How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

Mindtool-Assisted In-Field Learning (MAIL): An Advanced Ubiquitous Learning Project in Taiwan

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Hung, Pi-Hsia; Chen, Nian-Shing; Liu, Gi-Zen

2014-01-01

Scholars have identified that learning in an authentic environment with quality contextual and procedural supports can engage students in thorough observations and knowledge construction. Moreover, the target is that students are able to experience and make sense of all of the learning activities in the real-world environment with meaningful…

Making the Switch: Lightbulbs, Literacy, and Service-Learning

ERIC Educational Resources Information Center

Chiaravalloti, Laura A.

2009-01-01

Service-learning is an instructional methodology teachers can use to foster student engagement in rigorous curricula. Through service-learning, middle level students see that what they are learning in school is real and important, and that they can be valued, contributing members of society. In this article, the Titans team at Remington Middle…

Exploring Identities to Deepen Understanding of Urban High School Students' Sexual Health Decision-Making

ERIC Educational Resources Information Center

Brotman, Jennie S.; Mensah, Felicia Moore; Lesko, Nancy

2010-01-01

Sexual health is a controversial science topic that has received little attention in the field of science education, despite its direct relevance to students' lives and communities. Moreover, research from other fields indicates that a great deal remains to be learned about how to make school learning about sexual health influence the real-life…

Increasing Student Learning through Multimedia Projects.

ERIC Educational Resources Information Center

Simkins, Michael; Cole, Karen; Tavalin, Fern; Means, Barbara

This book discusses enhancing student achievement through project-based learning with multimedia. Chapter 1 describes project-based multimedia learning. Chapter 2 presents a multimedia primer, including the five basic types of media objects (i.e., images, text, sound, motion, and interactivity). Chapter 3 addresses making a real-world connection,…

Outdoor Classrooms--Planning Makes Perfect

ERIC Educational Resources Information Center

Haines, Sarah

2006-01-01

Schoolyard wildlife habitats aren't just for beauty and fun--they are outdoor classrooms where real science learning takes place. Schoolyard habitat projects involve conservation and restoration of wildlife habitat; however, the learning doesn't have to stop there--outdoor classrooms can foster many kinds of active learning across the curriculum…

Decision Making and Learning while Taking Sequential Risks

ERIC Educational Resources Information Center

Pleskac, Timothy J.

2008-01-01

A sequential risk-taking paradigm used to identify real-world risk takers invokes both learning and decision processes. This article expands the paradigm to a larger class of tasks with different stochastic environments and different learning requirements. Generalizing a Bayesian sequential risk-taking model to the larger set of tasks clarifies…

Looking Back--A Lesson Learned: From Videotape to Digital Media

ERIC Educational Resources Information Center

Lys, Franziska

2010-01-01

This paper chronicles the development of Drehort Neubrandenburg Online, an interactive, content-rich audiovisual language learning environment based on documentary film material shot on location in Neubrandenburg, Germany, in 1991 and 2002 and aimed at making language learning more interactive and more real. The paper starts with the description…

Aha! The Power of Using 6 E's.

ERIC Educational Resources Information Center

Tall, Lyssa; Luttrell-Montes, Sally

1999-01-01

Describes the 6 E's approach which enhances student learning and encourages stronger conceptual connections. Provides step-by-step details of how the 6E Teaching/Learning Model was incorporated during the Make It R.E.A.L. Institute. (CCM)

Making Connections: Where STEM Learning and Earth Science Data Services Meet

NASA Technical Reports Server (NTRS)

Bugbee, Kaylin; Ramachandran, Rahul; Maskey, Manil; Gatlin, Patrick; Weigel, Amanda

2016-01-01

STEM (Science, Technology, Engineering, Mathematics) learning is most effective when students are encouraged to see the connections between science, technology and real world problems. Helping to make these connections has become an increasingly important aspect of Earth Science data research. The Global Hydrology Resource Center (GHRC), one of NASA's 12 EOSDIS (Earth Observing System Data Information System) data centers, has developed a new type of documentation called the micro article to facilitate making connections between data and Earth science research problems.

Musical Futures in Victoria

ERIC Educational Resources Information Center

Jeanneret, Neryl

2010-01-01

Musical Futures is a music learning program that was established in the United Kingdom in 2003. It aims to make secondary classroom music more relevant to young people through engaging them in the informal learning practices of real world musicians, recognising that the way in which popular musicians learn is quite different from the pedagogy of…

Challenge of Supporting Vocational Learning: Empowering Collaboration in a Scripted 3D Game--How Does Teachers' Real-Time Orchestration Make a Difference?

ERIC Educational Resources Information Center

Hamalainen, Raija; Oksanen, Kimmo

2012-01-01

Along with the development of new technologies, orchestrating computer-supported collaborative learning (CSCL) has become a topic of discussion because new learning spaces challenge teacher to support collaborative learning in new ways. However, despite the optimistic notions of teachers' orchestration in CSCL situations, there are still no…

Earth Algebra: Real-Life Mathematics in Navajoland.

ERIC Educational Resources Information Center

Schaufele, Christopher; Srivastava, Ravindra

1995-01-01

An algebra class at Navajo Community College (Shiprock, New Mexico) uses traditional algebra topics to study real-life situations, focuses on environmental issues, encourages collaborative learning, uses modern technology, and promotes development of critical thinking and decision-making skills. Students follow principles of Dine educational…

Classroom Literacy Assessment. Making Sense of What Students Know and Do. Solving Problems in the Teaching of Literacy Series

ERIC Educational Resources Information Center

Paratore, Jeanne R. Ed.; McCormack, Rachel L. Ed.; Block, Cathy, Collins Ed.

2007-01-01

Showcasing assessment practices that can help teachers plan effective instruction, this book addresses the real-world complexities of teaching literacy in grades K-8. Leading contributors present trustworthy approaches that examine learning processes as well as learning products, that yield information on how the learning environment can be…

Expanded Learning Time and Opportunities: Key Principles, Driving Perspectives, and Major Challenges

ERIC Educational Resources Information Center

Blyth, Dale A.; LaCroix-Dalluhn, Laura

2011-01-01

If expanded learning is going to make a real difference, then three key principles must inform how communities overcome challenges and assure equitable access to learning opportunities. Much of today's debate is framed in the language of formal education systems--students, classrooms, schools--even though part of the expansion seeks to engage a…

Making a Low Cost Candy Floss Kit Gets Students Excited about Learning Physics

ERIC Educational Resources Information Center

Amir, Nazir; Subramaniam, R.

2009-01-01

An activity to excite kinaesthetically inclined students about learning physics is described in this article. Using only commonly available materials, a low cost candy floss kit is fabricated by students. A number of physics concepts are embedded contextually in the activity so that students get to learn these concepts in a real world setting…

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.

Teaching about Indians? Use the Real Stuff!

ERIC Educational Resources Information Center

Feldkamp-Price, Betsy; Smith, David Lee

1994-01-01

Provides suggestions for teaching students about American Indians. Teachers need to learn more about Indians; confront misconceptions and stereotypes; have students make Indian crafts and foods; play Indian games; learn about contemporary Indian culture; be critical of resources; and contact local Indian or cultural groups. (MDM)

Learning and Decision Making in Groups

ERIC Educational Resources Information Center

Rahimian, M. Amin

2017-01-01

Many important real-world decision-making problems involve group interactions among individuals with purely informational interactions. Such situations arise for example in jury deliberations, expert committees, medical diagnoses, etc. We model the purely informational interactions of group members, where they receive private information and act…

Online gaming for learning optimal team strategies in real time

NASA Astrophysics Data System (ADS)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

Making Entrepreneurship Education Work: The REAL Enterprises Model.

ERIC Educational Resources Information Center

Larson, Rick; King, Lisa; McGee, Mark; Shea, Brendon

This paper discusses the REAL (Rural Entrepreneurship through Action Learning) model as a necessary component of rural school-to-work (STW) programs. In rural areas where opportunities for traditional STW approaches (such as apprenticeships) are limited, entrepreneurial education teaches students to be job creators, not just job applicants. This…

Students' Reading Images in Kinematics: The Case of Real-Time Graphs.

ERIC Educational Resources Information Center

Testa, Italo; Monroy, Gabriella; Sassi, Elena

2002-01-01

Describes a study in which secondary school students were called upon to read and interpret documents containing images of real-time kinematics graphs specially designed to address common learning problems and minimize iconic difficulties. Makes suggestions regarding the acquisition of some specific capabilities that are needed to avoid…

Real and Imagined Body Movement Primes Metaphor Comprehension

ERIC Educational Resources Information Center

Wilson, Nicole L.; Gibbs, Raymond W., Jr.

2007-01-01

We demonstrate in two experiments that real and imagined body movements appropriate to metaphorical phrases facilitate people's immediate comprehension of these phrases. Participants first learned to make different body movements given specific cues. In two reading time studies, people were faster to understand a metaphorical phrase, such as push…

Deep Learning in Gastrointestinal Endoscopy.

PubMed

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Exploiting ICT and E-Learning in Teacher's Professional Development in Algeria: The Case of English Secondary School Teachers

ERIC Educational Resources Information Center

Guemide, Boutkhil; Benachaiba, Chellali

2012-01-01

The real potential of ICT is the way it changes learners to become autonomous in their learning process. E-learning also plays a crucial role in today's life and in modern education. Its importance lies in the fact that people are finding that e-learning can make a remarkable change in teaching/ or learning: to how quickly they master a skill; how…

In-Factory Learning - Qualification For The Factory Of The Future

NASA Astrophysics Data System (ADS)

Quint, Fabian; Mura, Katharina; Gorecky, Dominic

2015-07-01

The Industry 4.0 vision anticipates that internet technologies will find their way into future factories replacing traditional components by dynamic and intelligent cyber-physical systems (CPS) that combine the physical objects with their digital representation. Reducing the gap between the real and digital world makes the factory environment more flexible, more adaptive, but also more complex for the human workers. Future workers require interdisciplinary competencies from engineering, information technology, and computer science in order to understand and manage the diverse interrelations between physical objects and their digital counterpart. This paper proposes a mixed-reality based learning environment, which combines physical objects and visualisation of digital content via Augmented Reality. It uses reality-based interaction in order to make the dynamic interrelations between real and digital factory visible and tangible. We argue that our learning system does not work as a stand-alone solution, but should fit into existing academic and advanced training curricula.

The Learning Leader: Reflecting, Modeling, and Sharing

ERIC Educational Resources Information Center

Jacobs, Jacqueline E.; O'Gorman, Kevin L.

2012-01-01

With this book, principals, principals-in-training, and other school leaders get practical, easy-to-implement strategies for professional growth, strengthening relationships with faculty and staff, and making the necessary changes to improve K-12 learning environments. Grounded in specific, real-world examples and personal experiences, "The…

Machine learning in genetics and genomics

PubMed Central

Libbrecht, Maxwell W.; Noble, William Stafford

2016-01-01

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

Blogging and the Learning of Linear Algebra Concepts through Contextual Mathematics

ERIC Educational Resources Information Center

Nehme, Zeina

2011-01-01

Contextual mathematics is an area of mathematics teaching and learning through which researchers and educators believe that mathematics is better taught, and learned, if connected to real-life situations and problems. It is also very helpful if it makes sense in the students' world. Thus, the author decided to start a project by creating a blog,…

On the Changing Nature of Learning Context: Anticipating the Virtual Extensions of the World

ERIC Educational Resources Information Center

Westera, Wim

2011-01-01

Contextual learning starts from the premise that learning cannot take place in a vacuum, but should somehow be connected with real world attributes to make sense to learners. Today, digital media tend to bring about new dimensions of context: internet connections and mobile devices enable learners to overcome restrictions of time and location, and…

Ecocultural Factors in Students' Ability to Relate Science Concepts Learned at School and Experienced at Home: Implications for Chemistry Education

ERIC Educational Resources Information Center

Oloruntegbe, Kunle Oke; Ikpe, Adakole

2011-01-01

Making connections between science concepts taught in school and real-world phenomena is considered important in engaging students in learning. The present study examines students' abilities to relate their in-school science learning to everyday experiences at home. The sample comprised 200 senior secondary chemistry students drawn from Ondo…

Strategic Accountability Is Key to Making PLCs Effective

ERIC Educational Resources Information Center

Easton, Lois Brown

2017-01-01

Professional Learning Communities (PLCs) are often criticized for failing to focus on real problems of teaching and learning and for failing to deliver improvement. That is where accountability comes into play. Strategic accountability distinguishes PLCs that are effective from those that are not. Everyone knows what accountability is, but the…

Making Sense of Multitasking: Key Behaviours

ERIC Educational Resources Information Center

Judd, Terry

2013-01-01

Traditionally viewed as a positive characteristic, there is mounting evidence that multitasking using digital devices can have a range of negative impacts on task performance and learning. While the cognitive processes that cause these impacts are starting to be understood and the evidence that they occur in real learning contexts is mounting, the…

Applications of Generative Learning for the Survey of International Economics Course

ERIC Educational Resources Information Center

Sharp, David C.; Knowlton, Dave S.; Weiss, Renee E.

2005-01-01

Generative learning provides students with opportunities to organize course content, integrate new content with students' current knowledge, and elaborate on course content by making connections to real-world events. These opportunities promote less reliance on professors' lectures and simultaneously create more self-reliance among students. The…

Bringing Management Reality into the Classroom--The Development of Interactive Learning.

ERIC Educational Resources Information Center

Nicholson, Alastair

1997-01-01

Effective learning in management education can be enhanced by reproducing the real-world need to solve problems under pressure of time, inadequate information, and group interaction. An interactive classroom communication system involving problems in decision making and continuous improvement is one method for bridging theory and practice. (SK)

Using Open-Book Exams to Enhance Student Learning, Performance, and Motivation

ERIC Educational Resources Information Center

Green, Steve G.; Ferrante, Claudia J.; Heppard, Kurt A.

2016-01-01

This study investigated an alternative testing protocol used in an undergraduate managerial accounting course. Specifically, we assert that consistent open-book testing approaches will enhance learning and better prepare students for the real-world decision-making they will encounter. A semester-long testing protocol was executed incorporating a…

Place-Based Curriculum Making: Devising a Synthesis between Primary Geography and Outdoor Learning

ERIC Educational Resources Information Center

Dolan, Anne M.

2016-01-01

Outdoor learning provides children with an opportunity to experience the interdisciplinary nature of the real world through interactions with each other and the planet. Geographical enquiry involves exploring the outdoors in an investigative capacity. Space, place and sustainability are three core concepts in primary geography, although…

Oh, Deer!: Predator and Prey Relationships--Students Make Natural Connections through the Integration of Mathematics and Science

ERIC Educational Resources Information Center

Reeder, Stacy; Moseley, Christine

2006-01-01

This article describes an activity that integrates both mathematics and science while inviting students to make connections between the two and learn significant concepts in a meaningful way. Students work within the real-world context of wildlife population scenarios to make predictions, test their hypotheses, and determine and construct graphs…

Get Real: Augmented Reality for the Classroom

ERIC Educational Resources Information Center

Mitchell, Rebecca; DeBay, Dennis

2012-01-01

Kids love augmented reality (AR) simulations because they are like real-life video games. AR simulations allow students to learn content while collaborating face to face and interacting with a multimedia-enhanced version of the world around them. Although the technology may seem advanced, AR software makes it easy to develop content-based…

Exploring Weighty Matters with "Cucumber Soup": An Interdisciplinary Approach

ERIC Educational Resources Information Center

Columba, Lynn

2007-01-01

Children's literature can play a significant role in integrating math and science concepts into real-world applications. One particularly delightful selection is "Cucumber Soup" (Krudwig, 1998). This book can create a context--making cucumber soup--for weighing and for a real-life on adding fractions. This kind of learning context takes children…

Easy rider: monkeys learn to drive a wheelchair to navigate through a complex maze.

PubMed

Etienne, Stephanie; Guthrie, Martin; Goillandeau, Michel; Nguyen, Tho Hai; Orignac, Hugues; Gross, Christian; Boraud, Thomas

2014-01-01

The neurological bases of spatial navigation are mainly investigated in rodents and seldom in primates. The few studies led on spatial navigation in both human and non-human primates are performed in virtual, not in real environments. This is mostly because of methodological difficulties inherent in conducting research on freely-moving monkeys in real world environments. There is some incertitude, however, regarding the extrapolation of rodent spatial navigation strategies to primates. Here we present an entirely new platform for investigating real spatial navigation in rhesus monkeys. We showed that monkeys can learn a pathway by using different strategies. In these experiments three monkeys learned to drive the wheelchair and to follow a specified route through a real maze. After learning the route, probe tests revealed that animals successively use three distinct navigation strategies based on i) the place of the reward, ii) the direction taken to obtain reward or iii) a cue indicating reward location. The strategy used depended of the options proposed and the duration of learning. This study reveals that monkeys, like rodents and humans, switch between different spatial navigation strategies with extended practice, implying well-conserved brain learning systems across different species. This new task with freely driving monkeys provides a good support for the electrophysiological and pharmacological investigation of spatial navigation in the real world by making possible electrophysiological and pharmacological investigations.

Active learning in the presence of unlabelable examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri

2004-01-01

We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

Personal Decision Making. Focus on Economics.

ERIC Educational Resources Information Center

Leet, Don R.; Charkins, R. J.; Lang, Nancy A.; Lopus, Jane S.; Tamaribuchi, Gail

This book highlights and examines basic economic concepts as they relate to consumer, business, social, and personal choices. Students are shown connections between their classroom learning and their real-world experiences in budgeting, career planning, credit management, and housing. The set of 15 lessons include: (1) "Decision Making: Scarcity,…

From Action to English: Reality in the Classroom.

ERIC Educational Resources Information Center

Zuern, Guenther

1982-01-01

Describes use of total physical response as a teaching strategy in English-as-a-second-language classes. Students act out commands from teacher with no initial emphasis on oral production. This approach makes a lesson more real to students and physically involving them makes for more successful learning. (Author/BK)

Guiding Exploration through Three-Dimensional Virtual Environments: A Cognitive Load Reduction Approach

ERIC Educational Resources Information Center

Chen, Chwen Jen; Fauzy Wan Ismail, Wan Mohd

2008-01-01

The real-time interactive nature of three-dimensional virtual environments (VEs) makes this technology very appropriate for exploratory learning purposes. However, many studies have shown that the exploration process may cause cognitive overload that affects the learning of domain knowledge. This article reports a quasi-experimental study that…

Real Progress in Maryland: Student Learning Objectives and Teacher and Principal Evaluation

ERIC Educational Resources Information Center

Slotnik, William J.; Bugler, Daniel; Liang, Guodong

2014-01-01

The Maryland State Department of Education (MSDE) is making significant strides in guiding and supporting the implementation of Student Learning Objectives (SLOs) as well as a teacher and principal evaluation (TPE) system statewide. MSDE support focuses on helping districts prepare for full SLO implementation by providing technical assistance with…

No Longer Invisible

ERIC Educational Resources Information Center

Thomas, Cornell

2014-01-01

In a real teaching and learning community of learners, the belief exists that all can and will learn, that the teacher has the ability to connect new information with the knowledge base of each student, and that students will become empowered to make their own connections. This premise is built on a foundation that sees each student as a unique…

The Twin Twin Paradox: Exploring Student Approaches to Understanding Relativistic Concepts

ERIC Educational Resources Information Center

Cormier, Sebastien; Steinberg, Richard

2010-01-01

A great deal has long been known about student difficulties connecting real-world experiences with what they are learning in their physics classes, making learning basic ideas of classical physics challenging. Understanding these difficulties has led to the development of many instructional approaches that have been shown to help students make…

The Benefits & Drawbacks of Integrating Cloud Computing and Interactive Whiteboards in Teacher Preparation

ERIC Educational Resources Information Center

Blue, Elfreda; Tirotta, Rose

2011-01-01

Twenty-first century technology has changed the way tools are used to support and enhance learning and instruction. Cloud computing and interactive white boards, make it possible for learners to interact, simulate, collaborate, and document learning experiences and real world problem-solving. This article discusses how various technologies (blogs,…

Not Just a Fall Tree

ERIC Educational Resources Information Center

Miller-Hewes, Kathy A.

