organizations: fostering innovation through interactive learning.

    PubMed

    Dias, Casimiro; Escoval, Ana

    2015-01-01

    The article aims to provide an analytical understanding of hospitals as "learning organizations." It further analyzes the development of learning organizations as a way to enhance innovation and performance in the hospital sector. The article pulls together primary data on organizational flexibility, innovation, and performance from 95 administrators from hospital boards in Portugal, collected through a survey, interviews with hospital's boards, and a nominal group technique with a panel of experts on health systems. Results show that a combination of several organizational traits of the learning organization enhances its capacity for innovation development. The logistic model presented reveals that hospitals classified as "advanced learning organizations" have 5 times more chance of developing innovation than "basic learning organizations." Empirical findings further pointed out incentives, standards, and measurement requirements as key elements for integration of service delivery systems and expansion of the current capacity for structured and real-time learning in the hospital sector. The major implication arising from this study is that policy needs to combine instruments that promote innovation opportunities and incentives, with instruments stimulating the further development of the core components of learning organizations. Such a combination of policy instruments has the potential to ensure a wide external cooperation through a learning infrastructure.

  20. In and Out 101 Activities to Enrich the Learning Experience.

    ERIC Educational Resources Information Center

    Johnson, Patricia; And Others

    Activities developed and used with children and adults participating in the program offerings of the Edwin Gould Outdoor Education Centers are presented. Information describing most activities includes name, description of the activity, objectives, supervision or help required, procedures, time involved, size of area required, materials,…

  1. 48 CFR 301.603-72 - FAC-C and HHS SAC certification requirements.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... HUMAN SERVICES GENERAL HHS ACQUISITION REGULATION SYSTEM Career Development, Contracting Authority, and... retention of certification, including the requirement to earn continuous learning points (CLPs). FAC-C... to employees for the first time at a department or agency.) (c) The FAC-C certification is based on...

  2. Usability of Computerized Lung Auscultation-Sound Software (CLASS) for learning pulmonary auscultation.

    PubMed

    Machado, Ana; Oliveira, Ana; Jácome, Cristina; Pereira, Marco; Moreira, José; Rodrigues, João; Aparício, José; Jesus, Luis M T; Marques, Alda

    2018-04-01

    The mastering of pulmonary auscultation requires complex acoustic skills. Computer-assisted learning tools (CALTs) have potential to enhance the learning of these skills; however, few have been developed for this purpose and do not integrate all the required features. Thus, this study aimed to assess the usability of a new CALT for learning pulmonary auscultation. Computerized Lung Auscultation-Sound Software (CLASS) usability was assessed by eight physiotherapy students using computer screen recordings, think-aloud reports, and facial expressions. Time spent in each task, frequency of messages and facial expressions, number of clicks and problems reported were counted. The timelines of the three methods used were matched/synchronized and analyzed. The tasks exercises and annotation of respiratory sounds were the ones requiring more clicks (median 132, interquartile range [23-157]; 93 [53-155]; 91 [65-104], respectively) and where most errors (19; 37; 15%, respectively) and problems (n = 7; 6; 3, respectively) were reported. Each participant reported a median of 6 problems, with a total of 14 different problems found, mainly related with CLASS functionalities (50%). Smile was the only facial expression presented in all tasks (n = 54). CLASS is the only CALT available that meets all the required features for learning pulmonary auscultation. The combination of the three usability methods identified advantages/disadvantages of CLASS and offered guidance for future developments, namely in annotations and exercises. This will allow the improvement of CLASS and enhance students' activities for learning pulmonary auscultation skills.

  3. Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System

    PubMed Central

    Mikaitis, Mantas; Pineda García, Garibaldi; Knight, James C.; Furber, Steve B.

    2018-01-01

    SpiNNaker is a digital neuromorphic architecture, designed specifically for the low power simulation of large-scale spiking neural networks at speeds close to biological real-time. Unlike other neuromorphic systems, SpiNNaker allows users to develop their own neuron and synapse models as well as specify arbitrary connectivity. As a result SpiNNaker has proved to be a powerful tool for studying different neuron models as well as synaptic plasticity—believed to be one of the main mechanisms behind learning and memory in the brain. A number of Spike-Timing-Dependent-Plasticity(STDP) rules have already been implemented on SpiNNaker and have been shown to be capable of solving various learning tasks in real-time. However, while STDP is an important biological theory of learning, it is a form of Hebbian or unsupervised learning and therefore does not explain behaviors that depend on feedback from the environment. Instead, learning rules based on neuromodulated STDP (three-factor learning rules) have been shown to be capable of solving reinforcement learning tasks in a biologically plausible manner. In this paper we demonstrate for the first time how a model of three-factor STDP, with the third-factor representing spikes from dopaminergic neurons, can be implemented on the SpiNNaker neuromorphic system. Using this learning rule we first show how reward and punishment signals can be delivered to a single synapse before going on to demonstrate it in a larger network which solves the credit assignment problem in a Pavlovian conditioning experiment. Because of its extra complexity, we find that our three-factor learning rule requires approximately 2× as much processing time as the existing SpiNNaker STDP learning rules. However, we show that it is still possible to run our Pavlovian conditioning model with up to 1 × 104 neurons in real-time, opening up new research opportunities for modeling behavioral learning on SpiNNaker. PMID:29535600

  4. Exogenous Attention Enables Perceptual Learning

    PubMed Central

    Szpiro, Sarit F. A.; Carrasco, Marisa

    2015-01-01

    Practice can improve visual perception, and these improvements are considered to be a form of brain plasticity. Training-induced learning is time-consuming and requires hundreds of trials across multiple days. The process of learning acquisition is understudied. Can learning acquisition be potentiated by manipulating visual attentional cues? We developed a protocol in which we used task-irrelevant cues for between-groups manipulation of attention during training. We found that training with exogenous attention can enable the acquisition of learning. Remarkably, this learning was maintained even when observers were subsequently tested under neutral conditions, which indicates that a change in perception was involved. Our study is the first to isolate the effects of exogenous attention and to demonstrate its efficacy to enable learning. We propose that exogenous attention boosts perceptual learning by enhancing stimulus encoding. PMID:26502745

  5. Learning inverse kinematics: reduced sampling through decomposition into virtual robots.

    PubMed

    de Angulo, Vicente Ruiz; Torras, Carme

    2008-12-01

    We propose a technique to speedup the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any requirement on the robot architecture, and thus, it is completely general. Parametrized self-organizing maps are particularly adequate for this type of learning, and permit comparing results directly obtained and through the decomposition. Experimentation shows that time reductions of up to two orders of magnitude are easily attained.

  6. Navy Planning, Programming, Budgeting, and Execution: A Reference Guide for Senior Leaders, Managers, and Action Officers

    DTIC Science & Technology

    2016-01-01

    one- or two-day course to members still trying to learn the abbreviations and acronyms is not an effective way to spend anyone’s time. It takes a...requirements genera- tion process. This guide does not take a position in that debate, but it can offer lessons learned over the past few decades. First...and some lead- ers spend most of their careers learning about just one. But one can be effective sooner and more consistently by remembering and

  7. Integrating Popular Web Applications in Classroom Learning Environments and Its Effects on Teaching, Student Learning Motivation and Performance

    ERIC Educational Resources Information Center

    Lin, Yen-Ting; Jou, Min

    2013-01-01

    Advancements in information and communication technology (ICT) allowed several tools and systems to be proposed for improving classroom experiences to both instructors and students. However, most of these tools were brand-new and stand-alone programs that require users to invest additional time and effort to become familiar with their use. This…

  8. Promoting Critical Reflection: An Evaluation of the Longer-Term Impact of a Substantial Faculty Development Program

    ERIC Educational Resources Information Center

    Calkins, Susanna; Harris, Muveddet

    2017-01-01

    For many faculty, critical reflection on teaching and learning requires space and time that is not readily available. For fifteen years, we have run a substantial year-long faculty development program designed to help participants: (1) reflect critically on their teaching and their students' learning; and (2) develop a project related to their…

  9. Trajectories of Engagement: A Repeated Cross-Sectional Investigation of Student Perceptions of an Online Learning Environment

    ERIC Educational Resources Information Center

    Palmer, Stuart; Holt, Dale

    2012-01-01

    Evaluations of online learning environments (OLEs) often present a snapshot of system use. It has been identified in the literature that extended evaluation is required to reveal statistically significant developments in the evolution of system use over time. The research presented here draws on student OLE evaluations surveys run over the period…

  10. Student Service Learning (SSL): Give a Little Time...Make a Big Difference. 2014-2015 Guide for Students and Parents

    ERIC Educational Resources Information Center

    Montgomery County Public Schools, 2014

    2014-01-01

    Student Service Learning (SSL) provides students the opportunity to actively participate in the community and build the skills they need to be successful students and citizens. This booklet provides information about the Maryland State Department of Education SSL graduation mandate. Completing 75 SSL hours is a requirement for high school…

  11. How to Stop Dealing with the Same Types of Problems Day after Day, Part 2

    ERIC Educational Resources Information Center

    Martin, Gary

    2005-01-01

    Gaining expertise in leadership requires time, commitment, an adequate knowledge base, and a working plan for learning and growth. Without a plan for learning, only tacit or "how-to" expertise is developed. Leaders often know how to solve the problems facing them, but fail to analyze and act on the underlying causes. This results in administrators…

  12. How to Stop Dealing with the Same Types of Problems Day after Day, Part 1

    ERIC Educational Resources Information Center

    Martin, Gary

    2005-01-01

    Gaining expertise in leadership requires time, commitment, an adequate knowledge base, and a working plan for learning and growth. Without a plan for learning, only tacit or "how-to" expertise is developed. Leaders often know how to solve the problems facing them, but they fail to analyze and act on the underlying causes. This results in…

  13. Learning to Love the Questions: How Essential Questions Promote Creativity and Deep Learning

    ERIC Educational Resources Information Center

    Wilhelm, Jeffrey D.

    2014-01-01

    Educators know that creativity and innovation involve questioning and the capacity to frame topics as problems to be solved. They know that we are living in a time of a new generation of standards, including the Common Core State Standards (CCSS). In the U.S., compliance with these standards requires that educators encourage students to ask…

  14. Student Learning and Instructor Investment in Online and Face-to-Face Natural Resources Courses

    ERIC Educational Resources Information Center

    Wuellner, Melissa R.

    2013-01-01

    Substantial growth in online education in the United States has prompted questions on the levels of student learning and satisfaction achieved and the amount of instructor time investment required in the online environment compared to the face-to-face (F2F) environment. To date, very few have studied these measurements in science courses, and none…

  15. The Conflagration of a Straw Stalking Horse: Or People Learn All the Time.

    ERIC Educational Resources Information Center

    Campbell, James H.

    If students are to learn as much as they can, they should themselves choose what they wish to study. College faculties who set up course requirements, for whatever ostensible reason, are usurping a function which belongs to the student. Explicit guidance, in the form of advice from a faculty member, is better than the implicit guidance of course…

  16. Creating Thoughtful Salespeople: Experiential Learning to Improve Critical Thinking Skills in Traditional and Online Sales Education

    ERIC Educational Resources Information Center

    Alvarez, Cecilia M. O.; Taylor, Kimberly A.; Rauseo, Nancy A.

    2015-01-01

    Most undergraduate marketing majors will spend at least some time in a sales role, and employers are requiring greater professionalism and more varied skill sets from their sales hires. In addition, there is an increasing demand for online and higher order learning in sales education. In response, this article proposes that sales courses using…

  17. Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

    PubMed

    Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

    2016-05-15

    Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Learning curves for single incision and conventional laparoscopic right hemicolectomy: a multidimensional analysis

    PubMed Central

    Park, Yoonah; Yong, Yuen Geng; Jung, Kyung Uk; Huh, Jung Wook; Cho, Yong Beom; Kim, Hee Cheol; Lee, Woo Yong; Chun, Ho-Kyung

    2015-01-01

    Purpose This study aimed to compare the learning curves and early postoperative outcomes for conventional laparoscopic (CL) and single incision laparoscopic (SIL) right hemicolectomy (RHC). Methods This retrospective study included the initial 35 cases in each group. Learning curves were evaluated by the moving average of operative time, mean operative time of every five consecutive cases, and cumulative sum (CUSUM) analysis. The learning phase was considered overcome when the moving average of operative times reached a plateau, and when the mean operative time of every five consecutive cases reached a low point and subsequently did not vary by more than 30 minutes. Results Six patients with missing data in the CL RHC group were excluded from the analyses. According to the mean operative time of every five consecutive cases, learning phase of SIL and CL RHC was completed between 26 and 30 cases, and 16 and 20 cases, respectively. Moving average analysis revealed that approximately 31 (SIL) and 25 (CL) cases were needed to complete the learning phase, respectively. CUSUM analysis demonstrated that 10 (SIL) and two (CL) cases were required to reach a steady state of complication-free performance, respectively. Postoperative complications rate was higher in SIL than in CL group, but the difference was not statistically significant (17.1% vs. 3.4%). Conclusion The learning phase of SIL RHC is longer than that of CL RHC. Early oncological outcomes of both techniques were comparable. However, SIL RHC had a statistically insignificant higher complication rate than CL RHC during the learning phase. PMID:25960990

  19. A model to teach concomitant patient communication during psychomotor skill development.

    PubMed

    Nicholls, Delwyn; Sweet, Linda; Muller, Amanda; Hyett, Jon

    2018-01-01

    Many health professionals use psychomotor or task-based skills in clinical practice that require concomitant communication with a conscious patient. Verbally engaging with the patient requires highly developed verbal communication skills, enabling the delivery of patient-centred care. Historically, priority has been given to learning the psychomotor skills essential to clinical practice. However, there has been a shift towards also ensuring competent communication with the patient during skill performance. While there is literature outlining the steps to teach and learn verbal communication skills, little is known about the most appropriate instructional approach to teach how to verbally engage with the patient when also learning to perform a task. A literature review was performed and it identified that there was no model or proven approach which could be used to integrate the learning of both psychomotor and communication skills. This paper reviews the steps to teach a communication skill and provides a suggested model to guide the acquisition and development of the concomitant -communication skills required with a patient at the time a psychomotor skill is performed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

    PubMed Central

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2013-01-01

    Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

  1. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    PubMed

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2013-04-01

    Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  2. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

    PubMed

    Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

    2013-01-01

    Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

  3. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

    PubMed Central

    Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

    2013-01-01

    Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

  4. D1 Receptor Activation in the Mushroom Bodies Rescues Sleep Loss Induced Learning Impairments in Drosophila

    PubMed Central

    Seugnet, Laurent; Suzuki, Yasuko; Vine, Lucy; Gottschalk, Laura; Shaw, Paul J

    2008-01-01

    Background Extended wakefulness disrupts acquisition of short term memories in mammals. However, the underlying molecular mechanisms triggered by extended waking and restored by sleep are unknown. Moreover, the neuronal circuits that depend on sleep for optimal learning remain unidentified. Results Learning was evaluated using Aversive Phototaxic Suppression (APS). In this task, flies learn to avoid light that is paired with an aversive stimulus (quinine /humidity). We demonstrate extensive homology in sleep deprivation induced learning impairment between flies and humans. Both 6 h and 12 h of sleep deprivation are sufficient to impair learning in Canton-S (Cs) flies. Moreover, learning is impaired at the end of the normal waking-day in direct correlation with time spent awake. Mechanistic studies indicate that this task requires intact mushroom bodies (MBs) and requires the Dopamine D1-like receptor (dDA1). Importantly, sleep deprivation induced learning impairments could be rescued by targeted gene expression of the dDA1 receptor to the MBs. Conclusion These data provide direct evidence that extended wakefulness disrupts learning in Drosophila. These results demonstrate that it is possible to prevent the effects of sleep deprivation by targeting a single neuronal structure and identify cellular and molecular targets adversely affected by extended waking in a genetically tractable model organism. PMID:18674913

  5. Unsupervised learning on scientific ocean drilling datasets from the South China Sea

    NASA Astrophysics Data System (ADS)

    Tse, Kevin C.; Chiu, Hon-Chim; Tsang, Man-Yin; Li, Yiliang; Lam, Edmund Y.

    2018-06-01

    Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

  6. q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.

    PubMed

    Golkov, Vladimir; Dosovitskiy, Alexey; Sperl, Jonathan I; Menzel, Marion I; Czisch, Michael; Samann, Philipp; Brox, Thomas; Cremers, Daniel

    2016-05-01

    Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.

  7. Just in time? Using QR codes for multi-professional learning in clinical practice.

    PubMed

    Jamu, Joseph Tawanda; Lowi-Jones, Hannah; Mitchell, Colin

    2016-07-01

    Clinical guidelines and policies are widely available on the hospital intranet or from the internet, but can be difficult to access at the required time and place. Clinical staff with smartphones could use Quick Response (QR) codes for contemporaneous access to relevant information to support the Just in Time Learning (JIT-L) paradigm. There are several studies that advocate the use of smartphones to enhance learning amongst medical students and junior doctors in UK. However, these participants are already technologically orientated. There are limited studies that explore the use of smartphones in nursing practice. QR Codes were generated for each topic and positioned at relevant locations on a medical ward. Support and training were provided for staff. Website analytics and semi-structured interviews were performed to evaluate the efficacy, acceptability and feasibility of using QR codes to facilitate Just in Time learning. Use was intermittently high but not sustained. Thematic analysis of interviews revealed a positive assessment of the Just in Time learning paradigm and context-sensitive clinical information. However, there were notable barriers to acceptance, including usability of QR codes and appropriateness of smartphone use in a clinical environment. The use of Just in Time learning for education and reference may be beneficial to healthcare professionals. However, alternative methods of access for less technologically literate users and a change in culture of mobile device use in clinical areas may be needed. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Technology Education Using a Novel Approach in e-Learning-Towards Optimizing the Quality of Learning Outcomes

    NASA Astrophysics Data System (ADS)

    Malkawi, M. I.; Hawarey, M. M.

    2012-04-01

    Ever since the advent of the new era in presenting taught material in Electronic Form, international bodies, academic institutions, public sectors, as well as specialized entities in the private sector, globally, have all persevered to exploit the power of Distance Learning and e-Learning to disseminate the knowledge in Science and Art using the ubiquitous World Wide Web and its supporting Internet and Internetworking. Many Science & Education-sponsoring bodies, like UNESCO, the European Community, and the World Bank have been keen at funding multinational Distance Learning projects, many of which were directed at an educated audience in certain technical areas. Many countries around the Middle East have found a number of interested European partners to launch funding requests, and were generally successful in their solicitation efforts for the needed funds from these funding bodies. Albeit their intricacies in generating a wealth of knowledge in electronic form, many of the e-Learning schemas developed thus far, have only pursued their goals in the most conventional of ways; In essence, there had been little innovation introduced to gain anything, if any, above traditional classroom lecturing, other than, of course, the gained advantage of the simultaneous online testing and evaluation of the learned material by the examinees. In a sincere effort to change the way in which people look at the merits of e-Learning, and seek the most out of it, we shall propose a novel approach aimed at optimizing the learning outcomes of presented materials. In this paper we propose what shall henceforth be called as Iterative e-Learning. In Iterative e-Learning, as the name implies, a student uses some form of electronic media to access course material in a specific subject. At the end of each phase (Section, Chapter, Session, etc.) on a specific topic, the student is assessed online of how much he/she would have achieved before he/she would move on. If the student fails, due to some delinquency on a particular topic, the online process of e-Learning would take the student at some more detailed and deeper level on the subject matter where he/she had failed; once the student bridges the gap, to this end, then the ongoing e-Learning process would carry him/her further up the next level of the subject matter he/she is pursuing. This process is carried on at all levels of learning: section, chapter, and course level. A student may not progress to the next course level before he/she would pass the entire course at 80% or more. If in the process of repeating some section, chapter, or a whole course, then the student shall be required to score a higher percentage than the mere 80% he was required to attain the first time around; say 5% more per iteration he/she makes. Here, students going through Iterative e-Learning shall be allowed to move on to the next level of learning sooner than others if the time that takes them to learn a particular topic is shorter than would normally require an average student to expend, provided, of course, they make it through all the required assessment phases. Unlike the traditional ways of classroom or online lecturing, a student going through Iterative e-Learning is expected to achieve a quality of learning never before achieved via standard pedagogical methodologies. With Iterative e-Learning, it is expected that poorly accredited academic institutions will be able, for the first time, to produce the quality of graduates who are more capable of competing for highly paying jobs globally, and to be of the quality of contributing in more industry-supported economies.

  9. Improving patient care through work-based learning.

    PubMed

    Chapman, Linda

    To record post-registration community nurses' perceptions of the impact of work-based learning on the quality of patient care. Ten nurses were interviewed. Each interviewee, who had successfully completed work-based learning programmes, was asked to describe their impact on the quality of patient care. The participants valued work-based learning. Four themes emerged where work-based learning contributed to improving the quality of care: increased health promotion, increased access to services, increased patient choice and reduced risk of infection. The relevance of studies and distance learning materials were perceived to be the main aspects that influenced changes in practice. The study provides insight into how work-based learning helped staff develop practice. It highlights that time for learning and mentoring are paramount for changes in practice to occur through work-based learning. Further studies are required to establish the best structure and style of distance learning materials needed to meet the needs of post-registration community nurses.

  10. Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

    PubMed

    Smith, Kevin; Horvath, Peter

    2014-06-01

    High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

  11. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

    PubMed

    Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

    2017-08-01

    When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or by restricting movement preparation time. Here, we compared the two methods. Restricting preparation times did not eliminate reaiming but was sufficient to suppress reaiming during adaptation with widely distributed targets. The self-report method produced a discrepancy in implicit learning estimated by subtracting aiming directions and implicit learning measured in no-feedback trials. Copyright © 2017 the American Physiological Society.

  12. A self-taught artificial agent for multi-physics computational model personalization.

    PubMed

    Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

    2016-12-01

    Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

  13. Mechanisms and time course of vocal learning and consolidation in the adult songbird.

    PubMed

    Warren, Timothy L; Tumer, Evren C; Charlesworth, Jonathan D; Brainard, Michael S

    2011-10-01

    In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry.

  14. Mechanisms and time course of vocal learning and consolidation in the adult songbird

    PubMed Central

    Tumer, Evren C.; Charlesworth, Jonathan D.; Brainard, Michael S.

    2011-01-01

    In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry. PMID:21734110

  15. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

    PubMed Central

    Frémaux, Nicolas; Gerstner, Wulfram

    2016-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

  16. Pharmacists' perceptions of facilitators and barriers to lifelong learning.

    PubMed

    Hanson, Alan L; Bruskiewitz, Ruth H; Demuth, James E

    2007-08-15

    To reevaluate facilitators of and barriers to pharmacists' participation in lifelong learning previously examined in a 1990 study. A survey instrument was mailed to 274 pharmacists who volunteered to participate based on a prior random sample survey. Data based on perceptions of facilitators and barriers to lifelong learning, as well as self-perception as a lifelong learner, were analyzed and compared to a similar 1990 survey. The response rate for the survey was 88%. The top 3 facilitators and barriers to lifelong learning from the 2003 and the 1990 samples were: (1) personal desire to learn; (2) requirement to maintain professional licensure; and (3) enjoyment/relaxation provided by learning as change of pace from the "routine." The top 3 barriers were: (1) job constraints; (2) scheduling (location, distance, time) of group learning activities; and (3) family constraints (eg, spouse, children, personal). Respondents' broad self-perception as lifelong learners continued to be highly positive overall, but remained less positive relative to more specific lifelong learning skills such as the ability to identify learning objectives as well as to evaluate learning outcomes. Little has changed in the last decade relative to how pharmacists view themselves as lifelong learners, as well as what they perceive as facilitators and barriers to lifelong learning. To address factors identified as facilitators and barriers, continuing education (CE) providers should focus on pharmacists' time constraints, whether due to employment, family responsibilities, or time invested in the educational activity itself, and pharmacists' internal motivations to learn (personal desire, enjoyment), as well as external forces such as mandatory CE for relicensure.

  17. Pharmacists' Perceptions of Facilitators and Barriers to Lifelong Learning

    PubMed Central

    Bruskiewitz, Ruth H.; DeMuth, James E.

    2007-01-01

    Objectives To reevaluate facilitators of and barriers to pharmacists' participation in lifelong learning previously examined in a 1990 study. Methods A survey instrument was mailed to 274 pharmacists who volunteered to participate based on a prior random sample survey. Data based on perceptions of facilitators and barriers to lifelong learning, as well as self-perception as a lifelong learner, were analyzed and compared to a similar 1990 survey. Results The response rate for the survey was 88%. The top 3 facilitators and barriers to lifelong learning from the 2003 and the 1990 samples were: (1) personal desire to learn; (2) requirement to maintain professional licensure; and (3) enjoyment/relaxation provided by learning as change of pace from the “routine.” The top 3 barriers were: (1) job constraints; (2) scheduling (location, distance, time) of group learning activities; and (3) family constraints (eg, spouse, children, personal). Respondents' broad self-perception as lifelong learners continued to be highly positive overall, but remained less positive relative to more specific lifelong learning skills such as the ability to identify learning objectives as well as to evaluate learning outcomes. Conclusions Little has changed in the last decade relative to how pharmacists view themselves as lifelong learners, as well as what they perceive as facilitators and barriers to lifelong learning. To address factors identified as facilitators and barriers, continuing education (CE) providers should focus on pharmacists' time constraints, whether due to employment, family responsibilities, or time invested in the educational activity itself, and pharmacists' internal motivations to learn (personal desire, enjoyment), as well as external forces such as mandatory CE for relicensure. PMID:17786254

  18. Exogenous Attention Enables Perceptual Learning.

    PubMed

    Szpiro, Sarit F A; Carrasco, Marisa

    2015-12-01

    Practice can improve visual perception, and these improvements are considered to be a form of brain plasticity. Training-induced learning is time-consuming and requires hundreds of trials across multiple days. The process of learning acquisition is understudied. Can learning acquisition be potentiated by manipulating visual attentional cues? We developed a protocol in which we used task-irrelevant cues for between-groups manipulation of attention during training. We found that training with exogenous attention can enable the acquisition of learning. Remarkably, this learning was maintained even when observers were subsequently tested under neutral conditions, which indicates that a change in perception was involved. Our study is the first to isolate the effects of exogenous attention and to demonstrate its efficacy to enable learning. We propose that exogenous attention boosts perceptual learning by enhancing stimulus encoding. © The Author(s) 2015.

  19. Removing Obstacles to the Pedagogical Changes Required by Jonassen's Vision of Authentic Technology-Enabled Learning

    ERIC Educational Resources Information Center

    Ertmer, Peggy A.; Ottenbreit-Leftwich, Anne

    2013-01-01

    Educators have been striving to achieve meaningful technology use in our K-12 classrooms for over 30 years. Yet, despite significant investments of time and money in infrastructure, training, and support "we have few assurances that [educators] are able to use technology for teaching and learning" (NEA, 2008, p. 1). In this article, we call for a…

  20. No Certification, No Training...No Problem: The Influence of Formative Beliefs and Learning Experiences in the Instructional Practice of New Independent School Teachers

    ERIC Educational Resources Information Center

    Smith, Derick Graham

    2012-01-01

    This study sought to answer the question: "To what extent do prior beliefs about and experiences of teaching and learning influence the instructional practices of new independent school teachers," who are generally not required to have any formal pedagogical training or hold teacher certification prior to beginning full-time employment.…

  1. A Little Bit Can Go a Long Way: An Examination of Required Service in the Basic Communication Course

    ERIC Educational Resources Information Center

    McIntyre, Kristen A.; Sellnow, Deanna D.