2004-01-01

Trees burst with color in the northern states. Autumn leaves dust the ground. Painting the fall landscape is nothing new. Teachers have been doing it in classrooms for decades. The approach, however, can make the difference between whether the fall landscape is simply painting for fun, or a real learning experience. Students learn best when they…

Second Life as a Support Element for Learning Electronic Related Subjects: A Real Case

ERIC Educational Resources Information Center

Beltran Sierra, Luis M.; Gutierrez, Ronald S.; Garzon-Castro, Claudia L.

2012-01-01

Looking for more active and motivating methodological alternatives from the students' perspective, which promote analysis and investigation abilities that make the student a more participative agent and some learning processes are facilitated, a practical study was conducted in the University of La Sabana (Chia, Colombia), in Computing Engineering…

Teachers in the desert: Creating ecological research opportunities for teachers and students on the US-Mexico border

USDA-ARS?s Scientific Manuscript database

Considerable research provides evidence for the value of teaching science using enhanced context strategies. These strategies include making learning relevant to students by using real-world examples and problems as well as taking students out of the classroom to learn about the topic. Unfortunately...

Telepresence: A "Real" Component in a Model to Make Human-Computer Interface Factors Meaningful in the Virtual Learning Environment

ERIC Educational Resources Information Center

Selverian, Melissa E. Markaridian; Lombard, Matthew

2009-01-01

A thorough review of the research relating to Human-Computer Interface (HCI) form and content factors in the education, communication and computer science disciplines reveals strong associations of meaningful perceptual "illusions" with enhanced learning and satisfaction in the evolving classroom. Specifically, associations emerge…

Maximizing the Online Learning Experience: Suggestions for Educators and Students

ERIC Educational Resources Information Center

Cicco, Gina

2011-01-01

This article will discuss ways of maximizing the online course experience for teachers- and counselors-in-training. The widespread popularity of online instruction makes it a necessary learning experience for future teachers and counselors (Ash, 2011). New teachers and counselors take on the responsibility of preparing their students for real-life…

Making Real-World Issues Our Business: Critical Literacy in a Third-Grade Classroom.

ERIC Educational Resources Information Center

Heffernan, Lee; Lewison, Mitzi

2000-01-01

Reflects on the events that occurred during a six-month period in a suburban classroom. Documents the transformation that took place in learning and teaching as students took part in a critical literacy curriculum. Examines the significant curricular changes that occur when the "real world" is allowed to enter classroom discussions and…

Kids Are Consumers, Too! Real-World Reading and Language Arts.

ERIC Educational Resources Information Center

Fair, Jan; Melvin, Mary; Bantz, Carol; Vause, Kate

Designed to help youngsters with real-world learning, and with being a smart consumer, this book focuses on having students participate in decisions facing consumers every day. The book contends that this is the best way to help students think critically and solve problems. Activities in the book require students to make consumer decisions related…

Realistic Real World Contexts: Model Eliciting Activities

ERIC Educational Resources Information Center

Doruk, Bekir Kürsat

2016-01-01

Researchers have proposed a variety of methods to make a connection between real life and mathematics so that it can be learned in a practical way and enable people to utilise mathematics in their daily lives. Model-eliciting activities (MEAs) were developed to fulfil this need and are very capable of serving this purpose. The reason MEAs are so…

Moral Emotions and Moral Judgments in Children's Narratives: Comparing Real-Life and Hypothetical Transgressions

ERIC Educational Resources Information Center

Gutzwiller-Helfenfinger, Eveline; Gasser, Luciano; Malti, Tina

2010-01-01

How children make meaning of their own social experiences in situations involving moral issues is central to their subsequent affective and cognitive moral learning. Our study of young children's narratives describing their interpersonal conflicts shows that the emotions and judgments constructed in the course of these real-life narratives differ…

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.

Development of an Interactive Augmented Environment and Its Application to Autonomous Learning for Quadruped Robots

NASA Astrophysics Data System (ADS)

Kobayashi, Hayato; Osaki, Tsugutoyo; Okuyama, Tetsuro; Gramm, Joshua; Ishino, Akira; Shinohara, Ayumi

This paper describes an interactive experimental environment for autonomous soccer robots, which is a soccer field augmented by utilizing camera input and projector output. This environment, in a sense, plays an intermediate role between simulated environments and real environments. We can simulate some parts of real environments, e.g., real objects such as robots or a ball, and reflect simulated data into the real environments, e.g., to visualize the positions on the field, so as to create a situation that allows easy debugging of robot programs. The significant point compared with analogous work is that virtual objects are touchable in this system owing to projectors. We also show the portable version of our system that does not require ceiling cameras. As an application in the augmented environment, we address the learning of goalie strategies on real quadruped robots in penalty kicks. We make our robots utilize virtual balls in order to perform only quadruped locomotion in real environments, which is quite difficult to simulate accurately. Our robots autonomously learn and acquire more beneficial strategies without human intervention in our augmented environment than those in a fully simulated environment.

Educational Support System for Experiments Involving Construction of Sound Processing Circuits

ERIC Educational Resources Information Center

Takemura, Atsushi

2012-01-01

This paper proposes a novel educational support system for technical experiments involving the production of practical electronic circuits for sound processing. To support circuit design and production, each student uses a computer during the experiments, and can learn circuit design, virtual circuit making, and real circuit making. In the…

PhET: Interactive Simulations for Teaching and Learning Physics

NASA Astrophysics Data System (ADS)

Perkins, Katherine; Adams, Wendy; Dubson, Michael; Finkelstein, Noah; Reid, Sam; Wieman, Carl; LeMaster, Ron

2006-01-01

The Physics Education Technology (PhET) project creates useful simulations for teaching and learning physics and makes them freely available from the PhET website (http://phet.colorado.edu). The simulations (sims) are animated, interactive, and game-like environments in which students learn through exploration. In these sims, we emphasize the connections between real-life phenomena and the underlying science, and seek to make the visual and conceptual models of expert physicists accessible to students. We use a research-based approach in our design—incorporating findings from prior research and our own testing to create sims that support student engagement with and understanding of physics concepts.

Teaching Naked Techniques: Leveraging Research on Learning to Improve the Effectiveness of Teaching

ERIC Educational Resources Information Center

Bowen, José Antonio; Watson, C. Edward

2017-01-01

Ultimately, the overarching purpose of each faculty member in the classroom is to increase the probability that their students will do the work to learn the material on their own, appreciate new perspectives, and, ideally, make connections between one's course, other courses, and the real world. This article discusses how "Teaching Naked…

Game-Based Experiential Learning in Online Management Information Systems Classes Using Intel's IT Manager 3

ERIC Educational Resources Information Center

Bliemel, Michael; Ali-Hassan, Hossam

2014-01-01

For several years, we used Intel's flash-based game "IT Manager 3: Unseen Forces" as an experiential learning tool, where students had to act as a manager making real-time prioritization decisions about repairing computer problems, training and upgrading systems with better technologies as well as managing increasing numbers of technical…

New Perspectives on Context-Based Chemistry Education: Using a Dialectical Sociocultural Approach to View Teaching and Learning

ERIC Educational Resources Information Center

King, Donna

2012-01-01

Context-based chemistry education aims to improve student interest and motivation in chemistry by connecting canonical chemistry concepts with real-world contexts. Implementation of context-based chemistry programmes began 20 years ago in an attempt to make the learning of chemistry meaningful for students. This paper reviews such programmes…

Adaptive Management: From More Talk to Real Action

NASA Astrophysics Data System (ADS)

Williams, Byron K.; Brown, Eleanor D.

2014-02-01

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.

A new proof of the generalized Hamiltonian–Real calculus

PubMed Central

Gao, Hua; Mandic, Danilo P.

2016-01-01

The recently introduced generalized Hamiltonian–Real (GHR) calculus comprises, for the first time, the product and chain rules that makes it a powerful tool for quaternion-based optimization and adaptive signal processing. In this paper, we introduce novel dual relationships between the GHR calculus and multivariate real calculus, in order to provide a new, simpler proof of the GHR derivative rules. This further reinforces the theoretical foundation of the GHR calculus and provides a convenient methodology for generic extensions of real- and complex-valued learning algorithms to the quaternion domain.

Learning Application of Astronomy Based Augmented Reality using Android Platform

NASA Astrophysics Data System (ADS)

Maleke, B.; Paseru, D.; Padang, R.

2018-02-01

Astronomy is a branch of science involving observations of celestial bodies such as stars, planets, nebular comets, star clusters, and galaxies as well as natural phenomena occurring outside the Earth’s atmosphere. The way of learning of Astronomy is quite varied, such as by using a book or observe directly with a telescope. But both ways of learning have shortcomings, for example learning through books is only presented in the form of interesting 2D drawings. While learning with a telescope requires a fairly expensive cost to buy the equipment. This study will present a more interesting way of learning from the previous one, namely through Augmented Reality (AR) application using Android platform. Augmented Reality is a combination of virtual world (virtual) and real world (real) made by computer. Virtual objects can be text, animation, 3D models or videos that are combined with the actual environment so that the user feels the virtual object is in his environment. With the use of the Android platform, this application makes the learning method more interesting because it can be used on various Android smartphones so that learning can be done anytime and anywhere. The methodology used in making applications is Multimedia Lifecycle, along with C # language for AR programming and flowchart as a modelling tool. The results of research on some users stated that this application can run well and can be used as an alternative way of learning Astronomy with more interesting.

Integration Head Mounted Display Device and Hand Motion Gesture Device for Virtual Reality Laboratory

NASA Astrophysics Data System (ADS)

Rengganis, Y. A.; Safrodin, M.; Sukaridhoto, S.

2018-01-01

Virtual Reality Laboratory (VR Lab) is an innovation for conventional learning media which show us whole learning process in laboratory. There are many tools and materials are needed by user for doing practical in it, so user could feel new learning atmosphere by using this innovation. Nowadays, technologies more sophisticated than before. So it would carry in education and it will be more effective, efficient. The Supported technologies are needed us for making VR Lab such as head mounted display device and hand motion gesture device. The integration among them will be used us for making this research. Head mounted display device for viewing 3D environment of virtual reality laboratory. Hand motion gesture device for catching user real hand and it will be visualized in virtual reality laboratory. Virtual Reality will show us, if using the newest technologies in learning process it could make more interesting and easy to understand.

Academic-Hospital Partnership: Conducting a Community Health Needs Assessment as a Service Learning Project.

PubMed

Krumwiede, Kelly A; Van Gelderen, Stacey A; Krumwiede, Norma K

2015-01-01

The purposes of this service learning project were to trial nursing student application of the Community-Based Collaborative Action Research (CBCAR) framework while conducting a community health needs assessment and to assess the effectiveness of the CBCAR framework in providing real-world learning opportunities for enhancing baccalaureate nursing students' public health knowledge. In this case study analysis, the CBCAR framework linked service learning and community health needs assessment with public health nursing core competencies. Fifteen nursing students partnered with collaborative members. Student observational field notes and narrative reflections were analyzed qualitatively for fidelity to the CBCAR framework and to evaluate student public health knowledge. Students successfully employed the CBCAR framework in collaboration with the critical access hospital and community stakeholders to design and conduct the community health needs assessment. Service learning themes were real-world solutions, professional development, community collaboration, and making a difference. Students developed skills in six of the eight domains of the Quad Council's core competencies for public health nurses. Community-Based Collaborative Action Research facilitates collaborative partnerships and relationships throughout the research process. Students benefited by applying what they have learned from their education to a real community who lacks resources. © 2014 Wiley Periodicals, Inc.

Learning fuzzy information in a hybrid connectionist, symbolic model

NASA Technical Reports Server (NTRS)

Romaniuk, Steve G.; Hall, Lawrence O.

1993-01-01

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

A brief review of augmented reality science learning

NASA Astrophysics Data System (ADS)

Gopalan, Valarmathie; Bakar, Juliana Aida Abu; Zulkifli, Abdul Nasir

2017-10-01

This paper reviews several literatures concerning the theories and model that could be applied for science motivation for upper secondary school learners (16-17 years old) in order to make the learning experience more amazing and useful. The embedment of AR in science could bring an awe-inspiring transformation on learners' viewpoint towards the respective subject matters. Augmented Reality is able to present the real and virtual learning experience with the addition of multiple media without replacing the real environment. Due to the unique feature of AR, it attracts the mass attention of researchers to implement AR in science learning. This impressive technology offers learners with the ultimate visualization and provides an astonishing and transparent learning experience by bringing to light the unseen perspective of the learning content. This paper will attract the attention of researchers in the related field as well as academicians in the related discipline. This paper aims to propose several related theoretical guidance that could be applied in science motivation to transform the learning in an effective way.

Learned saliency transformations for gaze guidance

NASA Astrophysics Data System (ADS)

Vig, Eleonora; Dorr, Michael; Barth, Erhardt

2011-03-01

The saliency of an image or video region indicates how likely it is that the viewer of the image or video fixates that region due to its conspicuity. An intriguing question is how we can change the video region to make it more or less salient. Here, we address this problem by using a machine learning framework to learn from a large set of eye movements collected on real-world dynamic scenes how to alter the saliency level of the video locally. We derive saliency transformation rules by performing spatio-temporal contrast manipulations (on a spatio-temporal Laplacian pyramid) on the particular video region. Our goal is to improve visual communication by designing gaze-contingent interactive displays that change, in real time, the saliency distribution of the scene.

From Aristotle to Today: Making the History and Nature of Science Relevant

ERIC Educational Resources Information Center

Sterling, Donna R.

2009-01-01

Students connect to science in multiple ways. For some students, learning how real people have developed and defended their scientific ideas makes science relevant and interesting. Tracking the changes in scientific thought over time can be fascinating for students as they see how scientists based their growing understanding on empirical data that…

Deliberative Pedagogy in a Nonmajors Biology Course: Active Learning That Promotes Student Engagement with Science Policy and Research

ERIC Educational Resources Information Center

Weasel, Lisa H.; Finkel, Liza

2016-01-01

Deliberative democracy, a consensus model of decision making, has been used in real-life policy making involving controversial, science-related issues to increase citizen participation and engagement. Here, we describe a pedagogical approach based on this model implemented in a large, lecture-based, nonmajors introductory biology course at an…

Community-based, Experiential Learning for Second Year Neuroscience Undergraduates

PubMed Central

Yu, Heather J.; Ramos-Goyette, Sharon; McCoy, John G.; Tirrell, Michael E.

2013-01-01

Service learning is becoming a keystone of the undergraduate learning experience. At Stonehill College, we implemented a service learning course, called a Learning Community, in Neuroscience. This course was created to complement the basic research available to Stonehill Neuroscience majors with experience in a more applied and “clinical” setting. The Neuroscience Learning Community is designed to promote a deep understanding of Neuroscience by combining traditional classroom instruction with clinical perspectives and real-life experiences. This Neuroscience Learning Community helps students translate abstract concepts within the context of neurodevelopment by providing students with contextual experience in a real-life, unscripted setting. The experiential learning outside of the classroom enabled students to participate in informed discussions in the classroom, especially with regard to neurodevelopmental disorders. We believe that all students taking this course gain an understanding of the importance of basic and applied Neuroscience as it relates to the individual and the community. Students also have used this concrete, learning-by-doing experience to make informed decisions about career paths and choice of major. PMID:24319392

Making It Real: Project Managing Strategic e-Learning Development Processes in a Large, Campus-Based University

ERIC Educational Resources Information Center

Ward, Mary-Helen; West, Sandra; Peat, Mary; Atkinson, Susan

2010-01-01

The University of Sydney is a large, research-intensive, campus-based Australian University. Since 2004 a strategic initiative of project-based eLearning support has been creating teams of non-academic and academic staff, who have worked together to develop online resources to meet identified needs. The University's aims in continuing to provide…

A Case Study on Using Prediction Markets as a Rich Environment for Active Learning

ERIC Educational Resources Information Center

Buckley, Patrick; Garvey, John; McGrath, Fergal

2011-01-01

In this paper, prediction markets are presented as an innovative pedagogical tool which can be used to create a Rich Environment for Active Learning (REAL). Prediction markets are designed to make forecasts about specific future events by using a market mechanism to aggregate the information held by a large group of traders about that event into a…

Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making.

PubMed

Scholl, Jacqueline; Klein-Flügge, Miriam

2017-09-28

Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.

PubMed

Xu, Dongpo; Xia, Yili; Mandic, Danilo P

2016-02-01

The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.

Maths to Make Things Happen.

ERIC Educational Resources Information Center

Haylock, Derek; Morgans, Helen

1986-01-01

A class of 20 children eight to nine years old with low mathematic attainments assumed responsibility for planning the school's Easter Parade. Real life problems of purchasing prizes, timing the parade, and taking photographs provided opportunities for mathematics learning. (CL)

The connection: schooling, youth development, and community building-The Futures Academy case.

PubMed

Taylor, Henry Louis; McGlynn, Linda Greenough

2009-01-01

Universities, because of their vast human and fiscal resources, can play the central role in assisting in the development of school-centered community development programs that make youth development their top priority. The Futures Academy, a K-8 public school in the Fruit Belt, an inner-city neighborhood in Buffalo, New York, offers a useful model of community development in partnership with the Center for Urban Studies at the State University of New York at Buffalo. The goal of the project is to create opportunities for students to apply the knowledge and skills they learn in the classroom to the goal of working with others to make the neighborhood a better place to live. The efforts seek to realize in practice the Dewey dictum that individuals learn best when they have "a real motive behind and a real outcome ahead."

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

PubMed

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija

2017-01-01

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

Towards the Future "Earthquake" School in the Cloud: Near-real Time Earthquake Games Competition in Taiwan

NASA Astrophysics Data System (ADS)

Chen, K. H.; Liang, W. T.; Wu, Y. F.; Yen, E.

2014-12-01

To prevent the future threats of natural disaster, it is important to understand how the disaster happened, why lives were lost, and what lessons have been learned. By that, the attitude of society toward natural disaster can be transformed from training to learning. The citizen-seismologists-in-Taiwan project is designed to elevate the quality of earthquake science education by means of incorporating earthquake/tsunami stories and near-real time earthquake games competition into the traditional curricula in schools. Through pilot of courses and professional development workshops, we have worked closely with teachers from elementary, junior high, and senior high schools, to design workable teaching plans through a practical operation of seismic monitoring at home or school. We will introduce how the 9-years-old do P- and S-wave picking and measure seismic intensity through interactive learning platform, how do scientists and school teachers work together, and how do we create an environment to facilitate continuous learning (i.e., near-real time earthquake games competition), to make earthquake science fun.

Scientists in the Classroom Mentor Model Program - Bringing real time science into the K - 12 classroom

NASA Astrophysics Data System (ADS)

Worssam, J. B.

2017-12-01

Field research finally within classroom walls, data driven, hands on with students using a series of electronic projects to show evidence of scientific mentor collaboration. You do not want to miss this session in which I will be sharing the steps to develop an interactive mentor program between scientists in the field and students in the classroom. Using next generation science standards and common core language skills you will be able to blend scientific exploration with scientific writing and communication skills. Learn how to make connections in your own community with STEM businesses, agencies and organizations. Learn how to connect with scientists across the globe to make your classroom instruction interactive and live for all students. Scientists, you too will want to participate, see how you can reach out and be a part of the K-12 educational system with students learning about YOUR science, a great component for NSF grants! "Scientists in the Classroom," a model program for all, bringing real time science, data and knowledge into the classroom.

Reinforcement learning improves behaviour from evaluative feedback

NASA Astrophysics Data System (ADS)

Littman, Michael L.

2015-05-01

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Reinforcement learning improves behaviour from evaluative feedback.

PubMed

Littman, Michael L

2015-05-28

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Adaptation pathways: ecoregion and land ownership influences on climate adaptation decision-making in forest management

Treesearch

Todd A. Ontl; Chris Swanston; Leslie A. Brandt; Patricia R. Butler; Anthony W. D’Amato; Stephen D. Handler; Maria K. Janowiak; P. Danielle Shannon

2018-01-01

Climate adaptation planning and implementation are likely to increase rapidly within the forest sector not only as climate continues to change but also as we intentionally learn from real-world examples. We sought to better understand how adaptation is being incorporated in land management decision-making across diverse land ownership types in the Midwest by evaluating...

The Impact of a Museum Travelling Exhibition on Middle School Teachers and Students from Rural, Low-Income Homes

ERIC Educational Resources Information Center

Badger, James; Harker, Richard J. W.

2016-01-01

Schools may be places of learning, but a great deal of learning occurs outside of school. A growing body of literature investigates how school field trips allow rural students to make real-life connections with their school curriculum. This paper contributes to that area of research by describing how students from five middle schools in the United…

Professional Development That Sticks: How Do I Create Meaningful Learning Experiences for Educators? (ASCD Arias)

ERIC Educational Resources Information Center

Ende, Fred

2016-01-01

How can we approach professional development in a thoughtful way, keep teachers motivated, and make the process worthwhile? It's a truth that school leaders can't deny: teachers tend to think of PD as a distraction from the "real work" of the classroom--as something to get through instead of an opportunity to engage, learn, and grow as…

Real life narratives enhance learning about the 'art and science' of midwifery practice.

PubMed

Gilkison, Andrea; Giddings, Lynne; Smythe, Liz

2016-03-01

Health professional educators have long grappled with how to teach the more elusive art of practice alongside the science (a term that encompasses the sort of professional knowledge that can be directly passed on). A competent practitioner is one who knows when, how and for whom to apply knowledge and skills, thereby making the links between theory and practice. They combine art and science in such a way that integrates knowledge with insight. This participatory hermeneutic study explored the experience of teachers and students of implementing a narrative-centred curriculum in undergraduate midwifery education. It revealed that when real life narratives were central to the learning environment, students' learning about the art of midwifery practice was enhanced as they learned about midwifery decisions, reflected on their own values and beliefs and felt an emotional connection with the narrator. Further, art and science became melded together in the context specific wisdom of practice (phronesis).

Making God real and making God good: some mechanisms through which prayer may contribute to healing.

PubMed

Luhrmann, Tanya Marie

2013-10-01

Many social scientists attribute the health-giving properties of religious practice to social support. This paper argues that another mechanism may be a positive relationship with the supernatural, a proposal that builds upon anthropological accounts of symbolic healing. Such a mechanism depends upon the learned cultivation of the imagination and the capacity to make what is imagined more real and more good. This paper offers a theory of the way that prayer enables this process and provides some evidence, drawn from experimental and ethnographic work, for the claim that a relationship with a loving God, cultivated through the imagination in prayer, may contribute to good health and may contribute to healing in trauma and psychosis.

Memristive device based learning for navigation in robots.

PubMed

Sarim, Mohammad; Kumar, Manish; Jha, Rashmi; Minai, Ali A

2017-11-08

Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.

Making connections: Where STEM learning and Earth science data services meet

NASA Astrophysics Data System (ADS)

Bugbee, K.; Ramachandran, R.; Maskey, M.; Gatlin, P. N.; Weigel, A. M.

2016-12-01

STEM learning is most effective when students are encouraged to see the connections between science, technology and real world problems. Helping to make these connections has become an increasingly important aspect of Earth science data research. The Global Hydrology Resource Center (GHRC), one of NASA's 12 EOSDIS data centers, has developed a new type of documentation called the micro article to facilitate making connections between data and Earth science research problems. Micro articles are short academic texts that enable a reader to quickly understand a scientific phenomena, a case study, or an instrument used to collect data. While originally designed to increase data discovery and usability, micro articles also serve as a reliable starting point for project-based learning, an educational approach in STEM education, for high school and higher education environments. This presentation will highlight micro articles at the Global Hydrology Resource Center data center and will demonstrate the potential applications of micro articles in project-based learning.

Novel Advancements in Internet-Based Real-Time Data Technologies

NASA Technical Reports Server (NTRS)

Myers, Gerry; Welch, Clara L. (Technical Monitor)

2002-01-01

AZ Technology has been working with NASA MSFC (Marshall Space Flight Center) to find ways to make it easier for remote experimenters (RPI's) to monitor their International Space Station (ISS) payloads in real-time from anywhere using standard/familiar devices. That effort resulted in a product called 'EZStream' which is in use on several ISS-related projects. Although the initial implementation is geared toward ISS, the architecture and lessons learned are applicable to other space-related programs. This paper begins with a brief history on why Internet-based real-time data is important and where EZStream or products like it fit in the flow of data from orbit to experimenter/researcher. A high-level architecture is then presented along with explanations of the components used. A combination of commercial-off-the-shelf (COTS), Open Source, and custom components are discussed. The use of standard protocols is shown along with some details on how data flows between server and client. Some examples are presented to illustrate how a system like EZStream can be used in real world applications and how care was taken to make the end-user experience as painless as possible. A system such as EZStream has potential in the commercial (non-ISS) arena and some possibilities are presented. During the development and fielding of EZStream, a lot was learned. Good and not so good decisions were made. Some of the major lessons learned will be shared. The development of EZStream is continuing and the future of EZStream will be discussed to shed some light over the technological horizon.

Nursing Student Perceptions Regarding Simulation Experience Sequencing.

PubMed

Woda, Aimee A; Gruenke, Theresa; Alt-Gehrman, Penny; Hansen, Jamie

2016-09-01

The use of simulated learning experiences (SLEs) have increased within nursing curricula with positive learning outcomes for nursing students. The purpose of this study is to explore nursing students' perceptions of their clinical decision making (CDM) related to the block sequencing of different patient care experiences, SLEs versus hospital-based learning experiences (HLEs). A qualitative descriptive design used open-ended survey questions to generate information about the block sequencing of SLEs and its impact on nursing students' perceived CDM. Three themes emerged from the data: Preexperience Anxiety, Real-Time Decision Making, and Increased Patient Care Experiences. Nursing students identified that having SLEs prior to HLEs provided several benefits. Even when students preferred SLEs prior to HLEs, the sequence did not impact their CDM. This suggests that alternating block sequencing can be used without impacting the students' perceptions of their ability to make decisions. [J Nurs Educ. 2016;55(9):528-532.]. Copyright 2016, SLACK Incorporated.

No More Robots: Building Kids' Character, Competence, and Sense of Place. [Re]Thinking Environmental Education. Volume 2

ERIC Educational Resources Information Center

Coulter, Bob

2014-01-01

Place-based education offers a compelling opportunity to engage students in the life of their community. More than just taking a field trip, participants in a place-based project make sustained efforts to make a difference and learn basic skills along the way. Academic concepts come to life as real-world problems are investigated from a local…

A heterogeneous artificial stock market model can benefit people against another financial crisis

PubMed Central

2018-01-01

This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-intelligent and less-intelligent agents run steadily. Moreover, considering real stock markets change violently due to the financial crisis; the real stock markets are divided into two segments, before the financial crisis and after it. The optimal modified model before the financial crisis fails to replicate the statistical features of the real market after the financial crisis. Then, the optimal model after the financial crisis is shown. The experiments indicate that the optimal model after the financial crisis is able to replicate several of real market phenomena, including the first-order autocorrelation, kurtosis, standard deviation of yield series and first-order autocorrelation of yield square. We point out that there is a structural change in stock markets after the financial crisis, which can benefit people forecast the financial crisis. PMID:29912893

A heterogeneous artificial stock market model can benefit people against another financial crisis.

PubMed

Yang, Haijun; Chen, Shuheng

2018-01-01

This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-intelligent and less-intelligent agents run steadily. Moreover, considering real stock markets change violently due to the financial crisis; the real stock markets are divided into two segments, before the financial crisis and after it. The optimal modified model before the financial crisis fails to replicate the statistical features of the real market after the financial crisis. Then, the optimal model after the financial crisis is shown. The experiments indicate that the optimal model after the financial crisis is able to replicate several of real market phenomena, including the first-order autocorrelation, kurtosis, standard deviation of yield series and first-order autocorrelation of yield square. We point out that there is a structural change in stock markets after the financial crisis, which can benefit people forecast the financial crisis.

The Secrets of Taking Any Test: Learn the Techniques Successful Test-Takers Know. The Basics Made Easy...in 20 Minutes a Day.

ERIC Educational Resources Information Center

Meyers, Judith N.

The test-preparation program in this guide covers all forms of test taking to help students deal with real-world problems like test anxiety and insufficient preparation time. The chapters are: (1) "Finding Out about the Tests You Must Take"; (2) "Making a Study Plan"; (3) "Carrying Out Your Study Plan"; (4) "Learning Strategies"; (5) "Coping with…

Making STEM Connections

ERIC Educational Resources Information Center

Stump, Sheryl L.; Bryan, Joel A.; McConnell, Tom J.

2016-01-01

Integrated approaches to education in science, technology, engineering, and mathematics (STEM), especially those set in the context of real-world situations, can motivate and deepen students' learning of the STEM subjects (National Academy of Engineering and National Research Council 2014). This article describes two integrated investigations used…

Making Online Homework Work

ERIC Educational Resources Information Center

Lunsford, M. Leigh; Pendergrass, Marcus

2016-01-01

Online homework systems, which deliver homework assignments to students and provide real-time feedback on their responses, have the potential to increase student learning in college mathematics classes. However, current research on their effectiveness is inconclusive, with some studies showing gains in student achievement, whereas others report…

Online Mentoring.

ERIC Educational Resources Information Center

Duff, Carole

2000-01-01

When Urseline Academy girls need career advice, academic guidance, or personal support, they e-mail their mentors--professional women in the Dallas area whose "real-world" knowledge helps the students make informed choices. The program is an outgrowth of a summer internship program stressing student-centered learning. (MLH)

"Making a difference" - Medical students' opportunities for transformational change in health care and learning through quality improvement projects.

PubMed

Bergh, Anne-Marie; Bac, Martin; Hugo, Jannie; Sandars, John

2016-07-11

Quality improvement is increasingly becoming an essential aspect of the medical curriculum, with the intention of improving the health care system to provide better health care. The aim of this study was to explore undergraduate medical students' experiences of their involvement in quality improvement projects during a district health rotation. Student group reports from rotations in learning centres of the University of Pretoria in Mpumalanga Province, South Africa were analysed for the period 2012 to 2015. Interviews were conducted with health care providers at four learning centres in 2013. Three main themes were identified: (1) 'Situated learning', describing students' exposure to the discrepancies between ideal and reality in a real-life situation and how they learned to deal with complex situations, individually and as student group; (2) 'Facing dilemmas', describing how students were challenged about the non-ideal reality; (3) 'Making a difference', describing the impact of the students' projects, with greater understanding of themselves and others through working in teams but also making a change in the health care system. Quality improvement projects can provide an opportunity for both the transformation of health care and for transformative learning, with individual and 'collective' self-authorship.

Attributes of quality programs in universities in developing countries: Case studies of two private universities in Ecuador and beyond

NASA Astrophysics Data System (ADS)

Uriguen, Monica I.

This study sought to identify the key attributes of high-quality programs with an eye toward helping developing countries such as Ecuador advance program quality. The dissertation is divided into five chapters: (1) introduction to high-quality programs; (2) literature review of attributes of high-quality programs; (3) grounded theory method (including interviews with 60 individuals) used to identify program attributes that enhance student learning; (4) findings; and (5) conclusions and recommendations. Following are the five clusters and thirteen attributes of high-quality programs that I identified: Cluster One: Highly Qualified Participants: (1) Highly Qualified Faculty, and (2) Highly Qualified Students; Cluster Two: Learning-Centered Cultures: (3) Shared Program Direction Focused on Learning, (4) Real-World Learning Experiences, (5) Reading-Centered Culture, and (6) Supportive and Risk-Taking Environment; Cluster Three: Interactive Teaching and Learning: (7) Integrative learning: Theory with Practice, Self with Subject, and (8) Exclusive Tutoring and Mentoring; Cluster Four: Connected Program Requirements: (9) Planned Breadth and Depth Course Work, and (10) Tangible Products; and Cluster Five: Adequate Resources: (11) Support for Students, (12) Support for Faculty, and (13) Support for Campus Infrastructure. The study was guided by Haworth and Conrad's (1997) "Engagement Theory of High-Quality Programs." Eleven of the attributes of high-quality programs are closely connected to Haworth and Conrad's theory and the other two attributes---real-world learning experiences and a reading-centered culture---make the signature theoretical contributions of the study. Real-world learning experiences encourage the active involvement of stakeholders in designing curricula with real-world learning experiences. The second attribute---a reading-centered culture---has never before been identified in the literature. There are four key differences between Haworth and Conrad's theory and the theory developed in this study. This study identified four attributes that are highly important in Ecuador and, possibly, other developing countries: highly-qualified faculty, highly-qualified students, reading-centered cultures, and real-world learning experiences. If Latin American universities implement the recommendations proposed in the study, particularly Ecuadorian universities, there is a foundation for envisioning a better future for Ecuadorian universities.

Exploration of the functions of health impact assessment in real-world policymaking in the field of social health inequality: towards a conception of conceptual learning.

PubMed

Feyaerts, Gille; Deguerry, Murielle; Deboosere, Patrick; De Spiegelaere, Myriam

2017-06-01

With the implementation of health impact assessment (HIA)'s conceptual model into real-world policymaking, a number of fundamental issues arise concerning its decision-support function. Rooted in a rational vision of the decision-making process, focus regarding both conceptualisation and evaluation has been mainly on the function of instrumental policy-learning. However, in the field of social health inequalities, this function is strongly limited by the intrinsic 'wickedness' of the policy issue. Focusing almost exclusively on this instrumental function, the real influence HIA can have on policymaking in the longer term is underestimated and remains largely unexploited. Drawing insights from theoretical models developed in the field of political science and sociology, we explore the different decision-support functions HIA can fulfill and identify conceptual learning as potentially the most important. Accordingly, dominant focus on the technical engineering function, where knowledge is provided in order to 'rationalise' the policy process and to tackle 'tame' problems, should be complemented with an analysis of the conditions for conceptual learning, where knowledge introduces new information and perspectives and, as such, contributes in the longer term to a paradigm change.

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort

PubMed Central

Kolling, Nils; Nelissen, Natalie; Wittmann, Marco K.; Harmer, Catherine J.; Rushworth, Matthew F. S.

2015-01-01

Natural environments are complex, and a single choice can lead to multiple outcomes. Agents should learn which outcomes are due to their choices and therefore relevant for future decisions and which are stochastic in ways common to all choices and therefore irrelevant for future decisions between options. We designed an experiment in which human participants learned the varying reward and effort magnitudes of two options and repeatedly chose between them. The reward associated with a choice was randomly real or hypothetical (i.e., participants only sometimes received the reward magnitude associated with the chosen option). The real/hypothetical nature of the reward on any one trial was, however, irrelevant for learning the longer-term values of the choices, and participants ought to have only focused on the informational content of the outcome and disregarded whether it was a real or hypothetical reward. However, we found that participants showed an irrational choice bias, preferring choices that had previously led, by chance, to a real reward in the last trial. Amygdala and ventromedial prefrontal activity was related to the way in which participants' choices were biased by real reward receipt. By contrast, activity in dorsal anterior cingulate cortex, frontal operculum/anterior insula, and especially lateral anterior prefrontal cortex was related to the degree to which participants resisted this bias and chose effectively in a manner guided by aspects of outcomes that had real and more sustained relationships with particular choices, suppressing irrelevant reward information for more optimal learning and decision making. SIGNIFICANCE STATEMENT In complex natural environments, a single choice can lead to multiple outcomes. Human agents should only learn from outcomes that are due to their choices, not from outcomes without such a relationship. We designed an experiment to measure learning about reward and effort magnitudes in an environment in which other features of the outcome were random and had no relationship with choice. We found that, although people could learn about reward magnitudes, they nevertheless were irrationally biased toward repeating certain choices as a function of the presence or absence of random reward features. Activity in different brain regions in the prefrontal cortex either reflected the bias or reflected resistance to the bias. PMID:26269633

Adding Realism to Technical Drafting Programs

ERIC Educational Resources Information Center

Weaver, Gerald L.

1976-01-01

Suggestions for improved, relevant technical drafting programs are presented: (1) making realistic assignments, (2) viewing real projects, (3) duplicating industrial projects, (4) practicing lettering, (5) conducting research, (6) engaging in teamwork, (7) adapting to change, (8) learning to meet deadlines, and (9) stressing the importance of…

Making STEM Real

ERIC Educational Resources Information Center

Hoachlander, Gary; Yanofsky, Dave

2011-01-01

In too many schools, science and mathematics are taught separately with little or no attention to technology and engineering. Also, science and mathematics tend to function in isolation from other core subjects. In California, Linked Learning: Pathways to College and Career Success connects core academics to challenging professional and technical…

How do students implement collaborative testing in real-world contexts?

PubMed

Wissman, Kathryn T; Rawson, Katherine A

2016-01-01

Recent research has explored the effects of collaborative testing, showing costs and benefits during learning and for subsequent memory. However, no prior research is informative about whether and how students use collaborative testing in real-world contexts. Accordingly, the primary purpose of the current research was to explore the extent to which students use collaborative testing during self-regulated learning. We conducted three surveys (n = 692 across three samples) asking students about their use of collaborative testing, with a particular interest in conditions under which students report implementing collaborative testing. Among the key outcomes, a majority of students reported using collaborative testing when studying in a group. Additionally, students reported that key term definitions are the material most often used during collaborative testing. Students are also more motivated to use testing and believe testing is more effective and more fun when implemented in a group versus alone. Outcomes also shed light on metacognitive components of collaborative testing, with the student asking (versus answering) the question making the monitoring judgement whereas both students make the control decision about when to terminate practice. We discuss ways in which the collaborative memory literature can be extended to support more successful student learning.