    2014-01-01

    This study examines the utility of service-learning pedagogy in the general education basic communication course to meet service-learning outcomes, with an emphasis on civic engagement. Results of the data suggest that students in both a one-time service site and multiple-site condition indicated that the service experience enhanced three of the…

  2. Learning by Doing or Learning by Studying the History of Statistics? A Response to "The Sociology of Teaching Graduate Statistics"

    ERIC Educational Resources Information Center

    Farkas, George

    2005-01-01

    This article presents the author's response to Timothy Patrick Moran's article "The Sociology of Teaching Graduate Statistics." Since 1972, the author has taught the required graduate-level social statistics course in three different departments. During this time, he has seen the truth of the concerns that Moran expresses at the beginning of his…

  3. Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps

    PubMed Central

    Bowen, Chris; Ye, Gu; Alterovitz, Ron

    2015-01-01

    In unstructured environments in people’s homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task’s motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method’s effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints. Note to Practitioners Motivated by the desire to enable robots to autonomously operate in cluttered home and workplace environments, this paper presents an approach for intuitively training a robot in a manner that enables it to repeat the task in novel scenarios and in the presence of unforeseen obstacles in the environment. Based on user-provided demonstrations of the task, our method learns features of the task that are consistent across the demonstrations and that we expect should be repeated by the robot when performing the task. We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. We demonstrate the effectiveness of our motion planner for scenarios requiring transferring a powder and pushing a button in environments with obstacles, and we plan to extend our results to more complex tasks in the future. PMID:26279642

  4. Cognitive task analysis-based design and authoring software for simulation training.

    PubMed

    Munro, Allen; Clark, Richard E

    2013-10-01

    The development of more effective medical simulators requires a collaborative team effort where three kinds of expertise are carefully coordinated: (1) exceptional medical expertise focused on providing complete and accurate information about the medical challenges (i.e., critical skills and knowledge) to be simulated; (2) instructional expertise focused on the design of simulation-based training and assessment methods that produce maximum learning and transfer to patient care; and (3) software development expertise that permits the efficient design and development of the software required to capture expertise, present it in an engaging way, and assess student interactions with the simulator. In this discussion, we describe a method of capturing more complete and accurate medical information for simulators and combine it with new instructional design strategies that emphasize the learning of complex knowledge. Finally, we describe three different types of software support (Development/Authoring, Run Time, and Post Run Time) required at different stages in the development of medical simulations and the instructional design elements of the software required at each stage. We describe the contributions expected of each kind of software and the different instructional control authoring support required. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

  5. Liminal Spaces and Learning Computing

    ERIC Educational Resources Information Center

    McCartney, Robert; Boustedt, Jonas; Eckerdal, Anna; Mostrom, Jan Erik; Sanders, Kate; Thomas, Lynda; Zander, Carol

    2009-01-01

    "Threshold concepts" are concepts that, among other things, transform the way a student looks at a discipline. Although the term "threshold" might suggest that the transformation occurs at a specific point in time, an "aha" moment, it seems more common (at least in computing) that a longer time period is required.…

  6. Benefits and Barriers of E-Learning for Staff Training in a Medical University.

    PubMed

    Franz, Stefan; Behrends, Marianne; Haack, Claudia; Marschollek, Michael

    2015-01-01

    Learning Management Systems (LMS) are a feasible solution to fulfill the various requirements for e-learning based training in a medical university. Using the LMS ILIAS, the Institute of Diagnostic and Interventional Radiology has designed an e-learning unit about data protection, which has been used by 73% of the department's employees in the first three months. To increase the use of e-learning for staff training, it is necessary to identify barriers and benefits, which encourage the use of e-learning. Therefore, we started an online survey to examine how the employees evaluate this learning opportunity. The results show that 87% of the employees had no technical problems and also competence of Information and Communication Technology (ICT) was no barrier. If anything, reported issues were time shortages and tight schedules. Therefore, short learning modules (less than 20 minutes) are preferred. Furthermore, temporal flexibility for learning is important for 83% of employees.

  7. Students' performance in phonological awareness, rapid naming, reading, and writing.

    PubMed

    Capellini, Simone Aparecida; Lanza, Simone Cristina

    2010-01-01

    phonological awareness, rapid naming, reading and writing in students with learning difficulties of a municipal public school. to characterize and compare the performance of students from public schools with and without learning difficulties in phonological awareness, rapid naming, reading and writing. participants were 60 students from the 2nd to the 4th grades of municipal public schools divided into 6 groups. Each group was composed by 10 students, being 3 groups of students without learning difficulties and 3 groups with students with learning difficulties. As testing procedure phonological awareness, rapid automatized naming, oral reading and writing under dictation assessments were used. the results highlighted the better performance of students with no learning difficulties. Students with learning difficulties presented a higher ratios considering time/speed in rapid naming tasks and, consequently, lower production in activities of phonological awareness and reading and writing, when compared to students without learning difficulties. students with learning difficulties presented deficits when considering the relationship between naming and automatization skills, and among lexical access, visual discrimination, stimulus frequency use and competition in using less time for code naming, i.e. necessary for the phoneme-grapheme conversion process required in the reading and writing alphabetic system like the Portuguese language.

  8. Fast temporal neural learning using teacher forcing

    NASA Technical Reports Server (NTRS)

    Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

    1992-01-01

    A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

  9. Fast temporal neural learning using teacher forcing

    NASA Technical Reports Server (NTRS)

    Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

    1995-01-01

    A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

  10. Effects of learning duration on implicit transfer.

    PubMed

    Tanaka, Kanji; Watanabe, Katsumi

    2015-10-01

    Implicit learning and transfer in sequence acquisition play important roles in daily life. Several previous studies have found that even when participants are not aware that a transfer sequence has been transformed from the learning sequence, they are able to perform the transfer sequence faster and more accurately; this suggests implicit transfer of visuomotor sequences. Here, we investigated whether implicit transfer could be modulated by the number of trials completed in a learning session. Participants learned a sequence through trial and error, known as the m × n task (Hikosaka et al. in J Neurophysiol 74:1652-1661, 1995). In the learning session, participants were required to successfully perform the same sequence 4, 12, 16, or 20 times. In the transfer session, participants then learned one of two other sequences: one where the button configuration Vertically Mirrored the learning sequence, or a randomly generated sequence. Our results show that even when participants did not notice the alternation rule (i.e., vertical mirroring), their total working time was less and their total number of errors was lower in the transfer session compared with those who performed a Random sequence, irrespective of the number of trials completed in the learning session. This result suggests that implicit transfer likely occurs even over a shorter learning duration.

  11. Just-in-Time Teaching Exercises to Engage Students in an Introductory-Level Dinosaur Course

    NASA Astrophysics Data System (ADS)

    Guertin, Laura A.; Zappe, Sarah E.; Kim, Heeyoung

    2007-12-01

    The Just-in-Time Teaching (JiTT) technique allows students to be engaged in course material outside of the classroom by answering web-based questions. The responses are summarized and presented to students in class with a follow-up active learning exercise. College students enrolled in an introductory-level general education geoscience course were surveyed over a two-semester period on their engagement level during lecture and perceived learning of course content. Data show that students are able to reflect on their prior knowledge and construct new knowledge with weekly graded JiTT exercises. Despite increasing and competing pressures outside of the classroom, students reported increased learning and engagement in a course with required weekly assignments.

  12. The implementation of multiple interprofessional integrated modules by health sciences faculty in Chile.

    PubMed

    Castillo-Parra, Silvana; Oyarzo Torres, Sandra; Espinoza Barrios, Mónica; Rojas-Serey, Ana María; Maya, Juan Diego; Sabaj Diez, Valeria; Aliaga Castillo, Verónica; Castillo Niño, Manuel; Romero Romero, Luis; Foster, Jennifer; Hawes Barrios, Gustavo

    2017-11-01

    Multiple interprofessional integrated modules (MIIM) 1 and 2 are two required, cross-curricular courses developed by a team of health professions faculty, as well as experts in education, within the Faculty of Medicine of the University of Chile. MIIM 1 focused on virtual cases requiring team decision-making in real time. MIIM 2 focused on a team-based community project. The evaluation of MIIM included student, teacher, and coordinator perspectives. To explore the perceptions of this interprofessional experience quantitative data in the form of standardised course evaluations regarding teaching methodology, interpersonal relations and the course organisation and logistics were gathered. In addition, qualitative perceptions were collected from student focus groups and meetings with tutors and coordinators. Between 2010 and 2014, 881 students enrolled in MIIM. Their evaluation scores rated interpersonal relations most highly, followed by organisation and logistics, and then teaching methodology. A key result was the learning related to interprofessional team work by the teaching coordinators, as well as the participating faculty. The strengths of this experience included student integration and construction of new knowledge, skill development in making decisions, and collective self-learning. Challenges included additional time management and tutors' role. This work requires valuation of an alternative way of learning, which is critical for the performance of future health professionals.

  13. Multiple Motor Learning Strategies in Visuomotor Rotation

    PubMed Central

    Saijo, Naoki; Gomi, Hiroaki

    2010-01-01

    Background When exposed to a continuous directional discrepancy between movements of a visible hand cursor and the actual hand (visuomotor rotation), subjects adapt their reaching movements so that the cursor is brought to the target. Abrupt removal of the discrepancy after training induces reaching error in the direction opposite to the original discrepancy, which is called an aftereffect. Previous studies have shown that training with gradually increasing visuomotor rotation results in a larger aftereffect than with a suddenly increasing one. Although the aftereffect difference implies a difference in the learning process, it is still unclear whether the learned visuomotor transformations are qualitatively different between the training conditions. Methodology/Principal Findings We examined the qualitative changes in the visuomotor transformation after the learning of the sudden and gradual visuomotor rotations. The learning of the sudden rotation led to a significant increase of the reaction time for arm movement initiation and then the reaching error decreased, indicating that the learning is associated with an increase of computational load in motor preparation (planning). In contrast, the learning of the gradual rotation did not change the reaction time but resulted in an increase of the gain of feedback control, suggesting that the online adjustment of the reaching contributes to the learning of the gradual rotation. When the online cursor feedback was eliminated during the learning of the gradual rotation, the reaction time increased, indicating that additional computations are involved in the learning of the gradual rotation. Conclusions/Significance The results suggest that the change in the motor planning and online feedback adjustment of the movement are involved in the learning of the visuomotor rotation. The contributions of those computations to the learning are flexibly modulated according to the visual environment. Such multiple learning strategies would be required for reaching adaptation within a short training period. PMID:20195373

  14. Prolonged Perceptual Learning of Positional Acuity in Adult Amblyopia

    PubMed Central

    Li, Roger W; Klein, Stanley A; Levi, Dennis M

    2009-01-01

    Amblyopia is a developmental abnormality that results in physiological alterations in the visual cortex and impairs form vision. It is often successfully treated by patching the sound eye in infants and young children, but is generally considered to be untreatable in adults. However, a number of recent studies suggest that repetitive practice of a visual task using the amblyopic eye results in improved performance in both children and adults with amblyopia. These perceptual learning studies have used relatively brief periods of practice; however, clinical studies have shown that the time-constant for successful patching is long. The time-constant for perceptual learning in amblyopia is still unknown. Here we show that the time-constant for perceptual learning depends on the degree of amblyopia. Severe amblyopia requires more than 50 hours (≈35,000 trials) to reach plateau, yielding as much as a five-fold improvement in performance at a rate of ≈1.5% per hour. There is significant transfer of learning from the amblyopic to the dominant eye, suggesting that the learning reflects alterations in higher decision stages of processing. Using a reverse correlation technique, we document, for the first time, a dynamic retuning of the amblyopic perceptual decision template and a substantial reduction in internal spatial distortion. These results show that the mature amblyopic brain is surprisingly malleable, and point to more intensive treatment methods for amblyopia. PMID:19109504

  15. Teaching with Moodle in Soil Science

    NASA Astrophysics Data System (ADS)

    Roca, Núria

    2014-05-01

    Soil is a 3-dimensional body with properties that reflect the impact of climate, vegetation, fauna, man and topography on the soil's parent material over a variable time span. Therefore, soil is integral to many ecological and social systems and it holds potential solutions for many of the world's economic and scientific problems as climate change or scarcity of food and water. The teaching of Soil Science, as a natural science in its own right, requires principles that reflect the unique features and behaviour of soil and the practices of soil scientists. It could be argued that a unique set of teaching practices applies to Soil Science; however specific teaching practices are scarce in literature. The present work was triggered by the need to develop new techniques of teaching to speed up the learning process and to experiment with new methods of teaching. For such, it is necessary to adopt virtual learning environment to new learning requirements regarding Soil Science. This paper proposes a set of e-teaching techniques (as questionnaires, chats as well as forums) introduced in Moodle virtual learning Environment in order to increase student motivation and interest in Soil Science. Such technologies can be used to: a)Increase the amount of time a teacher allots for student reflection after asking a question and before a student responds (wait-time). This practice increases the quantity and quality of students' answers. The students give longer responses, students give more evidence for their ideas and conclusions, students speculate and hypothesize more and more students participated in responding. Furthermore, students ask more questions and talk more to other students. b)Improve active learning, an essential paradigm in education. In contrast to learning-before-doing, we propose to focus on learning-in-doing, a model where learners are increasingly involved in the authentic practices of communities through learning conversations and activities involving expert practitioners, educators and peers. c)Introduce the specific specialised technical language (jargon) gradually. The excessive use of Soil Science jargon confuses students and frequently put obstacles in the way of learning. d)Encourage the students to take responsibility for their learning, continuous assessment with direct error correction and content feedback and peer review with comments sent to forum. The student interest to learn using e-project is clearly strong.

  16. A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning.

    PubMed

    Tan, Javan; Quek, Chai

    2010-06-01

    Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-associative structures under time-invariant conditions. To maximize their operative value for online reasoning, these self-sustaining mechanisms must also be able to reorganize fuzzy-associative knowledge in real-time dynamic environments. Hence, it is critical to recognize that they would require self-reorganizational skills to rebuild fluid associative structures when their existing organizations fail to respond well to changing circumstances. In this light, while Hebbian theory (Hebb, 1949) is the basic computational framework for associative learning, it is less attractive for time-variant online learning because it suffers from stability limitations that impedes unlearning. Instead, this paper adopts the Bienenstock-Cooper-Munro (BCM) theory of neurological learning via meta-plasticity principles (Bienenstock et al., 1982) that provides for both online associative and dissociative learning. For almost three decades, BCM theory has been shown to effectively brace physiological evidence of synaptic potentiation (association) and depression (dissociation) into a sound mathematical framework for computational learning. This paper proposes an interpretation of the BCM theory of meta-plasticity for an online self-reorganizing fuzzy-associative learning system to realize online-reasoning capabilities. Experimental findings are twofold: 1) the analysis using S&P-500 stock index illustrated that the self-reorganizing approach could follow the trajectory shifts in the time-variant S&P-500 index for about 60 years, and 2) the benchmark profiles showed that the fuzzy-associative approach yielded comparable results with other fuzzy-precision models with similar online objectives.

  17. Twelve tips for implementing effective service learning.

    PubMed

    Playford, Denese; Bailey, Susan; Fisher, Colleen; Stasinska, Ania; Marshall, Lewis; Gawlinski, Michele; Young, Susan

    2017-11-24

    Service learning is an educational methodology that facilitates transformation of students' knowledge, attitudes and attitudes around holistic care through work with community organizations. To implement academically, defensible service learning requires faculty endorsement, consideration of course credit, an enthusiastic champion able to negotiate agreements with organizations, organizations' identification of their own projects so they are willing to both fund and supervise them, curricular underpinning that imparts the project skills necessary for success, embedding at a time when students' clinical identity is being formed, small packets of curriculum elements delivered "just in time" as students engage with their project, flexible online platform/s, assessment that is organically related to the project, providing cross cultural up-skilling, and focused on the students' responsibility for their own product. The result is a learning experience that is engaging for medical students, links the university to the community, and encourages altruism which is otherwise reported to decline through medical school.

  18. Counterbalancing clinical supervision and independent practice: case studies in learning thoracic epidural catheter insertion.

    PubMed

    Johnson, T

    2010-12-01

    Thoracic epidural catheter placement is an example of a demanding and high-risk clinical skill that junior anaesthetists need to learn by experience and under the supervision of consultants. This learning is known to present challenges that require further study. Ten consultant and 10 trainee anaesthetists in a teaching hospital were interviewed about teaching and learning this skill in the operating theatre, and a phenomenological analysis of their experience was performed. Trainee participation was limited by time pressure, lack of familiarity with consultants, and consultants' own need for clinical experience. There was a particular tension between safe and effective consultant practice and permitting trainees' independence. Three distinct stages of participation and assistance were identified from reports of ideal practice: early (part-task or basic procedure, consultant always present giving instruction and feedback), middle (independent practice with straightforward cases without further instruction), and late (skill extension and transfer). Learning assistance provided by consultants varied, but it was often not matched to the trainees' stages of learning. Negotiation of participation and assistance was recognized as being useful, but it did not happen routinely. There are many obstacles to trainees' participation in thoracic epidural catheter insertion, and learning assistance is not matched to need. A more explicit understanding of stages of learning is required to benefit the learning of this and other advanced clinical skills.

  19. Real-time Mainshock Forecast by Statistical Discrimination of Foreshock Clusters

    NASA Astrophysics Data System (ADS)

    Nomura, S.; Ogata, Y.

    2016-12-01

    Foreshock discremination is one of the most effective ways for short-time forecast of large main shocks. Though many large earthquakes accompany their foreshocks, discreminating them from enormous small earthquakes is difficult and only probabilistic evaluation from their spatio-temporal features and magnitude evolution may be available. Logistic regression is the statistical learning method best suited to such binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Statistical learning methods can keep learning discreminating features from updating catalog and give probabilistic recognition of forecast in real time. We estimated a non-linear function of foreshock proportion by smooth spline bases and evaluate the possibility of foreshocks by the logit function. In this study, we classified foreshocks from earthquake catalog by the Japan Meteorological Agency by single-link clustering methods and learned spatial and temporal features of foreshocks by the probability density ratio estimation. We use the epicentral locations, time spans and difference in magnitudes for learning and forecasting. Magnitudes of main shocks are also predicted our method by incorporating b-values into our method. We discuss the spatial pattern of foreshocks from the classifier composed by our model. We also implement a back test to validate predictive performance of the model by this catalog.

  20. Hidden Process Models

    DTIC Science & Technology

    2009-12-18

    cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

  1. Rapid Training of Information Extraction with Local and Global Data Views

    DTIC Science & Technology

    2012-05-01

    56 xiii 4.1 An example of words and their bit string representations. Bold ones are transliterated Arabic words...Natural Language Processing ( NLP ) community faces new tasks and new domains all the time. Without enough labeled data of a new task or a new domain to...conduct supervised learning, semi-supervised learning is particularly attractive to NLP researchers since it only requires a handful of labeled examples

  2. What Will They Learn? 2014-15. A Survey of Core Requirements at Our Nation's Colleges and Universities

    ERIC Educational Resources Information Center

    Kempson, Lauri; Lewin, Greg; Burt, Evan; Poliakoff, Michael

    2014-01-01

    A college education is rightly part of the American Dream. It is seen as the ticket to success in career and community, a credential that repays the investment of time and money in higher education that students, families, and taxpayers make. In "What Will They Learn?"™ the authors take as a premise that the core purpose of attending…

  3. Man-Machine Interaction: Operator.

    DTIC Science & Technology

    1984-06-01

    EASTER OF SCIENCI I COMPUTER SCIENCE Justification from the Distribution/ Availability Codes NAVal POSTGBADUATE SCHOOL Avail and/or June 1984 Dlst...Few pecple, if any, remember everything they see or hear but an anazingly large amount of material can be recalled years after it has been acquired...and skill, learning takes tine. The time required for the learning process will generally vary with the coaplexity of the material cr task he is

  4. Understanding and responding the students in learning mathematics through the differentiated instruction

    NASA Astrophysics Data System (ADS)

    Hapsari, T.; Darhim; Dahlan, J. A.

    2018-05-01

    This research discusses the differentiated instruction, a mathematic learning which is as expected by the students in connection with the differentiated instruction itself, its implementation, and the students’ responses. This research employs a survey method which involves 62 students as the research respondents. The mathematics learning types required by the students and their responses to the differentiated instruction are examined through questionnaire and interview. The mathematics learning types in orderly required by the students, from the highest frequency cover the easily understood instructions, slowly/not rushing teaching, fun, not complicated, interspersed with humour, various question practices, not too serious, and conducive class atmosphere for the instructions. Implementing the differentiated instruction is not easy. The teacher should be able to constantly assess the students, s/he should have good knowledge of relevant materials and instructions, and properly prepare the instructions, although it is time-consuming. The differentiated instruction is implemented on the instructions of numerical pattern materials. The strategies implemented are flexible grouping, tiered assignment, and compacting. The students positively respond the differentiated learning instruction that they become more motivated and involved in the instruction.

  5. Teacher perceptions of usefulness of mobile learning devices in rural secondary science classrooms

    NASA Astrophysics Data System (ADS)

    Tighe, Lisa

    The internet and easy accessibility to a wide range of digital content has created the necessity for teachers to embrace and integrate digitial media in their curriculums. Although there is a call for digital media integration in curriculum by current learning standards, rural schools continue to have access to fewer resources due to limited budgets, potentially preventing teachers from having access to the most current technology and science instructional materials. This dissertation identifies the perceptions rural secondary science teachers have on the usefulness of mobile learning devices in the science classroom. The successes and challenges in using mobile learning devices in the secondary classroom were also explored. Throughout this research, teachers generally supported the integration of mobile devices in the classroom, while harboring some concerns relating to student distractability and the time required for integrating mobile devices in exisiting curriculum. Quantitative and qualitative data collected through surveys, interviews, and classroom observations revealed that teachers perceive that mobile devices bring benefits such as ease of communication and easy access to digitial information. However, there are perceived challenges with the ability to effectively communicate complex scientific information via mobile devices, distractibility of students, and the time required to develop effective curriculum to integrate digital media into the secondary science classroom.

  6. Drosophila Learn Opposing Components of a Compound Food Stimulus

    PubMed Central

    Das, Gaurav; Klappenbach, Martín; Vrontou, Eleftheria; Perisse, Emmanuel; Clark, Christopher M.; Burke, Christopher J.; Waddell, Scott

    2014-01-01

    Summary Dopaminergic neurons provide value signals in mammals and insects [1–3]. During Drosophila olfactory learning, distinct subsets of dopaminergic neurons appear to assign either positive or negative value to odor representations in mushroom body neurons [4–9]. However, it is not known how flies evaluate substances that have mixed valence. Here we show that flies form short-lived aversive olfactory memories when trained with odors and sugars that are contaminated with the common insect repellent DEET. This DEET-aversive learning required the MB-MP1 dopaminergic neurons that are also required for shock learning [7]. Moreover, differential conditioning with DEET versus shock suggests that formation of these distinct aversive olfactory memories relies on a common negatively reinforcing dopaminergic mechanism. Surprisingly, as time passed after training, the behavior of DEET-sugar-trained flies reversed from conditioned odor avoidance into odor approach. In addition, flies that were compromised for reward learning exhibited a more robust and longer-lived aversive-DEET memory. These data demonstrate that flies independently process the DEET and sugar components to form parallel aversive and appetitive olfactory memories, with distinct kinetics, that compete to guide learned behavior. PMID:25042590

  7. Cooperative learning as applied to resident instruction in radiology reporting.

    PubMed

    Mueller, Donald; Georges, Alexandra; Vaslow, Dale

    2007-12-01

    The study is designed to evaluate the effectiveness of an active form of resident instruction, cooperative learning, and the residents' response to that form of instruction. The residents dictated three sets of reports both before and after instruction in radiology reporting using the cooperative learning method. The reports were evaluated for word count, Flesch-Kincaid grade level, advancement on clinical spectrum, clarity, and comparison to prior reports. The reports were evaluated for changes in performance characteristics between the pre- and postinstruction dictations. The residents' response to this form of instruction was evaluated by means of a questionnaire. The instruction was effective in changing the resident dictations. The results became shorter (P<.035), more complex (P<.0126), and demonstrated increased advancement on clinical spectrum (P<.0204). The resident response to this form of instruction was positive. One hundred percent or respondents indicated enjoyment working with their groups. Seventy-five percent stated they would like to participate in more cooperative learning activities. The least positive responses related to the amount of time devoted to the project. Sixty-three percent of respondents stated that the time devoted to the project was appropriate. Cooperative learning can be an effective tool in the setting of the radiology residency. Instructional time requirements must be strongly considered in designing a cooperative learning program.

  8. Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

    PubMed

    Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

    2016-06-21

    Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Employment and First-Year College Achievement: The Role of Self-Regulation and Motivation

    ERIC Educational Resources Information Center

    Huie, Faye C.; Winsler, Adam; Kitsantas, Anastasia

    2014-01-01

    Students often work in order to meet monetary requirements for college. However, employment reduces the time students can devote to their studies, which can hinder performance. This study examined whether motivation (self-efficacy goal orientation) and self-regulated learning (help-seeking, metacognitive self-regulation, time management and effort…

  10. LO + EPSS = Just-in-Time Reuse of Content to Support Employee Performance

    ERIC Educational Resources Information Center

    Nguyen, Frank; Hanzel, Matthew

    2007-01-01

    Those involved in training know that creating instructional materials can become a tedious, repetitive process. They also know that business conditions often require training interventions to be delivered in ways that are not ideally structured or timed. This article examines the notion that learning objects can be reused and adapted for…

  11. Designing, implementing and evaluating an online problem-based learning (PBL) environment--a pilot study.

    PubMed

    Ng, Manwa L; Bridges, Susan; Law, Sam Po; Whitehill, Tara

    2014-01-01

    Problem-based learning (PBL) has been shown to be effective for promoting student competencies in self-directed and collaborative learning, critical thinking, self-reflection and tackling novel situations. However, the need for face-to-face interactions at the same place and time severely limits the potential of traditional PBL. The requirements of space and for meeting at a specific location at the same time create timetabling difficulties. Such limitations need to be tackled before all potentials of PBL learning can be realized. The present study aimed at designing and implementing an online PBL environment for undergraduate speech/language pathology students, and assessing the associated pedagogical effectiveness. A group of eight PBL students were randomly selected to participate in the study. They underwent 4 weeks of online PBL using Adobe Connect. Upon completion of the experiment, they were assessed via a self-reported questionnaire and quantitative comparison with traditional PBL students based on the same written assignment. The questionnaire revealed that all participating students enjoyed online PBL, without any perceived negative effects on learning. Online PBL unanimously saved the students travel time to and from school. Statistical analysis indicated no significant difference in assignment grades between the online and traditional PBL groups, indicating that online PBL learning appears to be similarly effective as traditional face-to-face PBL learning.

  12. E-learning implementation in superior technical educational system

    NASA Astrophysics Data System (ADS)

    Musca, Gavril; Mihalache, Andrei; Musca, Elena

    2016-11-01

    E-learning methods apply to most modern and various domains but also represent a great tool for the mechanical educational system where there are a lot of sustained efforts for its implementation. Using, administrating and maintaining an e-learning system for a certain field of study requires knowledge related to computation system's utilization but also the understanding the working mechanisms behind it that allows the system to be fully customized in order to be perfect fitted to the user's needs and requirements. A Moodle based test is evaluated from several points of views such as coherence clarity, concise content, information synthesis capacity and the presentation mode which makes the difference between clear or fuzzy graphical representations or terms. The authors appreciate that the ability of managing information in real time by the professor is a decisive decision in order to successfully implement an e-learning web platform. Updating information and structuring trainee's activities from thoroughgoing study up to their individual proposals for conceived applications leads to a better understanding and practical knowledge of theory.

  13. CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.

    PubMed

    Hamanaka, Masatoshi; Taneishi, Kei; Iwata, Hiroaki; Ye, Jun; Pei, Jianguo; Hou, Jinlong; Okuno, Yasushi

    2017-01-01

    Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

    NASA Astrophysics Data System (ADS)

    Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

    2017-09-01

    Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in exascale simulations.

  15. A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    PubMed

    Halloran, John T; Rocke, David M

    2018-05-04

    Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

  16. Negotiating the use of formative assessment for learning in an era of accountability testing

    NASA Astrophysics Data System (ADS)

    Yin, Xinying

    The purpose of this collaborative action research was to understand how science educators can negotiate the tension between integrating formative assessment (FA) for students' learning and meeting the need for standardized summative assessment (testing) from a critical perspective. Using formative assessment in the era of accountability testing was a process in which the science educators identified the ways that the standardized testing system constrained the teacher's use of FA to improve students' learning, sought solutions to overcome the obstacles and came to understand how FA can be utilized to neutralize the power relationship between the institutional requirement and classroom teaching and learning. The challenge of doing FA under the pressure of standardized testing mainly lie in two dimensions: one was the demand of teaching all the desired standard-based content to all students in a limited amount of time and the sufficient time and flexibility required by doing FA to improve students' understanding, the other was the different levels of knowledge and forms of knowledge representation on FA and tests. The negotiation of doing FA for teaching standards and preparing students for tests entailed six aspects for the collaborative team, including clarifying teaching objectives, reconstructing instructional activities, negotiating with time constraints, designing effective FA activities, attending to students' needs in doing FA, and modifying end-of-unit tests to better assess the learning goals. As the teacher's instructional goals evolved to be more focused on conceptual understanding of standards and more thorough understanding for less activities, she perceived doing FA for learning and preparing students for standardized tests as more congruent. By integrating both divergent and convergent FA into instruction as well as modifying tests to be more aligned with standards, students' learning were enhanced and they were also being prepared for tests. This study added to our understandings about the relationship between formative assessment and summative accountability tests in science education and classroom teachers' conceptions and practices in making the relationship more coherent for learning. It also provided implications for science teacher professional development of formative assessment and for educational accountability policies.

  17. Blended Learning Implementation in “Guru Pembelajar” Program

    NASA Astrophysics Data System (ADS)

    Mahdan, D.; Kamaludin, M.; Wendi, H. F.; Simanjuntak, M. V.

    2018-02-01

    The rapid development of information and communication technology (ICT), especially the internet, computers and communication devices requires the innovation in learning; one of which is Blended Learning. The concept of Blended Learning is the mixing of face-to-face learning models by learning online. Blended learning used in the learner teacher program organized by the Indonesian department of education and culture that a program to improve the competence of teachers, called “Guru Pembelajar” (GP). Blended learning model is perfect for learning for teachers, due to limited distance and time because online learning can be done anywhere and anytime. but the problems that arise from the implementation of this activity are many teachers who do not follow the activities because teachers, especially the elderly do not want to follow the activities because they cannot use computers and the internet, applications that are difficult to understand by participants, unstable internet connection in the area where the teacher lives and facilities and infrastructure are not adequate.

  18. Dictionary learning and time sparsity in dynamic MRI.

    PubMed

    Caballero, Jose; Rueckert, Daniel; Hajnal, Joseph V

    2012-01-01

    Sparse representation methods have been shown to tackle adequately the inherent speed limits of magnetic resonance imaging (MRI) acquisition. Recently, learning-based techniques have been used to further accelerate the acquisition of 2D MRI. The extension of such algorithms to dynamic MRI (dMRI) requires careful examination of the signal sparsity distribution among the different dimensions of the data. Notably, the potential of temporal gradient (TG) sparsity in dMRI has not yet been explored. In this paper, a novel method for the acceleration of cardiac dMRI is presented which investigates the potential benefits of enforcing sparsity constraints on patch-based learned dictionaries and TG at the same time. We show that an algorithm exploiting sparsity on these two domains can outperform previous sparse reconstruction techniques.

  19. Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

    PubMed

    Pedretti, G; Milo, V; Ambrogio, S; Carboni, R; Bianchi, S; Calderoni, A; Ramaswamy, N; Spinelli, A S; Ielmini, D

    2017-07-13

    Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10 4 ) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

  20. Role Played by the Passage of Time in Reversal Learning.

    PubMed

    Goarin, Estelle H F; Lingawi, Nura W; Laurent, Vincent

    2018-01-01

    Reversal learning is thought to involve an extinction-like process that inhibits the expression of the initial learning. However, behavioral evidence for this inhibition remains difficult to interpret as various procedures have been employed to study reversal learning. Here, we used a discrimination task in rats to examine whether the inhibition produced by reversal learning is as sensitive to the passage of time as the inhibition produced by extinction. Experiment 1 showed that when tested immediately after reversal training, rats were able to use the reversed contingencies to solve the discrimination task in an outcome-specific manner. This ability to use outcome-specific information was lost when a delay was inserted between reversal training and test. However, interpretation of these data was made difficult by a potential floor effect. This concern was addressed in Experiment 2 in which it was confirmed that the passage of time impaired the ability of the rats to use the reversed contingencies in an outcome-specific manner to solve the task. Further, it revealed that the delay between initial learning and test was not responsible for this impairment. Additional work demonstrated that solving the discrimination task was unaffected by Pavlovian extinction but that the discriminative stimuli were able to block conditioning to a novel stimulus, suggesting that Pavlovian processes were likely to contribute to solving the discrimination. We therefore concluded that the expression of reversal and extinction learning do share the same sensitivity to the effect of time. However, this sensitivity was most obvious when we assessed outcome-specific information following reversal learning. This suggests that the processes involved in reversal learning are somehow distinct from those underlying extinction learning, as the latter has usually been found to leave outcome-specific information relatively intact. Thus, the present study reveals that a better understanding of the mechanisms supporting reversal training requires assessing the impact that this training exerts on the content of learning rather than performance per se .

  1. Role Played by the Passage of Time in Reversal Learning

    PubMed Central

    Goarin, Estelle H. F.; Lingawi, Nura W.; Laurent, Vincent

    2018-01-01

    Reversal learning is thought to involve an extinction-like process that inhibits the expression of the initial learning. However, behavioral evidence for this inhibition remains difficult to interpret as various procedures have been employed to study reversal learning. Here, we used a discrimination task in rats to examine whether the inhibition produced by reversal learning is as sensitive to the passage of time as the inhibition produced by extinction. Experiment 1 showed that when tested immediately after reversal training, rats were able to use the reversed contingencies to solve the discrimination task in an outcome-specific manner. This ability to use outcome-specific information was lost when a delay was inserted between reversal training and test. However, interpretation of these data was made difficult by a potential floor effect. This concern was addressed in Experiment 2 in which it was confirmed that the passage of time impaired the ability of the rats to use the reversed contingencies in an outcome-specific manner to solve the task. Further, it revealed that the delay between initial learning and test was not responsible for this impairment. Additional work demonstrated that solving the discrimination task was unaffected by Pavlovian extinction but that the discriminative stimuli were able to block conditioning to a novel stimulus, suggesting that Pavlovian processes were likely to contribute to solving the discrimination. We therefore concluded that the expression of reversal and extinction learning do share the same sensitivity to the effect of time. However, this sensitivity was most obvious when we assessed outcome-specific information following reversal learning. This suggests that the processes involved in reversal learning are somehow distinct from those underlying extinction learning, as the latter has usually been found to leave outcome-specific information relatively intact. Thus, the present study reveals that a better understanding of the mechanisms supporting reversal training requires assessing the impact that this training exerts on the content of learning rather than performance per se. PMID:29740293

  2. Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children

    PubMed Central

    Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

    2016-01-01

    Background Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. Methods The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6–12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. Results TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. Conclusions These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures. PMID:27384671

  3. Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children.

    PubMed

    Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

    2016-01-01

    Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6-12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures.

  4. Supervised Learning Applied to Air Traffic Trajectory Classification

    NASA Technical Reports Server (NTRS)

    Bosson, Christabelle S.; Nikoleris, Tasos

    2018-01-01

    Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

  5. DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

    PubMed

    Kim, Lok-Won

    2018-05-01

    Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

  6. The Earth System Course at the University of Oklahoma: Science and Pedagogy Aimed at Pre-service Teachers

    NASA Astrophysics Data System (ADS)

    Postawko, S.; Soreghan, M.; Marek, E.

    2005-12-01

    Traditionally, education majors at the University of Oklahoma took either Introduction to Physical Geology or Introduction to Meteorology to fulfill their physical sciences requirement. Science education majors were required to take both courses. These courses are large-enrollment lecture type courses, with required lab sections taught by graduate teaching assistants. Beginning in 1997, faculty from the Colleges of Education and Geosciences at the University of Oklahoma began working together to provide effective earth science education for pre-service teachers. The first step in this collaboration was the development of a new course on The Earth System that focuses on Earth as a whole rather than on the more narrow focus of either the geology or meteorology courses. The new course, which was taught for the first time in the Spring of 2001, covers a number of major themes related to Earth Science, including the Carbon Cycle, Earth Materials, Plate Tectonics, Atmosphere and Oceans. The particular concepts within each theme were chosen based on two criteria: 1) alignment with content advocated by national (NSES) and state (Priority Academic Student Skills-PASS) standards; and 2) they are amenable to a learning cycle pedagogical approach. Besides an interdisciplinary approach to the content, the new course features pedagogical innovations. In lieu of independent laboratory and lecture times, we scheduled two class periods of longer duration, so that active learning, involving hands-on activities and experiments were possible throughout each class period. The activities modeled the learning-cycle approach with an exploration, concept invention, and an expansion phase (Marek and Cavallo, 1997). Therefore, the pre-service teachers experienced the learning cycle in practice prior to learning the theory in their upper division "methods" course. In the first 3 years that the course was taught, students were given surveys early in the semester and at the end of the semester. The surveys aimed to both assess the students' learning and retention (compared to students in the more traditional Introductory Geology course, who were given similar surveys), and solicit the students' opinions of the inquiry-based learning approach compared to more traditional lecture/lab classroom teaching methods.

  7. Active Learning with Irrelevant Examples

    NASA Technical Reports Server (NTRS)

    Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

    2006-01-01

    Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

  8. Critical Success Factor for Implementing Vocational Blended Learning

    NASA Astrophysics Data System (ADS)

    Dewi, K. C.; Ciptayani, P. I.; Surjono, H. D.; Priyanto

    2018-01-01

    Blended learning provides many benefits to the flexibility of time, place and situation constraints. The research’s objectives was describing the factors that determine the successful implementation of blended learning in vocational higher education. The research used a qualitative approach, data collected through observations and interviews by questionnare based on the CSFs indicators refers to TAM and Kliger. Data analysis was inductive method. The result provided an illustration that the success of vocational blended learning implementation was largely determined by the selection of instructional models that are inline with learning achievement target. The effectiveness of blended learning required the existence of policy support, readiness of IT infrastructure. Changing lecturer’s culture by utilizing ICT can also encourage the accelerated process of successful implementation. It can concluded that determinant factor of successful implementation of blended learning in vocational education is determined by teacher’s ability in mastering the pedagogical knowledge of designing instructional models.

  9. On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

    NASA Technical Reports Server (NTRS)

    Jen, Chian-Li; Tilwick, Leon

    2000-01-01

    This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

  10. Cashew Nut Positioning during Stone Tool Use by Wild Bearded Capuchin Monkeys (Sapajus libidinosus).

    PubMed

    Falótico, Tiago; Luncz, Lydia V; Svensson, Magdalena S; Haslam, Michael

    2016-01-01

    Wild capuchin monkeys (Sapajus libidinosus) at Serra da Capivara National Park, Brazil, regularly use stone tools to break open cashew nuts (Anacardium spp.). Here we examine 2 approaches used by the capuchins to position the kidney-shaped cashew nuts on an anvil before striking with a stone tool. Lateral positioning involves placing the nut on its flatter, more stable side, therefore requiring less attention from the monkey during placement. However, the less stable and never previously described arched position, in which the nut is balanced with its curved side uppermost, requires less force to crack the outer shell. We observed cashew nut cracking in a field experimental setting. Only 6 of 20 adults, of both sexes, were observed to deliberately place cashew nuts in an arched position, which may indicate that the technique requires time and experience to learn. We also found that use of the arched position with dry nuts, but not fresh, required, in 63% of the time, an initial processing to remove one of the cashew nut lobes, creating a more stable base for the arch. This relatively rare behaviour appears to have a complex ontogeny, but further studies are required to establish the extent to which social learning is involved. © 2017 S. Karger AG, Basel.

  11. Formation in professional education: an examination of the relationship between theories of meaning and theories of the self.

    PubMed

    Benner, Patricia

    2011-08-01

    Being formed through learning a practice is best understood within a constitutive theory of meaning as articulated by Charles Taylor. Disengaged views of the person cannot account for the formative changes in a person's identity and capacities upon learning a professional practice. Representational or correspondence theories of meaning cannot account for formation. Formation occurs over time because students actively seek and take up new concerns and learn new knowledge and skills. Engaged situated reasoning about underdetermined practice situations requires well-formed skillful clinicians caring for particular patients in particular situations.

  12. Model-free distributed learning

    NASA Technical Reports Server (NTRS)

    Dembo, Amir; Kailath, Thomas

    1990-01-01

    Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights. The perturbation may be either via noise sources or orthogonal signals. The invariance to detailed network structure mitigates large variability between supposedly identical networks as well as implementation defects. This local, regular, and completely distributed mechanism requires no central control and involves only a few global signals. Thus it allows for integrated on-chip learning in large analog and optical networks.

  13. Linking Mission to Learning Activities for Assurance of Learning

    ERIC Educational Resources Information Center

    Yeung, Shirley Mo-ching

    2011-01-01

    Can accreditation-related requirements and mission statements measure learning outcomes? This study focuses on triangulating accreditation-related requirements with mission statements and learning activities to learning outcomes. This topic has not been comprehensively explored in the past. After looking into the requirements of AACSB, ISO, and…

  14. Displays mounted on cutting blocks reduce the learning curve in navigated total knee arthroplasty.

    PubMed

    Schnurr, Christoph; Eysel, Peer; König, Dietmar Pierre

    2011-01-01

    The use of computer navigation in total knee arthroplasty (TKA) improves the implant alignment but increases the operation time. Studies have shown that the operation time is further prolonged due to the surgeon's learning curve, and longer operation times have been associated with higher morbidity risks. It has been our hypothesis that an improvement in the human-machine interface might reduce the time required during the learning curve. Accordingly, we asked whether the use of navigation devices with a display fixed on the surgical instruments would reduce the operation time in navigated TKAs performed by navigation beginners. Thirty medical students were randomized and used two navigation devices in rotation: these were the Kolibri® device with an external display and the Dash® device with a display that was fixed on the cutting blocks. The time for adjustment of the tibial and femoral cutting blocks on knee models while using these devices was measured. A significant time reduction was demonstration when the Dash® device was used: The time reduction was 21% for the tibial block (p = 0.007), 40% for the femoral block (p < 0.001), and 32% for the whole procedure (p < 0.001). The integrated display, fixed on surgical instruments in a manner similar to a spirit level, seems to be more user-friendly for navigation beginners. Hence, unproductive time losses during the learning curve may be diminished.

  15. Experiential and authentic learning approaches in vaccine management.

    PubMed

    Kartoglu, Umit; Vesper, James; Teräs, Hanna; Reeves, Thomas

    2017-04-19

    A high level of concern is placed on the storage, handling, transportation, and distribution of vaccines and other pharmaceutical products, particularly those that are time and temperature sensitive. While active and passive cooling equipment and monitoring devices are important, it is the various personnel responsible for executing and writing procedures, designing and operating systems, and investigating problems and helping prevent them who are paramount in establishing and maintaining a "cold chain" for time and temperature sensitive pharmaceutical products (TTSPPs). These professionals must possess the required competencies, knowledge, skills and abilities so they can effectively perform these activities with appropriate levels of expertise. These are complex tasks that require the development of higher cognitive skills that cannot be adequately addressed through professional development opportunities based on simple information delivery and content acquisition. This paper describes two unique learning solutions (one on a bus called the "wheels course" and the other online called "e-learning") that have been developed by WHO Global Learning Opportunities (WHO/GLO) to provide participants with opportunities not just to learn about cold chain systems or vaccine management, but, rather, to develop high levels of expertise in their respective fields through experiential and authentic learning activities. In these interactive learning environments, participants have opportunities to address real-life situations in contexts similar to what they may face in their own work environments and develop solutions and critical thinking skills they can apply when they return to their jobs. This paper further delineates the managerial and operational vaccine management functions encompassed in these two unique learning environments. The paper also describes the alignment of the objectives addressed in the "wheels course" and the e-learning version with effective vaccine management (EVM) criteria as prescribed by WHO. The paper concludes with an example of a real world product developed by course graduates (specifically a decision tree that is now used by some national programmes). These types of products, valuable in their own right, often emerge when learning environments based on authentic learning principles are designed and implemented as they were by WHO/GLO. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  16. Learning style versus time spent studying and career choice: Which is associated with success in a combined undergraduate anatomy and physiology course?

    PubMed

    Farkas, Gary J; Mazurek, Ewa; Marone, Jane R

    2016-01-01

    The VARK learning style is a pedagogical focus in health care education. This study examines relationships of course performance vs. VARK learning preference, study time, and career plan among students enrolled in an undergraduate anatomy and physiology course at a large urban university. Students (n = 492) from the fall semester course completed a survey consisting of the VARK questionnaire, gender, academic year, career plans, and estimated hours spent per week in combined classroom and study time. Seventy-eight percent of students reported spending 15 or fewer hours per week studying. Study time and overall course score correlated significantly for the class as a whole (r = 0.111, P = 0.013), which was mainly due to lecture (r = 0.118, P = 0.009) performance. No significant differences were found among students grouped by learning styles. When corrected for academic year, overall course scores (mean ± SEM) for students planning to enter dentistry, medicine, optometry or pharmacy (79.89 ± 0.88%) were significantly higher than those of students planning to enter physical or occupational therapies (74.53 ± 1.15%; P = 0.033), as well as nurse/physician assistant programs (73.60 ± 1.3%; P = 0.040). Time spent studying was not significantly associated with either learning style or career choice. Our findings suggest that specific career goals and study time, not learning preferences, are associated with better performance among a diverse group of students in an undergraduate anatomy and physiology course. However, the extent to which prior academic preparation, cultural norms, and socioeconomic factors influenced these results requires further investigation. © 2015 American Association of Anatomists.

  17. Learning style versus time spent studying and career choice: Which is associated with success in a combined undergraduate anatomy and physiology course?

    PubMed Central

    Farkas, Gary J.; Mazurek, Ewa; Marone, Jane R.

    2016-01-01

    The VARK learning style is a pedagogical focus in health care education. This study examines relationships of course performance vs. VARK learning preference, study time, and career plan among students enrolled in an undergraduate anatomy and physiology course at a large urban university. Students (n = 492) from the fall semester course completed a survey consisting of the VARK questionnaire, gender, academic year, career plans, and estimated hours spent per week in combined classroom and study time. Seventy-eight percent of students reported spending 15 or fewer hours per week studying. Study time and overall course score correlated significantly for the class as a whole (r = 0.111, P = 0.013), which was mainly due to lecture (r = 0.118, P = 0.009) performance. No significant differences were found among students grouped by learning styles. When corrected for academic year, overall course scores (mean ± SEM) for students planning to enter “medicines” (79.89 ± 0.88%) were significantly higher than those of students planning to enter physical/occupational therapies (74.53 ± 1.15%; P = 0.033), as well as nurse/physician assistant programs (73.60 ± 1.3%; P = 0.040). Time spent studying was not significantly associated with either learning style or career choice. Our findings suggest that specific career goals and study time, not learning preferences, are associated with better performance among a diverse group of students in an undergraduate anatomy/physiology course. However, the extent to which prior academic preparation, cultural norms, and socioeconomic factors influenced these results requires further investigation. PMID:26301828

  18. MO-F-16A-03: AAPM Online Learning Support of New ABR MOC Requirements

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Bloch, C; Ogburn, J; Woodward, M

    2014-06-15

    In 2002 the American Board of Radiology (ABR) discontinued issuing lifetime board certification. After that time diplomates received a timelimited certificate and must participate in the Maintenance of Certification (MOC) program in order to maintain their certification. Initially certificates were issued with a 10 year expiration period and the MOC had requirements to be met over that 10 year period. The goal was to demonstrate continuous maintenance of clinical competency, however some diplomates were attempting to fulfill most or all of the requirements near the end of the 10 year period. This failed to meet the continuous aspect of themore » goal and so the ABR changed to a sliding 3-year window. This was done to recognize that not every year would be the same, but that diplomates should be able to maintain a reasonable average over any 3 year period.A second significant change occurred in 2013. The initial requirements included 20 selfassessment modules (SAMs) over the original 10 year term. SAMs are a special type of continuing education (CE) credit that were an addition to the 250 standard CE credits required over the 10 year period. In 2013, however, the new requirement is 75 CE credits over the previous 3 years, of which 25 must include self-assessment. Effectively this raised the self-assessment requirement from 20 in 10 years to 25 in 3 years. Previously SAMs were an interactive presentation available in limited quantities at live meetings. However, the new requirement is not for SAMs but CE-SA which includes SAMs, but also includes the online quizzes provided at the AAPM online learning center. All credits earned at the AAPM online learning center fulfill the ABR SA requirement.This talk will be an interactive demonstration of the AAPM online learning center along with a discussion of the MOC requirements.« less

  19. Toward accelerating landslide mapping with interactive machine learning techniques

    NASA Astrophysics Data System (ADS)

    Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

    2013-04-01

    Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

  20. Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.

    PubMed

    Kaufhold, John P; Tsai, Philbert S; Blinder, Pablo; Kleinfeld, David

    2012-08-01

    A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by "learned threshold relaxation"; (2) removes spurious segments by "learning to eliminate deletion candidate strands"; and (3) enforces consistency in the joint space of learned vascular graph corrections through "consistency learning." Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with >800(3) voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5-21% and strand elimination performance by 18-57%. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. Copyright © 2012 Elsevier B.V. All rights reserved.

  1. Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments

    PubMed Central

    Kaufhold, John P.; Tsai, Philbert S.; Blinder, Pablo; Kleinfeld, David

    2012-01-01

    A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by “learned threshold relaxation”; (2) removes spurious segments by “learning to eliminate deletion candidate strands”; and (3) enforces consistency in the joint space of learned vascular graph corrections through “consistency learning.” Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with > 8003 voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5 to 21 % and strand elimination performance by 18 to 57 %. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. PMID:22854035

  2. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

    PubMed

    Newton, Katherine M; Peissig, Peggy L; Kho, Abel Ngo; Bielinski, Suzette J; Berg, Richard L; Choudhary, Vidhu; Basford, Melissa; Chute, Christopher G; Kullo, Iftikhar J; Li, Rongling; Pacheco, Jennifer A; Rasmussen, Luke V; Spangler, Leslie; Denny, Joshua C

    2013-06-01

    Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats. To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies. The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University. By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results. Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.

  3. Lessons Learned From an Epidemiologist-Led Countywide Community Assessment for Public Health Emergency Response (CASPER) in Oregon.

    PubMed

    Repp, Kimberly K; Hawes, Eva; Rees, Kathleen J; Vorderstrasse, Beth; Mohnkern, Sue

    2018-06-07

    Conducting a large-scale Community Assessment for Public Health Emergency Response (CASPER) in a geographically and linguistically diverse county presents significant methodological challenges that require advance planning. The Centers for Disease Control and Prevention (CDC) has adapted methodology and provided a toolkit for a rapid needs assessment after a disaster. The assessment provides representative data of the sampling frame to help guide effective distribution of resources. This article describes methodological considerations and lessons learned from a CASPER exercise conducted by Washington County Public Health in June 2016 to assess community emergency preparedness. The CDC's CASPER toolkit provides detailed guidance for exercises in urban areas where city blocks are well defined with many single family homes. Converting the exercise to include rural areas with challenging geographical terrain, including accessing homes without public roads, required considerable adjustments in planning. Adequate preparations for vulnerable populations with English linguistic barriers required additional significant resources. Lessons learned are presented from the first countywide CASPER exercise in Oregon. Approximately 61% of interviews were completed, and 85% of volunteers reported they would participate in another CASPER exercise. Results from the emergency preparedness survey will be presented elsewhere. This experience indicates the most important considerations for conducting a CASPER exercise are oversampling clusters, overrecruiting volunteers, anticipating the actual cost of staff time, and ensuring timely language services are available during the event.

  4. Integrating Real-Time Antecedent Rubrics via Blackboard™ into a Community College General Psychology Class

    ERIC Educational Resources Information Center

    Goomas, David

    2015-01-01

    Numerous studies have reported on the innovative and effective delivery of online course content by community colleges, but not much has been done on how learning management systems (LMS) can deliver real-time (immediate data delivery) antecedents that inform students of performance requirements. This pilot study used Blackboard's™ interactive…

  5. Assessment Competence through In Situ Practice for Preservice Educators

    ERIC Educational Resources Information Center

    Hurley, Kimberly S.

    2018-01-01

    Effective assessment is the cornerstone of the teaching and learning process and a benchmark of teaching competency. P-12 assessment in physical activity can be complex and dynamic, often requiring a set of skills developed over time through trial and error. Novice teachers have limited time to hone an assessment process that can showcase their…

  6. Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates

    NASA Astrophysics Data System (ADS)

    Borgnat, Pierre; Flandrin, Patrick; Richard, Cédric; Ferrari, André; Amoud, Hassan; Honeine, Paul

    2012-03-01

    Time-frequency representations provide a powerful tool for nonstationary signal analysis and classification, supporting a wide range of applications [12]. As opposed to conventional Fourier analysis, these techniques reveal the evolution in time of the spectral content of signals. In Ref. [7,38], time-frequency analysis is used to test stationarity of any signal. The proposed method consists of a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogate signals for defining the null hypothesis of stationarity and, based upon this information, to derive statistical tests. An open question remains, however, about how to choose relevant time-frequency features. Over the last decade, a number of new pattern recognition methods based on reproducing kernels have been introduced. These learning machines have gained popularity due to their conceptual simplicity and their outstanding performance [30]. Initiated by Vapnik’s support vector machines (SVM) [35], they offer now a wide class of supervised and unsupervised learning algorithms. In Ref. [17-19], the authors have shown how the most effective and innovative learning machines can be tuned to operate in the time-frequency domain. This chapter follows this line of research by taking advantage of learning machines to test and quantify stationarity. Based on one-class SVM, our approach uses the entire time-frequency representation and does not require arbitrary feature extraction. Applied to a set of surrogates, it provides the domain boundary that includes most of these stationarized signals. This allows us to test the stationarity of the signal under investigation. This chapter is organized as follows. In Section 22.2, we introduce the surrogate data method to generate stationarized signals, namely, the null hypothesis of stationarity. The concept of time-frequency learning machines is presented in Section 22.3, and applied to one-class SVM in order to derive a stationarity test in Section 22.4. The relevance of the latter is illustrated by simulation results in Section 22.5.