Teachers' experiences of teaching in a blended learning environment.

PubMed

Jokinen, Pirkko; Mikkonen, Irma

2013-11-01

This paper considers teachers' experiences of teaching undergraduate nursing students in a blended learning environment. The basic idea of the study programme was to support students to reflect on theory and practice, and provide with access to expert and professional knowledge in real-life problem-solving and decision making. Learning was organised to support learning in and about work: students worked full-time and this provided excellent opportunities for learning both in practice, online and face-to-face sessions. The aim of the study was to describe teachers' experiences of planning and implementing teaching and learning in a blended-learning-based adult nursing programme. The research method was qualitative, and the data were collected by three focus group interviews, each with four to six participants. The data were analysed using qualitative content analysis. The results show that the blended learning environment constructed by the combination of face-to-face learning and learning in practice with technology-mediated learning creates challenges that must be taken into consideration when planning and implementing blended teaching and learning. However, it provides good opportunities to enhance students' learning in and about work. This is because such programmes support student motivation through the presence of "real-life" and their relevance to the students' own places of work. Nevertheless, teachers require knowledge of different pedagogical approaches; they need professional development support in redesigning teaching and learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Towards a Unified Theory of Engineering Education

ERIC Educational Resources Information Center

Salcedo Orozco, Oscar H.

2017-01-01

STEM education is an interdisciplinary approach to learning where rigorous academic concepts are coupled with real-world lessons and activities as students apply science, technology, engineering, and mathematics in contexts that make connections between school, community, work, and the global enterprise enabling STEM literacy (Tsupros, Kohler and…

Drafting.

ERIC Educational Resources Information Center

Hughes, Larry R.

This guide to teaching drafting, one in a series of instructional materials for junior high industrial arts education, is designed to assist teachers as they plan and implement new courses of study and as they make revisions and improvements in existing courses in order to integrate classroom learning with real-life experiences. This drafting…

Subjective Criticism: From Cognition to Morality.

ERIC Educational Resources Information Center

Goldberg, Marilyn K.

Reflecting on the contributions to individual education that literature study should make, this paper synthesizes some major directions taken by contemporary literary critics, examines the cognitive requirements for real learning, and puts these considerations into perspective as a goal for the teaching of literature. The major theories of…

Learning the effects of psychotropic drugs during pregnancy using real-world safety data: a paradigm shift toward modern pharmacovigilance.

PubMed

Lupattelli, Angela; Spigset, Olav; Nordeng, Hedvig

2018-06-15

The growing evidence on psychotropic drug safety in pregnancy has been possible thanks to the increasing availability of real-world data, i.e. data not collected in conventional randomised controlled trials. Use of these data is a key to establish psychotropic drug effects on foetal, child, and maternal health. Despite the inherent limitations and pitfalls of observational data, these can still be informative after a critical appraisal of the collective body of evidence has been done. By valuing real-world safety data, and making these a larger part of the regulatory decision-making process, we move toward a modern pregnancy pharmacovigilance. The recent uptake of real-world safety data by health authorities has set the basis for an important paradigm shift, which is integrating such data into drug labelling. The recent safety assessment of sodium valproate in pregnant and childbearing women is probably one of the first examples of modern pregnancy pharmacovigilance.

Big data learning and suggestions in modern apps

NASA Astrophysics Data System (ADS)

Sharma, G.; Nadesh, R. K.; ArivuSelvan, K.

2017-11-01

Among many other tasks involved for emergent location-based applications such as those involved in prescribing touring places and those focused on publicizing based on destination, destination prediction is vital. Dealing with destination prediction involves determining the probability of a location (destination) depending on historical trajectories. In this paper, a destination prediction based on probabilistic model (Machine Learning Model) feed-forward neural networks will be presented, which will work by making the observation of driver’s habits. Some individuals drive to same locations such as work involving same route every day of the working week. Here, streaming of real-time driving data will be sent through Kafka queue in apache storm for real-time processing and finally storing the data in MongoDB.

Making Science Real: Photo-Sharing in Biology and Chemistry

ERIC Educational Resources Information Center

Waycott, Jenny; Dalgarno, Barney; Kennedy, Gregor; Bishop, Andrea

2012-01-01

In this paper, we examine students' reflections about the value of two photo-sharing activities that were implemented in undergraduate Biology and Chemistry subjects. Both activities aimed, broadly, to provide support for authentic and meaningful learning experiences in undergraduate science. Although the activities were similar--both required…

Paternity Testing in a PBL Environment

ERIC Educational Resources Information Center

Casla, Alberto Vicario; Zubiaga, Isabel Smith

2010-01-01

Problem Based Learning (PBL) makes use of real-life scenarios to stimulate students' prior knowledge and to provide a meaningful context that is also related to the student's future professional work. In this article, Paternity testing is presented using a PBL approach that involves a combination of classroom, laboratory, and out-of-class…

Is That Penguin Stuffed or Real?

ERIC Educational Resources Information Center

Ohanian, Susan

1996-01-01

Like sugaring, teaching requires immense patience. Superintendents can force textbooks on teachers but cannot make them use them. Not every high schooler needs an elitist, college-bound education, but no one needs to be bribed or threatened into learning or reading. Alternative texts and approaches can be used to help students discover a…

Geometry Challenges

ERIC Educational Resources Information Center

Robinson, James

2012-01-01

What is it that makes learners "think"? What geometrical problem, simply posed, can switch on the thinking processes of students? The author describes one such starting point that worked in a real classroom with Year 9 students. A group of learners for whom "mathematics comes easy" experience being stuck, they learn to handle this unfamiliar…

Enhancing Geographic Learning and Literacy through Filmmaking

ERIC Educational Resources Information Center

Dando, Christina E.; Chadwick, Jacob J.

2014-01-01

In this media-saturated society, students need to think more critically about the media they encounter and that they are producing. Through filmmaking, students can link geographic theory and the real world, bridging the distance from readings/lectures/discussions to the geography on the ground, making the abstract concrete. But constructing films…

Learning from Real-Life Problems: Functional Education in Bangladesh.

ERIC Educational Resources Information Center

Islam, Mahmood Aminul

1980-01-01

Describes a program in Bangladesh designed to make the rural poor understand their social and economic problems in order to begin to bring about change through their own efforts. The program is functional education and includes topics in family planning, health, housing, nutrition, and agriculture. (Author/SA)

Making Statistics "Real" for Social Work Students

ERIC Educational Resources Information Center

Wells, Melissa

2006-01-01

This article presents results from an evaluation of service learning in statistics courses for master of social work students. The article provides an overview of the application of a community-based statistics project, describes student feedback regarding the project, and illustrates some strengths and limitations of using this pedagogy with…

Using Student Managed Businesses to Integrate the Business Curriculum

ERIC Educational Resources Information Center

Massad, Victor J.; Tucker, Joanne M.

2009-01-01

To teach business today requires that we go beyond classroom learning and encourage real world, cross-functional experiences and applied management decision-making. This paper describes an innovative approach that requires students to apply their function-specific knowledge of business, integrated with other functional areas, to an authentic…

Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

DOT National Transportation Integrated Search

2016-12-25

The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

Video Tutorial of Continental Food

NASA Astrophysics Data System (ADS)

Nurani, A. S.; Juwaedah, A.; Mahmudatussa'adah, A.

2018-02-01

This research is motivated by the belief in the importance of media in a learning process. Media as an intermediary serves to focus on the attention of learners. Selection of appropriate learning media is very influential on the success of the delivery of information itself both in terms of cognitive, affective and skills. Continental food is a course that studies food that comes from Europe and is very complex. To reduce verbalism and provide more real learning, then the tutorial media is needed. Media tutorials that are audio visual can provide a more concrete learning experience. The purpose of this research is to develop tutorial media in the form of video. The method used is the development method with the stages of analyzing the learning objectives, creating a story board, validating the story board, revising the story board and making video tutorial media. The results show that the making of storyboards should be very thorough, and detailed in accordance with the learning objectives to reduce errors in video capture so as to save time, cost and effort. In video capturing, lighting, shooting angles, and soundproofing make an excellent contribution to the quality of tutorial video produced. In shooting should focus more on tools, materials, and processing. Video tutorials should be interactive and two-way.

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

PubMed

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Space Mysteries: Making Science and Astronomy Learning Fun

NASA Astrophysics Data System (ADS)

Plait, P.; Tim, G.; Cominsky, L.

2001-12-01

How do you get and keep a student's attention during class? Make learning fun! Using a game to teach students ensures that they have fun, enjoy the lesson and remember it. We have developed a series of interactive web and CD based games called "Space Mysteries" to teach students math, physics and astronomy. Using real NASA data, the students must find out Who (or What) dunit in an engaging astronomy mystery. The games include video interviews with famous scientists, actors playing roles who give clues to the solution, and even a few blind alleys and red herrings. The first three games are currently online in beta release at http://mystery.sonoma.edu.

Active Learning Using Hint Information.

PubMed

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Technology in postgraduate medical education: a dynamic influence on learning?

PubMed Central

Bullock, Alison; Webb, Katie

2015-01-01

The influence of technology in medical workplace learning is explored by focusing on three uses: m-learning (notably apps), simulation and social media. Smartphones with point-of-care tools (such as textbooks, drug guides and medical calculators) can support workplace learning and doctors’ decision-making. Simulations can help develop technical skills and team interactions, and ‘in situ’ simulations improve the match between the virtual and the real. Social media (wikis, blogs, networking, YouTube) heralds a more participatory and collaborative approach to knowledge development. These uses of technology are related to Kolb's learning cycle and Eraut's intentions of informal learning. Contentions and controversies with these technologies exist. There is a problem with the terminology commonly adopted to describe the use of technology to enhance learning. Using learning technology in the workplace changes the interaction with others and raises issues of professionalism and etiquette. Lack of regulation makes assessment of app quality a challenge. Distraction and dependency are charges levelled at smartphone use in the workplace and these need further research. Unless addressed, these and other challenges will impede the benefits that technology may bring to postgraduate medical education. PMID:26341127

Use of learning miniprojects in a chemistry laboratory for engineering

NASA Astrophysics Data System (ADS)

Cancela, Angeles; Maceiras, Rocio; Sánchez, Angel; Izquierdo, Milagros; Urréjola, Santiago

2016-01-01

The aim of this paper is to describe the design of chemical engineering laboratory sessions in order to focus them on the learning company approach. This is an activity carried out in the classroom similar to the activities that exist in real companies. This could lead classroom practice to a more cooperative learning and a different style of experimentation. The stated goal is to make a design that seeks to motivate students in a cooperative manner to perform their experiments self-directed and self-organised. The teaching organisation and development of participatory action research are described.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Moral emotions and moral judgments in children's narratives: comparing real-life and hypothetical transgressions.

PubMed

Gutzwiller-Helfenfinger, Eveline; Gasser, Luciano; Malti, Tina

2010-01-01

How children make meaning of their own social experiences in situations involving moral issues is central to their subsequent affective and cognitive moral learning. Our study of young children's narratives describing their interpersonal conflicts shows that the emotions and judgments constructed in the course of these real-life narratives differ from the emotions and judgments generated in the context of hypothetical transgressions. In the narratives, all emotions mentioned spontaneously were negative. In contrast, emotions attributed in the interview part covered a broader spectrum. One's own real-life transgressions were judged less severe and more justified than hypothetical transgressions. © Wiley Periodicals, Inc.

Impairment of decision-making in multiple sclerosis: A neuroeconomic approach.

PubMed

Sepúlveda, Maria; Fernández-Diez, Begoña; Martínez-Lapiscina, Elena H; Llufriu, Sara; Sola-Valls, Nuria; Zubizarreta, Irati; Blanco, Yolanda; Saiz, Albert; Levy, Dino; Glimcher, Paul; Villoslada, Pablo

2017-11-01

To assess the decision-making impairment in patients with multiple sclerosis (MS) and how they relate to other cognitive domains. We performed a cross-sectional analysis in 84 patients with MS, and 21 matched healthy controls using four tasks taken from behavioral economics: (1) risk preferences, (2) choice consistency, (3) delay of gratification, and (4) rate of learning. All tasks were conducted using real-world reward outcomes (food or money) in different real-life conditions. Participants underwent cognitive examination using the Brief Repeatable Battery-Neuropsychology. Patients showed higher risk aversion (general propensity to choose the lottery was 0.51 vs 0.64, p = 0.009), a trend to choose more immediate rewards over larger but delayed rewards ( p = 0.108), and had longer reactions times ( p = 0.033). Choice consistency and learning rates were not different between groups. Progressive patients chose slower than relapsing patients. In relation to general cognitive impairments, we found correlations between impaired decision-making and impaired verbal memory ( r = 0.29, p = 0.009), visual memory ( r = -0.37, p = 0.001), and reduced processing speed ( r = -0.32, p = 0.001). Normalized gray matter volume correlated with deliberation time ( r = -0.32, p = 0.005). Patients with MS suffer significant decision-making impairments, even at the early stages of the disease, and may affect patients' quality and social life.

The Role of Interactional Quality in Learning from Touch Screens during Infancy: Context Matters.

PubMed

Zack, Elizabeth; Barr, Rachel

2016-01-01

Interactional quality has been shown to enhance learning during book reading and play, but has not been examined during touch screen use. Learning to apply knowledge from a touch screen is complex for infants because it involves transfer of learning between a two-dimensional (2D) screen and three-dimensional (3D) object in the physical world. This study uses a touch screen procedure to examine interactional quality measured via maternal structuring, diversity of maternal language, and dyadic emotional responsiveness and infant outcomes during a transfer of learning task. Fifty 15-month-old infants and their mothers participated in this semi-naturalistic teaching task. Mothers were given a 3D object, and a static image of the object presented on a touch screen. Mothers had 5 min to teach their infant that a button on the real toy works in the same way as a virtual button on the touch screen (or vice versa). Overall, 64% of infants learned how to make the button work, transferring learning from the touch screen to the 3D object or vice versa. Infants were just as successful in the 3D to 2D transfer direction as they were in the 2D to 3D transfer direction. A cluster analysis based on emotional responsiveness, the proportion of diverse maternal verbal input, and amount of maternal structuring resulted in two levels of interactional quality: high quality and moderate quality. A logistic regression revealed the level of interactional quality predicted infant transfer. Infants were 19 times more likely to succeed and transfer learning between the touch screen and real object if they were in a high interactional quality dyad, even after controlling for infant activity levels. The present findings suggest that interactional quality between mother and infant plays an important role in making touch screens effective teaching tools for infants' learning.

The Role of Interactional Quality in Learning from Touch Screens during Infancy: Context Matters

PubMed Central

Zack, Elizabeth; Barr, Rachel

2016-01-01

Interactional quality has been shown to enhance learning during book reading and play, but has not been examined during touch screen use. Learning to apply knowledge from a touch screen is complex for infants because it involves transfer of learning between a two-dimensional (2D) screen and three-dimensional (3D) object in the physical world. This study uses a touch screen procedure to examine interactional quality measured via maternal structuring, diversity of maternal language, and dyadic emotional responsiveness and infant outcomes during a transfer of learning task. Fifty 15-month-old infants and their mothers participated in this semi-naturalistic teaching task. Mothers were given a 3D object, and a static image of the object presented on a touch screen. Mothers had 5 min to teach their infant that a button on the real toy works in the same way as a virtual button on the touch screen (or vice versa). Overall, 64% of infants learned how to make the button work, transferring learning from the touch screen to the 3D object or vice versa. Infants were just as successful in the 3D to 2D transfer direction as they were in the 2D to 3D transfer direction. A cluster analysis based on emotional responsiveness, the proportion of diverse maternal verbal input, and amount of maternal structuring resulted in two levels of interactional quality: high quality and moderate quality. A logistic regression revealed the level of interactional quality predicted infant transfer. Infants were 19 times more likely to succeed and transfer learning between the touch screen and real object if they were in a high interactional quality dyad, even after controlling for infant activity levels. The present findings suggest that interactional quality between mother and infant plays an important role in making touch screens effective teaching tools for infants’ learning. PMID:27625613

A Simple Classification Model for Debriefing Simulation Games

ERIC Educational Resources Information Center

Peters, Vincent A. M.; Vissers, Geert A. N.

2004-01-01

Debriefing is an important phase in using simulation games. Participants are invited to make a connection between experiences gained from playing the game and experiences in real-life situations. Thus, debriefing is the phase meant to encourage learning from the simulation game. Although design and practice of debriefing sessions should be aligned…

Assessing the Flipped Classroom in Operations Management: A Pilot Study

ERIC Educational Resources Information Center

Prashar, Anupama

2015-01-01

The author delved into the results of a flipped classroom pilot conducted for an operations management course module. It assessed students' perception of a flipped learning environment after making them experience it in real time. The classroom environment was construed using a case research approach and students' perceptions were studied using…

TV's Version of Education (And What to Do about It).

ERIC Educational Resources Information Center

Kaplan, George

1990-01-01

While television's potential as the nation's "great educator" is expanding, messages about schooling come across as colorless and forgettable. The "Learning in America" series had limited mass appeal. The "real" stories (George Bush as "Education President," school control, and the future of teaching) are neglected. Cable TV could make the…

Towards an Effective Use of Audio Conferencing in Distance Language Courses

ERIC Educational Resources Information Center

Hampel, Regine; Hauck, Mirjam

2004-01-01

In order to respond to learners' need for more flexible speaking opportunities and to overcome the geographical challenge of students spread over the United Kingdom and continental Western Europe, the Open University recently introduced Internet-based, real-time audio conferencing, thus making a groundbreaking move in the distance learning and…

Mobile Experiences of Historical Place: A Multimodal Analysis of Emotional Engagement

ERIC Educational Resources Information Center

Sakr, Mona; Jewitt, Carey; Price, Sara

2016-01-01

This article explores how to research the opportunities for emotional engagement that mobile technologies provide for the design and enactment of learning environments. In the context of mobile technologies that foster location-based linking, we make the case for the centrality of in situ real-time observational research on how emotional…

Making Meaning of Scientific Practices: Exploring the Pathways and Variations of Classrooms Engaging in Science Practices

ERIC Educational Resources Information Center

Ko, Mon-Lin Monica

2013-01-01

A focus of reforms in standards, learning environments, teacher preparation programs and professional development is to support teachers' and students' engagement with scientific practices such as argumentation, modeling and generating explanations for real-world phenomena (NRC, 2011). Engaging in these practices in authentic ways…

Ageing, Learning and Health: Making Connections

ERIC Educational Resources Information Center

Mestheneos, Elizabeth; Withnall, Alexandra

2016-01-01

The health of ageing populations is a real concern across the world so that the concept of active ageing has been advocated as a framework for appropriate educational policies and programmes to support people as they grow older. The other elements discussed here are health and healthy life expectancy (HLE) acknowledging that as people age, they…

Recipes for Life

ERIC Educational Resources Information Center

Zehr, Mary Ann

2006-01-01

This article presents a school-based summer camp called the Get FIT program. Students in Eagle Pass, Texas, go to summer camp to learn how to eat better, play harder, and make smarter decisions about their health. This article also presents an experience of an eleven-year-old child who was once "real fat." The child improved his eating…

Sizing up the Solar System

ERIC Educational Resources Information Center

Wiebke, Heidi; Rogers, Meredith Park; Nargund-Joshi, Vanashri

2011-01-01

The American Association for the Advancement of Science (AAAS 1993) states that by the end of fifth grade, students should understand that a model, such as those depicting the solar system, is a smaller version of the real product, making it easier to physically work with and therefore learn from. However, for students and even adults,…

Research and Teaching: Undergraduate Students' Scientifically Informed Decision Making about Socio-Hydrological Issues

ERIC Educational Resources Information Center

Sabel, Jaime L.; Vo, Tina; Alred, Ashley; Dauer, Jenny M.; Forbes, Cory T.

2017-01-01

Although knowledge of disciplinary concepts and epistemic understanding of science are foundations of scientific literacy, students must learn to apply their knowledge to real-world situations. To engage effectively with contemporary water-related challenges with scientific and social dimensions, students need to understand the properties of water…

A Commentary on "Contextualising the Intermediate Financial Accounting Courses in the Global Financial Crisis"

ERIC Educational Resources Information Center

Carnegie, Garry D.; West, Brian

2011-01-01

Accounting is a practical discipline, existing to satisfy particular human needs which are usually depicted in terms of decision-making processes and accountability evaluations. Proposals for how accounting education may be infused with learning from the "real-world" contexts in which it operates are always welcome. However, as the…

Imitation, Interaction and Imagery: Learning to Improvise Drawing with Music

ERIC Educational Resources Information Center

Huovinen, Erkki; Manneberg, Avigail

2013-01-01

This article describes a project in which undergraduate students of beginning drawing were brought together with free improvising musicians to explore interaction in collective real-time art-making. Following a series of guided rehearsals, the students were free to choose their own strategies for interactive group projects. We discuss these…

Aim, Shoot, Ready! Future Teachers Learn to Do Video

ERIC Educational Resources Information Center

Hernandez-Ramos, Pedro

2007-01-01

This paper describes an intensive 2-hr workshop designed to introduce preservice teachers to digital video in the context of an instructional technology course or as a stand-alone activity. Acknowledging time constraints in most real-life instructional situations, this format takes novices with no or very limited knowledge of video making to the…

A Thematic Review of Studies into the Effectiveness of Context-Based Chemistry Curricula

ERIC Educational Resources Information Center

Ultay, Neslihan; Calik, Muammer

2012-01-01

Context-based chemistry education aims at making connections between real life and the scientific content of chemistry courses. The purpose of this study was to evaluate context-based chemistry studies. In looking for the context-based chemistry studies, the authors entered the keywords "context-based", "contextual learning" and "chemistry…

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

NASA Astrophysics Data System (ADS)

Mølgaard, Lasse L.; Buus, Ole T.; Larsen, Jan; Babamoradi, Hamid; Thygesen, Ida L.; Laustsen, Milan; Munk, Jens Kristian; Dossi, Eleftheria; O'Keeffe, Caroline; Lässig, Lina; Tatlow, Sol; Sandström, Lars; Jakobsen, Mogens H.

2017-05-01

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.

Comparative Studies of Prediction Strategies for Solar X-ray Time Series

NASA Astrophysics Data System (ADS)

Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.

2016-12-01

Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

NASA Astrophysics Data System (ADS)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

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.

"Heart Shots": a classroom activity to instigate active learning.

PubMed

Abraham, Reem Rachel; Vashe, Asha; Torke, Sharmila

2015-09-01

The present study aimed to provide undergraduate medical students at Melaka Manipal Medical College (Manipal Campus), Manipal University, in Karnataka, India, an opportunity to apply their knowledge in cardiovascular concepts to real-life situations. A group activity named "Heart Shots" was implemented for a batch of first-year undergraduate students (n = 105) at the end of a block (teaching unit). Students were divided into 10 groups each having 10-11 students. They were requested to make a video/PowerPoint presentation about the application of cardiovascular principles to real-life situations. The presentation was required to be of only pictures/photos and no text material, with a maximum duration of 7 min. More than 95% of students considered that the activity helped them to apply their knowledge in cardiovascular concepts to real-life situations and understand the relevance of physiology in medicine and to revise the topic. More than 90% of students agreed that the activity helped them to apply their creativity in improving their knowledge and to establish a link between concepts rather than learning them as isolated facts. Based on the feedback, we conclude that the activity was student centered and that it facilitated learning. Copyright © 2015 The American Physiological Society.

Learning Physics from the Real World by Direct Observation

NASA Astrophysics Data System (ADS)

Shaibani, Saami J.

2012-03-01

It is axiomatic that hands-on experience provides many learning opportunities, which lectures and textbooks cannot match. Moreover, experiments involving the real world are beneficial in helping students to gain a level of understanding that they might not otherwise achieve. One practical limitation with the real world is that simplifications and approximations are sometimes necessary to make the material accessible; however, these types of adjustments can be viewed with misgiving when they appear arbitrary and/or convenience-based. The present work describes a very familiar feature of everyday life, whose underlying physics is examined without modifications to mitigate difficulties from the lack of control in a non-laboratory environment. In the absence of any immediate formula to process results, students are encouraged to reach ab initio answers with guidance provided by a structured series of worksheets. Many of the latter can be completed as homework assignments prior to activity in the field. This approach promotes thinking and inquiry as valuable attributes instead of unquestioningly following a prescribed path.

Using NCLab-karel to improve computational thinking skill of junior high school students

NASA Astrophysics Data System (ADS)

Kusnendar, J.; Prabawa, H. W.

2018-05-01

Increasingly human interaction with technology and the increasingly complex development of digital technology world make the theme of computer science education interesting to study. Previous studies on Computer Literacy and Competency reveal that Indonesian teachers in general have fairly high computational skill, but their skill utilization are limited to some applications. This engenders limited and minimum computer-related learning for the students. On the other hand, computer science education is considered unrelated to real-world solutions. This paper attempts to address the utilization of NCLab- Karel in shaping the computational thinking in students. This computational thinking is believed to be able to making learn students about technology. Implementation of Karel utilization provides information that Karel is able to increase student interest in studying computational material, especially algorithm. Observations made during the learning process also indicate the growth and development of computing mindset in students.

Sophisticated epistemologies of physics versus high-stakes tests: How do elite high school students respond to competing influences about how to learn physics?

NASA Astrophysics Data System (ADS)

Yerdelen-Damar, Sevda; Elby, Andrew

2016-06-01

This study investigates how elite Turkish high school physics students claim to approach learning physics when they are simultaneously (i) engaged in a curriculum that led to significant gains in their epistemological sophistication and (ii) subject to a high-stakes college entrance exam. Students reported taking surface (rote) approaches to learning physics, largely driven by college entrance exam preparation and therefore focused on algorithmic problem solving at the expense of exploring concepts and real-life examples more deeply. By contrast, in recommending study strategies to "Arzu," a hypothetical student who doesn't need to take a college entrance exam and just wants to understand physics deeply, the students focused more on linking concepts and real-life examples and on making sense of the formulas and concepts—deep approaches to learning that reflect somewhat sophisticated epistemologies. These results illustrate how students can epistemically compartmentalize, consciously taking different epistemic stances—different views of what counts as knowing and learning—in different contexts even within the same discipline.

Learning Building Layouts with Non-geometric Visual Information: The Effects of Visual Impairment and Age

PubMed Central

Kalia, Amy A.; Legge, Gordon E.; Giudice, Nicholas A.

2009-01-01

Previous studies suggest that humans rely on geometric visual information (hallway structure) rather than non-geometric visual information (e.g., doors, signs and lighting) for acquiring cognitive maps of novel indoor layouts. This study asked whether visual impairment and age affect reliance on non-geometric visual information for layout learning. We tested three groups of participants—younger (< 50 years) normally sighted, older (50–70 years) normally sighted, and low vision (people with heterogeneous forms of visual impairment ranging in age from 18–67). Participants learned target locations in building layouts using four presentation modes: a desktop virtual environment (VE) displaying only geometric cues (Sparse VE), a VE displaying both geometric and non-geometric cues (Photorealistic VE), a Map, and a Real building. Layout knowledge was assessed by map drawing and by asking participants to walk to specified targets in the real space. Results indicate that low-vision and older normally-sighted participants relied on additional non-geometric information to accurately learn layouts. In conclusion, visual impairment and age may result in reduced perceptual and/or memory processing that makes it difficult to learn layouts without non-geometric visual information. PMID:19189732

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm.

PubMed

Kamath, Uday; Domeniconi, Carlotta; De Jong, Kenneth

2018-01-01

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing

NASA Astrophysics Data System (ADS)

Ribera, Javier; Tahboub, Khalid; Delp, Edward J.

2015-03-01

Video surveillance systems are widely deployed for public safety. Real-time monitoring and alerting are some of the key requirements for building an intelligent video surveillance system. Real-life settings introduce many challenges that can impact the performance of real-time video analytics. Video analytics are desired to be resilient to adverse and changing scenarios. In this paper we present various approaches to characterize the uncertainty of a classifier and incorporate crowdsourcing at the times when the method is uncertain about making a particular decision. Incorporating crowdsourcing when a real-time video analytic method is uncertain about making a particular decision is known as online active learning from crowds. We evaluate our proposed approach by testing a method we developed previously for crowd flow estimation. We present three different approaches to characterize the uncertainty of the classifier in the automatic crowd flow estimation method and test them by introducing video quality degradations. Criteria to aggregate crowdsourcing results are also proposed and evaluated. An experimental evaluation is conducted using a publicly available dataset.

Post learning sleep improves cognitive-emotional decision-making: evidence for a 'deck B sleep effect' in the Iowa Gambling Task.

PubMed

Seeley, Corrine J; Beninger, Richard J; Smith, Carlyle T

2014-01-01

The Iowa Gambling Task (IGT) is widely used to assess real life decision-making impairment in a wide variety of clinical populations. Our study evaluated how IGT learning occurs across two sessions, and whether a period of intervening sleep between sessions can enhance learning. Furthermore, we investigate whether pre-sleep learning is necessary for this improvement. A 200-trial version of the IGT was administered at two sessions separated by wake, sleep or sleep and wake (time-of-day control). Participants were categorized as learners and non-learners based on initial performance in session one. In session one, participants initially preferred the high-frequency reward decks B and D, however, a subset of learners decreased choice from negative expected value 'bad' deck B and increased choices towards with a positive expected value 'good' decks (decks C and D). The learners who had a period of sleep (sleep and sleep/wake control conditions) between sessions showed significantly larger reduction in choices from deck B and increase in choices from good decks compared to learners that had intervening wake. Our results are the first to show that post-learning sleep can improve performance on a complex decision-making task such as the IGT. These results provide new insights into IGT learning and have important implications for understanding the neural mechanisms of "sleeping on" a decision.

Children's Learning from Touch Screens: A Dual Representation Perspective.

PubMed

Sheehan, Kelly J; Uttal, David H

2016-01-01

Parents and educators often expect that children will learn from touch screen devices, such as during joint e-book reading. Therefore an essential question is whether young children understand that the touch screen can be a symbolic medium - that entities represented on the touch screen can refer to entities in the real world. Research on symbolic development suggests that symbolic understanding requires that children develop dual representational abilities, meaning children need to appreciate that a symbol is an object in itself (i.e., picture of a dog) while also being a representation of something else (i.e., the real dog). Drawing on classic research on symbols and new research on children's learning from touch screens, we offer the perspective that children's ability to learn from the touch screen as a symbolic medium depends on the effect of interactivity on children's developing dual representational abilities. Although previous research on dual representation suggests the interactive nature of the touch screen might make it difficult for young children to use as a symbolic medium, the unique interactive affordances may help alleviate this difficulty. More research needs to investigate how the interactivity of the touch screen affects children's ability to connect the symbols on the screen to the real world. Given the interactive nature of the touch screen, researchers and educators should consider both the affordances of the touch screen as well as young children's cognitive abilities when assessing whether young children can learn from it as a symbolic medium.

History matching through dynamic decision-making

PubMed Central

Maschio, Célio; Santos, Antonio Alberto; Schiozer, Denis; Rocha, Anderson

2017-01-01

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark. PMID:28582413

A new supervised learning algorithm for spiking neurons.

PubMed

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

Infant Statistical Learning

PubMed Central

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

Evaluating the co-production of a near real time Earthquake Aftershock forecasting tool for humanitarian risk assessment and emergency planning

NASA Astrophysics Data System (ADS)

Quinn, Keira; Hope, Max; McCloskey, John; NicBhloscaidh, Mairead; Jimenez, Abigail; Dunlop, Paul

2015-04-01

Concern Worldwide and the University of Ulster Geophysics Research Group are engaged in a project to co-produce a suite of software and mapping tools to assess aftershock hazard in near real-time during the emergency response phase of earthquake disaster, and inform humanitarian emergency planning and response activities. This paper uses a social learning approach to evaluate this co-production process. Following Wenger (1999) we differentiate between the earthquake science and humanitarian communities of practice (CoP) along three dimensions: enterprise (the purpose of CoPs and the problems participants are working to address), repertoire (knowledge, skills, language), and identity (values and boundaries). We examine the effectiveness of learning between CoP, focusing on boundary work and objects, and various organisational structures and aspects of the wider political economy of learning that enable and hinder the co-production process. We conclude by identifying a number of ways to more effectively integrate earthquake science into humanitarian decision-making, policy development and programme design.

Multi-Perspective Indexing of Diverse Spatial Characteristics of an Outdoor Field toward Redesigning of Real-World Learning

ERIC Educational Resources Information Center

Okada, Masaya; Tada, Masahiro

2014-01-01

Real-world learning is important because it encourages learners to obtain knowledge through various experiences. To design effective real-world learning, it is necessary to analyze the diverse learning activities that occur in real-world learning and to develop effective strategies for learning support. By inventing the technologies of multimodal…

Evaluating the use of augmented reality to support undergraduate student learning in geomorphology

NASA Astrophysics Data System (ADS)

Ockelford, A.; Bullard, J. E.; Burton, E.; Hackney, C. R.

2016-12-01

Augmented Reality (AR) supports the understanding of complex phenomena by providing unique visual and interactive experiences that combine real and virtual information and help communicate abstract problems to learners. With AR, designers can superimpose virtual graphics over real objects, allowing users to interact with digital content through physical manipulation. One of the most significant pedagogic features of AR is that it provides an essentially student-centred and flexible space in which students can learn. By actively engaging participants using a design-thinking approach, this technology has the potential to provide a more productive and engaging learning environment than real or virtual learning environments alone. AR is increasingly being used in support of undergraduate learning and public engagement activities across engineering, medical and humanities disciplines but it is not widely used across the geosciences disciplines despite the obvious applicability. This paper presents preliminary results from a multi-institutional project which seeks to evaluate the benefits and challenges of using an augmented reality sand box to support undergraduate learning in geomorphology. The sandbox enables users to create and visualise topography. As the sand is sculpted, contours are projected onto the miniature landscape. By hovering a hand over the box, users can make it `rain' over the landscape and the water `flows' down in to rivers and valleys. At undergraduate level, the sand-box is an ideal focus for problem-solving exercises, for example exploring how geomorphology controls hydrological processes, how such processes can be altered and the subsequent impacts of the changes for environmental risk. It is particularly valuable for students who favour a visual or kinesthetic learning style. Results presented in this paper discuss how the sandbox provides a complex interactive environment that encourages communication, collaboration and co-design.

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

PubMed

Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

2013-12-01

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Technology in postgraduate medical education: a dynamic influence on learning?

PubMed

Bullock, Alison; Webb, Katie

2015-11-01

The influence of technology in medical workplace learning is explored by focusing on three uses: m-learning (notably apps), simulation and social media. Smartphones with point-of-care tools (such as textbooks, drug guides and medical calculators) can support workplace learning and doctors' decision-making. Simulations can help develop technical skills and team interactions, and 'in situ' simulations improve the match between the virtual and the real. Social media (wikis, blogs, networking, YouTube) heralds a more participatory and collaborative approach to knowledge development. These uses of technology are related to Kolb's learning cycle and Eraut's intentions of informal learning. Contentions and controversies with these technologies exist. There is a problem with the terminology commonly adopted to describe the use of technology to enhance learning. Using learning technology in the workplace changes the interaction with others and raises issues of professionalism and etiquette. Lack of regulation makes assessment of app quality a challenge. Distraction and dependency are charges levelled at smartphone use in the workplace and these need further research. Unless addressed, these and other challenges will impede the benefits that technology may bring to postgraduate medical education. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Designing learning apparatus to promote twelfth grade students’ understanding of digital technology concept: A preliminary studies

NASA Astrophysics Data System (ADS)

Marlius; Kaniawati, I.; Feranie, S.

2018-05-01

A preliminary learning design using relay to promote twelfth grade student’s understanding of logic gates concept is implemented to see how well it’s to adopted by six high school students, three male students and three female students of twelfth grade. This learning design is considered for next learning of digital technology concept i.e. data digital transmition and analog. This work is a preliminary study to design the learning for large class. So far just a few researches designing learning design related to digital technology with relay. It may due to this concept inserted in Indonesian twelfth grade curriculum recently. This analysis is focus on student difficulties trough video analysis to learn the concept. Based on our analysis, the recommended thing for redesigning learning is: students understand first about symbols and electrical circuits; the Student Worksheet is made in more detail on the assembly steps to the project board; mark with symbols at points in certain places in the circuit for easy assembly; assembly using relays by students is enough until is the NOT’s logic gates and the others that have been assembled so that effective time. The design of learning using relays can make the relay a liaison between the abstract on the digital with the real thing of it, especially in the circuit of symbols and real circuits. Besides it is expected to also enrich the ability of teachers in classroom learning about digital technology.

The Science of Cycling

ERIC Educational Resources Information Center

Crompton, Zoe; Daniels, Shelley

2014-01-01

Children are engaged by finding out about science in the real world (Harlen, 2010). Many children will be cyclists or will have seen or heard about the success of British cyclists in the Olympics and the Tour de France. This makes cycling a good hook to draw children into learning science. It is also a good cross-curricular topic, with strong…

Developing Computational Methods to Measure and Track Learners' Spatial Reasoning in an Open-Ended Simulation

ERIC Educational Resources Information Center

Mallavarapu, Aditi; Lyons, Leilah; Shelley, Tia; Minor, Emily; Slattery, Brian; Zellner, Moria

2015-01-01

Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the…

Confronting the Nation's Urban Crisis: From Watts (1965) to South Central Los Angeles (1992).

ERIC Educational Resources Information Center

Peterson, George E.; And Others

What has been learned about making cities better since civil disturbances first arose in American cities is summarized, and guidelines are offered for constructing an urban agenda for improvement. In some respects the country has made real progress since the Watts riots, but in other areas, conditions are unambiguously worse, with increasing…

Who Can Be a Hero?: Helen Keller, Annie Sullivan, and Discovering Strength of Character

ERIC Educational Resources Information Center

Morin, Kathleen Dunlevy; Bernheim, Rachel Oestreicher

2005-01-01

"A Study of Heroes: Making a Difference Using Your Heart, Intellect, and Talents" is a program originally developed in diverse school communities. Students learn to distinguish between the concepts of hero and celebrity and to discover the real heroes in their own families, schools, communities, and most importantly--within themselves. This…

Alternate Reality Games as Learning Environments for Student Induction

ERIC Educational Resources Information Center

Whitton, Nicola; Jones, Rosie; Wilson, Scott; Whitton, Peter

2014-01-01

Alternate reality games (ARGs) are a relatively new form of collaborative game that make use of both the virtual and real worlds to engage players in a series of challenges within a compelling narrative. The Alternate Reality Games for Orientation, Socialisation and Induction (ARGOSI) project aimed to use this game format to provide an alternative…

Eating up Experiments: Teaching Research Methods with Classroom Simulation and "Food Detectives"

ERIC Educational Resources Information Center

Gray, Jennifer B.