  7. Training-Dependent Associative Learning Induced Neocortical Structural Plasticity: A Trace Eyeblink Conditioning Analysis

    PubMed Central

    Chau, Lily S.; Prakapenka, Alesia V.; Zendeli, Liridon; Davis, Ashley S.; Galvez, Roberto

    2014-01-01

    Studies utilizing general learning and memory tasks have suggested the importance of neocortical structural plasticity for memory consolidation. However, these learning tasks typically result in learning of multiple different tasks over several days of training, making it difficult to determine the synaptic time course mediating each learning event. The current study used trace-eyeblink conditioning to determine the time course for neocortical spine modification during learning. With eyeblink conditioning, subjects are presented with a neutral, conditioned stimulus (CS) paired with a salient, unconditioned stimulus (US) to elicit an unconditioned response (UR). With multiple CS-US pairings, subjects learn to associate the CS with the US and exhibit a conditioned response (CR) when presented with the CS. Trace conditioning is when there is a stimulus free interval between the CS and the US. Utilizing trace-eyeblink conditioning with whisker stimulation as the CS (whisker-trace-eyeblink: WTEB), previous findings have shown that primary somatosensory (barrel) cortex is required for both acquisition and retention of the trace-association. Additionally, prior findings demonstrated that WTEB acquisition results in an expansion of the cytochrome oxidase whisker representation and synaptic modification in layer IV of barrel cortex. To further explore these findings and determine the time course for neocortical learning-induced spine modification, the present study utilized WTEB conditioning to examine Golgi-Cox stained neurons in layer IV of barrel cortex. Findings from this study demonstrated a training-dependent spine proliferation in layer IV of barrel cortex during trace associative learning. Furthermore, findings from this study showing that filopodia-like spines exhibited a similar pattern to the overall spine density further suggests that reorganization of synaptic contacts set the foundation for learning-induced neocortical modifications through the different neocortical layers. PMID:24760074

  8. Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

    NASA Astrophysics Data System (ADS)

    Wang, Shibing; Baron, Stanislas; Kachwala, Nishrin; Kallingal, Chidam; Sun, Dezheng; Shu, Vincent; Fong, Weichun; Li, Zero; Elsaid, Ahmad; Gao, Jin-Wei; Su, Jing; Ser, Jung-Hoon; Zhang, Quan; Chen, Been-Der; Howell, Rafael; Hsu, Stephen; Luo, Larry; Zou, Yi; Zhang, Gary; Lu, Yen-Wen; Cao, Yu

    2018-03-01

    Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time. Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO). Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy. ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges. In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.

  9. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    PubMed

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Operant Conditioning in Honey Bees (Apis mellifera L.): The Cap Pushing Response.

    PubMed

    Abramson, Charles I; Dinges, Christopher W; Wells, Harrington

    2016-01-01

    The honey bee has been an important model organism for studying learning and memory. More recently, the honey bee has become a valuable model to understand perception and cognition. However, the techniques used to explore psychological phenomena in honey bees have been limited to only a few primary methodologies such as the proboscis extension reflex, sting extension reflex, and free flying target discrimination-tasks. Methods to explore operant conditioning in bees and other invertebrates are not as varied as with vertebrates. This may be due to the availability of a suitable response requirement. In this manuscript we offer a new method to explore operant conditioning in honey bees: the cap pushing response (CPR). We used the CPR to test for difference in learning curves between novel auto-shaping and more traditional explicit-shaping. The CPR protocol requires bees to exhibit a novel behavior by pushing a cap to uncover a food source. Using the CPR protocol we tested the effects of both explicit-shaping and auto-shaping techniques on operant conditioning. The goodness of fit and lack of fit of these data to the Rescorla-Wagner learning-curve model, widely used in classical conditioning studies, was tested. The model fit well to both control and explicit-shaping results, but only for a limited number of trials. Learning ceased rather than continuing to asymptotically approach the physiological most accurate possible. Rate of learning differed between shaped and control bee treatments. Learning rate was about 3 times faster for shaped bees, but for all measures of proficiency control and shaped bees reached the same level. Auto-shaped bees showed one-trial learning rather than the asymptotic approach to a maximal efficiency. However, in terms of return-time, the auto-shaped bees' learning did not carry over to the covered-well test treatments.

  11. Operant Conditioning in Honey Bees (Apis mellifera L.): The Cap Pushing Response

    PubMed Central

    Abramson, Charles I.; Dinges, Christopher W.; Wells, Harrington

    2016-01-01

    The honey bee has been an important model organism for studying learning and memory. More recently, the honey bee has become a valuable model to understand perception and cognition. However, the techniques used to explore psychological phenomena in honey bees have been limited to only a few primary methodologies such as the proboscis extension reflex, sting extension reflex, and free flying target discrimination-tasks. Methods to explore operant conditioning in bees and other invertebrates are not as varied as with vertebrates. This may be due to the availability of a suitable response requirement. In this manuscript we offer a new method to explore operant conditioning in honey bees: the cap pushing response (CPR). We used the CPR to test for difference in learning curves between novel auto-shaping and more traditional explicit-shaping. The CPR protocol requires bees to exhibit a novel behavior by pushing a cap to uncover a food source. Using the CPR protocol we tested the effects of both explicit-shaping and auto-shaping techniques on operant conditioning. The goodness of fit and lack of fit of these data to the Rescorla-Wagner learning-curve model, widely used in classical conditioning studies, was tested. The model fit well to both control and explicit-shaping results, but only for a limited number of trials. Learning ceased rather than continuing to asymptotically approach the physiological most accurate possible. Rate of learning differed between shaped and control bee treatments. Learning rate was about 3 times faster for shaped bees, but for all measures of proficiency control and shaped bees reached the same level. Auto-shaped bees showed one-trial learning rather than the asymptotic approach to a maximal efficiency. However, in terms of return-time, the auto-shaped bees’ learning did not carry over to the covered-well test treatments. PMID:27626797

  12. Learning new faces in typical and atypical populations of children.

    PubMed

    Jones, Rebecca R; Blades, Mark; Coleman, Mike; Pascalis, Olivier

    2013-02-01

    Recognizing an individual as familiar is an important aspect of our social cognition, which requires both learning a face and recalling it. It has been suggested that children with autistic spectrum disorder (ASD) have deficits and abnormalities in face processing. We investigated whether the process by which unfamiliar faces become familiar differs in typically developing (TD) children, children with ASD, and children with developmental delay. Children were familiarized with a set of moving novel faces presented over a three-day period. Recognition of the learned faces was assessed at five time points during the three-day period. Both immediate and delayed recall of faces was tested. All groups showed improvements in face recognition at immediate recall, which indicated that learning had occurred. The TD population showed slightly better performance than the two other groups, however no difference was specific to the ASD group. All groups showed similar levels of improvements with time. Our results are discussed in terms of learning in ASD. © 2013 The Authors. Scandinavian Journal of Psychology © 2013 The Scandinavian Psychological Associations.

  13. Social and monetary reward learning engage overlapping neural substrates.

    PubMed

    Lin, Alice; Adolphs, Ralph; Rangel, Antonio

    2012-03-01

    Learning to make choices that yield rewarding outcomes requires the computation of three distinct signals: stimulus values that are used to guide choices at the time of decision making, experienced utility signals that are used to evaluate the outcomes of those decisions and prediction errors that are used to update the values assigned to stimuli during reward learning. Here we investigated whether monetary and social rewards involve overlapping neural substrates during these computations. Subjects engaged in two probabilistic reward learning tasks that were identical except that rewards were either social (pictures of smiling or angry people) or monetary (gaining or losing money). We found substantial overlap between the two types of rewards for all components of the learning process: a common area of ventromedial prefrontal cortex (vmPFC) correlated with stimulus value at the time of choice and another common area of vmPFC correlated with reward magnitude and common areas in the striatum correlated with prediction errors. Taken together, the findings support the hypothesis that shared anatomical substrates are involved in the computation of both monetary and social rewards. © The Author (2011). Published by Oxford University Press.

  14. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    PubMed

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  15. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

    PubMed Central

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845

  16. Transforming Change in the Military: A Systems Approach

    DTIC Science & Technology

    2007-06-01

    organization, such as Matthias Finger and Silvia Burgin Brand, argue that the learning organization concept is still very vague.46 And there is still...Matthias Finger and S. B. Brand, “The Concept of the ‘Learning Organization’ Applied to the Transformation of the Public Sector,” in Organizational...for an officer to immerse herself in intellectual study and the time required in a “muddy boot” billet. According to Murray and Millet , “the Naval

  17. Use of an automated learning management system to validate nursing competencies.

    PubMed

    Dumpe, Michelle L; Kanyok, Nancy; Hill, Kristin

    2007-01-01

    Maintaining nurse competencies in a dynamic environment is not an easy task and requires the use of resources already strained. An online learning management system was created, and 24 annual competencies were redesigned for online validation. As a result of this initiative, competencies have been standardized across many disciplines and are completed in a more timely manner, nurses and managers are more satisfied with this method of annual assessments, and cost savings have been realized.

  18. DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

    PubMed

    Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

    2016-09-01

    Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Implementation of Competency-Based Pharmacy Education (CBPE)

    PubMed Central

    Koster, Andries; Schalekamp, Tom; Meijerman, Irma

    2017-01-01

    Implementation of competency-based pharmacy education (CBPE) is a time-consuming, complicated process, which requires agreement on the tasks of a pharmacist, commitment, institutional stability, and a goal-directed developmental perspective of all stakeholders involved. In this article the main steps in the development of a fully-developed competency-based pharmacy curriculum (bachelor, master) are described and tips are given for a successful implementation. After the choice for entering into CBPE is made and a competency framework is adopted (step 1), intended learning outcomes are defined (step 2), followed by analyzing the required developmental trajectory (step 3) and the selection of appropriate assessment methods (step 4). Designing the teaching-learning environment involves the selection of learning activities, student experiences, and instructional methods (step 5). Finally, an iterative process of evaluation and adjustment of individual courses, and the curriculum as a whole, is entered (step 6). Successful implementation of CBPE requires a system of effective quality management and continuous professional development as a teacher. In this article suggestions for the organization of CBPE and references to more detailed literature are given, hoping to facilitate the implementation of CBPE. PMID:28970422

  20. Learning experience of Chinese nursing students in an online clinical English course: qualitative study.

    PubMed

    Tang, Anson C Y; Wong, Nick; Wong, Thomas K S

    2015-02-01

    The low English proficiency of Chinese nurse/nursing students affects their performance when they work in English-speaking countries. However, limited resources are available to help them improve their workplace English, i.e. English used in a clinical setting. To this end, it is essential to look for an appropriate and effective means to assist them in improving their clinical English. The objective of this study is to evaluate the learning experience of Chinese nursing students after they have completed an online clinical English course. Focus group interview was used to explore their learning experience. 100 students in nursing programs at Tung Wah College were recruited. The inclusion criteria were: (1) currently enrolled in a nursing program; and (2) having clinical experience. Eligible participants self-registered for the online English course, and were required to complete the course within 3 months. After that, semi-structured interviews were conducted on students whom completed the whole and less than half of the course. One of the researchers joined each of the interviews as a facilitator and an observer. Thematic analysis was used to analyze the data. Finally, 7 themes emerged from the interviews: technical issues, adequacy of support, time requirement, motivation, clarity of course instruction, course design, and relevancy of the course. Participants had varied opinions on the 2 themes: motivation and relevancy of the course. Overall, results of this study suggest that the online English course helped students improve their English. Factors which support their learning are interactive course design, no time constraint, and relevancy to their work/study. Factors which detracted from their learning are poor accessibility, poor technical and learning support and no peer support throughout the course. Copyright © 2014. Published by Elsevier Ltd.

  1. Development of an international online learning platform for hepatopancreatobiliary surgical training: a needs assessment

    PubMed Central

    Warner, Susanne G; Connor, Saxon; Christophi, Christopher; Azodo, Ijeoma A; Kent, Tara; Pier, David; Minter, Rebecca M

    2014-01-01

    Background The Americas Hepato-Pancreato-Biliary Association (AHPBA) and the Australian and New Zealand Hepatic, Pancreatic and Biliary Association (ANZHPBA) are developing an online distance learning curriculum to facilitate an interactive didactic experience for hepatopancreatobiliary (HPB) fellows in the operationalization of existing HPB fellow curricula. Two needs assessment surveys were carried out to identify the optimal structure and process for deployment in fellow education. Methods A 22-question survey querying fellows' learning styles and current and anticipated use of learning tools was disseminated electronically to 38 North American and Australasian HPB fellows. A follow-up 20-question survey was administered to assess fellows' feelings regarding online content. Results Response rates were 55% (n = 21) for the first survey and 42% for the second (n = 16). In the first survey, 67% of respondents claimed familiarity with the required HPB curriculum, and 43% indicated dissatisfaction with current personal study strategies. A total of 62% (n = 13) reported studying with focused clinical relevance versus using a prescribed curriculum (n = 1, 5%). Fellows anticipated participating using online tools once (n = 10, 48%) or two or three times (n = 5, 24%) per week. Most respondents (n = 18, 86%) would meaningfully follow one or two discussions per month. The second survey identified themes for improvement such as discussion topics of interest, avoidance of holiday timing and mandatory participation. Conclusions An international online distance learning format is an appealing mechanism for improved dissemination and operationalization of the established HPB fellow curricula. Fellows will engage in interactive discussions monthly. Controversial topics or those requiring complex decision making are best suited to this learning format. PMID:24961380

  2. Website Redesign: A Case Study.

    PubMed

    Wu, Jin; Brown, Janis F

    2016-01-01

    A library website redesign is a complicated and at times arduous task, requiring many different steps including determining user needs, analyzing past user behavior, examining other websites, defining design preferences, testing, marketing, and launching the site. Many different types of expertise are required over the entire process. Lessons learned from the Norris Medical Library's experience with the redesign effort may be useful to others undertaking a similar project.

  3. Defense Logistics Standard Systems Functional Requirements.

    DTIC Science & Technology

    1987-03-01

    Artificial Intelligence - the development of a machine capability to perform functions normally concerned with human intelligence, such as learning , adapting...Basic Data Base Machine Configurations .... ......... D- 18 xx ~ ?f~~~vX PART I: MODELS - DEFENSE LOGISTICS STANDARD SYSTEMS FUNCTIONAL REQUIREMENTS...On-line, Interactive Access. Integrating user input and machine output in a dynamic, real-time, give-and- take process is considered the optimum mode

  4. What Will They Learn? 2015-16. A Survey of Core Requirements at Our Nation's Colleges and Universities

    ERIC Educational Resources Information Center

    Kempson, Lauri; Burt, Evan; Bledsoe, Eric; Poliakoff, Michael

    2015-01-01

    At a time when 87% of employers believe that our colleges must raise the quality of students' educations in order for the United States to remain competitive globally, and four in five Americans say they believe all graduates should have to take the key courses outlined in the study, few colleges require a real liberal arts education. "What…

  5. The effects of flipped learning for bystander cardiopulmonary resuscitation on undergraduate medical students.

    PubMed

    Nakanishi, Taizo; Goto, Tadahiro; Kobuchi, Taketsune; Kimura, Tetsuya; Hayashi, Hiroyuki; Tokuda, Yasuharu

    2017-12-22

    To compare bystander cardiopulmonary resuscitation skills retention between conventional learning and flipped learning for first-year medical students. A post-test only control group design. A total of 108 participants were randomly assigned to either the conventional learning or flipped learning. The primary outcome measures of time to the first chest compression and the number of total chest compressions during a 2-minute test period 6 month after the training were assessed with the Mann-Whitney U test. Fifty participants (92.6%) in the conventional learning group and 45 participants (83.3%) in the flipped learning group completed the study. There were no statistically significant differences 6 months after the training in the time to the first chest compression of 33.0 seconds (interquartile range, 24.0-42.0) for the conventional learning group and 31.0 seconds (interquartile range, 25.0-41.0) for the flipped learning group (U=1171.0, p=0.73) or in the number of total chest compressions of 101.5 (interquartile range, 90.8-124.0) for the conventional learning group and 104.0 (interquartile range, 91.0-121.0) for the flipped learning group (U=1083.0, p=0.75). The 95% confidence interval of the difference between means of the number of total chest compressions 6 months after the training did not exceed a clinically important difference defined a priori. There were no significant differences between the conventional learning group and the flipped learning group in our main outcomes. Flipped learning might be comparable to conventional learning, and seems a promising approach which requires fewer resources and enables student-centered learning without compromising the acquisition of CPR skills.

  6. Learning curve for peroral endoscopic myotomy

    PubMed Central

    El Zein, Mohamad; Kumbhari, Vivek; Ngamruengphong, Saowanee; Carson, Kathryn A.; Stein, Ellen; Tieu, Alan; Chaveze, Yamile; Ismail, Amr; Dhalla, Sameer; Clarke, John; Kalloo, Anthony; Canto, Marcia Irene; Khashab, Mouen A.

    2016-01-01

    Background and study aims: Although peroral endoscopic myotomy (POEM) is being performed more frequently, the learning curve for gastroenterologists performing the procedure has not been well studied. The aims of this study were to define the learning curve for POEM and determine which preoperative and intraoperative factors predict the time that will be taken to complete the procedure and its different steps. Patients and methods: Consecutive patients who underwent POEM performed by a single expert gastroenterologist for the treatment of achalasia or spastic esophageal disorders were included. The POEM procedure was divided into four steps: mucosal entry, submucosal tunneling, myotomy, and closure. Nonlinear regression was used to determine the POEM learning plateau and calculate the learning rate. Results: A total of 60 consecutive patients underwent POEM in an endoscopy suite. The median length of procedure (LOP) was 88 minutes (range 36 – 210), and the mean (± standard deviation [SD]) LOP per centimeter of myotomy was 9 ± 5 minutes. The total operative time decreased significantly as experience increased (P < 0.001), with a “learning plateau” at 102 minutes and a “learning rate” of 13 cases. The mucosal entry, tunneling, and closure times decreased significantly with experience (P < 0.001). The myotomy time showed no significant decrease with experience (P = 0.35). When the mean (± SD) total procedure times for the learning phase and the corresponding comparator groups were compared, a statistically significant difference was observed between procedures 11 – 15 and procedures 16 – 20 (15.5 ± 2.4 min/cm and 10.1 ± 2.7 min/cm, P = 0.01) but not thereafter. A higher case number was significantly associated with a decreased LOP (P < 0.001). Conclusion: In this single-center retrospective study, the minimum threshold number of cases required for an expert interventional endoscopist performing POEM to reach a plateau approached 13. PMID:27227118

  7. Correlates and perceived outcomes of four types of employee development activity.

    PubMed

    Birdi, K; Allan, C; Warr, P

    1997-12-01

    Participation in 4 different types of development activity was studied in a sample of manufacturing employees (N = 1,798). It was found that similar sets of variables were linked to greater participation in 3 activities: required training courses in work time, work-based development activity in work time, and career planning activity in work time or an individual's own time. Three kinds of reported benefits were studied, and the occurrence of these benefits was found to vary between different types of development activity. Overall job satisfaction and organizational commitment were significantly associated with prior participation in required training courses and work-based development activity. However, voluntary learning in one's own time was completely unrelated to these work attitudes.

  8. Generalized SMO algorithm for SVM-based multitask learning.

    PubMed

    Cai, Feng; Cherkassky, Vladimir

    2012-06-01

    Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

  9. Back to the future with hands-on science: students' perceptions of learning anatomy and physiology.

    PubMed

    Johnston, Amy Nicole Burne; McAllister, Margaret

    2008-09-01

    This article examines student perceptions of learning related to anatomy and physiology in a bachelor of nursing program. One strategy to teach the sciences is simulated learning, a technology that offers exciting potential. Virtual environments for laboratory learning may offer numerous benefits: teachers can convey information to a larger group of students, reducing the need for small laboratory classes; less equipment is required, thus containing ongoing costs; and students can learn in their own time and place. However, simulated learning may also diminish access to the teacher-student relationship and the opportunity for guided practice and guided linking of theory with practice. Without this hands-on experience, there is a risk that students will not engage as effectively, and thus conceptual learning and the development of critical thinking skills are diminished. However, student perceptions of these learning experiences are largely unknown. Thus, this study examined students' perceptions of anatomy and physiology laboratory experiences and the importance they placed on hands-on experience in laboratory settings.

  10. Evidence for social learning in wild lemurs (Lemur catta).

    PubMed

    Kendal, Rachel L; Custance, Deborah M; Kendal, Jeremy R; Vale, Gillian; Stoinski, Tara S; Rakotomalala, Nirina Lalaina; Rasamimanana, Hantanirina

    2010-08-01

    Interest in social learning has been fueled by claims of culture in wild animals. These remain controversial because alternative explanations to social learning, such as asocial learning or ecological differences, remain difficult to refute. Compared with laboratory-based research, the study of social learning in natural contexts is in its infancy. Here, for the first time, we apply two new statistical methods, option-bias analysis and network-based diffusion analysis, to data from the wild, complemented by standard inferential statistics. Contrary to common thought regarding the cognitive abilities of prosimian primates, our evidence is consistent with social learning within subgroups in the ring-tailed lemur (Lemur catta), supporting the theory of directed social learning (Coussi-Korbel & Fragaszy, 1995). We also caution that, as the toolbox for capturing social learning in natural contexts grows, care is required in ensuring that the methods employed are appropriate-in particular, regarding social dynamics among study subjects. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.

  11. Effective learning strategies for real-time image-guided adaptive control of multiple-source hyperthermia applicators.

    PubMed

    Cheng, Kung-Shan; Dewhirst, Mark W; Stauffer, Paul R; Das, Shiva

    2010-03-01

    This paper investigates overall theoretical requirements for reducing the times required for the iterative learning of a real-time image-guided adaptive control routine for multiple-source heat applicators, as used in hyperthermia and thermal ablative therapy for cancer. Methods for partial reconstruction of the physical system with and without model reduction to find solutions within a clinically practical timeframe were analyzed. A mathematical analysis based on the Fredholm alternative theorem (FAT) was used to compactly analyze the existence and uniqueness of the optimal heating vector under two fundamental situations: (1) noiseless partial reconstruction and (2) noisy partial reconstruction. These results were coupled with a method for further acceleration of the solution using virtual source (VS) model reduction. The matrix approximation theorem (MAT) was used to choose the optimal vectors spanning the reduced-order subspace to reduce the time for system reconstruction and to determine the associated approximation error. Numerical simulations of the adaptive control of hyperthermia using VS were also performed to test the predictions derived from the theoretical analysis. A thigh sarcoma patient model surrounded by a ten-antenna phased-array applicator was retained for this purpose. The impacts of the convective cooling from blood flow and the presence of sudden increase of perfusion in muscle and tumor were also simulated. By FAT, partial system reconstruction directly conducted in the full space of the physical variables such as phases and magnitudes of the heat sources cannot guarantee reconstructing the optimal system to determine the global optimal setting of the heat sources. A remedy for this limitation is to conduct the partial reconstruction within a reduced-order subspace spanned by the first few maximum eigenvectors of the true system matrix. By MAT, this VS subspace is the optimal one when the goal is to maximize the average tumor temperature. When more than 6 sources present, the steps required for a nonlinear learning scheme is theoretically fewer than that of a linear one, however, finite number of iterative corrections is necessary for a single learning step of a nonlinear algorithm. Thus, the actual computational workload for a nonlinear algorithm is not necessarily less than that required by a linear algorithm. Based on the analysis presented herein, obtaining a unique global optimal heating vector for a multiple-source applicator within the constraints of real-time clinical hyperthermia treatments and thermal ablative therapies appears attainable using partial reconstruction with minimum norm least-squares method with supplemental equations. One way to supplement equations is the inclusion of a method of model reduction.

  12. Utilization of exploration-based learning and video-assisted learning to teach GlideScope videolaryngoscopy.

    PubMed

    Johnston, Lindsay C; Auerbach, Marc; Kappus, Liana; Emerson, Beth; Zigmont, Jason; Sudikoff, Stephanie N

    2014-01-01

    GlideScope (GS) is used in pediatric endotracheal intubation (ETI) but requires a different technique compared to direct laryngoscopy (DL). This article was written to evaluate the efficacy of exploration-based learning on procedural performance using GS for ETI of simulated pediatric airways and establish baseline success rates and procedural duration using DL in airway trainers among pediatric providers at various levels. Fifty-five pediatric residents, fellows, and faculty from Pediatric Critical Care, NICU, and Pediatric Emergency Medicine were enrolled. Nine physicians from Pediatric Anesthesia benchmarked expert performance. Participants completed a demographic survey and viewed a video by the GS manufacturer. Subjects spent 15 minutes exploring GS equipment and practicing the intubation procedure. Participants then intubated neonatal, infant, child, and adult airway simulators, using GS and DL, in random order. Time to ETI was recorded. Procedural performance after exploration-based learning, measured as time to successful ETI, was shorter for DL than for GS for neonatal and child airways at the.05 significance level. Time to ETI in adult airway using DL was correlated with experience level (p =.01). Failure rates were not different among subgroups. A brief video and period of exploration-based learning is insufficient for implementing a new technology. Pediatricians at various levels of training intubated simulated airways faster using DL than GS.

  13. Ultrafast learning in a hard-limited neural network pattern recognizer

    NASA Astrophysics Data System (ADS)

    Hu, Chia-Lun J.

    1996-03-01

    As we published in the last five years, the supervised learning in a hard-limited perceptron system can be accomplished in a noniterative manner if the input-output mapping to be learned satisfies a certain positive-linear-independency (or PLI) condition. When this condition is satisfied (for most practical pattern recognition applications, this condition should be satisfied,) the connection matrix required to meet this mapping can be obtained noniteratively in one step. Generally, there exist infinitively many solutions for the connection matrix when the PLI condition is satisfied. We can then select an optimum solution such that the recognition of any untrained patterns will become optimally robust in the recognition mode. The learning speed is very fast and close to real-time because the learning process is noniterative and one-step. This paper reports the theoretical analysis and the design of a practical charter recognition system for recognizing hand-written alphabets. The experimental result is recorded in real-time on an unedited video tape for demonstration purposes. It is seen from this real-time movie that the recognition of the untrained hand-written alphabets is invariant to size, location, orientation, and writing sequence, even the training is done with standard size, standard orientation, central location and standard writing sequence.

  14. Using a Genetic Algorithm to Learn Behaviors for Autonomous Vehicles,

    DTIC Science & Technology

    1992-08-12

    Truly autonomous vehicles will require both projective planning and reactive components in order to perform robustly. Projective components are...long time period. This work addresses the problem of creating reactive components for autonomous vehicles . Creating reactive behaviors (stimulus

  15. Quality Assurance in School Health

    ERIC Educational Resources Information Center

    Newell, Susan; Schoenike, Sumner L.; Lisko, Elaine A.

    2003-01-01

    School nurses need to become more influential administrators, managers, and entrepreneurs. They must learn to lead and collaborate effectively in designing, implementing, and evaluating coordinated school health programs. Quality assurance is an essential ingredient in this process that requires accurate, timely, and confidential incident…

  16. Stop the Stretching. Grades 6-8.

    ERIC Educational Resources Information Center

    Rushton, Erik; Ryan, Emily; Swift, Charles

    In this activity, students learn about composite materials, tension as a force, and how they act on structural components through the design and testing of a strip of plastic chair webbing. This activity requires a 60-minute time period for completion. (Author/NB)

  17. A forecast-based STDP rule suitable for neuromorphic implementation.

    PubMed

    Davies, S; Galluppi, F; Rast, A D; Furber, S B

    2012-08-01

    Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that the STDP TTS algorithm allows the neural network to adapt and detect the incoming pattern with improvements both in the reliability of, and the time required for, consistent output. Through the approximations we suggest in this paper, we introduce a learning rule that is easy to implement both in event-driven simulators and in dedicated hardware, reducing computational complexity relative to the standard STDP rule. Such a rule offers a promising solution, complementary to standard STDP evaluation algorithms, for real-time learning using spiking neural networks in time-critical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Scene recognition based on integrating active learning with dictionary learning

    NASA Astrophysics Data System (ADS)

    Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

    2018-04-01

    Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

  19. The effectiveness of robotic training depends on motor task characteristics.

    PubMed

    Marchal-Crespo, Laura; Rappo, Nicole; Riener, Robert

    2017-12-01

    Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies. Motor tasks can be classified based on the time characteristics of the task, in particular the task's duration (discrete vs. continuous). Continuous tasks require movements without distinct beginning or end. Discrete tasks require fast movements that include well-defined postures at the beginning and the end. We developed two games, one that requires a continuous movement-a tracking task-and one that requires discrete movements-a fast reaching task. We conducted an experiment with thirty healthy subjects to evaluate the effectiveness of three error-modulating training strategies-no guidance, error amplification (i.e., repulsive forces proportional to errors) and haptic guidance-on self-reported motivation and learning of the continuous and discrete games. Training with error amplification resulted in better motor learning than haptic guidance, besides the fact that error amplification reduced subjects' interest/enjoyment and perceived competence during training. Only subjects trained with error amplification improved their performance after training the discrete game. In fact, subjects trained without guidance improved the performance in the continuous game significantly more than in the discrete game, probably because the continuous task required greater attentional levels. Error-amplifying training strategies have a great potential to provoke better motor learning in continuous and discrete tasks. However, their long-lasting negative effects on motivation might limit their applicability in intense neurorehabilitation programs.