2014-01-01

The subject of research methods is often unknown, foreboding, and unappealing to undergraduate communication majors. Thus, in the research methods course, two ways to overcome such issues and achieve learning are by: (1) making the unfamiliar more familiar and accessible; and (2) placing abstract knowledge in its useful real-world context. Making…

Teacher-Designed Software for Interactive Linear Equations: Concepts, Interpretive Skills, Applications & Word-Problem Solving.

ERIC Educational Resources Information Center

Lawrence, Virginia

No longer just a user of commercial software, the 21st century teacher is a designer of interactive software based on theories of learning. This software, a comprehensive study of straightline equations, enhances conceptual understanding, sketching, graphic interpretive and word problem solving skills as well as making connections to real-life and…

The Effect of Authentic Problem-Based Vocabulary Tasks on Vocabulary Learning of EFL Learners

ERIC Educational Resources Information Center

Mohammadi, Fateme Shir

2017-01-01

Language learners' cognitive engagement with the content in language classes has been advocated in the last few decades (Laufer & Hulstjin, 2001). To this end, the researcher designed authentic problem-based tasks which make use of learners' cognitive and metacognitive skills to solve real-life vocabulary tasks. Nelson vocabulary test was…

A Walk in the "Tall, Tall Grass"

ERIC Educational Resources Information Center

Kaatz, Kathryn

2008-01-01

This inquiry-based lesson was inspired by Denise Fleming's book entitled, "In the Tall, Tall Grass" (1991). The author used the book and a real study of prairie grasses to teach kindergartners how to make careful observations and record what they see. In addition, they learn how to "draw as scientists." Here the author describes her class's yearly…

Learning in the real place: medical students' learning and socialization in clerkships at one medical school.

PubMed

Han, Heeyoung; Roberts, Nicole K; Korte, Russell

2015-02-01

To understand medical students' learning experiences in clerkships: learning expectations (what they expect to learn), learning process (how they learn), and learning outcomes (what they learn). Using a longitudinal qualitative research design, the authors followed the experiences of 12 participants across their clerkship year (2011-2012) at the Southern Illinois University School of Medicine. Interview data from each participant were collected at three points (preclerkship, midclerkship, and postclerkship) and analyzed using a grounded theory approach. Additionally, the authors observed participants through a full clerkship day to augment the interviews. Before clerkships, students expected to have more hands-on experiences and become more knowledgeable by translating textbook knowledge to real patients and practicing diagnostic thinking. During clerkships, students experienced ambiguity and subjectivity of attending physicians' expectations and evaluation criteria. They perceived that impression management was important to ensure that they received learning opportunities and good evaluations. After clerkships, students perceived that their confidence increased in navigating the health care environments and interacting with patients, attendings, and residents. However, they felt that there were limited opportunities to practice diagnostic thinking. Students could not clearly discern the decision-making processes used by attending physicians. Although they saw many patients, they perceived that their learning was at the surface level. Students' experiential learning in clerkships occurred through impression management as a function of dynamic social and reciprocal relationships between students and attendings or residents. Students reported that they did not learn comprehensive clinical reasoning skills to the degree they expected in clerkships.

Conceptualisation of knowledge construction in community service-learning programmes in nursing education.

PubMed

Mthembu, Sindi Z; Mtshali, Fikile G

2013-01-01

Practices in higher education have been criticised for not developing and preparing students for the expertise required in real environments. Literature reports that educational programmes tend to favour knowledge conformation rather than knowledge construction; however, community service learning (CSL) is a powerful pedagogical strategy that encourages students to make meaningful connections between the content in the classroom and real-life experiences as manifested by the communities. Through CSL, learning is achieved by the active construction of knowledge supported by multiple perspectives within meaningful real contexts, and the social interactions amongst students are seen to play a critical role in the processes of learning and cognition. This article reflects facilitators’ perspective of the knowledge construction process as used with students doing community service learning in basic nursing programmes. The aim of this article was to conceptualise the phenomenon of knowledge construction and thereby provide educators with a shared meaning and common understanding, and to analyse the interaction strategies utilised by nurse educators in the process of knowledge construction in community service-learning programmes in basic nursing education. A qualitative research approach based on a grounded theory research design was used in this article. Two nursing education institutions were purposively selected. Structured interviews were conducted with 16 participants. The results revealed that the knowledge construction in community service-learning programmes is conceptualised as having specific determinants, including the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue, interactive learning in communities of learners, active learning, continuous reflection as well as collaborative and inquiry-based learning. Upon completion of an experience, students create and test generated knowledge in different contextual health settings. It was concluded that knowledge is constructed by students as a result of their interaction with the communities in their socio-cultural context and is mediated by their prior concrete experiences. The implication of this is that students construct knowledge that can be applied in their future work places.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

Making Teamwork Work: Team Knowledge for Team Effectiveness.

PubMed

Guchait, Priyanko; Lei, Puiwa; Tews, Michael J

2016-01-01

This study examined the impact of two types of team knowledge on team effectiveness. The study assessed the impact of taskwork knowledge and teamwork knowledge on team satisfaction and performance. A longitudinal study was conducted with 27 service-management teams involving 178 students in a real-life restaurant setting. Teamwork knowledge was found to impact both team outcomes. Furthermore, team learning behavior was found to mediate the relationships between teamwork knowledge and team outcomes. Educators and managers should therefore ensure these types of knowledge are developed in teams along with learning behavior for maximum effectiveness.

Energy Project professional development: Promoting positive attitudes about science among K-12 teachers

NASA Astrophysics Data System (ADS)

Robertson, Amy D.; Daane, Abigail R.

2017-12-01

Promoting positive attitudes about science among teachers has important implications for teachers' classroom practice and for their relationship to science as a discipline. In this paper, we report positive shifts in teachers' attitudes about science, as measured by the Colorado Learning Attitudes about Science (CLASS) survey, over the course of their participation in a professional development course that emphasized the flexible use of energy representations to understand real world scenarios. Our work contributes to the larger effort to make the case that professional development matters for teacher learning and attitudes.

Expert system and process optimization techniques for real-time monitoring and control of plasma processes

NASA Astrophysics Data System (ADS)

Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.

1991-03-01

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).

Can you go the distance? Attending the virtual classroom.

PubMed

Bigony, Lorraine

2010-01-01

Distance learning via the World Wide Web offers convenience and flexibility. Online education connects nurses geographically in a manner that the traditional face-to-face learning environment lacks. Delivered in both a synchronous (real time interaction) or asynchronous (delayed interaction) format, distance programs continue to provide nurses with choice, especially in the pursuit of advanced degrees. This article explores the pros and cons of distance education, in addition to the most popular platform used in distance learning today, the Blackboard Academic Suite. Characteristics of the potential enrollee to ensure a successful distance education experience are also discussed. Distance nursing programs are here to stay. Although rigorous, the ease of accessibility makes distance learning a viable alternative for busy nurses.

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

PubMed

Ribeiro, C H; Hemerly, E M

2000-02-01

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.

Teaching Basic Field Skills Using Screen-Based Virtual Reality Landscapes

NASA Astrophysics Data System (ADS)

Houghton, J.; Robinson, A.; Gordon, C.; Lloyd, G. E. E.; Morgan, D. J.

2016-12-01

We are using screen-based virtual reality landscapes, created using the Unity 3D game engine, to augment the training geoscience students receive in preparing for fieldwork. Students explore these landscapes as they would real ones, interacting with virtual outcrops to collect data, determine location, and map the geology. Skills for conducting field geological surveys - collecting, plotting and interpreting data; time management and decision making - are introduced interactively and intuitively. As with real landscapes, the virtual landscapes are open-ended terrains with embedded data. This means the game does not structure student interaction with the information as it is through experience the student learns the best methods to work successfully and efficiently. These virtual landscapes are not replacements for geological fieldwork rather virtual spaces between classroom and field in which to train and reinforcement essential skills. Importantly, these virtual landscapes offer accessible parallel provision for students unable to visit, or fully partake in visiting, the field. The project has received positive feedback from both staff and students. Results show students find it easier to focus on learning these basic field skills in a classroom, rather than field setting, and make the same mistakes as when learning in the field, validating the realistic nature of the virtual experience and providing opportunity to learn from these mistakes. The approach also saves time, and therefore resources, in the field as basic skills are already embedded. 70% of students report increased confidence with how to map boundaries and 80% have found the virtual training a useful experience. We are also developing landscapes based on real places with 3D photogrammetric outcrops, and a virtual urban landscape in which Engineering Geology students can conduct a site investigation. This project is a collaboration between the University of Leeds and Leeds College of Art, UK, and all our virtual landscapes are freely available online at www.see.leeds.ac.uk/virtual-landscapes/.

Using real objects to teach about climate change: an ethnographic perspective

NASA Astrophysics Data System (ADS)

Conner, L.; Perin, S.; Coats, V.; Sturm, M.

2017-12-01

Informal educators frequently use real objects to connect visitors with science content that can otherwise seem abstract. Our NSF-funded project, "Hot Times in Cold Places," leverages this premise to teach about climate change through real objects associated with the nation's only permafrost tunnel, located in Fox, Alaska. We posit that touching real ice, holding Pleistocene bones, and seeing ice wedges in context allows learners to understand climate change in a direct and visceral manner. We are conducting ethnographic research to understand visitor experience at both the tunnel itself and at a permafrost museum exhibit that we are creating as part of the project. Research questions include: 1) What is the nature of visitor talk with respect to explanations about permafrost, tipping points, climate change, and geological time? 2) How do attributes of "realness" (scale, resolution, uniqueness, history and adherence to an original) affect visitor's experience of objects, as perceived through the senses and emotions? We use naturalistic observation, interviews, and videotaping to answer these questions. Analysis focuses on child-to-child talk, reciprocal talk between educator and child, and reciprocal talk between parent and child. Our results elucidate the value of real, vs. replicated and virtual objects, in informal learning, especially in the context of climate change education. An understanding of these factors can help informal learning educators make informed choices about program and exhibit design.

Smarter Instruments, Smarter Archives: Machine Learning for Tactical Science

NASA Astrophysics Data System (ADS)

Thompson, D. R.; Kiran, R.; Allwood, A.; Altinok, A.; Estlin, T.; Flannery, D.

2014-12-01

There has been a growing interest by Earth and Planetary Sciences in machine learning, visualization and cyberinfrastructure to interpret ever-increasing volumes of instrument data. Such tools are commonly used to analyze archival datasets, but they can also play a valuable real-time role during missions. Here we discuss ways that machine learning can benefit tactical science decisions during Earth and Planetary Exploration. Machine learning's potential begins at the instrument itself. Smart instruments endowed with pattern recognition can immediately recognize science features of interest. This allows robotic explorers to optimize their limited communications bandwidth, triaging science products and prioritizing the most relevant data. Smart instruments can also target their data collection on the fly, using principles of experimental design to reduce redundancy and generally improve sampling efficiency for time-limited operations. Moreover, smart instruments can respond immediately to transient or unexpected phenomena. Examples include detections of cometary plumes, terrestrial floods, or volcanism. We show recent examples of smart instruments from 2014 tests including: aircraft and spacecraft remote sensing instruments that recognize cloud contamination, field tests of a "smart camera" for robotic surface geology, and adaptive data collection by X-Ray fluorescence spectrometers. Machine learning can also assist human operators when tactical decision making is required. Terrestrial scenarios include airborne remote sensing, where the decision to re-fly a transect must be made immediately. Planetary scenarios include deep space encounters or planetary surface exploration, where the number of command cycles is limited and operators make rapid daily decisions about where next to collect measurements. Visualization and modeling can reveal trends, clusters, and outliers in new data. This can help operators recognize instrument artifacts or spot anomalies in real time. We show recent examples from science data pipelines deployed onboard aircraft as well as tactical visualizations for non-image instrument data.

Overlay improvements using a real time machine learning algorithm

NASA Astrophysics Data System (ADS)

Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

2014-04-01

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

Building a Conversation: Preservice Teachers' Use of Video as Data for Making Evidence Based Arguments About Practice

ERIC Educational Resources Information Center

McDonald, Scott

2010-01-01

For decades teacher educators have used video to support developing preservice teachers, but new technologies open the possibility of a much more dynamic and real-time use for video of teaching. This article describes an initial attempt to leverage these technologies to develop a teacher learning community focused on evidence-based arguments about…

Using DVI To Teach Physics: Making the Abstract More Concrete.

ERIC Educational Resources Information Center

Knupfer, Nancy Nelson; Zollman, Dean

The ways in which Digital Video Interactive (DVI), a new video technology, can help students learn concepts of physics were studied in a project that included software design and production as well as formative and summative evaluation. DVI provides real-time motion, with the full-motion image contained to a window on part of the screen so that…

Some Decks Are "Better" than Others: The Effect of Reinforcer Type and Task Instructions on Learning in the Iowa Gambling Task

ERIC Educational Resources Information Center

Fernie, Gordon; Tunney, Richard J.

2006-01-01

The Iowa Gambling Task (Bechara, Damasio, Damasio, & Anderson, 1994) has become widely used as a laboratory test of "real-life" decision-making. However, aspects of its administration that have been varied by researchers may differentially affect performance and the conclusions researchers can draw. Some researchers have used facsimile money…

Separate Tracks or Real Synergy? Achieving a Closer Relationship between Education and SD, Post-2015

ERIC Educational Resources Information Center

Sterling, Stephen

2014-01-01

This article is based upon a longer concept paper commissioned by UNESCO in preparation for the World Conference on ESD, and entitled "Winning the Future We Want--the pivotal role of education and learning". Neither this article nor the original paper necessarily represents UNESCO'S views. The brief for this paper was to make a…

Connecting with What Is Out There!: Using Twitter in the Large Lecture

ERIC Educational Resources Information Center

Tyma, Adam

2011-01-01

With the desire for more and more campuses to develop their online or hybrid curricula, expanding pedagogy to include real-time technology in the classroom not only makes sense but can also be done with little or no additional technological investment. The use of technology in the classroom to aid in student learning, help streamline grading,…

The Role of Microcomputer-Based Laboratories in Learning To Make Graphs of Distance and Velocity.

ERIC Educational Resources Information Center

Brasell, Heather

Two questions about the effects of microcomputer-based laboratory (MBL) activities on graphing skills were addressed in this study: (1) the extent to which activities help students link their concrete experiences with motion with graphic representations of these experiences; and (2) the degree of importance of the real-time aspect of the MBL in…

OERs in Context--Case Study of Innovation and Sustainability of Educational Practices at the University of Mauritius

ERIC Educational Resources Information Center

Issack, Santally Mohammad

2011-01-01

Over the recent years, there has been a growing interest in Open Educational Resources (OER). A similar trend was observed about a decade ago in the concept of Learning Objects, which inevitably faded without really making an impact in real-world educational contexts. A number of repositories were created that contain thousands of learning…

Real Life Narratives Enhance Learning about the "Art and Science" of Midwifery Practice

ERIC Educational Resources Information Center

Gilkison, Andrea; Giddings, Lynne; Smythe, Liz

2016-01-01

Health professional educators have long grappled with how to teach the more elusive art of practice alongside the science (a term that encompasses the sort of professional knowledge that can be directly passed on). A competent practitioner is one who knows when, how and for whom to apply knowledge and skills, thereby making the links between…

No Second Chance to Make a First Impression: The "Thin-Slice" Effect on Instructor Ratings and Learning Outcomes in Higher Education

ERIC Educational Resources Information Center

Samudra, Preeti G.; Min, Inah; Cortina, Kai S.; Miller, Kevin F.

2016-01-01

Prior research has found strong and persistent effects of instructor first impressions on student evaluations. Because these studies look at real classroom lessons, this finding fits two different interpretations: (1) first impressions may color student experience of instruction regardless of lesson quality, or (2) first impressions may provide…

A Transcultural Wisdom Bank in the Classroom: Making Cultural Diversity a Key Resource in Teaching and Learning

ERIC Educational Resources Information Center

Chang, Jui-shan

2006-01-01

This article presents an account of how the author has made cultural diversity a powerful pedagogical resource for teaching rather than a burden. It is suggested that such an approach can deliver real educational benefits, rather than simply being a protocol to meet the requirements of "political correctness" or commercial imperatives. The issues…

Information and Communication Technology in Foreign Language Teaching: Leveraging the Internet to Make Language Learning Real

ERIC Educational Resources Information Center

Watson, Etáin

2013-01-01

The internet is the largest communications network in the world. It has become the virtual backbone of all communication. Therefore, it seems natural to leverage it as a major tool in any education involving communication skills, especially language skills. This chapter outlines a practitioner's experience on how this can be done in a foreign…

Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

PubMed Central

Kaggal, Vinod C.; Elayavilli, Ravikumar Komandur; Mehrabi, Saeed; Pankratz, Joshua J.; Sohn, Sunghwan; Wang, Yanshan; Li, Dingcheng; Rastegar, Majid Mojarad; Murphy, Sean P.; Ross, Jason L.; Chaudhry, Rajeev; Buntrock, James D.; Liu, Hongfang

2016-01-01

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future. PMID:27385912

Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

PubMed

Kaggal, Vinod C; Elayavilli, Ravikumar Komandur; Mehrabi, Saeed; Pankratz, Joshua J; Sohn, Sunghwan; Wang, Yanshan; Li, Dingcheng; Rastegar, Majid Mojarad; Murphy, Sean P; Ross, Jason L; Chaudhry, Rajeev; Buntrock, James D; Liu, Hongfang

2016-01-01

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

The Benefits of a Real-Time Web-Based Response System for Enhancing Engaged Learning in Classrooms and Public Science Events.

PubMed

Sarvary, Mark A; Gifford, Kathleen M

2017-01-01

Large introduction to neuroscience classes and small science cafés have the same goal: bridging the gap between the presenter and the audience to convey the information while being engaging. Early classroom response systems became the cornerstone of flipped and engaged learning. These "clickers" helped turn lectures into dialogues, allowing the presenter to become a facilitator rather than a "sage on the stage." Rapid technological developments, especially the increase of computing power opened up new opportunities, moving these systems from a clicker device onto cellphones and laptops. This allowed students to use their own devices, and instructors to use new question types, such as clicking on a picture or ranking concepts. A variety of question types makes the learning environment more engaging, allows better examples for creative and critical thinking, and facilitates assessment. Online access makes these response systems scalable, bringing the strength of formative assessments and surveys to public science communication events, neuroscience journal clubs and distance learning. In addition to the new opportunities, online polling systems also create new challenges for the presenters. For example, allowing mobile devices in the classroom can be distracting. Here, a web-based, real-time response system called Poll Everywhere was compared to iClickers, highlighting the benefits and the pitfalls of both systems. In conclusion, the authors observe that the benefits of web-based response systems outweigh the challenges, and this form of digital pedagogy can help create a rich dialogue with the audience in large classrooms as well as in public science events.