  20. Effects of cerebellar nuclear inactivation on the learning of a complex forelimb movement in cats.

    PubMed

    Wang, J J; Shimansky, Y; Bracha, V; Bloedel, J R

    1998-05-01

    The purpose of this study was to determine the effects of inactivating concurrently the cerebellar interposed and dentate nuclei on the capacity of cats to acquire and retain a complex, goal-directed forelimb movement. To assess the effects on acquisition, cats were required to learn to move a vertical manipulandum bar through a two-segment template with a shape approximating an inverted "L" after the injection of muscimol (saline for the control group) in the interposed and dentate cerebellar nuclei. During training periods, they were exposed progressively to more difficult templates, which were created by decreasing the angle between the two segments of the template. After determining the most difficult template the injected animals could learn within the specified time and performance constraints, the retraining phase of the experiment was initiated in which the cats were required to execute the same sequence of templates in the absence of any injection. This stage of the experiment assessed retention and determined the extent of any relearning required to execute the task at criterion levels. Next, the animals were overtrained without any injection on the most difficult template they could perform. Finally, to determine the effects of nuclear inactivation on retention after extensive retraining, their capacity to perform the same template was determined after muscimol injection in the interposed and dentate nuclei. The findings show that during the inactivation of the dentate and interposed nuclei the animals could learn to execute the more difficult templates. However, when required to execute the most difficult template learned under muscimol on the day after injections were discontinued, the cats had to "relearn" (reacquire) the movement. Finally, when the cerebellar nuclei were inactivated after the animals learned the task in the absence of any injections during the retraining phase, retention was not blocked. The data indicate that the intermediate and lateral cerebellum are not required either for learning this type of complex voluntary movement or for retaining the capacity to perform the task once it is learned. Nevertheless, when the cerebellum becomes available for executing a task learned in the absence of this structure, reacquisition of the behavior usually is necessary. It is hypothesized that the relearning observed after acquisition during muscimol inactivation reflects the tendency of the system to incorporate the cerebellum into the interactions responsible for the learning and performance of a motor sequence that is optimal for executing the task.

  1. Collaborative learning of clinical skills in health professions education: the why, how, when and for whom.

    PubMed

    Tolsgaard, Martin G; Kulasegaram, Kulamakan M; Ringsted, Charlotte V

    2016-01-01

    This study is designed to provide an overview of why, how, when and for whom collaborative learning of clinical skills may work in health professions education. Collaborative learning of clinical skills may influence learning positively according to the non-medical literature. Training efficiency may therefore be improved if the outcomes of collaborative learning of clinical skills are superior or equivalent to those attained through individual learning. According to a social interaction perspective, collaborative learning of clinical skills mediates its effects through social interaction, motivation, accountability and positive interdependence between learners. Motor skills learning theory suggests that positive effects rely on observational learning and action imitation, and negative effects may include decreased hands-on experience. Finally, a cognitive perspective suggests that learning is dependent on cognitive co-construction, shared knowledge and reduced cognitive load. The literature on the collaborative learning of clinical skills in health science education is reviewed to support or contradict the hypotheses provided by the theories outlined above. Collaborative learning of clinical skills leads to improvements in self-efficacy, confidence and performance when task processing is observable or communicable. However, the effects of collaborative learning of clinical skills may decrease over time as benefits in terms of shared cognition, scaffolding and cognitive co-construction are outweighed by reductions in hands-on experience and time on task. Collaborative learning of clinical skills has demonstrated promising results in the simulated setting. However, further research into how collaborative learning of clinical skills may work in clinical settings, as well as into the role of social dynamics between learners, is required. © 2015 John Wiley & Sons Ltd.

  2. A theory of local learning, the learning channel, and the optimality of backpropagation.

    PubMed

    Baldi, Pierre; Sadowski, Peter

    2016-11-01

    In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Transformative learning through international immersion: building multicultural competence in family therapy and counseling.

    PubMed

    McDowell, Teresa; Goessling, Kristen; Melendez, Tatiana

    2012-04-01

    This study explores the experiences of graduate students who completed one of two international courses facilitated by family therapy faculty in a U.S. master's-level counseling psychology department. Participants reported that international courses were personally and professionally transformative. Spending time in a foreign country gave them opportunities to learn from cultural differences, ultimately increasing the social and global awareness required for multicultural sensitivity. Experiential learning, reflection, and dialogue resulted in raised critical consciousness among participants. In this article, we discuss the transformational learning processes embedded in international courses and the potential benefits of these experiences on the development of multicultural sensitivity in family therapists and counselors in training. © 2010 American Association for Marriage and Family Therapy.

  4. Are Serious Games a Good Strategy for Pharmacy Education?

    PubMed

    Cain, Jeff; Piascik, Peggy

    2015-05-25

    Serious gaming is the use of game principles for the purposes of learning, skill acquisition, and training. Higher education is beginning to incorporate serious gaming into curricula, and health professions education is the most common area for serious game use. Advantages of serious gaming in pharmacy education include authentic, situated learning without risk of patient consequences, collaborative learning, ability to challenge students of all performance levels, high student motivation with increased time on task, immediate feedback, ability to learn from mistakes without becoming discouraged, and potential for behavior and attitude change. Development of quality games for pharmacy education requires content expertise as well as expertise in the science and design of gaming. When well done, serious gaming provides a valuable additional tool for pharmacy education.

  5. Effective e-learning for health professionals and students--barriers and their solutions. A systematic review of the literature--findings from the HeXL project.

    PubMed

    Childs, Sue; Blenkinsopp, Elizabeth; Hall, Amanda; Walton, Graham

    2005-12-01

    In 2003/4 the Information Management Research Institute, Northumbria University, conducted a research project to identify the barriers to e-learning for health professionals and students. The project also established possible ways to overcome these barriers. The North of England Workforce Development Confederation funded the project. The project comprised a systematic review of the literature on barriers to and solutions/critical success factors for e-learning in the health field. Fifty-seven references were suitable for analysis. This review was supplemented by a questionnaire survey of learners and an interview study of learning providers to ensure that data identified from the literature were grounded in reality. The main barriers are: requirement for change; costs; poorly designed packages; inadequate technology; lack of skills; need for a component of face-to-face teaching; time intensive nature of e-learning; computer anxiety. A range of solutions can solve these barriers. The main solutions are: standardization; strategies; funding; integration of e-learning into the curriculum; blended teaching; user friendly packages; access to technology; skills training; support; employers paying e-learning costs; dedicated work time for e-learning. The authors argue that librarians can play an important role in e-learning: providing support and support materials; teaching information skills; managing and providing access to online information resources; producing their own e-learning packages; assisting in the development of other packages.

  6. Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

    NASA Astrophysics Data System (ADS)

    Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

    2017-01-01

    Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

  7. IoGET: Internet of Geophysical and Environmental Things

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Mudunuru, Maruti Kumar

    The objective of this project is to provide novel and fast reduced-order models for onboard computation at sensor nodes for real-time analysis. The approach will require that LANL perform high-fidelity numerical simulations, construct simple reduced-order models (ROMs) using machine learning and signal processing algorithms, and use real-time data analysis for ROMs and compressive sensing at sensor nodes.

  8. Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific Phenomena

    ERIC Educational Resources Information Center

    Corredor, Javier; Gaydos, Matthew; Squire, Kurt

    2014-01-01

    This article explores how learning biological concepts can be facilitated by playing a video game that depicts interactions and processes at the subcellular level. Particularly, this article reviews the effects of a real-time strategy game that requires players to control the behavior of a virus and interact with cell structures in a way that…

  9. Learning curve analysis of mitral valve repair using telemanipulative technology.

    PubMed

    Charland, Patrick J; Robbins, Tom; Rodriguez, Evilio; Nifong, Wiley L; Chitwood, Randolph W

    2011-08-01

    To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. We found a statistically significant learning curve (P < .01). The institutional learning percentage∗ derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2) = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis. Copyright © 2011 The American Association for Thoracic Surgery. All rights reserved.

  10. Learning in robotic manipulation: The role of dimensionality reduction in policy search methods. Comment on "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands" by Marco Santello et al.

    NASA Astrophysics Data System (ADS)

    Ficuciello, Fanny; Siciliano, Bruno

    2016-07-01

    A question that often arises, among researchers working on artificial hands and robotic manipulation, concerns the real meaning of synergies. Namely, are they a realistic representation of the central nervous system control of manipulation activities at different levels and of the sensory-motor manipulation apparatus of the human being, or do they constitute just a theoretical framework exploiting analytical methods to simplify the representation of grasping and manipulation activities? Apparently, this is not a simple question to answer and, in this regard, many minds from the field of neuroscience and robotics are addressing the issue [1]. The interest of robotics is definitely oriented towards the adoption of synergies to tackle the control problem of devices with high number of degrees of freedom (DoFs) which are required to achieve motor and learning skills comparable to those of humans. The synergy concept is useful for innovative underactuated design of anthropomorphic hands [2], while the resulting dimensionality reduction simplifies the control of biomedical devices such as myoelectric hand prostheses [3]. Synergies might also be useful in conjunction with the learning process [4]. This aspect is less explored since few works on synergy-based learning have been realized in robotics. In learning new tasks through trial-and-error, physical interaction is important. On the other hand, advanced mechanical designs such as tendon-driven actuation, underactuated compliant mechanisms and hyper-redundant/continuum robots might exhibit enhanced capabilities of adapting to changing environments and learning from exploration. In particular, high DoFs and compliance increase the complexity of modelling and control of these devices. An analytical approach to manipulation planning requires a precise model of the object, an accurate description of the task, and an evaluation of the object affordance, which all make the process rather time consuming. The integration of learning into control naturally leads to relaxing the above requirements through the adoption of coordinated motion patterns and sensory-motor synergies as useful tools leading to a problem of reduced dimension. To this purpose, model-based control strategies relying on synergistic models of manipulation activities learned from human experience can be integrated with real-time learning from actions strategies [5]. In [6] a classification of learning strategies for robotics is provided, while the difference between imitation learning and reinforcement learning (RL) is highlighted in [7]. From recent research in the field [8,9], it seems that RL represents the future toward autonomous and intelligent robots since it provides learning capabilities as those of humans, i.e. based on exploration and trial-and-error policies. In this context, suitable policy search methods to be implemented in a synergy-based framework are to be sought in order to reduce the search space dimension while guaranteeing the convergence and efficiency of the learning algorithm.

  11. Kanaka Maoli and Kamáāina Seascapes - Knowing Our Ocean Through Times of Change

    NASA Astrophysics Data System (ADS)

    Puniwai, N.

    2017-12-01

    In Hawaíi our oceans define us, we come from the ocean. Our oceans change, and we change with them, as we always have. By learning from people who are dependent on their environment, we learn how to observe and how to adapt. Through the lens of climate change, we interviewed respected ocean observers and surfers to learn about changes they have witnessed over time and the spatial scales and ocean conditions important to them. We looked at our ancient and historical texts to see what processes they recorded and the language they used to ascribe their observations, interactions and relationships to these places. Yet, we also integrate what our mechanical data sensors have recorded over recent time. By expanding our time scales of reference, knowledge sources, and collaborators, these methods teach us how our ancestors adapted and how climate change may impact our subsistence, recreation, and interactions with the environment. Managing complex seascapes requires the integration of multiple ways of knowing; strengthening our understanding of seascapes and their resiliency in this changing environment.

  12. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    PubMed

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  13. Contrasts in Student Affect by Institution and Instructor: Establishing a National Baseline for Geoscience Courses

    NASA Astrophysics Data System (ADS)

    Mcconnell, D. A.; Perkins, D.; Stempien, J.; Husman, J.

    2011-12-01

    The GARNET (Geoscience Affective Research Network) project examines the connection between student learning and the affective domain, which includes student motivations, values, attitudes and learning strategies - factors that can both promote and limit learning. This is the first study to compare and contrast the relationship between student motivation and learning strategies, the nature of classroom instruction, and learning outcomes across a common course taught by multiple instructors at different types of academic institutions. In 2009-2011 we administered pre- and post-course Motivated Strategies for Learning Questionnaires (MSLQ; Pintrich et al, 1993) to 1990 students in more than 40 introductory geology classes taught by 25 instructors at nine colleges and universities. Students primarily register for the introductory courses to fulfill a general education requirement with a relatively modest proportion (25%) declaring a prior interest in the course topic. This institutional requirement produces a situation where students' motivational orientation is not likely to adjust to their newfound academic environment. The students do not have an interest in the topic, they have little prior knowledge about the content, they do not see connections between the content and their future goals, and they have limited autonomy in their choice of a course (the course is required). In general, we find that across different institutions and instructors, students' motivation and self-regulation degrades. Through classroom observations, and student surveys we have evidence that specific faculty are able to help students maintain some of the positive motivational orientations students bring to the class. The MSLQ contains 15 subscales, six measure motivation (e.g., task value, self-efficacy), and nine focus on different learning strategies (e.g., elaboration, effort regulation). Regardless of institution or instructor, MSLQ scores on many subscales declined from beginning to the end of the semester indicating that students lost confidence and adopted less effective learning strategies as the semester progressed. Our results suggest that instructors can use a variety of approaches to improve aspects of student motivation and learning. These interventions may range from simple opportunities for students to reflect on their learning to more sophisticated efforts to help students develop greater confidence in their ability or interest in the topic. Such interventions require a reallocation of time in or out of the classroom, and may involve a significant effort by instructors.

  14. Acceptability of the flipped classroom approach for in-house teaching in emergency medicine.

    PubMed

    Tan, Eunicia; Brainard, Andrew; Larkin, Gregory L

    2015-10-01

    To evaluate the relative acceptability of the flipped classroom approach compared with traditional didactics for in-house teaching in emergency medicine. Our department changed its learning model from a 'standard' lecture-based model to a 'flipped classroom' model. The 'flipped classroom' included provided pre-session learning objectives and resources before each 2 h weekly session. In-session activities emphasised active learning strategies and knowledge application. Feedback was sought from all medical staff regarding the acceptability of the new approach using an online anonymous cross-sectional qualitative survey. Feedback was received from 49/57 (86%) medical staff. Ninety-eight per cent (48/49) of respondents preferred the flipped classroom over the traditional approach. Aspects of the flipped classroom learners liked most included case-based discussion, interaction with peers, application of knowledge, self-directed learning and small-group learning. Barriers to pre-session learning include work commitments, 'life', perceived lack of time, family commitments, exam preparation and high volume of learning materials. Reported motivational factors promoting pre-session learning include formal assessment, participation requirements, more time, less material, more clinical relevance and/or more interesting material. Case studies and 'hands-on' activities were perceived to be the most useful in-session activities. The flipped classroom shows promise as an acceptable approach to in-house emergency medicine teaching. © 2015 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

  15. Video-Assisted Thoracic Surgical Lobectomy for Lung Cancer: Description of a Learning Curve.

    PubMed

    Yao, Fei; Wang, Jian; Yao, Ju; Hang, Fangrong; Cao, Shiqi; Cao, Yongke

    2017-07-01

    Video-assisted thoracic surgical (VATS) lobectomy is gaining popularity in the treatment of lung cancer. The aim of this study is to investigate the learning curve of VATS lobectomy by using multidimensional methods and to compare the learning curve groups with respect to perioperative clinical outcomes. We retrospectively reviewed a prospective database to identify 67 consecutive patients who underwent VATS lobectomy for lung cancer by a single surgeon. The learning curve was analyzed by using moving average and the cumulative sum (CUSUM) method. With the moving average and CUSUM analyses for the operation time, patients were stratified into two groups, with chronological order defining early and late experiences. Perioperative clinical outcomes were compared between the two learning curve groups. According to the moving average method, the peak point for operation time occurred at the 26th case. The CUSUM method also showed the operation time peak point at the 26th case. When results were compared between early- and late-experience periods, the operation time, duration of chest drainage, and postoperative hospital stay were significantly longer in the early-experience group (cases 1 to 26). The intraoperative estimated blood loss was significantly less in the late-experience group (cases 27 to 67). CUSUM charts showed a decreasing duration of chest drainage after the 36th case and shortening postoperative hospital stay after the 37th case. Multidimensional statistical analyses suggested that the learning curve for VATS lobectomy for lung cancer required ∼26 cases. Favorable intraoperative and postoperative care parameters for VATS lobectomy were observed in the late-experience group.

  16. Learning and Earning: 1974 and the Decade

    ERIC Educational Resources Information Center

    Harris, Stanley; Fredericks, Ted

    1974-01-01

    There are many jobs available for persons with the required training. The article explores some of the areas where job openings exist, discusses the types of schooling appropriate, and provides examples for relating military training and prison rehabilitation programs to present times. (AG)

  17. Transit Operations Decision Support System (TODSS) core requirements prototype development case study and lessons learned.

    DOT National Transportation Integrated Search

    2010-02-01

    Transit Operations Decision Support Systems (TODSS) are systems designed to support dispatchers and others in real-time operations : management in response to incidents, special events, and other changing conditions. As part of a joint Federal Transi...

  18. Effects of pre-conditioning on behavior and physiology of horses during a standardised learning task

    PubMed Central

    Webb, Holly; Starling, Melissa J.; Freire, Rafael; Buckley, Petra; McGreevy, Paul D.

    2017-01-01

    Rein tension is used to apply pressure to control both ridden and unridden horses. The pressure is delivered by equipment such as the bit, which may restrict voluntary movement and cause changes in behavior and physiology. Managing the effects of such pressure on arousal level and behavioral indicators will optimise horse learning outcomes. This study examined the effect of training horses to turn away from bit pressure on cardiac outcomes and behavior (including responsiveness) over the course of eight trials in a standardised learning task. The experimental procedure consisted of a resting phase, treatment/control phase, standardised learning trials requiring the horses (n = 68) to step backwards in response to bit pressure and a recovery phase. As expected, heart rate increased (P = 0.028) when the handler applied rein tension during the treatment phase. The amount of rein tension required to elicit a response during treatment was higher on the left than the right rein (P = 0.009). Total rein tension required for trials reduced (P < 0.001) as they progressed, as did time taken (P < 0.001) and steps taken (P < 0.001). The incidence of head tossing decreased (P = 0.015) with the progression of the trials and was higher (P = 0.018) for the control horses than the treated horses. These results suggest that preparing the horses for the lesson and slightly raising their arousal levels, improved learning outcomes. PMID:28358892

  19. Incorporating inquiry and the process of science into introductory astronomy labs at the George Washington University

    NASA Astrophysics Data System (ADS)

    Cobb, Bethany E.

    2018-01-01

    Since 2013, the Physics Department at GWU has used student-centered active learning in the introductory astronomy course “Introduction to the Cosmos.” Class time is spent in groups on questions, math problems, and hands-on activities, with multiple instructors circulating to answer questions and engage with the students. The students have responded positively to this active-learning. Unfortunately, in transitioning to active-learning there was no time to rewrite the labs. Very quickly, the contrast between the dynamic classroom and the traditional labs became apparent. The labs were almost uniformly “cookie-cutter” in that the procedure and analysis were specified step-by-step and there was just one right answer. Students rightly criticized the labs for lacking a clear purpose and including busy-work. Furthermore, this class fulfills the GWU scientific reasoning general education requirement and thus includes learning objectives related to understanding the scientific method, testing hypotheses with data, and considering uncertainty – but the traditional labs did not require these skills. I set out to rejuvenate the lab sequence by writing new inquiry labs based on both topic-specific and scientific reasoning learning objectives. While inquiry labs can be challenging for the students, as they require active thinking and creativity, these labs engage the students more thoroughly in the scientific process. In these new labs, whenever possible, I include real astronomical data and ask the students to use digital tools (SDSS SkyServer, SOHO archive) as if they are real astronomers. To allow students to easily plot, manipulate and analyze data, I built “smart” Excel files using formulas, dropdown menus and macros. The labs are now much more authentic and thought-provoking. Whenever possible, students independently develop questions, hypotheses, and procedures and the scientific method is “scaffolded” over the semester by providing more guidance in the early labs and more independence later on. Finally, in every lab, students must identify and reflect on sources of error. These labs are more challenging for the instructors to run and to grade, but they are much more satisfying when it comes to student learning.

  20. Interdisciplinary curriculum to train internal medicine and obstetrics-gynecology residents in ambulatory women's health: adapting problem-based learning to residency education.

    PubMed

    Spencer, Abby L; McNeil, Melissa

    2009-09-01

    Although residents in internal medicine (IM) and obstetrics-gynecology (OG) must provide primary care for women, studies indicate that both groups require more skills and training in women's health. Our goals were to assess the needs of residents at our academic medical center and to design an interdisciplinary curriculum that addresses these needs utilizing a modified problem-based learning (PBL) format. The aim of this article is to report on the development, logistics, and successful implementation of our innovative curriculum. Based on results from a targeted needs-assessment, we designed a curriculum for both IM and OG residents to address curricular deficiencies in an efficient and effective manner. Procurement of support was achieved by reviewing overlapping competency requirements and results of the needs-assessment with the program directors. The curriculum consists of six ambulatory clinical cases which lead residents through a discussion of screening, diagnosis, prevention, and management within a modified PBL format. Residents select one learning objective each week which allows them to serve as content experts during case discussions, applying what they learned from their literature review to guide the group as they decide upon the next step for the case. This format helps accommodate different experience levels of learners, encourages discussion from less-vocal residents, and utilizes theories of adult learning. Sixty-five residents have participated in the curriculum since it was successfully implemented. IM residents report that the cases were their first opportunity to discuss the health concerns of younger women; OG residents felt similarly about cases related to older women. Implementation challenges included resident accountability. Residents identified the timing of the sessions and clinical coverage requirements as barriers to conference attendance. Interdisciplinary modified PBL conferences focusing on shared curricular needs in ambulatory women's health are well-received by both IM and OG residents. This format utilizes theories of adult learning and maximizes limited time and resources by teaching IM and OG residents concurrently, and can be successfully implemented at a large academic medical center.

  1. An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes.

    PubMed

    Fravolini, M L; Fabietti, P G

    2014-01-01

    This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.

  2. E-learning & information communication technology (ICT) in nursing education: A review of the literature.

    PubMed

    Button, Didy; Harrington, Ann; Belan, Ingrid

    2014-10-01

    To examine primary research articles published between January 2001 and December 2012 that focused on the issues for students and educators involved with E-learning in preregistration nursing programs. The literature was systematically reviewed, critically appraised and thematically analyzed. E-learning is arguably the most significant change to occur in nursing education since the move from hospital training to the tertiary sector. Differences in computer and information literacy for both students and educators influence the success of implementation of E-learning into current curricula. Online databases including CINAHL, MEDLINE, OVID, the ProQuest Central, PubMed, ERIC and Science Direct were used. The criteria used for selecting studies reviewed were: primary focus on electronic learning and issues faced by nursing students and/or nurse educators from undergraduate preregistration nursing programs; all articles had to be primary research studies, published in English in peer reviewed journals between January 2001 and December 2012. Analysis of the 28 reviewed studies revealed the following three themes: issues relating to E-learning for students; use of information technologies; educator (faculty) issues involving pedagogy, workload and staff development in E-learning and associated technology. The review highlighted that commencing preregistration nursing students required ongoing education and support surrounding nursing informatics. This support would enable students to progress and be equipped with the life-long learning skills required to provide safe evidence based care. The review also identified the increased time and skill demands placed on nurse educators to adapt their current education methodologies and teaching strategies to incorporate E-learning. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  3. Improving Preservice Teachers' Self-Efficacy through Service Learning: Lessons Learned

    ERIC Educational Resources Information Center

    Bernadowski, Carianne; Perry, Ronald; Del Greco, Robert

    2013-01-01

    University students have been barraged with service learning opportunities both as course required and as volunteer opportunities in recent years. Currently, many universities now require students to participate in engaged learning as a graduation requirement. Situated in Bandura's theory of self-efficacy, this study examines the effects service…

  4. Effect of an Individual Readiness Assurance Test on a Team Readiness Assurance Test in the Team-Based Learning of Physiology

    ERIC Educational Resources Information Center

    Gopalan, Chaya; Fox, Dainielle J.; Gaebelein, Claude J.

    2013-01-01

    We examined whether requiring an individual readiness assurance test (iRAT) before a team readiness assurance test (tRAT) would benefit students in becoming better problem solvers in physiology. It was tested in the form of tRAT scores, the time required to complete the tRAT assignment, and individual performance on the unit examinations. Students…

  5. Towards DMD-Based Estimation and Control of Flow Separation using an Array of Surface Pressure Sensors

    NASA Astrophysics Data System (ADS)

    Deem, Eric; Cattafesta, Louis; Zhang, Hao; Rowley, Clancy

    2016-11-01

    Closed-loop control of flow separation requires the spatio-temporal states of the flow to be fed back through the controller in real time. Previously, static and dynamic estimation methods have been employed that provide reduced-order model estimates of the POD-coefficients of the flow velocity using surface pressure measurements. However, this requires a "learning" dataset a priori. This approach is effective as long as the dynamics during control do not stray from the learning dataset. Since only a few dynamical features are required for feedback control of flow separation, many of the details provided by full-field snapshots are superfluous. This motivates a state-observation technique that extracts key dynamical features directly from surface pressure, without requiring PIV snapshots. The results of identifying DMD modes of separated flow through an array of surface pressure sensors in real-time are presented. This is accomplished by employing streaming DMD "on the fly" to surface pressure snapshots. These modal characteristics exhibit striking similarities to those extracted from PIV data and the pressure field obtained via solving Poisson's equation. Progress towards closed-loop separation control based on the dynamic modes of surface pressure will be discussed. Supported by AFOSR Grant FA9550-14-1-0289.

  6. Technical proficiency in hand-assisted laparoscopic colon and rectal surgery: determining how many cases are required to achieve mastery.

    PubMed

    Pendlimari, Rajesh; Holubar, Stefan D; Dozois, Eric J; Larson, David W; Pemberton, John H; Cima, Robert R

    2012-04-01

    To determine how many cases are required to achieve technical proficiency for hand-assisted laparoscopic surgery (HALS). Retrospective study. Tertiary care hospital. Using a prospective database, all HALS colorectal resections from 2003 to 2009 by 2 surgeons (A and B) were reviewed. Over 6 years, surgeons A and B performed 397 and 322 cases. Change-Point Analysis (CUSUM) was used to define the number of cases required to effect improvement in operative time. Cases before and after the change point were considered as being in the "learning period" and "skilled period." Operative time; short-term outcomes. The change point occurred after 108 and 105 cases for surgeons A and B, respectively. The learning period and skilled period were similar with respect to age, sex, body mass index, prior abdominal surgery, medical comorbidities, and American Society of Anesthesiologists class. Mean overall operative time decreased from 263 to 185 minutes (P < .001). The decrease in mean operative duration for specific resections were as follows: right colectomy, 35 minutes (P = .003); left colectomy, 63 minutes (P = .006); sigmoid colectomy, 63 minutes (P < .001); anterior resection, 70 minutes (P < .001); coloanal anastomosis, 52 minutes (P = .003); subtotal colectomy, 75 minutes (P < .001); and total proctocolectomy with ileal reservoir, 80 minutes (P < .001). Intraoperative complications and conversion rate were similar, but overall morbidity, infectious complications, readmissions, and length of stay were all significantly (P < .05) lower during the skilled period. For HALS colorectal resection, technical proficiency occurred after approximately 105 cases, and increased surgeon experience resulted in improved short-term outcomes. These data suggest that the learning curve for HALS colorectal resection will extend beyond fellowship training for many colorectal surgeons.

  7. The learning curve of robot-assisted radical cystectomy: results from the International Robotic Cystectomy Consortium.

    PubMed

    Hayn, Matthew H; Hussain, Abid; Mansour, Ahmed M; Andrews, Paul E; Carpentier, Paul; Castle, Erik; Dasgupta, Prokar; Rimington, Peter; Thomas, Raju; Khan, Shamim; Kibel, Adam; Kim, Hyung; Manoharan, Murugesan; Menon, Mani; Mottrie, Alex; Ornstein, David; Peabody, James; Pruthi, Raj; Palou Redorta, Joan; Richstone, Lee; Schanne, Francis; Stricker, Hans; Wiklund, Peter; Chandrasekhar, Rameela; Wilding, Greg E; Guru, Khurshid A

    2010-08-01

    Robot-assisted radical cystectomy (RARC) has evolved as a minimally invasive alternative to open radical cystectomy for patients with invasive bladder cancer. We sought to define the learning curve for RARC by evaluating results from a multicenter, contemporary, consecutive series of patients who underwent this procedure. Utilizing the International Robotic Cystectomy Consortium database, a prospectively maintained and institutional review board-approved database, we identified 496 patients who underwent RARC by 21 surgeons at 14 institutions from 2003 to 2009. Cut-off points for operative time, lymph node yield (LNY), estimated blood loss (EBL), and margin positivity were identified. Using specifically designed statistical mixed models, we were able to inversely predict the number of patients required for an institution to reach the predetermined cut-off points. Mean operative time was 386 min, mean EBL was 408 ml, and mean LNY was 18. Overall, 34 of 482 patients (7%) had a positive surgical margin (PSM). Using statistical models, it was estimated that 21 patients were required for operative time to reach 6.5h and 8, 20, and 30 patients were required to reach an LNY of 12, 16, and 20, respectively. For all patients, PSM rates of <5% were achieved after 30 patients. For patients with pathologic stage higher than T2, PSM rates of <15% were achieved after 24 patients. RARC is a challenging procedure but is a technique that is reproducible throughout multiple centers. This report helps to define the learning curve for RARC and demonstrates an acceptable level of proficiency by the 30th case for proxy measures of RARC quality. Copyright (c) 2010 European Association of Urology. Published by Elsevier B.V. All rights reserved.

  8. A switched systems approach to image-based estimation

    NASA Astrophysics Data System (ADS)

    Parikh, Anup

    With the advent of technological improvements in imaging systems and computational resources, as well as the development of image-based reconstruction techniques, it is necessary to understand algorithm performance when subject to real world conditions. Specifically, this dissertation focuses on the stability and performance of a class of image-based observers in the presence of intermittent measurements, caused by e.g., occlusions, limited FOV, feature tracking losses, communication losses, or finite frame rates. Observers or filters that are exponentially stable under persistent observability may have unbounded error growth during intermittent sensing, even while providing seemingly accurate state estimates. In Chapter 3, dwell time conditions are developed to guarantee state estimation error convergence to an ultimate bound for a class of observers while undergoing measurement loss. Bounds are developed on the unstable growth of the estimation errors during the periods when the object being tracked is not visible. A Lyapunov-based analysis for the switched system is performed to develop an inequality in terms of the duration of time the observer can view the moving object and the duration of time the object is out of the field of view. In Chapter 4, a motion model is used to predict the evolution of the states of the system while the object is not visible. This reduces the growth rate of the bounding function to an exponential and enables the use of traditional switched systems Lyapunov analysis techniques. The stability analysis results in an average dwell time condition to guarantee state error convergence with a known decay rate. In comparison with the results in Chapter 3, the estimation errors converge to zero rather than a ball, with relaxed switching conditions, at the cost of requiring additional information about the motion of the feature. In some applications, a motion model of the object may not be available. Numerous adaptive techniques have been developed to compensate for unknown parameters or functions in system dynamics; however, persistent excitation (PE) conditions are typically required to ensure parameter convergence, i.e., learning. Since the motion model is needed in the predictor, model learning is desired; however, PE is difficult to insure a priori and infeasible to check online for nonlinear systems. Concurrent learning (CL) techniques have been developed to use recorded data and a relaxed excitation condition to ensure convergence. In CL, excitation is only required for a finite period of time, and the recorded data can be checked to determine if it is sufficiently rich. However, traditional CL requires knowledge of state derivatives, which are typically not measured and require extensive filter design and tuning to develop satisfactory estimates. In Chapter 5 of this dissertation, a novel formulation of CL is developed in terms of an integral (ICL), removing the need to estimate state derivatives while preserving parameter convergence properties. Using ICL, an estimator is developed in Chapter 6 for simultaneously estimating the pose of an object as well as learning a model of its motion for use in a predictor when the object is not visible. A switched systems analysis is provided to demonstrate the stability of the estimation and prediction with learning scheme. Dwell time conditions as well as excitation conditions are developed to ensure estimation errors converge to an arbitrarily small bound. Experimental results are provided to illustrate the performance of each of the developed estimation schemes. The dissertation concludes with a discussion of the contributions and limitations of the developed techniques, as well as avenues for future extensions.

  9. Are There Age-Related Differences in the Ability to Learn Configural Responses?

    PubMed Central

    Clark, Rachel; Freedberg, Michael; Hazeltine, Eliot; Voss, Michelle W.

    2015-01-01

    Age is often associated with a decline in cognitive abilities that are important for maintaining functional independence, such as learning new skills. Many forms of motor learning appear to be relatively well preserved with age, while learning tasks that involve associative binding tend to be negatively affected. The current study aimed to determine whether age differences exist on a configural response learning task, which includes aspects of motor learning and associative binding. Young (M = 24 years) and older adults (M = 66.5 years) completed a modified version of a configural learning task. Given the requirement of associative binding in the configural relationships between responses, we predicted older adults would show significantly less learning than young adults. Older adults demonstrated lower performance (slower reaction time and lower accuracy). However, contrary to our prediction, older adults showed similar rates of learning as indexed by a configural learning score compared to young adults. These results suggest that the ability to acquire knowledge incidentally about configural response relationships is largely unaffected by cognitive aging. The configural response learning task provides insight into the task demands that constrain learning abilities in older adults. PMID:26317773

  10. Learning to be different: Acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations

    USGS Publications Warehouse

    Tinker, M.T.; Mangel, M.; Estes, J.A.

    2009-01-01

    Question: How does the ability to improve foraging skills by learning, and to transfer that learned knowledge, affect the development of intra-population foraging specializations? Features of the model: We use both a state-dependent life-history model implemented by stochastic dynamic programming (SDPM) and an individual-based model (IBM) to capture the dynamic nature of behavioural preferences in feeding. Variables in the SDPM include energy reserves, skill levels, energy and handling time per single prey item, metabolic rate, the rates at which skills are learned and forgotten, the effect of skills on handling time, and the relationship between energy reserves and fitness. Additional variables in the IBM include the probability of successful weaning, the logistic dynamics of the prey species with stochastic recruitment, the intensity of top-down control of prey by predators, the mean and variance in skill levels of new recruits, and the extent to which learned Information can be transmitted via matrilineal social learning. Key range of variables: We explore the effects of approaching the time horizon in the SDPM, changing the extent to which skills can improve with experience, increasing the rates of learning or forgetting of skills, changing whether the learning curve is constant, accelerating (T-shaped) or decelerating ('r'-shaped), changing both mean and maximum possible energy reserves, changing metabolic costs of foraging, and changing the rate of encounter with prey. Conclusions: The model results show that the following factors increase the degree of prey specialization observed in a predator population: (1) Experience handling a prey type can substantially improve foraging skills for that prey. (2) There is limited ability to retain complex learned skills for multiple prey types. (3) The learning curve for acquiring new foraging skills is accelerating, or J-shaped. (4) The metabolic costs of foraging are high relative to available energy reserves. (5) Offspring can learn foraging skills from their mothers (matrilineal social learning). (6) Food abundance is limited, such that average individual energy reserves are low Additionally, the following factors increase the likelihood of alternative specializations co-occurring in a predator population: (1) The predator exerts effective top-down control of prey abundance, resulting in frequency-dependent dynamics. (2) There is stochastic Variation in prey population dynamics, but this Variation is neither too extreme in magnitude nor too 'slow' with respect to the time required for an individual forager to learn new foraging skills. For a given predator population, we deduce that the degree of specialization will be highest for those prey types requiring complex capture or handling skills, while prey species that are both profitable and easy to capture and handle will be included in the diet of all individuals. Frequency-dependent benefits of selecting alternative prey types, combined with the ability of foragers to improve their foraging skills by learning, and transmit learned skills to offspring, can result in behaviourally mediated foraging specialization, and also lead to the co-existence of alternative specializations. The extent of such specialization is predicted to be a variable trait, increasing in locations or years when intra-specific competition is high relative to inter-specific competition. ?? 2009 M. Tim Tinker.

  11. Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

    PubMed

    Cantley, Kurtis D; Subramaniam, Anand; Stiegler, Harvey J; Chapman, Richard A; Vogel, Eric M

    2012-04-01

    Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

  12. FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch

    PubMed Central

    Day, Nancy F; Kimball, Todd Haswell; Aamodt, Caitlin M; Heston, Jonathan B; Hilliard, Austin T; Xiao, Xinshu; White, Stephanie A

    2018-01-01

    Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans. PMID:29360038

  13. A smart-pixel holographic competitive learning network

    NASA Astrophysics Data System (ADS)

    Slagle, Timothy Michael

    Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization-multiplexed PCM readout, and gains of up to 60 were observed when a time-sequential read-out technique was used. Finally, the optical competitive learning laboratory system is described, including some necessary modifications to the previous architectures, and the data acquisition and control system developed for the system. Experimental results showing phase conjugation of the WTA outputs, holographic interconnect storage, associative storage between input images and WTA neuron outputs, and WTA array switching are presented, demonstrating the functions necessary for the operation of the optical learning system.

  14. Cognitive Models for Learning to Control Dynamic Systems

    DTIC Science & Technology

    2008-05-30

    2 3N NM NM NMK NK M− + + + + constraints, including KN M+ equality constraints, 7 2NM M+ inequality non- timing constraints and the rest are... inequality timing constraints. The size of the MILP model grows rapidly with the increase of problem size. So it is a big challenge to deal with more...task requirement, are studied in the section. An assumption is made in advance that the time of attack delay and flight time to the sink node are

  15. Teaching and assessing procedural skills using simulation: metrics and methodology.

    PubMed

    Lammers, Richard L; Davenport, Moira; Korley, Frederick; Griswold-Theodorson, Sharon; Fitch, Michael T; Narang, Aneesh T; Evans, Leigh V; Gross, Amy; Rodriguez, Elliot; Dodge, Kelly L; Hamann, Cara J; Robey, Walter C

    2008-11-01

    Simulation allows educators to develop learner-focused training and outcomes-based assessments. However, the effectiveness and validity of simulation-based training in emergency medicine (EM) requires further investigation. Teaching and testing technical skills require methods and assessment instruments that are somewhat different than those used for cognitive or team skills. Drawing from work published by other medical disciplines as well as educational, behavioral, and human factors research, the authors developed six research themes: measurement of procedural skills; development of performance standards; assessment and validation of training methods, simulator models, and assessment tools; optimization of training methods; transfer of skills learned on simulator models to patients; and prevention of skill decay over time. The article reviews relevant and established educational research methodologies and identifies gaps in our knowledge of how physicians learn procedures. The authors present questions requiring further research that, once answered, will advance understanding of simulation-based procedural training and assessment in EM.

  16. Peroral endoscopic myotomy: Time to change our opinion regarding the treatment of achalasia?

    PubMed Central

    Tantau, Marcel; Crisan, Dana

    2015-01-01

    Peroral endoscopic myotomy (POEM) is a new endoscopic treatment for achalasia. Compared to the classical surgical myotomy, POEM brings at least the advantage of minimal invasiveness. The data provided until now suggest that POEM offers excellent short-term symptom resolution, with improvement of dysphagia in more than 90% of treated patients, with encouraging manometric outcomes and low incidence of postprocedural gastroesophageal reflux. The effectiveness of this novel therapy requires long-term follow-up and comparative studies with other treatment modalities for achalasia. This technique requires experts in interventional endoscopy, with a learning curve requiring more than 20 cases, including training on animal and cadaver models, and with a need for structured proctoring during the first cases. This review aims to summarize the data on the technique, outcomes, safety and learning curve of this new endoscopic treatment of achalasia. PMID:25789094

  17. Adaptive management of rangeland systems

    USGS Publications Warehouse

    Allen, Craig R.; Angeler, David G.; Fontaine, Joseph J.; Garmestani, Ahjond S.; Hart, Noelle M.; Pope, Kevin L.; Twidwell, Dirac

    2017-01-01

    Adaptive management is an approach to natural resource management that uses structured learning to reduce uncertainties for the improvement of management over time. The origins of adaptive management are linked to ideas of resilience theory and complex systems. Rangeland management is particularly well suited for the application of adaptive management, having sufficient controllability and reducible uncertainties. Adaptive management applies the tools of structured decision making and requires monitoring, evaluation, and adjustment of management. Adaptive governance, involving sharing of power and knowledge among relevant stakeholders, is often required to address conflict situations. Natural resource laws and regulations can present a barrier to adaptive management when requirements for legal certainty are met with environmental uncertainty. However, adaptive management is possible, as illustrated by two cases presented in this chapter. Despite challenges and limitations, when applied appropriately adaptive management leads to improved management through structured learning, and rangeland management is an area in which adaptive management shows promise and should be further explored.

  18. Incremental learning of concept drift in nonstationary environments.

    PubMed

    Elwell, Ryan; Polikar, Robi

    2011-10-01

    We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn(++). NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn(++) family of algorithms, that is, without requiring access to previously seen data. Learn(++). NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn(++). NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper. © 2011 IEEE

  19. Virtual Learning Environment in Continuing Education for Nursing in Oncology: an Experimental Study.

    PubMed

    das Graças Silva Matsubara, Maria; De Domenico, Edvane Birelo Lopes

    2016-12-01

    Nurses working in oncology require continuing education and nowadays distance education is a possibility. To compare learning outcomes of the professionals participating in classroom learning versus distance learning; describing the sociodemographic characteristics and digital fluency of participants; comparing learning outcomes with independent variables; assessing the adequacy of educational practices in Virtual Environment Moodle Learning through the constructivist online learning environment survey. An experimental, randomized controlled study; conducted at the A C Camargo Cancer Center, located in São Paulo, SP, Brazil. The study included 97 nurses, with average training of 1 to 2 years. A control group (n = 44) had face to face training and the experiment group (n = 53) had training by distance learning, both with identical program content. The dependent variable was the result of learning, measured by applying a pre-assessment questionnaire and post-intervention for both groups. The sociodemographic and digital fluency data were uniform among the groups. The performance of both groups was statistically significant (p 0.005), and the control group had a greater advantage (40.4 %). Distance education has proven to be an effective alternative for training nurses, especially when they have more complex knowledge, more experience in the area and institutional time. Distance Education may be a possibility for the training of nurses for work in oncology. The association of age, training time and the institution, and the experience in Oncology interfered in the performance of both groups.

  20. Method of Real-Time Principal-Component Analysis

    NASA Technical Reports Server (NTRS)

    Duong, Tuan; Duong, Vu

    2005-01-01

    Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

  1. Working To Learn: Transforming Learning in the Workplace.

    ERIC Educational Resources Information Center

    Evans, Karen, Ed.; Hodkinson, Phil, Ed.; Unwin, Lorna, Ed.

    This book contains 13 papers on transformations in the nature of work that affect the learning and skill requirements of jobs and individuals and ways those requirements can be met. The following papers are included: "The Significance of Workplace Learning for a 'Learning Society'" (Karen Evans, Helen Rainbird); "Learning Careers:…

  2. Sustaining Change: The Answers Are Blowing in the Wind.

    ERIC Educational Resources Information Center

    Moffett, Cerylle A.

    2000-01-01

    Sustaining reform requires district leaders to develop a supportive infrastructure, nurture professional communities, reduce turnover, and use facilitators to build capacity. Bringing educators up to speed means providing abundant staff development, balancing pressure with support, providing adult learning time, and reducing fragmentation and…

  3. Folding Our Way to Productivity. Active Learning Lessons. Economics International.

    ERIC Educational Resources Information Center

    Baranova, Daira; Bottomoley, Alice; Brock, John; Shappo, Natalia

    This lesson plan was developed through "Economics International," an international program to help build economic education infrastructures in the emerging market economies. It provides a lesson description; economic concepts; content standards and benchmarks; related subject areas; instructional objectives; time required for lesson…

  4. Instruction in high schools: the evidence and the challenge.

    PubMed

    Corcoran, Tom; Silander, Megan

    2009-01-01

    The combined effects of standards-based reforms and accountability demands arising from recent technological and economic changes, say Tom Corcoran and Megan Silander, are requiring high schools to accomplish something they have never been required to do-ensure that substantially all students achieve at a relatively high level. Meeting that challenge, say the authors, will require high schools to improve the effectiveness of their core technology-instruction. The authors first examine how organizational structures affect instruction. Most high schools, they say, organize instruction by subject or discipline, thus encouraging an isolated and independent approach to teaching rather than one in which teachers are guided by a shared vision or goals. Many schools have focused on increasing teacher collaboration, often through teaming, interdisciplinary teaching, or professional learning communities. Citing limited evidence that these reforms improve instruction and learning, Corcoran and Silander urge researchers to examine whether the changes help schools implement specific instructional reforms and support sustained efforts to improve instruction. Next the authors explore the effects on student learning of instructional strategies such as interdisciplinary teaching, cooperative learning, project-based learning, adaptive instruction, inquiry, and dialogic teaching. The evidence suggests the power of well-designed student grouping strategies, of allowing students to express their ideas and questions, and of offering students challenging tasks. But, the authors say, less than half of American high school students report working in groups, and little class time is devoted to student-centered discussions. The authors conclude that schools should promote the use of proven instructional practices. In addition, teachers should systematically monitor how students vary in what they are learning and adapt their instruction in response to students' progress and needs, in the process learning more about what variations in instruction respond most effectively to common variations in students' learning. The authors argue that such "adaptive instruction" has the greatest potential for success in today's standards-based policy environment with its twin values of equity and excellence.

  5. Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

    NASA Technical Reports Server (NTRS)

    Niewoehner, Kevin R.; Carter, John (Technical Monitor)

    2001-01-01

    The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

  6. 1393 Ring Bus at JPL: Description and Status

    NASA Technical Reports Server (NTRS)

    Wysocky, Terry R.

    2007-01-01

    Completed Ring Bus IC V&V Phase - Ring Bus Test Plan Completed for SIM Project - Applicable to Other Projects Implemented a Avionics Bus Based upon the IEEE 1393 Standard - Excellent Starting Point for a General Purpose High-Speed Spacecraft Bus - Designed to Meet SIM Requirements for - Real-time deterministic, distributed systems. - Control system requirements - Fault detection and recovery Other JPL Projects Considering Implementation F'light Software Ring Bus Driver Module Began in 2006, Continues Participating in Standard Revision. Search for Earth-like planets orbiting nearby stars and measure the masses and orbits of the planets it finds. Survey 2000 nearby stars for planetary systems to learn whether our Solar System is unusual, or typical. Make a new catalog of star position 100 times more accurate than current measurements. Learn how our galaxy formed and will evolve by studying the dynamics of its stars. Critically test models of exactly how stars shine, including exotic objects like black holes, neutron stars and white dwarfs.

  7. EVA Skills Training

    NASA Technical Reports Server (NTRS)

    Parazynski, Scott

    2012-01-01

    Dr. Parazynski and a colleague from Extravehicular Activity (EVA), Robotics, & Crew Systems Operations (DX) worked closely to build the EVA Skills Training Program, and for the first time, defined the gold standards of EVA performance, allowing crewmembers to increase their performance significantly. As part of the program, individuals had the opportunity to learn at their own rate, taking additional water time as required, to achieve that level of performance. This focus on training to one's strengths and weaknesses to bolster them enabled the Crew Office and DX to field a much larger group of spacewalkers for the daunting "wall of EVA" required for the building and maintenance of the ISS. Parazynski also stressed the need for designers to understand the capabilities and the limitations of a human in a spacesuit, as well as opportunities to improve future generations of space. He shared lessons learned (how the Crew Office engaged in these endeavors) and illustrated the need to work as a team to develop these complex systems.

  8. Interactive, Online, Adsorption Lab to Support Discovery of the Scientific Process

    NASA Astrophysics Data System (ADS)

    Carroll, K. C.; Ulery, A. L.; Chamberlin, B.; Dettmer, A.

    2014-12-01

    Science students require more than methods practice in lab activities; they must gain an understanding of the application of the scientific process through lab work. Large classes, time constraints, and funding may limit student access to science labs, denying students access to the types of experiential learning needed to motivate and develop new scientists. Interactive, discovery-based computer simulations and virtual labs provide an alternative, low-risk opportunity for learners to engage in lab processes and activities. Students can conduct experiments, collect data, draw conclusions, and even abort a session. We have developed an online virtual lab, through which students can interactively develop as scientists as they learn about scientific concepts, lab equipment, and proper lab techniques. Our first lab topic is adsorption of chemicals to soil, but the methodology is transferrable to other topics. In addition to learning the specific procedures involved in each lab, the online activities will prompt exploration and practice in key scientific and mathematical concepts, such as unit conversion, significant digits, assessing risks, evaluating bias, and assessing quantity and quality of data. These labs are not designed to replace traditional lab instruction, but to supplement instruction on challenging or particularly time-consuming concepts. To complement classroom instruction, students can engage in a lab experience outside the lab and over a shorter time period than often required with real-world adsorption studies. More importantly, students can reflect, discuss, review, and even fail at their lab experience as part of the process to see why natural processes and scientific approaches work the way they do. Our Media Productions team has completed a series of online digital labs available at virtuallabs.nmsu.edu and scienceofsoil.com, and these virtual labs are being integrated into coursework to evaluate changes in student learning.

  9. Detection Learning Style Vark For Out Of School Children (OSC)

    NASA Astrophysics Data System (ADS)

    Amran, Ali; Desiani, Anita; Hasibuan, MS

    2017-04-01

    Learning style is different for every learner especially for out of school children or OSC. They are not like formal students, they are learners but they don’t have a teacher as a guide for learning. E-learning is one of the solutions to help OSC to get education. E-learning should have preferred learning styles of learners. Data for identifying the learning style in this study were collected with a VARK questionnaire from 25 OSC in junior high school level from 5 municipalities in Palembang. The validity of the questionnaire was considered on basis of experts’ views and its reliability was calculated by using Cronbach’s alpha coefficients (α=0.68). Overall, 55% preferred to use a single learning style (Uni-modal). Of these, 27,76% preferred Aural, 20,57% preferred Reading Writing, 33,33% preferred Kinaesthetic and 23,13% preferred Visual. 45% of OSC preferred more than one style, 30% chose two-modes (bimodal), and 15% chose three-modes (tri-modal). The Most preferred Learning style of OSC is kinaesthetic learning. Kinaesthetic learning requires body movements, interactivities, and direct contacts with learning materials, these things can be difficult to implement in eLearning, but E-learning should be able to adopt any learning styles which are flexible in terms of time, period, curriculum, pedagogy, location, and language.

  10. The sales learning curve.

    PubMed

    Leslie, Mark; Holloway, Charles A

    2006-01-01

    When a company launches a new product into a new market, the temptation is to immediately ramp up sales force capacity to gain customers as quickly as possible. But hiring a full sales force too early just causes the firm to burn through cash and fail to meet revenue expectations. Before it can sell an innovative product efficiently, the entire organization needs to learn how customers will acquire and use it, a process the authors call the sales learning curve. The concept of a learning curve is well understood in manufacturing. Employees transfer knowledge and experience back and forth between the production line and purchasing, manufacturing, engineering, planning, and operations. The sales learning curve unfolds similarly through the give-and-take between the company--marketing, sales, product support, and product development--and its customers. As customers adopt the product, the firm modifies both the offering and the processes associated with making and selling it. Progress along the manufacturing curve is measured by tracking cost per unit: The more a firm learns about the manufacturing process, the more efficient it becomes, and the lower the unit cost goes. Progress along the sales learning curve is measured in an analogous way: The more a company learns about the sales process, the more efficient it becomes at selling, and the higher the sales yield. As the sales yield increases, the sales learning process unfolds in three distinct phases--initiation, transition, and execution. Each phase requires a different size--and kind--of sales force and represents a different stage in a company's production, marketing, and sales strategies. Adjusting those strategies as the firm progresses along the sales learning curve allows managers to plan resource allocation more accurately, set appropriate expectations, avoid disastrous cash shortfalls, and reduce both the time and money required to turn a profit.

  11. E-Learning in Croatian Higher Education: An Analysis of Students' Perceptions

    NASA Astrophysics Data System (ADS)

    Dukić, Darko; Andrijanić, Goran

    2010-06-01

    Over the last years, e-learning has taken an important role in Croatian higher education as a result of strategies defined and measures undertaken. Nonetheless, in comparison to the developed countries, the achievements in e-learning implementation are still unsatisfactory. Therefore, the efforts to advance e-learning within Croatian higher education need to be intensified. It is further necessary to undertake ongoing activities in order to solve possible problems in e-learning system functioning, which requires the development of adequate evaluation instruments and methods. One of the key steps in this process would be examining and analyzing users' attitudes. This paper presents a study of Croatian students' perceptions with regard to certain aspects of e-learning usage. Given the character of this research, adequate statistical methods were required for the data processing. The results of the analysis indicate that, for the most part, Croatian students have positive perceptions of e-learning, particularly as support to time-honored forms of teaching. However, they are not prepared to completely give up the traditional classroom. Using factor analysis, we identified four underlying factors of a collection of variables related to students' perceptions of e-learning. Furthermore, a certain number of statistically significant differences in student attitudes have been confirmed, in terms of gender and year of study. In our study we used discriminant analysis to determine discriminant functions that distinguished defined groups of students. With this research we managed to a certain degree to alleviate the current data insufficiency in the area of e-learning evaluation among Croatian students. Since this type of learning is gaining in importance within higher education, such analyses have to be conducted continuously.

  12. 45 CFR 2517.730 - May an applicant submit more than one application to the Corporation for the same project at the...

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ...-BASED SERVICE-LEARNING PROGRAMS Funding Requirements § 2517.730 May an applicant submit more than one application to the Corporation for the same project at the same time? No. The Corporation will reject an... to the Corporation for the same project at the same time? 2517.730 Section 2517.730 Public Welfare...

  13. Learning curves for transapical transcatheter aortic valve replacement in the PARTNER-I trial: Technical performance, success, and safety.