Learning classification with auxiliary probabilistic information

PubMed Central

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2012-01-01

Finding ways of incorporating auxiliary information or auxiliary data into the learning process has been the topic of active data mining and machine learning research in recent years. In this work we study and develop a new framework for classification learning problem in which, in addition to class labels, the learner is provided with an auxiliary (probabilistic) information that reflects how strong the expert feels about the class label. This approach can be extremely useful for many practical classification tasks that rely on subjective label assessment and where the cost of acquiring additional auxiliary information is negligible when compared to the cost of the example analysis and labelling. We develop classification algorithms capable of using the auxiliary information to make the learning process more efficient in terms of the sample complexity. We demonstrate the benefit of the approach on a number of synthetic and real world data sets by comparing it to the learning with class labels only. PMID:25309141

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Simple webs of natural environment theme as a result of sharing in science teacher training

NASA Astrophysics Data System (ADS)

Tapilouw, M. C.; Firman, H.; Redjeki, S.; Chandra, D. T.

2018-03-01

Thematic learning is one type of integrated science (Biology, Physics, Chemistry and Earth Science) in Science Education. This study is concerning about simple webs of natural environment theme in science learning, as one of training material in science teacher training program. Making simple web is a goal of first step in teacher training program. Every group explain their web illustration to other group. Twenty Junior High School science teacher above one education foundation participate in science teacher training program. In order to gather simple webs, sharing method was used in this first step of science teacher training. The result of this study is five different simple web of natural environment themes. These webs represent science learning in class VII/Semester I, class VII/Semester II, Class VIII, Class IX/Semester I, Class IX/Semester II based on basic competency in National Curriculum 2013. Each group discussed web of natural environment theme based on their learning experience in real class which basic competency and subject matters are linked with natural environment theme. As a conclusion, simple webs are potential to develop in the next step of science teacher training program and to be implemented in real class.

Virtual Learning is the Real Thing

ERIC Educational Resources Information Center

Tekaat-Davey, Diana

2006-01-01

In this article, the author discusses how in California, high school students are learning about real business through a virtual world. Virtual enterprise programs are helping students learn about the real business world. Learning about the business world has become about as real as it can in California high schools. Enrollment in the programs…

The impact of a museum travelling exhibition on middle school teachers and students from rural, low-income homes

NASA Astrophysics Data System (ADS)

Badger, James; Harker, Richard J. W.

2016-06-01

Schools may be places of learning, but a great deal of learning occurs outside of school. A growing body of literature investigates how school field trips allow rural students to make real-life connections with their school curriculum. This paper contributes to that area of research by describing how students from five middle schools in the United States responded to a travelling museum exhibition hosted at a non-museum site. The authors explore the impact of the exhibition on students from poor, rural backgrounds, discussing how it helped them to engage with themes such as freedom of expression, democracy, citizenship and Holocaust education. The results show that, by connecting curricular content with real-life situations, field trips such as this have the potential to change not only students' understanding of the curriculum, but also their teachers' estimation of their abilities.

The Flipped Classroom: An active teaching and learning strategy for making the sessions more interactive and challenging.

PubMed

Sultan, Amber Shamim

2018-04-01

Flipping the classroom is a pedagogical model that employs easy to use, readily accessible technology based resources such as video lectures, reading handouts, and practice problems outside the classroom, whereas interactive group-based, problem-solving activities conducted in the classroom. This strategy permits for an extended range of learning activities during the session. Using class time for active learning provides greater opportunity for mentoring and peer to peer collaboration. Instead of spending too much time on delivering lectures, class time can best be utilized by interacting with students, discussing their concerns related to the particular topic to be taught, providing real life examples relevant to the course content, challenging students to think in a broader aspect about complex process and encouraging different team based learning activities.

Hypothetical biotechnology companies: A role-playing student centered activity for undergraduate science students.

PubMed

Chuck, Jo-Anne

2011-01-01

Science students leaving undergraduate programs are entering the biotechnology industry where they are presented with issues which require integration of science content. Students find this difficult as through-out their studies, most content is limited to a single subdiscipline (e.g., biochemistry, immunology). In addition, students need knowledge of the ethical, economic, and legal frame work in which the industry operates. This article presents an approach to deliver these outcomes in a collaborative and active learning modality which promotes deep learning. In the model, groups of final year undergraduate students form hypothetical biotechnology companies and identify real issues of interest to industry, make integrative team decisions, use professional level technology, and develop appropriate communication skills. The final successful teaching paradigm was based on self reflection, observation, and student feedback to ensure appropriate attainment of content, group work skills and increased confidence in professional decision-making. It is these outcomes which will facilitate life long learning skills, a major outcome applicable for all tertiary education. Copyright © 2011 Wiley Periodicals, Inc.

Science for Diplomacy, Diplomacy for Science

NASA Astrophysics Data System (ADS)

Colglazier, E. Wiliam

2015-04-01

I was a strong proponent of ``science diplomacy'' when I became Science and Technology Adviser to the Secretary of State in 2011. I thought I knew a lot about the subject after being engaged for four decades on international S&T policy issues and having had distinguished scientists as mentors who spent much of their time using science as a tool for building better relations between countries and working to make the world more peaceful, prosperous, and secure. I learned a lot from my three years inside the State Department, including great appreciation and respect for the real diplomats who work to defuse conflicts and avoid wars. But I also learned a lot about science diplomacy, both using science to advance diplomacy and diplomacy to advance science. My talk will focus on the five big things that I learned, and from that the one thing where I am focusing my energies to try to make a difference now that I am a private citizen again.

Project Management in Real Time: A Service-Learning Project

ERIC Educational Resources Information Center

Larson, Erik; Drexler, John A., Jr.

2010-01-01

This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

NASA Astrophysics Data System (ADS)

Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

2016-10-01

The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists and educators, and offer "just-in-time" opportunities to support constituents exploring emerging NASA STEM education, from diverse educators to the curious learner of any age.

Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.

PubMed

Badal-Valero, Elena; Alvarez-Jareño, José A; Pavía, Jose M

2018-01-01

This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. We combine Benford's Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the context of a real Spanish court case. After mapping each supplier's set of accounting data into a 21-dimensional space using Benford's Law and applying machine learning algorithms, additional companies that could merit further scrutiny are flagged up. A new tool to detect money laundering criminals is proposed in this paper. The tool is tested in the context of a real case. Copyright © 2017 Elsevier B.V. All rights reserved.

Making Quantitative Genetics Relevant: Effectiveness of a Laboratory Investigation that Links Scientific Research, Commercial Applications, and Legal Issues

ERIC Educational Resources Information Center

Rutledge, Michael L.; Mathis, Philip M.; Seipelt, Rebecca L.

2005-01-01

As students apply their knowledge of scientific concepts and of science as a method of inquiry, learning becomes relevant. This laboratory exercise is designed to foster students' understanding of the genetics of quantitative traits and of the nature of science as a method of inquiry by engaging them in a real-world business scenario. During the…

How to Survive and Prosper in the Real World after Graduation: An Unconventional Approach to Achieving Your Success.

ERIC Educational Resources Information Center

Connors, Shawn M.

Unconventional advice to help college graduates be successful is offered. Ways to find sources of money (i.e., jobs) are described, including: using services or products offered through the mail, making phone calls to build a network of contacts in a particular industry, attending seminars to further one's knowledge of a field, learning about…

Making Maths Useful: How Two Teachers Prepare Adult Learners to Apply Their Numeracy Skills in Their Lives outside the Classroom

ERIC Educational Resources Information Center

Brooks, Carolyn

2015-01-01

This pilot case study of two teachers and their learner groups from Adult and Community settings, investigates how numeracy teachers, working with adult learners in discrete numeracy classes, motivate and enable learners to build on their informal skills and apply new learning to their own real-life contexts. Teachers used a range of abstract and…

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

NASA Technical Reports Server (NTRS)

Wehner, Walter S.

2014-01-01

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

[Critical incidents].

PubMed

Scheidegger, D

2005-03-01

In medicine real severe mishaps are rare. On the other hand critical incidents are frequent. Anonymous critical incident reporting systems allow us to learn from these mishaps. This learning process will make our daily clinical work safer Unfortunately, before these systems can be used efficiently our professional culture has to be changed. Everyone in medicine has to admit that errors do occur to see the need for an open discussion. If we really want to learn from errors, we cannot punish the individual, who reported his or her mistake. The interest is primarily in what has happened and why it has happened and not who has committed this mistake. The cause for critical incidents in medicine is in over 80% the human factor Poor communication, work under enormous stress, conflicts and hierarchies are the main cause. This has been known for many years, therefore have already 15 years ago high-tech industries, like e.g. aviation, started to invest in special courses on team training. Medicine is a typical profession were until now only the individual performance decided about the professional career Communication, conflict management, stress management, decision making, risk management, team and team resource management were subjects that have never been taught during our preor postgraduate education. These points are the most important ones for an optimal teamwork. A multimodular course designed together with Swissair (Human Aspect Development medical, HADmedical) helps to cover, as in aviation, the soft factor and behavioural education in medicine and to prepare professionals in health care to work as a real team.

Context generalization in Drosophila visual learning requires the mushroom bodies

NASA Astrophysics Data System (ADS)

Liu, Li; Wolf, Reinhard; Ernst, Roman; Heisenberg, Martin

1999-08-01

The world is permanently changing. Laboratory experiments on learning and memory normally minimize this feature of reality, keeping all conditions except the conditioned and unconditioned stimuli as constant as possible. In the real world, however, animals need to extract from the universe of sensory signals the actual predictors of salient events by separating them from non-predictive stimuli (context). In principle, this can be achieved ifonly those sensory inputs that resemble the reinforcer in theirtemporal structure are taken as predictors. Here we study visual learning in the fly Drosophila melanogaster, using a flight simulator,, and show that memory retrieval is, indeed, partially context-independent. Moreover, we show that the mushroom bodies, which are required for olfactory but not visual or tactile learning, effectively support context generalization. In visual learning in Drosophila, it appears that a facilitating effect of context cues for memory retrieval is the default state, whereas making recall context-independent requires additional processing.

Making mathematics and science integration happen: key aspects of practice

NASA Astrophysics Data System (ADS)

Ríordáin, Máire Ní; Johnston, Jennifer; Walshe, Gráinne

2016-02-01

The integration of mathematics and science teaching and learning facilitates student learning, engagement, motivation, problem-solving, criticality and real-life application. However, the actual implementation of an integrative approach to the teaching and learning of both subjects at classroom level, with in-service teachers working collaboratively, at second-level education, is under-researched due to the complexities of school-based research. This study reports on a year-long case study on the implementation of an integrated unit of learning on distance, speed and time, within three second-level schools in Ireland. This study employed a qualitative approach and examined the key aspects of practice that impact on the integration of mathematics and science teaching and learning. We argue that teacher perspective, teacher knowledge of the 'other subject' and of technological pedagogical content knowledge (TPACK), and teacher collaboration and support all impact on the implementation of an integrative approach to mathematics and science education.

Learning a trajectory using adjoint functions and teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad B.; Barhen, Jacob

1992-01-01

A new methodology for faster supervised temporal learning in nonlinear neural networks is presented which builds upon the concept of adjoint operators to allow fast computation of the gradients of an error functional with respect to all parameters of the neural architecture, and exploits the concept of teacher forcing to incorporate information on the desired output into the activation dynamics. The importance of the initial or final time conditions for the adjoint equations is discussed. A new algorithm is presented in which the adjoint equations are solved simultaneously (i.e., forward in time) with the activation dynamics of the neural network. We also indicate how teacher forcing can be modulated in time as learning proceeds. The results obtained show that the learning time is reduced by one to two orders of magnitude with respect to previously published results, while trajectory tracking is significantly improved. The proposed methodology makes hardware implementation of temporal learning attractive for real-time applications.

Motor learning from virtual reality to natural environments in individuals with Duchenne muscular dystrophy.

PubMed

Quadrado, Virgínia Helena; Silva, Talita Dias da; Favero, Francis Meire; Tonks, James; Massetti, Thais; Monteiro, Carlos Bandeira de Mello

2017-11-10

To examine whether performance improvements in the virtual environment generalize to the natural environment. we had 64 individuals, 32 of which were individuals with DMD and 32 were typically developing individuals. The groups practiced two coincidence timing tasks. In the more tangible button-press task, the individuals were required to 'intercept' a falling virtual object at the moment it reached the interception point by pressing a key on the computer. In the more abstract task, they were instructed to 'intercept' the virtual object by making a hand movement in a virtual environment using a webcam. For individuals with DMD, conducting a coincidence timing task in a virtual environment facilitated transfer to the real environment. However, we emphasize that a task practiced in a virtual environment should have higher rates of difficulties than a task practiced in a real environment. IMPLICATIONS FOR REHABILITATION Virtual environments can be used to promote improved performance in ?real-world? environments. Virtual environments offer the opportunity to create paradigms similar ?real-life? tasks, however task complexity and difficulty levels can be manipulated, graded and enhanced to increase likelihood of success in transfer of learning and performance. Individuals with DMD, in particular, showed immediate performance benefits after using virtual reality.

Coming to the new D.A.R.E.: A preliminary test of the officer-taught elementary keepin' it REAL curriculum.

PubMed

Day, L Edward; Miller-Day, Michelle; Hecht, Michael L; Fehmie, Desiree

2017-11-01

The present study reports a preliminary evaluation of D.A.R.E.'s new elementary school keepin' it REAL substance abuse prevention program. Given the widespread dissemination of D.A.R.E., this evaluation, even though of short term effects, has important implications for national prevention efforts. The new prevention curriculum teaches social and emotional competencies such as decision making and resistance skills. Social and emotional competencies and other risk factors were examined among students (N=943) in 26 classrooms, 13 classrooms in the treatment condition (n=359) and 13 classrooms in the control condition (n=584) using a quasi-experimental matched group design. Pretest comparisons of treatment and control groups were completed, along with attrition analyses, and hierarchical logistic and linear regressions were computed to assess the intervention. The results revealed that the intervention produced significant effects on preventative factors such as the likelihood of resisting peer pressure, increased responsible decision making knowledge and decision-making skills, and confidence in being able to explain why they would refuse offers of cigarettes. The results of this study suggest that D.A.R.E.'s elementary keepin' it REAL program has promise as a social and emotional learning (SEL) based prevention program. Copyright © 2017 Elsevier Ltd. All rights reserved.

Virtual reality welder training

NASA Astrophysics Data System (ADS)

White, Steven A.; Reiners, Dirk; Prachyabrued, Mores; Borst, Christoph W.; Chambers, Terrence L.

2010-01-01

This document describes the Virtual Reality Simulated MIG Lab (sMIG), a system for Virtual Reality welder training. It is designed to reproduce the experience of metal inert gas (MIG) welding faithfully enough to be used as a teaching tool for beginning welding students. To make the experience as realistic as possible it employs physically accurate and tracked input devices, a real-time welding simulation, real-time sound generation and a 3D display for output. Thanks to being a fully digital system it can go beyond providing just a realistic welding experience by giving interactive and immediate feedback to the student to avoid learning wrong movements from day 1.

Home Exercise in a Social Context: Real-Time Experience Sharing Using Avatars

NASA Astrophysics Data System (ADS)

Aghajan, Yasmin; Lacroix, Joyca; Cui, Jingyu; van Halteren, Aart; Aghajan, Hamid

This paper reports on the design of a vision-based exercise monitoring system. The system aims to promote well-being by making exercise sessions enjoyable experiences, either through real-time interaction and instructions proposed to the user, or via experience sharing or group gaming with peers in a virtual community. The use of avatars is explored as means of representation of the user’s exercise movements or appearance, and the system employs user-centric approaches in visual processing, behavior modeling via history data accumulation, and user feedback to learn the preferences. A preliminary survey study has been conducted to explore the avatar preferences in two user groups.

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

NASA Astrophysics Data System (ADS)

Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

2017-10-01

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

Project-Based Learning and International Business Education

ERIC Educational Resources Information Center

Danford, Gerard L.

2006-01-01

Project-based Learning (PbL) mirrors that of real-world business situations. PbL engages students in real projects for real corporations. Furthermore, this is an effective learning methodology which can be easily incorporated into a dynamic and challenging learning context such as international business education. Engaging in student-corporate…

"It's Like 'Lord of the Rings,' Sir. but Real!": Teaching, Learning and Sharing Medieval History for All

ERIC Educational Resources Information Center

Eldridge, Chris

2016-01-01

Medieval history is on the rise. Among the many recent reforms in the history curriculum is a requirement for medieval themes at GCSE and across the country the new linear A-level offers fresh opportunities for teachers to look beyond the traditional diet of Tudors and modern history. The huge divide between us and the medieval mind can make the…

Learning physical examination skills outside timetabled training sessions: what happens and why?

PubMed

Duvivier, Robbert J; van Geel, Koos; van Dalen, Jan; Scherpbier, Albert J J A; van der Vleuten, Cees P M

2012-08-01

Lack of published studies on students' practice behaviour of physical examination skills outside timetabled training sessions inspired this study into what activities medical students undertake to improve their skills and factors influencing this. Six focus groups of a total of 52 students from Years 1-3 using a pre-established interview guide. Interviews were recorded, transcribed and analyzed using qualitative methods. The interview guide was based on questionnaire results; overall response rate for Years 1-3 was 90% (n = 875). Students report a variety of activities to improve their physical examination skills. On average, students devote 20% of self-study time to skill training with Year 1 students practising significantly more than Year 3 students. Practice patterns shift from just-in-time learning to a longitudinal selfdirected approach. Factors influencing this change are assessment methods and simulated/real patients. Learning resources used include textbooks, examination guidelines, scientific articles, the Internet, videos/DVDs and scoring forms from previous OSCEs. Practising skills on fellow students happens at university rooms or at home. Also family and friends were mentioned to help. Simulated/real patients stimulated students to practise of physical examination skills, initially causing confusion and anxiety about skill performance but leading to increased feelings of competence. Difficult or enjoyable skills stimulate students to practise. The strategies students adopt to master physical examination skills outside timetabled training sessions are self-directed. OSCE assessment does have influence, but learning takes place also when there is no upcoming assessment. Simulated and real patients provide strong incentives to work on skills. Early patient contacts make students feel more prepared for clinical practice.

Decision Making: from Neuroscience to Psychiatry

PubMed Central

Lee, Daeyeol

2013-01-01

Adaptive behaviors increase the likelihood of survival and reproduction and improve the quality of life. However, it is often difficult to identify optimal behaviors in real life due to the complexity of the decision maker’s environment and social dynamics. As a result, although many different brain areas and circuits are involved in decision making, evolutionary and learning solutions adopted by individual decision makers sometimes produce suboptimal outcomes. Although these problems are exacerbated in numerous neurological and psychiatric disorders, their underlying neurobiological causes remain incompletely understood. In this review, theoretical frameworks in economics and machine learning and their applications in recent behavioral and neurobiological studies are summarized. Examples of such applications in clinical domains are also discussed for substance abuse, Parkinson’s disease, attention-deficit/hyperactivity disorder, schizophrenia, mood disorders, and autism. Findings from these studies have begun to lay the foundations necessary to improve diagnostics and treatment for various neurological and psychiatric disorders. PMID:23622061

Examining key design decisions involved in developing a serious game for child sexual abuse prevention.