    PubMed

    Suri, Rakesh M; Minha, Sa'ar; Alli, Oluseun; Waksman, Ron; Rihal, Charanjit S; Satler, Lowell P; Greason, Kevin L; Torguson, Rebecca; Pichard, Augusto D; Mack, Michael; Svensson, Lars G; Rajeswaran, Jeevanantham; Lowry, Ashley M; Ehrlinger, John; Mick, Stephanie L; Tuzcu, E Murat; Thourani, Vinod H; Makkar, Raj; Holmes, David; Leon, Martin B; Blackstone, Eugene H

    2016-09-01

    Introduction of hybrid techniques, such as transapical transcatheter aortic valve replacement (TA-TAVR), requires skills that a heart team must master to achieve technical efficiency: the technical performance learning curve. To date, the learning curve for TA-TAVR remains unknown. We therefore evaluated the rate at which technical performance improved, assessed change in occurrence of adverse events in relation to technical performance, and determined whether adverse events after TA-TAVR were linked to acquiring technical performance efficiency (the learning curve). From April 2007 to February 2012, 1100 patients, average age 85.0 ± 6.4 years, underwent TA-TAVR in the PARTNER-I trial. Learning curves were defined by institution-specific patient sequence number using nonlinear mixed modeling. Mean procedure time decreased from 131 to 116 minutes within 30 cases (P = .06) and device success increased to 90% by case 45 (P = .0007). Within 30 days, 354 patients experienced a major adverse event (stroke in 29, death in 96), with possibly decreased complications over time (P ∼ .08). Although longer procedure time was associated with more adverse events (P < .0001), these events were associated with change in patient risk profile, not the technical performance learning curve (P = .8). The learning curve for TA-TAVR was 30 to 45 procedures performed, and technical efficiency was achieved without compromising patient safety. Although fewer patients are now undergoing TAVR via nontransfemoral access, understanding TA-TAVR learning curves and their relationship with outcomes is important as the field moves toward next-generation devices, such as those to replace the mitral valve, delivered via the left ventricular apex. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

  14. Stimulus-response learning in long-term cocaine users: acquired equivalence and probabilistic category learning.

    PubMed

    Vadhan, Nehal P; Myers, Catherine E; Rubin, Eric; Shohamy, Daphna; Foltin, Richard W; Gluck, Mark A

    2008-01-11

    The purpose of this study was to examine stimulus-response (S-R) learning in active cocaine users. Twenty-two cocaine-dependent participants (20 males and 2 females) and 21 non-drug using control participants (19 males and 2 females) who were similar in age and education were administered two computerized learning tasks. The Acquired Equivalence task initially requires learning of simple antecedent-consequent discriminations, but later requires generalization of this learning when the stimuli are presented in novel recombinations. The Weather Prediction task requires the prediction of a dichotomous outcome based on different stimuli combinations when the stimuli predict the outcome only probabilistically. On the Acquired Equivalence task, cocaine users made significantly more errors than control participants when required to learn new discriminations while maintaining previously learned discriminations, but performed similarly to controls when required to generalize this learning. No group differences were seen on the Weather Prediction task. Cocaine users' learning of stimulus discriminations under conflicting response demands was impaired, but their ability to generalize this learning once they achieved criterion was intact. This performance pattern is consistent with other laboratory studies of long-term cocaine users that demonstrated that established learning interfered with new learning on incremental learning tasks, relative to healthy controls, and may reflect altered dopamine transmission in the basal ganglia of long-term cocaine users.

  15. Exploring the use of mobile technologies for the acquisition of clinical skills.

    PubMed

    Clay, Collette A

    2011-08-01

    Mobile learning has the potential to supplement information communication technology (ICT), online learning and the traditional teaching and learning methods to educate practitioners in the clinical practice area. Following the development of several Post Graduate modules of learning for the theory and clinical skills required to undertake the Newborn Infant Physical Examination (NIPE), a small research study was undertaken to combine mobile learning and NIPE. The research study explored the hypothesis that mobile devices could be used in pedagogically effective ways to support and enhance the learning and acquisition of clinical skills in the clinical arena. Participants in the study each received a handheld mobile device (iPod) that had been loaded with several Reusable Learning Objects (RLO) outlining each aspect of the physical examination to be performed. At the end of the module (12 weeks in duration), each participant completed an evaluation questionnaire. Participants confirmed that mobile learning afforded flexibility in time and place of learning and captured their interest in the learning material. This study reports that the use of mobile technology for skill acquisition is creative and innovative, placing learning firmly in the hands of the learner. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

    NASA Astrophysics Data System (ADS)

    Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

    2017-05-01

    For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

  17. Online learning for professional development.

    PubMed

    Purkis, Nick; Gabb, Carol A

    This article addresses how nurses in the UK can use online learning to meet the changing requirements of continuing professional development. Recent changes in post-registration nurse education are due to two main reasons: financial cutbacks and reduced use of agency staff make it difficult for nurse managers to release nurses for study time away from the ward; and healthcare becoming increasingly diverse and complex, so pre-registration education has changed. Since September 2013, only degree-level pre-registration nursing programmes have been available in the UK. Degree-level education is intended to sharpen critical thinking skills to improve future healthcare but it may also disadvantage nurses without degrees. One response to these challenges is to provide online learning, such as online personal learning programmes. portfolios (e-portfolios) or other onlinen

  18. Are Serious Games a Good Strategy for Pharmacy Education?

    PubMed Central

    Cain, Jeff

    2015-01-01

    Serious gaming is the use of game principles for the purposes of learning, skill acquisition, and training. Higher education is beginning to incorporate serious gaming into curricula, and health professions education is the most common area for serious game use. Advantages of serious gaming in pharmacy education include authentic, situated learning without risk of patient consequences, collaborative learning, ability to challenge students of all performance levels, high student motivation with increased time on task, immediate feedback, ability to learn from mistakes without becoming discouraged, and potential for behavior and attitude change. Development of quality games for pharmacy education requires content expertise as well as expertise in the science and design of gaming. When well done, serious gaming provides a valuable additional tool for pharmacy education. PMID:26089556

  19. Improving Group Work Practices in Teaching Life Sciences: Trialogical Learning

    NASA Astrophysics Data System (ADS)

    Tammeorg, Priit; Mykkänen, Anna; Rantamäki, Tomi; Lakkala, Minna; Muukkonen, Hanni

    2017-08-01

    Trialogical learning, a collaborative and iterative knowledge creation process using real-life artefacts or problems, familiarizes students with working life environments and aims to teach skills required in the professional world. We target one of the major limitation factors for optimal trialogical learning in university settings, inefficient group work. We propose a course design combining effective group working practices with trialogical learning principles in life sciences. We assess the usability of our design in (a) a case study on crop science education and (b) a questionnaire for university teachers in life science fields. Our approach was considered useful and supportive of the learning process by all the participants in the case study: the students, the stakeholders and the facilitator. Correspondingly, a group of university teachers expressed that the trialogical approach and the involvement of stakeholders could promote efficient learning. In our case in life sciences, we identified the key issues in facilitating effective group work to be the design of meaningful tasks and the allowance of sufficient time to take action based on formative feedback. Even though trialogical courses can be time consuming, the experience of applying knowledge in real-life cases justifies using the approach, particularly for students just about to enter their professional careers.

  20. Introducing an online community into a clinical education setting: a pilot study of student and staff engagement and outcomes using blended learning

    PubMed Central

    2010-01-01

    Background There are growing reasons to use both information and communication functions of learning technologies as part of clinical education, but the literature offers few accounts of such implementations or evaluations of their impact. This paper details the process of implementing a blend of online and face-to-face learning and teaching in a clinical education setting and it reports on the educational impact of this innovation. Methods This study designed an online community to complement a series of on-site workshops and monitored its use over a semester. Quantitative and qualitative data recording 43 final-year medical students' and 13 clinical educators' experiences with this blended approach to learning and teaching were analysed using access, adoption and quality criteria as measures of impact. Results The introduction of the online community produced high student ratings of the quality of learning and teaching and it produced student academic results that were equivalent to those from face-to-face-only learning and teaching. Staff had mixed views about using blended learning. Conclusions Projects such as this take skilled effort and time. Strong incentives are required to encourage clinical staff and students to use a new mode of communication. A more synchronous or multi-channel communication feedback system might stimulate increased adoption. Cultural change in clinical teaching is also required before clinical education can benefit more widely from initiatives such as this. PMID:20100354

  1. 34 CFR 85.350 - What must I do if I learn of information required under § 85.335 after entering into a covered...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 1 2010-07-01 2010-07-01 false What must I do if I learn of information required under...-Primary Tier Participants § 85.350 What must I do if I learn of information required under § 85.335 after... the transaction if you learn either that— (a) You failed to disclose information earlier, as required...

  2. 29 CFR 98.350 - What must I do if I learn of information required under § 98.335 after entering into a covered...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 29 Labor 1 2010-07-01 2010-07-01 true What must I do if I learn of information required under Â... What must I do if I learn of information required under § 98.335 after entering into a covered... transaction if you learn either that— (a) You failed to disclose information earlier, as required by § 98.335...

  3. 41 CFR 105-68.350 - What must I do if I learn of information required under § 105-68.335 after entering into a...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... learn of information required under § 105-68.335 after entering into a covered transaction with the....350 What must I do if I learn of information required under § 105-68.335 after entering into a covered... if you learn either that— (a) You failed to disclose information earlier, as required by § 105-68.335...

  4. 34 CFR 85.365 - What must I do if I learn of information required under § 85.355 after entering into a covered...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 1 2010-07-01 2010-07-01 false What must I do if I learn of information required under...-Lower Tier Participants § 85.365 What must I do if I learn of information required under § 85.355 after... to that person if you learn either that— (a) You failed to disclose information earlier, as required...

  5. 34 CFR 85.365 - What must I do if I learn of information required under § 85.355 after entering into a covered...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 1 2011-07-01 2011-07-01 false What must I do if I learn of information required under...-Lower Tier Participants § 85.365 What must I do if I learn of information required under § 85.355 after... to that person if you learn either that— (a) You failed to disclose information earlier, as required...

  6. 34 CFR 85.350 - What must I do if I learn of information required under § 85.335 after entering into a covered...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 1 2011-07-01 2011-07-01 false What must I do if I learn of information required under...-Primary Tier Participants § 85.350 What must I do if I learn of information required under § 85.335 after... the transaction if you learn either that— (a) You failed to disclose information earlier, as required...

  7. 29 CFR 98.350 - What must I do if I learn of information required under § 98.335 after entering into a covered...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 29 Labor 1 2011-07-01 2011-07-01 false What must I do if I learn of information required under Â... What must I do if I learn of information required under § 98.335 after entering into a covered... transaction if you learn either that— (a) You failed to disclose information earlier, as required by § 98.335...

  8. 41 CFR 105-68.350 - What must I do if I learn of information required under § 105-68.335 after entering into a...

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... learn of information required under § 105-68.335 after entering into a covered transaction with the....350 What must I do if I learn of information required under § 105-68.335 after entering into a covered... if you learn either that— (a) You failed to disclose information earlier, as required by § 105-68.335...

  9. Hidden physics models: Machine learning of nonlinear partial differential equations

    NASA Astrophysics Data System (ADS)

    Raissi, Maziar; Karniadakis, George Em

    2018-03-01

    While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

  10. KISS--a new approach to self-controlled e-learning of selected chapters in Medical Engineering and other fields at bachelor and master course level.

    PubMed

    Hutten, Helmut; Stiegmaier, Wolfgang; Rauchegger, Günter

    2005-09-01

    Modern life style requires new methods for individual lifelong learning, based on access at every time and from every place. This fundamental requirement is provided by the Internet. The Internet technology promises an increasing potential in the future for e-learning or tele-learning. Some special requirements are password-controlled access, applicability of most commercially available PCs and laptops equipped with standard software (Microsoft Internet Explorer 6.0), central evaluation of the students' performance, inclusion of an examination part, provision of a picture gallery and a comprehensive glossary accessible in the learning mode. The KISS-shell has been developed based on the Oracle 10g application server in combination with a relational data base (Oracle 8i) on the server side and a web browser based interface using JavaScript for user control of data input on the client side (Kontrolliertes Intelligentes Selbstgesteuertes Studium, KISS). The first tutorial application has been realized with a chapter about cardiac pacemakers. The weight of that chapter (or module) is about 2 ECTS (i.e. the equivalent of 30 working hours; European Credit Transfer System, ECTS). The internal structure of the chapter is organized in sequential mode. It consists of five main sections. Each of those five sections is subdivided into five subsections of comparable length. Progression from one subsection to the next is possible only after successfully passing through the respective examination. The whole learning programme with the pacemaker chapter has been evaluated by 10 students. The system will be presented together with first experiences including the evaluation results. Until now the program has not been used for training purposes.

  11. Contracted time and expanded space: The impact of circumnavigation on judgements of space and time.

    PubMed

    Brunec, Iva K; Javadi, Amir-Homayoun; Zisch, Fiona E L; Spiers, Hugo J

    2017-09-01

    The ability to estimate distance and time to spatial goals is fundamental for survival. In cases where a region of space must be navigated around to reach a location (circumnavigation), the distance along the path is greater than the straight-line Euclidean distance. To explore how such circumnavigation impacts on estimates of distance and time, we tested participants on their ability to estimate travel time and Euclidean distance to learned destinations in a virtual town. Estimates for approximately linear routes were compared with estimates for routes requiring circumnavigation. For all routes, travel times were significantly underestimated, and Euclidean distances overestimated. For routes requiring circumnavigation, travel time was further underestimated and the Euclidean distance further overestimated. Thus, circumnavigation appears to enhance existing biases in representations of travel time and distance. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  12. Nursing students' perceptions of factors influencing their learning environment in a clinical skills laboratory: A qualitative study.

    PubMed

    Haraldseid, Cecilie; Friberg, Febe; Aase, Karina

    2015-09-01

    The mastery of clinical skills learning is required to become a trained nurse. Due to limited opportunities for clinical skills training in clinical practice, undergraduate training at clinical skills laboratories (CSLs) is an essential part of nursing education. In a sociocultural learning perspective learning is situated in an environment. Growing student cohorts, rapid introduction of technology-based teaching methods and a shift from a teaching- to a learning-centered education all influence the environment of the students. These changes also affect CSLs and therefore compel nursing faculties to adapt to the changing learning environment. This study aimed to explore students' perceptions of their learning environment in a clinical skills laboratory, and to increase the knowledge base for improving CSL learning conditions identifying the most important environmental factors according to the students. An exploratory qualitative methodology was used. Nineteen second-year students enrolled in an undergraduate nursing program in Norway participated in the study. They took the same clinical skills course. Eight were part-time students (group A) and 11 were full-time students (group B). Focus group interviews and content analysis were conducted to capture the students' perception of the CSL learning environment. The study documents students' experience of the physical (facilities, material equipment, learning tools, standard procedures), psychosocial (expectations, feedback, relations) and organizational (faculty resources, course structure) factors that affect the CSL learning environment. Creating an authentic environment, facilitating motivation, and providing resources for multiple methods and repetitions within clinical skills training are all important for improving CSL learning environments from the student perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning.

    PubMed

    Török, Balázs; Janacsek, Karolina; Nagy, Dávid G; Orbán, Gergő; Nemeth, Dezso

    2017-04-01

    Learning complex structures from stimuli requires extended exposure and often repeated observation of the same stimuli. Learning induces stimulus-dependent changes in specific performance measures. The same performance measures, however, can also be affected by processes that arise because of extended training (e.g., fatigue) but are otherwise independent from learning. Thus, a thorough assessment of the properties of learning can only be achieved by identifying and accounting for the effects of such processes. Reactive inhibition is a process that modulates behavioral performance measures on a wide range of time scales and often has opposite effects than learning. Here we develop a tool to disentangle the effects of reactive inhibition from learning in the context of an implicit learning task, the alternating serial reaction time (ASRT) task. Our method highlights that the magnitude of the effect of reactive inhibition on measured performance is larger than that of the acquisition of statistical structure from stimuli. We show that the effect of reactive inhibition can be identified not only in population measures but also at the level of performance of individuals, revealing varying degrees of contribution of reactive inhibition. Finally, we demonstrate that a higher proportion of behavioral variance can be explained by learning once the effects of reactive inhibition are eliminated. These results demonstrate that reactive inhibition has a fundamental effect on the behavioral performance that can be identified in individual participants and can be separated from other cognitive processes like learning. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

    PubMed

    He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

    2018-04-17

    By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

  15. LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

    PubMed Central

    Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

    2018-01-01

    By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

  16. Learning through ferroelectric domain dynamics in solid-state synapses

    NASA Astrophysics Data System (ADS)

    Boyn, Sören; Grollier, Julie; Lecerf, Gwendal; Xu, Bin; Locatelli, Nicolas; Fusil, Stéphane; Girod, Stéphanie; Carrétéro, Cécile; Garcia, Karin; Xavier, Stéphane; Tomas, Jean; Bellaiche, Laurent; Bibes, Manuel; Barthélémy, Agnès; Saïghi, Sylvain; Garcia, Vincent

    2017-04-01

    In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

  17. Design of time-pulse coded optoelectronic neuronal elements for nonlinear transformation and integration

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lazareva, Maria V.

    2008-03-01

    In the paper the actuality of neurophysiologically motivated neuron arrays with flexibly programmable functions and operations with possibility to select required accuracy and type of nonlinear transformation and learning are shown. We consider neurons design and simulation results of multichannel spatio-time algebraic accumulation - integration of optical signals. Advantages for nonlinear transformation and summation - integration are shown. The offered circuits are simple and can have intellectual properties such as learning and adaptation. The integrator-neuron is based on CMOS current mirrors and comparators. The performance: consumable power - 100...500 μW, signal period- 0.1...1ms, input optical signals power - 0.2...20 μW time delays - less 1μs, the number of optical signals - 2...10, integration time - 10...100 of signal periods, accuracy or integration error - about 1%. Various modifications of the neuron-integrators with improved performance and for different applications are considered in the paper.

  18. Mitigation of time-varying distortions in Nyquist-WDM systems using machine learning

    NASA Astrophysics Data System (ADS)

    Granada Torres, Jhon J.; Varughese, Siddharth; Thomas, Varghese A.; Chiuchiarelli, Andrea; Ralph, Stephen E.; Cárdenas Soto, Ana M.; Guerrero González, Neil

    2017-11-01

    We propose a machine learning-based nonsymmetrical demodulation technique relying on clustering to mitigate time-varying distortions derived from several impairments such as IQ imbalance, bias drift, phase noise and interchannel interference. Experimental results show that those impairments cause centroid movements in the received constellations seen in time-windows of 10k symbols in controlled scenarios. In our demodulation technique, the k-means algorithm iteratively identifies the cluster centroids in the constellation of the received symbols in short time windows by means of the optimization of decision thresholds for a minimum BER. We experimentally verified the effectiveness of this computationally efficient technique in multicarrier 16QAM Nyquist-WDM systems over 270 km links. Our nonsymmetrical demodulation technique outperforms the conventional QAM demodulation technique, reducing the OSNR requirement up to ∼0.8 dB at a BER of 1 × 10-2 for signals affected by interchannel interference.

  19. Analysis of the vitreoretinal surgery learning curve.

    PubMed

    Martín-Avià, J; Romero-Aroca, P

    2017-06-01

    To describe intra- and post-operative complications, as well as the evolution of the surgical technique in first 4years of work of a novice retina surgeon, and evaluate minimal learning time required to reduce its complications, deciding which pathologies should still be referred to higher level hospitals, until further experience may be achieved. A study was conducted on patients that had undergone vitreoretinal surgery by a novice surgeon in Tarragona between 23rd October 2007 and 31st December 2011. The primary diagnosis, surgeon learning time, surgical technique, intra-operative and post-operative complications were recorded. A total of 247 surgeries were studied. The percentage of use of 20G and 23G calibres during the time, marks a change towards trans-conjunctival surgery from the ninth trimester (98 surgeries). Surgical complications decreased towards twelfth trimester (130 surgeries) with an increase in the previous months. The shift towards 23G technique around 100 surgeries is interpreted as greater comfort and safety by the surgeon. Increased surgical complications during the following months until its decline around 130 surgeries can be interpreted as an 'overconfidence'. It is arguable that the learning curve is slower than what the surgeon believes. An individual analysis of the complications and surgical outcomes is recommended to ascertain the status of the learning curve. Copyright © 2016 Sociedad Española de Oftalmología. Publicado por Elsevier España, S.L.U. All rights reserved.

  20. Vocal exploration is locally regulated during song learning

    PubMed Central

    Ravbar, Primoz; Parra, Lucas C.; Lipkind, Dina; Tchernichovski, Ofer

    2012-01-01

    Exploratory variability is essential for sensory-motor learning, but it is not known how and at what time scales it is regulated. We manipulated song learning in zebra finches to experimentally control the requirements for vocal exploration in different parts of their song. We first trained birds to perform a one-syllable song, and once they mastered it we added a new syllable to the song model. Remarkably, when practicing the modified song, birds rapidly alternated between high and low acoustic variability to confine vocal exploration to the newly added syllable. Further, even within syllables, acoustic variability changed independently across song elements that were only milliseconds apart. Analysis of the entire vocal output during learning revealed that the variability of each song element decreased as it approached the target, correlating with momentary local distance from the target and less so with the overall distance. We conclude that vocal error is computed locally in sub-syllabic time scales and that song elements can be learned and crystalized independently. Songbirds have dedicated brain circuitry for vocal babbling in the anterior forebrain pathway (AFP), which generates exploratory song patterns that drive premotor neurons at the song nucleus RA (robust nucleus of the arcopallium). We hypothesize that either AFP adjusts the gain of vocal exploration in fine time scales, or that the sensitivity of RA premotor neurons to AFP/HVC inputs varies across song elements. PMID:22399765

  1. Introducing the Learning Practice--III. Leadership, empowerment, protected time and reflective practice as core contextual conditions.

    PubMed

    Rushmer, Rosemary; Kelly, Diane; Lough, Murray; Wilkinson, Joyce E; Davies, Huw T O

    2004-08-01

    This paper is the third of three related papers exploring the ways in which the principles of Learning Organizations (LOs) could be applied in Primary Care settings at the point of service delivery. Here we provide a systematic literature review of contextual factors that either play a key role in providing a facilitative context for a Learning Practice or manifest themselves as barriers to any Practice's attempts to develop a learning culture. Core contextual conditions are identified as, first, the requirement for strong and visionary leadership. Leaders who support and develop others, ask challenging questions, are willing to be learners themselves, see possibilities and make things happen, facilitate learning environments. The second core condition is the involvement and empowerment of staff where changes grow from the willing participation of all concerned. The third prerequisite is the setting-aside of times and places for learning and reflection. This paper contributes to the wider quality improvement debate in three main ways. First, by highlighting the local contextual issues that are most likely to impact on the success or failure of a Practice's attempts to work towards a learning culture. Second, by demonstrating that the very same factors can either help or hinder depending on how they are manifest and played out in context. Third, it adds to the evidence available to support the case for LOs in health care settings.

  2. Cooperation and Coordination Between Fuzzy Reinforcement Learning Agents in Continuous State Partially Observable Markov Decision Processes

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Vengerov, David

    1999-01-01

    Successful operations of future multi-agent intelligent systems require efficient cooperation schemes between agents sharing learning experiences. We consider a pseudo-realistic world in which one or more opportunities appear and disappear in random locations. Agents use fuzzy reinforcement learning to learn which opportunities are most worthy of pursuing based on their promise rewards, expected lifetimes, path lengths and expected path costs. We show that this world is partially observable because the history of an agent influences the distribution of its future states. We consider a cooperation mechanism in which agents share experience by using and-updating one joint behavior policy. We also implement a coordination mechanism for allocating opportunities to different agents in the same world. Our results demonstrate that K cooperative agents each learning in a separate world over N time steps outperform K independent agents each learning in a separate world over K*N time steps, with this result becoming more pronounced as the degree of partial observability in the environment increases. We also show that cooperation between agents learning in the same world decreases performance with respect to independent agents. Since cooperation reduces diversity between agents, we conclude that diversity is a key parameter in the trade off between maximizing utility from cooperation when diversity is low and maximizing utility from competitive coordination when diversity is high.

  3. How can surgical training benefit from theories of skilled motor development, musical skill acquisition and performance psychology?

    PubMed

    McCaskie, Andrew W; Kenny, Dianna T; Deshmukh, Sandeep

    2011-05-02

    Trainee surgeons must acquire expert status in the context of reduced hours, reduced operating room time and the need to learn complex skills involving screen-mediated techniques, computers and robotics. Ever more sophisticated surgical simulation strategies have been helpful in providing surgeons with the opportunity to practise, but not all of these strategies are widely available. Similarities in the motor skills required in skilled musical performance and surgery suggest that models of music learning, and particularly skilled motor development, may be applicable in training surgeons. More attention should be paid to factors associated with optimal arousal and optimal performance in surgical training - lessons learned from helping anxious musicians optimise performance and manage anxiety may also be transferable to trainee surgeons. The ways in which the trainee surgeon moves from novice to expert need to be better understood so that this process can be expedited using current knowledge in other disciplines requiring the performance of complex fine motor tasks with high cognitive load under pressure.

  4. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    PubMed

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  5. Evolutionary online behaviour learning and adaptation in real robots.

    PubMed

    Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

    2017-07-01

    Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.

  6. A Decision Model for Merging Base Operations: Outsourcing Pest Management on Joint Base Anacostia-Bolling

    DTIC Science & Technology

    2011-11-30

    OH: South- Western Cengage Learning. Mankiw , N. G. (2006). Principles of economics (4th ed.). Mason, OH: Thompson South- Western. Private...When the choice to in-source or outsource an installation function or service requirement exists, in these challenging economic times, it is now more...decision uncertainties. When the choice to in-source or outsource an installation function or service requirement exists, in these challenging economic

  7. Consolidation of Vocabulary Is Associated with Sleep in Children

    ERIC Educational Resources Information Center

    Henderson, Lisa M.; Weighall, Anna R.; Brown, Helen; Gaskell, M. Gareth

    2012-01-01

    Although the acquisition of a novel word is apparently rapid, adult research suggests that integration of novel and existing knowledge (measured by engagement in lexical competition) requires sleep-associated consolidation. We present the first investigation of whether a similar time-course dissociation characterizes word learning across…

  8. 40 CFR 63.1401 - Compliance schedule.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... needed and the date when the owner or operator first learned of the circumstances necessitating a request for compliance extension. (e) All terms in this subpart that define a period of time for completion of required tasks (e.g., weekly, monthly, quarterly, annual), unless specified otherwise, refer to the...

  9. It's about Time!

    ERIC Educational Resources Information Center

    Smith, Diane

    2012-01-01

    Proficiency-based instruction focuses on what students know and can do. Proficiency-based teaching and learning is a teacher-led initiative in Oregon that began in the early 1990s with efforts to establish a proficiency-based admissions requirement system for Oregon's state colleges and universities. Meanwhile, many public alternative schools…

  10. How to Make Guided Discovery Learning Practical for Student Teachers

    ERIC Educational Resources Information Center

    Janssen, Fred J. J. M.; Westbroek, Hanna B.; van Driel, Jan H.

    2014-01-01

    Many innovative teaching approaches lack classroom impact because teachers consider the proposals impractical. Making a teaching approach practical requires instrumentality (procedures), congruence (local fit), and affordable cost (limited time and resources).This paper concerns a study on the development and effects of a participatory design…

  11. Quiet Quincy Quarter. Teacher's Guide [and] Student Materials.

    ERIC Educational Resources Information Center

    Zishka, Phyllis

    This document suggests learning activities, teaching methods, objectives, and evaluation measures for a second grade consumer education unit on quarters. The unit, which requires approximately six hours of class time, reinforces basic social studies and mathematics skills including following sequences of numbers, distinguishing left from right,…

  12. Basic Skills Applications in Occupational Investigation.

    ERIC Educational Resources Information Center

    Hendrix, Mary

    This guide contains 50 lesson plans for learning activities that incorporate basic skills into content areas of career education, mathematics, science, social studies, communications, and productive work habits. Each lesson consists of a purpose, basic skills applications, approximate time required, materials needed, things for the teacher to do…

  13. Using Common Planning Time to Foster Professional Learning

    ERIC Educational Resources Information Center

    Dever, Robin; Lash, Martha J.

    2013-01-01

    Increased emphasis on meeting state standards, more stringent requirements for designation as highly qualified, and intensified accountability for student performance have foisted new expectations upon teachers and stimulated changes in professional development models in which the greater urgency is clearly to attend to the teacher's role as…

  14. Transmorphosis: Negotiating Discontinuities in Academic Work

    ERIC Educational Resources Information Center

    Rhea, Zane Ma

    2010-01-01

    The idea of "transmorphosis" will be used in this article to discuss the impact of new teaching and learning environments on the highly mobile global academic pedagogue. "Trans-" implies "movement across", whether it be space, time, place, culture, or institution. "-morphosis" evokes the possibility/requirement to shapeshift one's pedagogical…

  15. What We've Learned about Assessing Hands-On Science.

    ERIC Educational Resources Information Center

    Shavelson, Richard J.; Baxter, Gail P.

    1992-01-01

    A recent study compared hands-on scientific inquiry assessment to assessments involving lab notebooks, computer simulations, short-answer paper-and-pencil problems, and multiple-choice questions. Creating high quality performance assessments is a costly, time-consuming process requiring considerable scientific and technological know-how. Improved…

  16. Digital Reenactments: Using Green Screen Technology to Recreate the Past

    ERIC Educational Resources Information Center

    Sheffield, Caroline C.; Swan, Stephen B.

    2012-01-01

    Historical reenactments are a frequently utilized active learning strategy that encourages students to engage in historical thinking. They require students to critically read and synthesize information, consider multiple perspectives, and write a coherent narrative demonstrating an understanding of the time period, event, and the individuals…

  17. Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

    PubMed

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

    2012-01-01

    Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

  18. MLBCD: a machine learning tool for big clinical data.

    PubMed

    Luo, Gang

    2015-01-01

    Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

  19. Pedagogical underpinnings of computer-based learning.

    PubMed

    Adams, Audrey M

    2004-04-01

    E-learning is becoming increasingly incorporated into educational programmes. Digital materials usually require a lot of investment in terms of time, money and human resources. With advances in technology, delivery of content has much improved in terms of multimedia elements. However, often only low-level learning is achieved as a result of using these materials. The purpose of this article is to give a comprehensive overview of some of the most important issues to consider when incorporating e-learning into educational programmes. Computer-based learning has three components: hardware, software and 'underware', the pedagogy that underpins its development. The latter is the most important, as the approach adopted will influence the creation of computer-based learning materials and determine the way in which students engage with subject matter. Teachers are responsible for the quality of their courses and have a vital role in helping to develop the most appropriate electronic learning activities that will facilitate students to acquire the knowledge and skills necessary for clinical practice. Therefore, they need to have an awareness of what contributes to educationally effective, computer-based learning materials.

  20. Measuring Cognitive Load in Embodied Learning Settings.

    PubMed

    Skulmowski, Alexander; Rey, Günter Daniel

    2017-01-01

    In recent years, research on embodied cognition has inspired a number of studies on multimedia learning and instructional psychology. However, in contrast to traditional research on education and multimedia learning, studies on embodied learning (i.e., focusing on bodily action and perception in the context of education) in some cases pose new problems for the measurement of cognitive load. This review provides an overview over recent studies on embodied learning in which cognitive load was measured using surveys, behavioral data, or physiological measures. The different methods are assessed in terms of their success in finding differences of cognitive load in embodied learning scenarios. At the same time, we highlight the most important challenges for researchers aiming to include these measures into their study designs. The main issues we identified are: (1) Subjective measures must be appropriately phrased to be useful for embodied learning; (2) recent findings indicate potentials as well as problematic aspects of dual-task measures; (3) the use of physiological measures offers great potential, but may require mobile equipment in the context of embodied scenarios; (4) meta-cognitive measures can be useful extensions of cognitive load measurement for embodied learning.

  1. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

    PubMed

    Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

    2018-02-01

    Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

  2. A comparison of hysteroscopic mechanical tissue removal with bipolar electrical resection for the management of endometrial polyps in an ambulatory care setting: preliminary results.

    PubMed

    Pampalona, Jennifer Rovira; Bastos, Maria Degollada; Moreno, Gemma Mancebo; Pust, Andrea Buron; Montesdeoca, Gemma Escribano; Guerra Garcia, Angel; Pruñonosa, Juan Carles Mateu; Collado, Ramon Carreras; Torras, Pere Bresco

    2015-01-01

    To assess and compare efficacy, pain, and the learning curve associated with diagnostic therapeutic hysteroscopy using mechanical tissue removal versus bipolar electrical resection in the management of endometrial polyps in an ambulatory care setting. A randomized controlled clinical trial (Canadian Task Force classification I). Hospital de Igulada, Barcelona, Spain. A total of 133 patients diagnosed with endometrial polyp(s) were included and randomly assigned to 1 of the 2 hysteroscopic methods. Criteria assessed were total hysteroscopy time, full polypectomy procedure time, pain experienced by patients, and learning curve of staff in training. The average time to perform total hysteroscopy using the mechanical tissue removal system (TRUCLEAR 5.0 System; Smith & Nephew Inc., Andover, MD) was 6 minutes 49 seconds versus 11 minutes 37 seconds required for the bipolar electrosurgery system (GYNECARE VERSAPOINT; Ethicon Inc, Somerville, NJ) (p < .01). Results for complete polypectomy time favored the TRUCLEAR System at 3 minutes 7 seconds over the VERSAPOINT System at 8 minutes 25 seconds (p < .01). If a successful procedure is predicated on access to cavity, visualization, and complete resection and excision of endometrial polyp, the mechanical TRUCLEAR Tissue Removal System shows a higher success rate than the VERSAPOINT Bipolar Electrosurgery System at 92% and 77%, respectively. Analysis of pain using the visual analog scale revealed no significant differences between the 2 techniques (p > .05). A study of the residents' learning curve showed a higher level of autonomy with hysteroscopy using the TRUCLEAR Tissue Removal System with which residents showed a higher level of confidence compared with hysteroscopy with the VERSAPOINT Bipolar Electrosurgery System. In hysteroscopic polypectomy, the mechanical tissue removal system was significantly faster, achieved a greater success rate for complete polypectomy, and required a shorter learning curve from staff being trained in the management of endometrial polyps when compared with bipolar electrical resection. Copyright © 2015 AAGL. Published by Elsevier Inc. All rights reserved.

  3. MO-B-19A-01: MOC: A How-To Guide

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ibbott, G; Seibert, J; Allison, J

    2014-06-15

    Medical physicists who were certified in 2002 or later, as well as those who become certified in the future, are enrolled in Maintenance of Certification. Many physicists with life-time certificates have voluntarily enrolled in MOC, as have physicists who volunteer their time to participate in the ABR exam development and administration processes. MOC consists of four components: Part 1, Professional standing; Part 2, Lifelong learning and self-assessment; Part 3, Cognitive expertise; and Part 4, Practice quality improvement. These four components together evaluate six competencies: Medical knowledge, patient care and procedural skills, interpersonal and communication skills, professionalism, practice-based learning and improvement,more » and systems-based practice. Parts 1, 2, and 3 of MOC are fairly straightforward, although many participants have questions about the process for attesting to professional standing, the opportunities for obtaining self-assessed continuing education, and the timing of the cognitive exam. MOC participants also have questions about Part 4, Practice Quality Improvement. PQI projects are powerful tools for improving the quality and safety of the environments in which we practice medical physics. In the current version of MOC known as “Continuous Certification” a medical physicist must have completed a PQI project within the previous three years, at the time of the ABR's annual look-back each March. For the first “full” annual look-back in March 2016, diplomates will be given an additional year, so that a PQI project completed in 2012, 2013, 2014, or 2015 will fulfill this requirement. Each component of MOC will be addressed, and the specifics of interest to medical physicists will be discussed. Learning Objectives: Understand the four components and six competencies evaluated by MOC. Become familiar with the annual requirements of Continuous Certification. Learn about opportunities for Practice Quality Improvement projects. Understand refinements occurring in the MOC program.« less

  4. Microinvasive Glaucoma Stent (MIGS) Surgery With Concomitant Phakoemulsification Cataract Extraction: Outcomes and the Learning Curve.

    PubMed

    Al-Mugheiry, Toby S; Cate, Heidi; Clark, Allan; Broadway, David C

    2017-07-01

    To evaluate learning effects with respect to outcomes of a microinvasive glaucoma stent (MIGS) inserted during cataract surgery in glaucoma patients. Single surgeon, observational cohort study of 25 consecutive Ivantis Hydrus microstent insertions, with a minimum follow-up of 12 months. A learning curve analysis was performed by assessing hypotensive effect, adverse effects, and surgical procedure duration, with respect to consecutive case number. Success was defined with respect to various intraocular pressure (IOP) targets (21, 18, 15 mm Hg) and reduction in required antiglaucoma medications. Complete success was defined as achieving target IOP without antiglaucoma therapy. No clinically significant adverse events or learning effects were identified, although surgical time reduced with consecutive case number. Mean follow-up was 16.8 months. At final follow-up the mean IOP for all eyes was reduced from 18.1 (±3.6) mm Hg [and a simulated untreated value of 25.9 (±5.2) mm Hg] to 15.3 (±2.2) mm Hg (P=0.007; <0.0001) and the mean number of topical antiglaucoma medications was reduced from 1.96 (±0.96) to 0.04 (±0.20) (P<0.0001). Complete success (IOP<21 mm Hg, no medications) was 96% at final follow-up. Complete success (IOP<18 mm Hg, no medications) was 80% at final follow-up, but only 32% with a target IOP of <15 mm Hg (no medications). No significant learning curve effects were observed for a trained surgeon with respect to MIGS microstent insertion performed at the time of cataract surgery. Adjunctive MIGS surgery was successful in lowering IOP to <18 mm Hg and reducing/abolishing the requirement for antiglaucoma medication in eyes with open-angle glaucoma, but less successful at achieving low IOP levels (<15 mm Hg).

  5. Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.

    PubMed

    Hübner, David; Verhoeven, Thibault; Schmid, Konstantin; Müller, Klaus-Robert; Tangermann, Michael; Kindermans, Pieter-Jan

    2017-01-01

    Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means. We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP.

  6. Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

    PubMed Central

    Verhoeven, Thibault; Schmid, Konstantin; Müller, Klaus-Robert; Tangermann, Michael; Kindermans, Pieter-Jan

    2017-01-01

    Objective Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means. Method We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Results Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP. PMID:28407016

  7. When Delays Improve Memory: Stabilizing Memory in Children May Require Time.

    PubMed

    Darby, Kevin P; Sloutsky, Vladimir M

    2015-12-01

    Memory is critical for learning, cognition, and cognitive development. Recent work has suggested that preschool-age children are vulnerable to catastrophic levels of memory interference, in which new learning dramatically attenuates memory for previously acquired knowledge. In the work reported here, we investigated the effects of consolidation on children's memory by introducing a 48-hr delay between learning and testing. In Experiment 1, the delay improved children's memory and eliminated interference. Results of Experiment 2 suggest that the benefit of this delay is limited to situations in which children are given enough information to form complex memory structures. These findings have important implications for understanding consolidation processes and memory development. © The Author(s) 2015.

  8. Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.

    PubMed

    Deco, G; Schürmann, B

    1999-05-15

    We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time.

  9. [Distance learning in postgraduate training of professionals. Example of occupational medicine specialization].

    PubMed

    Marcinkiewicz, Andrzej; Cybart, Adam; Chromińska-Szosland, Dorota

    2002-01-01

    The rapid development of science, technology, economy and the society has one along with the wide recognition of lifelong education and learning society concepts. Scientific centres worldwide conduct research how the access to the information and multimedia technology could bring about positive changes in our lives including improvement in education and the learning environment. Mankind development in conformity with social progress and sustainable development faces a new educational concept of learning society and open education in the information age, supported with multimedia and data processing technology. Constrains in resources availability for broadening the access to education had led to search for alternative, more time and cost-effective systems of education. One of them is distance learning, applied with success in many countries. The benefits of distance learning are well proven and can be extended to occupational medicine. Major advantages include: the integration of studies with work experience, flexibility, allowing studies to be matched to work requirements, perceived work and leisure timing, continuity of career progression. Likewise is in Poland this form of education becomes more and more popular. The distance education systems have been seen as an investment in human resource development. The vast variety of courses and educational stages makes possible the modern method of knowledge to be easily accessible. Experience of the School of Public Health in Łódź in distance learning had shown remarkable benefits of the method with comparable quality of intramural and distance learning in respect of the knowledge and experience gained by students.

  10. Evolutionary neural networks for anomaly detection based on the behavior of a program.

    PubMed

    Han, Sang-Jun; Cho, Sung-Bae

    2006-06-01

    The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

  11. The effect of subjective awareness measures on performance in artificial grammar learning task.

    PubMed

    Ivanchei, Ivan I; Moroshkina, Nadezhda V

    2018-01-01

    Systematic research into implicit learning requires well-developed awareness-measurement techniques. Recently, trial-by-trial measures have been widely used. However, they can increase complexity of a study because they are an additional experimental variable. We tested the effects of these measures on performance in artificial grammar learning study. Four groups of participants were assigned to different awareness measures conditions: confidence ratings, post-decision wagering, decision strategy attribution or none. Decision-strategy-attribution participants demonstrated better grammar learning and longer response times compared to controls. They also exhibited a conservative bias. Grammaticality by itself was a stronger predictor of strings endorsement in decision-strategy-attribution group compared to other groups. Confidence ratings and post-decision wagering only affected the response times. These results were supported by an additional experiment that used a balanced chunk strength design. We conclude that a decision-strategy-attribution procedure may force participants to adopt an analytical decision-making strategy and rely mostly on conscious knowledge of artificial grammar. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. VLSI realization of learning vector quantization with hardware/software co-design for different applications

    NASA Astrophysics Data System (ADS)

    An, Fengwei; Akazawa, Toshinobu; Yamasaki, Shogo; Chen, Lei; Jürgen Mattausch, Hans

    2015-04-01

    This paper reports a VLSI realization of learning vector quantization (LVQ) with high flexibility for different applications. It is based on a hardware/software (HW/SW) co-design concept for on-chip learning and recognition and designed as a SoC in 180 nm CMOS. The time consuming nearest Euclidean distance search in the LVQ algorithm’s competition layer is efficiently implemented as a pipeline with parallel p-word input. Since neuron number in the competition layer, weight values, input and output number are scalable, the requirements of many different applications can be satisfied without hardware changes. Classification of a d-dimensional input vector is completed in n × \\lceil d/p \\rceil + R clock cycles, where R is the pipeline depth, and n is the number of reference feature vectors (FVs). Adjustment of stored reference FVs during learning is done by the embedded 32-bit RISC CPU, because this operation is not time critical. The high flexibility is verified by the application of human detection with different numbers for the dimensionality of the FVs.

  13. Semantic Modelling for Learning Styles and Learning Material in an E-Learning Environment

    ERIC Educational Resources Information Center

    Alhasan, Khawla; Chen, Liming; Chen, Feng

    2017-01-01

    Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning…

  14. The emergence of online learning in PN Education.

    PubMed

    Hopkins, David D

    2008-01-01

    For the fifth year in a row the online learning sector outpaced growth rates of the traditional classroom. Online learning continues to garner increasing levels of positive support from administrators, employers, and students who value the option of online education at increasingly greater levels. PN Education has largely remained on the sidelines of this revolution. However, with the nursing crisis growing, students, governments, and institutions demanding more access and convenience to educational options, and the emergence of the Millennial Generation making up the majority of the students, the time has come for PN programs to embrace the potential of online learning. With its diverse mix of didactic, clinical, and lab requirements, PN education is ideally suited for the newest evolution of online delivery-Blended Learning 2.0. This paper will analyze in detail the overall state of affairs of online learning, especially as it pertains to educating the next generation of practical nurses, and finally to provide an overview of the key components of a quality online program in PN Education.

  15. Utilization of Illustrations during Learning of Science Textbook Passages among Low- and High-Ability Children.

    PubMed

    Hannus; Hyönä

    1999-04-01

    Effects of illustrations on learning authentic textbook materials were studied among 10-year-old elementary school children of high and low intellectual ability. Experiment 1 showed that the presence of illustrations improved learning of illustrated text content, but not that of nonillustrated text content. Comprehension scores were improved by the presence of illustrations for high-ability children, but not for low-ability children. In Experiment 2, children's eye movements were measured during learning of illustrated textbook passages to study how children divide their attention between text and illustrations. The results suggest that learning is heavily driven by the text and that children inspect illustrations only minimally. High-ability students were more strategic in processing in the sense that they spent relatively more time on pertinent segments of text and illustrations. It is concluded that the learning of illustrated science textbook materials involves requirements that may be more readily met by more intellectually capable students. Copyright 1999 Academic Press.

  16. A Blended Learning Experience for Teaching Microbiology

    PubMed Central

    Sancho, Pilar; Corral, Ricardo; Rivas, Teresa; González, María Jesús; Chordi, Andrés

    2006-01-01

    Objectives To create a virtual laboratory system in which experimental science students could learn required skills and competencies while overcoming such challenges as time limitations, high cost of resources, and lack of feedback often encountered in a traditional laboratory setting. Design A blended learning experience that combines traditional practices and e-learning was implemented to teach microbiological methods to pharmacy students. Virtual laboratory modules were used to acquire nonmanual skills such as visual and mental skills for data reading, calculations, interpretation of the results, deployment of an analytical protocol, and reporting results. Assesment Learning achievement was evaluated by questions about microbiology case-based problems. Students' perceptions were obtained by assessment questionnaire. Conclusion By combining different learning scenarios, the acquisition of the necessary but otherwise unreachable competences was achieved. Students achieved similar grades in the modules whose initiation was in the virtual laboratory to the grades they achieved with the modules whose complete or partial initiation took place in the laboratory. The knowledge acquired was satisfactory and the participants valued the experience. PMID:17149449

  17. Transgenerational latent early-life associated regulation unites environment and genetics across generations

    PubMed Central

    Lahiri, Debomoy K; Maloney, Bryan; Bayon, Baindu L; Chopra, Nipun; White, Fletcher A; Greig, Nigel H; Nurnberger, John I

    2016-01-01

    The origin of idiopathic diseases is still poorly understood. The latent early-life associated regulation (LEARn) model unites environmental exposures and gene expression while providing a mechanistic underpinning for later-occurring disorders. We propose that this process can occur across generations via transgenerational LEARn (tLEARn). In tLEARn, each person is a ‘unit’ accumulating preclinical or subclinical ‘hits’ as in the original LEARn model. These changes can then be epigenomically passed along to offspring. Transgenerational accumulation of ‘hits’ determines a sporadic disease state. Few significant transgenerational hits would accompany conception or gestation of most people, but these may suffice to ‘prime’ someone to respond to later-life hits. Hits need not produce symptoms or microphenotypes to have a transgenerational effect. Testing tLEARn requires longitudinal approaches. A recently proposed longitudinal epigenome/envirome-wide association study would unite genetic sequence, epigenomic markers, environmental exposures, patient personal history taken at multiple time points and family history. PMID:26950428

  18. Video Analysis of a Plucked String: An Example of Problem-based Learning

    NASA Astrophysics Data System (ADS)

    Wentworth, Christopher D.; Buse, Eric

    2009-11-01

    Problem-based learning is a teaching methodology that grounds learning within the context of solving a real problem. Typically the problem initiates learning of concepts rather than simply being an application of the concept, and students take the lead in identifying what must be developed to solve the problem. Problem-based learning in upper-level physics courses can be challenging, because of the time and financial requirements necessary to generate real data. Here, we present a problem that motivates learning about partial differential equations and their solution in a mathematical methods for physics course. Students study a plucked elastic cord using high speed digital video. After creating video clips of the cord motion under different tensions they are asked to create a mathematical model. Ultimately, students develop and solve a model that includes damping effects that are clearly visible in the videos. The digital video files used in this project are available on the web at http://physics.doane.edu .

  19. The learning curve of laparoendoscopic single-site (LESS) cholecystectomy: definable, short, and safe.

    PubMed

    Hernandez, Jonathan; Ross, Sharona; Morton, Connor; McFarlin, Kellie; Dahal, Sujat; Golkar, Farhaad; Albrink, Michael; Rosemurgy, Alexander

    2010-11-01

    The applications of laparoendoscopic single-site (LESS) surgery, including cholecystectomy, are occurring quickly, although little is generally known about issues associated with the learning curve of this new technique including operative time, conversion rates, and safety. We prospectively followed all patients undergoing LESS cholecystectomy, and compared operations undertaken at our institutions in cohorts of 25 patients with respect to operative times, conversion rates, and complications. One-hundred fifty patients of mean age 46 years underwent LESS cholecystectomy. No significant differences in operative times were demonstrable between any of the 25-patient cohorts operated on at our institution. A significant reduction in operative times (p < 0.001) after completion of 75 LESS procedures was, however, identified with the experience of a single surgeon. No significant reduction in the number of procedures requiring an additional trocar(s) or conversion to open operations was observed after completion of 25 LESS cholecystectomies. Complication rates were low, and not significantly different between any 25-patient cohorts. For surgeons proficient with multi-incision laparoscopic cholecystectomy, the learning curve for LESS cholecystectomy begins near proficiency. Operative complications and conversions were infrequent and unchanged across successive 25-patient cohorts, and were similar to those reported for multi-incision laparoscopic cholecystectomy after the learning curve. Copyright © 2010 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

  20. A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

    NASA Astrophysics Data System (ADS)

    Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

    2017-04-01

    This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

  1. Forecasting financial asset processes: stochastic dynamics via learning neural networks.

    PubMed

    Giebel, S; Rainer, M

    2010-01-01

    Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

  2. Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

    PubMed

    Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

    2017-07-01

    Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

  3. Missing the Mark: Is ICS Training Achieving Its Goal

    DTIC Science & Technology

    2016-12-01

    method achieves learning and actually gives students new knowledge, skills, and 281 Ibid. 282 Ibid...designed to be five days (40 hours) long.331 The class assumes that the student already has a general understanding of ICS and completion of at least...35–37. 64 The entire process is time consuming, as the student must complete the in- class time (as required for the specific class ) and

  4. Overcoming Resistance to New Ideas

    ERIC Educational Resources Information Center

    Powell, William; Kusuma-Powell, Ochan

    2015-01-01

    There are two types of challenges that adults face in their professional learning: technical and adaptive. Technical challenges simply require informational learning while adaptive challenges require transformational learning, which requires us to rethink our deeply held values, beliefs, assumptions, and even our professional identity. Adaptive…

  5. Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

    PubMed

    Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

    2017-06-01

    The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

  6. Criteria for successful uptake of AAL technologies: lessons learned from Norwegian pilot projects.

    PubMed

    Svagård, Ingrid; Ausen, Dag; Standal, Kristin

    2013-01-01

    Implementation of AAL-technology as an integrated part of public health and care services requires a systematic and multidisciplinary approach. There are several challenges that need to be handled in parallel and with sustained effort over time, to tackle the multidimensional problem of building the value chain that is required for widespread uptake of AAL technology. Several pilot projects are on-going in Norway, involving municipalities, technology providers and research partners. Examples are "Home Safety" (NO: Trygghetspakken) and "Safe Tracks" (NO: Trygge spor). This paper will elaborate on our lessons learned with focus on five main points: 1) User-friendly and robust technology 2) Technology adapted organization 3) Service oriented technology providers 4) Care service organizations as demanding customer and 5) Sustainable financial model.

  7. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

    PubMed Central

    Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system. PMID:27713685

  8. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

    PubMed

    Waytowich, Nicholas R; Lawhern, Vernon J; Bohannon, Addison W; Ball, Kenneth R; Lance, Brent J

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

  9. Cooperative Learning in Reservoir Simulation Classes: Overcoming Disparate Entry Skills

    NASA Astrophysics Data System (ADS)

    Awang, Mariyamni

    2006-10-01

    Reservoir simulation is one of the core courses in the petroleum engineering curriculum and it requires knowledge and skills in three major disciplines, namely programming, numerical methods and reservoir engineering. However, there were often gaps in the students' readiness to undertake the course, even after completing the necessary requirements. The disparate levels of competency of the good and poor students made it difficult to target a certain level. Cooperative learning in the form of projects and peer teaching was designed to address the major concern of disparate entry skills, and at the same time the method used should also succeed in keeping students interest in class, developing communication skills and improving self-learning. Slower and weaker students were expected to benefit from being taught by good students, who were better prepared, and good students would gain deeper comprehension of the subject matter. From evaluations, the approach was considered successful since the overall passing rate was greater than 95% compared to previous years of around 70-80%. It had also succeeded in improving the learning environment in class. Future simulation classes will continue to use the cooperative approach with minor adjustments.

  10. Reinforcement learning for resource allocation in LEO satellite networks.

    PubMed

    Usaha, Wipawee; Barria, Javier A

    2007-06-01

    In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.

  11. Alyce Annie: A New CPR Home Practice Manikin.

    ERIC Educational Resources Information Center

    Schultz, Alyce; And Others

    1981-01-01

    Alyce Annie, a lightweight, portable manikin, was designed to economize classroom time and to provide a method for learning cardiopulmonary resuscitation (CPR) independently. A study with high school students determined that the students trained in this method could attain the necessary psychomotor skills and knowledge level required for CPR…

  12. Faculty Impact on Persistence and Success in Developmental Writing Courses

    ERIC Educational Resources Information Center

    Bixler, L. Ann.

    2012-01-01

    In the next decade, community college English departments will expand their developmental course offerings. The students who take these developmental courses generally have higher incidence of diagnosed learning disabilities, bleak economic circumstances that require them to work full time, greater dependence on public transportation, and some…

  13. Mapping Generic Skills Curricula: Outcomes and Discussion

    ERIC Educational Resources Information Center

    Robley, Will; Whittle, Sue; Murdoch-Eaton, Deborah

    2005-01-01

    Generic skills development is increasingly being embedded into UK higher education curricula to improve the employability and lifelong learning skills of graduates. At the same time universities are being required to benchmark their curricular outcomes against national and employer standards. This paper presents and discusses the results of a…

  14. A Meta-Analysis of Individualized Instruction in Dental Education.

    ERIC Educational Resources Information Center

    Dacanay, Lakshmi S; Cohen, Peter A.

    1992-01-01

    Meta-analysis of 34 comparative studies on conventional vs. individualized instruction (II) found most favored the latter but with small-moderate overall effect. Pacing had significant effect, with teacher-pacing more effective than student-paced learning. On average, II required less time than conventional teaching. Additional research on this…

  15. Assessment Intelligence in Small Group Learning

    ERIC Educational Resources Information Center

    Xing, Wanli; Wu, Yonghe

    2014-01-01

    Assessment of groups in CSCL context is a challenging task fraught with many confounding factors collected and measured. Previous documented studies are by and large summative in nature and some process-oriented methods require time-intensive coding of qualitative data. This study attempts to resolve these problems for teachers to assess groups…

  16. An Evaluation of an Automated Approach to Concept-Based Grammar Instruction

    ERIC Educational Resources Information Center

    Lyddon, Paul A.

    2012-01-01

    Acquiring sufficient linguistic proficiency to perform competently in academic and professional contexts generally requires substantial study time beyond what most language programs can offer in the classroom. As such, teachers and students alike would benefit considerably from high quality self-access materials promoting independent learning out…

  17. A Stunning Admission

    ERIC Educational Resources Information Center

    Hu, Helen

    2012-01-01

    Few people set out to become admissions counselors, say people in the profession. But the field is requiring skills that are more demanding and varied than ever. And at a time when universities are looking especially hard at the bottom line, people in admissions need to constantly learn new things and make themselves indispensable. Counselors…

  18. Japan's Teacher Acculturation: Critical Analysis through Comparative Ethnographic Narrative

    ERIC Educational Resources Information Center

    Howe, Edward R.

    2005-01-01

    Cross-cultural teaching and research in Canada and Japan is reported. Ethnographic narrative methods were used to examine Japan's teacher acculturation. Canada's teachers are largely required to work in isolation, to learn their practice through trial and error. There is little provision for mentorship and insufficient time to reflect. In…

  19. Boosting Adult Learning. Working Brief.

    ERIC Educational Resources Information Center

    Boyer, David

    Too many of Britain's workforce lack the skills needed for a knowledge-based economy. To remedy this will require the commitment, in time and resources, of individuals, employers, the education and training infrastructure and the state. Adults with the lowest qualifications have the least access to employer-funded training, especially in small…

  20. In Defense of Snow Days

    ERIC Educational Resources Information Center

    Goodman, Joshua

    2015-01-01

    In snowy climates, school superintendents must frequently decide whether an impending storm warrants closing schools for the day. Concerns about student and teacher safety must be weighed against the loss of student learning time, along with state requirements for days of instruction and the cost and inconvenience of extending the school year into…

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