PubMed

Stieler-Hunt, Colleen; Jones, Christian M; Rolfe, Ben; Pozzebon, Kay

2014-01-01

This paper presents a case study of the key decisions made in the design of Orbit, a child sexual abuse prevention computer game targeted at school students between 8 and 10 years of age. Key decisions include providing supported delivery for the target age group, featuring adults in the program, not over-sanitizing game content, having a focus on building healthy self-concept of players, making the game engaging and relatable for all players and evaluating the program. This case study has implications for the design of Serious Games more generally, including that research should underpin game design decisions, game designers should consider ways of bridging the game to real life, the learning that arises from the game should go beyond rote-learning, designers should consider how the player can make the game-world their own and comprehensive evaluations of Serious Games should be undertaken.

Examining key design decisions involved in developing a serious game for child sexual abuse prevention

PubMed Central

Stieler-Hunt, Colleen; Jones, Christian M.; Rolfe, Ben; Pozzebon, Kay

2014-01-01

This paper presents a case study of the key decisions made in the design of Orbit, a child sexual abuse prevention computer game targeted at school students between 8 and 10 years of age. Key decisions include providing supported delivery for the target age group, featuring adults in the program, not over-sanitizing game content, having a focus on building healthy self-concept of players, making the game engaging and relatable for all players and evaluating the program. This case study has implications for the design of Serious Games more generally, including that research should underpin game design decisions, game designers should consider ways of bridging the game to real life, the learning that arises from the game should go beyond rote-learning, designers should consider how the player can make the game-world their own and comprehensive evaluations of Serious Games should be undertaken. PMID:24550880

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

PubMed

Zhang, Jing; Song, Yanlin; Xia, Fan; Zhu, Chenjing; Zhang, Yingying; Song, Wenpeng; Xu, Jianguo; Ma, Xuelei

2017-09-01

Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm. Large size of the training dataset is critical to increase the diagnostic accuracy. The performance of the trained machine could be tested by new images before clinical use. Real-time diagnosis, easy to use and potential high accuracy were the advantages of AI for IOPD. In sum, AI with deep learning technology is a promising method to help rapid and accurate IOPD. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Online Bioinformatics Curriculum

PubMed Central

Searls, David B.

2012-01-01

Online learning initiatives over the past decade have become increasingly comprehensive in their selection of courses and sophisticated in their presentation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article presents such a bioinformatics curriculum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. PMID:23028269

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

Toddlers’ referential understanding of pictures

PubMed Central

Ganea, Patricia A.; Preissler, Melissa Allen; Butler, Lucas; Carey, Susan; DeLoache, Judy S.

2010-01-01

Pictures are referential in that they can represent objects in the real world. Here we explore the emergence of understanding of the referential potential of pictures in the second year of life. In Study 1, 15-, 18-, and 24-month-old children learned a word for a picture of a novel object (e.g., “blicket”) in the context of a picture-book interaction. Later they were presented with the picture of a blicket along with the real object it depicted and asked to indicate “a blicket.” Many of the 24-, 18-month-olds and even 15-month-olds indicated the real object as an instance of a “blicket”, consistent with an understanding of the referential relation between pictures and objects. In Study 2, children were tested with an exemplar object that differed in color from the depicted object to determine if they would extend the label they had learned for the depicted object to a slightly different category member. The 15-, 18- and 24-month-old participants failed to make a consistent referential response. The results are discussed in terms of whether pictorial understanding at this age is associative or symbolic. PMID:19560783

LDA merging and splitting with applications to multiagent cooperative learning and system alteration.

PubMed

Pang, Shaoning; Ban, Tao; Kadobayashi, Youki; Kasabov, Nikola K

2012-04-01

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.

Project REAL (Real Educational Activities of Learning)

ERIC Educational Resources Information Center

Lindsay, Dolores

1977-01-01

Project REAL (Real Educational Activities of Learning) began as a program for 22 handicapped high school dropouts under age 21 and offered practical instruction in such areas as construction, electronic assembly, and merchandising. (JYC)

Real-time maneuver optimization of space-based robots in a dynamic environment: Theory and on-orbit experiments

NASA Astrophysics Data System (ADS)

Chamitoff, Gregory E.; Saenz-Otero, Alvar; Katz, Jacob G.; Ulrich, Steve; Morrell, Benjamin J.; Gibbens, Peter W.

2018-01-01

This paper presents the development of a real-time path-planning optimization approach to controlling the motion of space-based robots. The algorithm is capable of planning three dimensional trajectories for a robot to navigate within complex surroundings that include numerous static and dynamic obstacles, path constraints and performance limitations. The methodology employs a unique transformation that enables rapid generation of feasible solutions for complex geometries, making it suitable for application to real-time operations and dynamic environments. This strategy was implemented on the Synchronized Position Hold Engage Reorient Experimental Satellite (SPHERES) test-bed on the International Space Station (ISS), and experimental testing was conducted onboard the ISS during Expedition 17 by the first author. Lessons learned from the on-orbit tests were used to further refine the algorithm for future implementations.

Adapting proportional myoelectric-controlled interfaces for prosthetic hands.

PubMed

Pistohl, Tobias; Cipriani, Christian; Jackson, Andrew; Nazarpour, Kianoush

2013-01-01

Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses' full functionality, it is essential to find efficient ways to control their multiple actuators. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally. We investigated whether a similar control scheme, using signals from four hand muscles, could be adopted for real-time operation of a dexterous robotic hand. Despite different mapping strategies, learning to control the robotic hand over time was surprisingly similar to the learning of two-dimensional cursor control.

Alert Triage v 0.1 beta

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

Doak, Justin E.; Ingram, Joe; Johnson, Josh

2016-01-06

In the cyber security operations of a typical organization, data from multiple sources are monitored, and when certain conditions in the data are met, an alert is generated in an alert management system. Analysts inspect these alerts to decide if any deserve promotion to an event requiring further scrutiny. This triage process is manual, time-consuming, and detracts from the in-depth investigation of events. We have created a software system that uses supervised machine learning to automatically prioritize these alerts. In particular we utilize active learning to make efficient use of the pool of unlabeled alerts, thereby improving the performance ofmore » our ranking models over passive learning. We have demonstrated the effectiveness of our system on a large, real-world dataset of cyber security alerts.« less

Probability Learning: Changes in Behavior Across Time and Development

PubMed Central

Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

2017-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience—within a learning session and over development—influences people’s use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4–11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information. PMID:28121026

Problem-Based Learning and Earth System Science - The ESSEA High School Earth System Science Online Course

NASA Astrophysics Data System (ADS)

Myers, R.; Botti, J.

2002-12-01

The high school Earth system science course is web based and designed to meet the professional development needs of science teachers in grades 9-12. Three themes predominate this course: Earth system science (ESS) content, collaborative investigations, and problem-based learning (PBL) methodology. PBL uses real-world contexts for in-depth investigations of a subject matter. Participants predict the potential impacts of the selected event on Earth's spheres and the subsequent feedback and potential interactions that might result. PBL activities start with an ill-structured problem that serves as a springboard to team engagement. These PBL scenarios contain real-world situations. Teams of learners conduct an Earth system science analysis of the event and make recommendations or offer solutions regarding the problem. The course design provides an electronic forum for conversations, debate, development, and application of ideas. Samples of threaded discussions built around ESS thinking in science and PBL pedagogy will be presented.

Problem-Based Learning and Earth System Science - The ESSEA High School Earth System Science Online Course

NASA Astrophysics Data System (ADS)

Myers, R. J.; Botti, J. A.

2001-12-01

The high school Earth system science course is web based and designed to meet the professional development needs of science teachers in grades 9-12. Three themes predominate this course: Earth system science (ESS) content, collaborative investigations, and problem-based learning (PBL) methodology. PBL uses real-world contexts for in-depth investigations of a subject matter. Participants predict the potential impacts of the selected event on Earth's spheres and the subsequent feedback and potential interactions that might result. PBL activities start with an ill-structured problem that serves as a springboard to team engagement. These PBL scenarios contain real-world situations. Teams of learners conduct an Earth system science analysis of the event and make recommendations or offer solutions regarding the problem. The course design provides an electronic forum for conversations, debate, development, and application of ideas. Samples of threaded discussions built around ESS thinking in science and PBL pedagogy will be presented.

An online supervised learning method based on gradient descent for spiking neurons.

PubMed

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrator element as a promoter of active learning in engineering teaching

NASA Astrophysics Data System (ADS)

Oliveira, Paulo C.; Oliveira, Cristina G.

2014-03-01

In this paper, we present a teaching proposal used in an Introductory Physics course to civil engineering students from Porto's Engineering Institute/Instituto Superior de Engenharia do Porto (ISEP). The proposal was born from the need to change students' perception and motivation for learning physics. It consists in the use of an integrator element, called the physics elevator project. This integrator element allows us to use, in a single project, all the content taught in the course and uses several active learning strategies. In this paper, we analyse this project as: (i) a clarifying element of the contents covered in the course; (ii) a promoter element of motivation and active participation in class and finally and (iii) a link between the contents covered in the course and the 'real world'. The data were collected by a questionnaire and interviews to students. From the data collected, it seems that the integrator element improves students' motivation towards physics and develops several skills that they consider to be important to their professional future. It also acts as a clarifying element and makes the connection between the physics that is taught and the 'real world'.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Preschool children's Collaborative Science Learning Scaffolded by Tablets

NASA Astrophysics Data System (ADS)

Fridberg, Marie; Thulin, Susanne; Redfors, Andreas

2017-06-01

This paper reports on a project aiming to extend the current understanding of how emerging technologies, i.e. tablets, can be used in preschools to support collaborative learning of real-life science phenomena. The potential of tablets to support collaborative inquiry-based science learning and reflective thinking in preschool is investigated through the analysis of teacher-led activities on science, including children making timelapse photography and Slowmation movies. A qualitative analysis of verbal communication during different learning contexts gives rise to a number of categories that distinguish and identify different themes of the discussion. In this study, groups of children work with phase changes of water. We report enhanced and focused reasoning about this science phenomenon in situations where timelapse movies are used to stimulate recall. Furthermore, we show that children communicate in a more advanced manner about the phenomenon, and they focus more readily on problem solving when active in experimentation or Slowmation producing contexts.

Studienlandschaft Schwingbachtal: an out-door full-scale learning tool newly equipped with augmented reality

NASA Astrophysics Data System (ADS)

Aubert, A. H.; Schnepel, O.; Kraft, P.; Houska, T.; Plesca, I.; Orlowski, N.; Breuer, L.

2015-11-01

This paper addresses education and communication in hydrology and geosciences. Many approaches can be used, such as the well-known seminars, modelling exercises and practical field work but out-door learning in our discipline is a must, and this paper focuses on the recent development of a new out-door learning tool at the landscape scale. To facilitate improved teaching and hands-on experience, we designed the Studienlandschaft Schwingbachtal. Equipped with field instrumentation, education trails, and geocache, we now implemented an augmented reality App, adding virtual teaching objects on the real landscape. The App development is detailed, to serve as methodology for people wishing to implement such a tool. The resulting application, namely the Schwingbachtal App, is described as an example. We conclude that such an App is useful for communication and education purposes, making learning pleasant, and offering personalized options.

Robust reinforcement learning.

PubMed

Morimoto, Jun; Doya, Kenji

2005-02-01

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H(infinity) control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H(infinity) control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.

The World Well Lost, Found: Reality and Authenticity in Green's "New Classroom Pedagogy"

ERIC Educational Resources Information Center

Vakeva, Lauri

2009-01-01

In her recent work, Green (2001; 2008) builds on the idea that there is a gulf between "real-world music" and classroom music (Ibid., p. 2). One of her main goals seems to be to pave the way for the former in the latter: to make the music in schools more in touch with reality. The learning practices of popular music are taken to bring the needed…

JPRS Report, East Europe.

DTIC Science & Technology

1991-03-13

Pashko] Thank you , but you should know the real reasons which have drawn me into Albania’s democratic opposition movement. I am 36 years old...stability to go the course with its new commitments. [Radoncic] Because you are the leader of an opposition party, it makes it all the more interesting...everything depends on us ourselves. [Radoncic] Although there is obviously room for opti- mism, it would be interesting to learn how you foresee changing

A Lecture Supporting System Based on Real-Time Learning Analytics

ERIC Educational Resources Information Center

Shimada, Atsushi; Konomi, Shin'ichi

2017-01-01

A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

On the Auditory-Proprioception Substitution Hypothesis: Movement Sonification in Two Deafferented Subjects Learning to Write New Characters

PubMed Central

Danna, Jérémy; Velay, Jean-Luc

2017-01-01

The aim of this study was to evaluate the compensatory effects of real-time auditory feedback on two proprioceptively deafferented subjects. The real-time auditory feedback was based on a movement sonification approach, consisting of translating some movement variables into synthetic sounds to make them audible. The two deafferented subjects and 16 age-matched control participants were asked to learn four new characters. The characters were learned under two different conditions, one without sonification and one with sonification, respecting a within-subject protocol. The results revealed that characters learned with sonification were reproduced more quickly and more fluently than characters learned without and that the effects of sonification were larger in deafferented than in control subjects. Secondly, whereas control subjects were able to learn the characters without sounds the deafferented subjects were able to learn them only when they were trained with sonification. Thirdly, although the improvement was still present in controls, the performance of deafferented subjects came back to the pre-test level 2 h after the training with sounds. Finally, the two deafferented subjects performed differently from each other, highlighting the importance of studying at least two subjects to better understand the loss of proprioception and its impact on motor control and learning. To conclude, movement sonification may compensate for a lack of proprioception, supporting the auditory-proprioception substitution hypothesis. However, sonification would act as a “sensory prosthesis” helping deafferented subjects to better feel their movements, without permanently modifying their motor performance once the prosthesis is removed. Potential clinical applications for motor rehabilitation are numerous: people with a limb prosthesis, with a stroke, or with some peripheral nerve injury may potentially be interested. PMID:28386211

Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…

Experts in offside decision making learn to compensate for their illusory perceptions.

PubMed

Put, Koen; Baldo M, V C; Cravo, André M; Wagemans, Johan; Helsen, Werner F

2013-12-01

In association football, the flash-lag effect appears to be a viable explanation for erroneous offside decision making. Due to this spatiotemporal illusion, assistant referees (ARs) perceive the player who receives the ball ahead of his real position. In this experiment, a laboratory decision-making task was used to demonstrate that international top-class ARs, compared with amateur soccer players, do not have superior perceptual sensitivity. They clearly modify their decision criterion according to the contextual needs and, therefore, show a higher response bias toward not responding to the stimulus, in particular in the most difficult situations. Thus, international ARs show evidence for response-level compensation, resulting in a specific cost (i.e., more misses), which clearly reflects the use of particular (cognitive) strategies. In summary, it appears that experts in offside decision making can be distinguished from novices more on the cognitive or decision-making level than on the perceptual level.

Effects of emotional preferences on value-based decision making are mediated by mentalizing not reward networks

PubMed Central

Evans, Simon; Fleming, Stephen M.; Dolan, Raymond J.; Averbeck, Bruno B.

2012-01-01

Real-world decision-making often involves social considerations. Consequently, the social value of stimuli can induce preferences in choice behavior. However, it is unknown how financial and social values are integrated in the brain. Here, we investigated how smiling and angry face stimuli interacted with financial reward feedback in a stochastically-rewarded decision-making task. Subjects reliably preferred the smiling faces despite equivalent reward feedback, demonstrating a socially driven bias. We fit a Bayesian reinforcement learning model to factor the effects of financial rewards and emotion preferences in individual subjects, and regressed model predictions on the trial-by-trial fMRI signal. Activity in the sub-callosal cingulate and the ventral striatum, both involved in reward learning, correlated with financial reward feedback, whereas the differential contribution of social value activated dorsal temporo-parietal junction and dorsal anterior cingulate cortex, previously proposed as components of a mentalizing network. We conclude that the impact of social stimuli on value-based decision processes is mediated by effects in brain regions partially separable from classical reward circuitry. PMID:20946058

Attitudes about high school physics in relationship to gender and ethnicity: A mixed method analysis

NASA Astrophysics Data System (ADS)

Hafza, Rabieh Jamal

There is an achievement gap and lack of participation in science, technology, engineering, and math (STEM) by minority females. The number of minority females majoring in STEM related fields and earning advanced degrees in these fields has not significantly increased over the past 40 years. Previous research has evaluated the relationship between self-identity concept and factors that promote the academic achievement as well the motivation of students to study different subject areas. This study examined the interaction between gender and ethnicity in terms of physics attitudes in the context of real world connections, personal interest, sense making/effort, problem solving confidence, and problem solving sophistication. The Colorado Learning Attitudes about Science Survey (CLASS) was given to 131 students enrolled in physics classes. There was a statistically significant Gender*Ethnicity interaction for attitude in the context of Real World Connections, Personal Interest, Sense Making/Effort, Problem Solving Confidence, and Problem Solving Sophistication as a whole. There was also a statistically significant Gender*Ethnicity interaction for attitude in the context of Real World Connections, Personal Interest, and Sense Making/Effort individually. Five Black females were interviewed to triangulate the quantitative results and to describe the experiences of minority females taking physics classes. There were four themes that emerged from the interviews and supported the findings from the quantitative results. The data supported previous research done on attitudes about STEM. The results reported that Real World Connections and Personal Interest could be possible factors that explain the lack of participation and achievement gaps that exists among minority females.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

PubMed

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

Exploring Non-Traditional Learning Methods in Virtual and Real-World Environments

ERIC Educational Resources Information Center

Lukman, Rebeka; Krajnc, Majda

2012-01-01

This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…

Effects of Real and Recalled Success on Learned Helplessness and Depression

ERIC Educational Resources Information Center

Teasdale, John D.

1978-01-01

The effects of recalling past successes on the deficits in learned helplessness and depression were examined and, for learned helplessness, compared with those of real success. Results suggest real success does not have its therapeutic effects by modifying attributions for failure toward external factors. (Editor)

Learning from Dealing with Real World Problems

ERIC Educational Resources Information Center

Akcay, Hakan

2017-01-01

The purpose of this article is to provide an example of using real world issues as tools for science teaching and learning. Using real world issues provides students with experiences in learning in problem-based environments and encourages them to apply their content knowledge to solving current and local problems.

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

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

El Naqa, I; Ten, R

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with amore » month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the quantum approach averaged a more optimistic 0.57±0.4, but with high phase fluctuations. Conclusion: Our results demonstrate that quantum machine learning approaches provide a feasible and promising framework for real-time and sequential clinical decision-making in adaptive radiotherapy.« less

The viewing room: A lens for developing ethical comportment.

PubMed

McAllister, Margaret; Levett-Jones, Tracy; Petrini, Marcia A; Lasater, Kathie

2016-01-01

Healthcare is dynamic and complex, and against this background, nursing students must negotiate the transition from lay person to healthcare professional. Diverse life experiences and learning styles can further complicate this journey of transformation. The contemporary role of the nurse includes caring for and making clinical decisions about patients based on ethical principles. Learning about and integrating ethical comportment as part of the transformative journey requires nurse educators to create and implement learning experiences that challenge nursing students to think deeply and broadly about the experiences they encounter, to question their previous assumptions and prejudices, to consider the world of healthcare through a new lens, and to reflect on and learn from the process. The judicious use of film has the potential to assist students to recognize and develop ethical comportment as they prepare for real-world clinical practice experiences. In this paper, we present three film exemplars and related teaching strategies designed to facilitate transformative learning and development of ethical comportment. Copyright © 2015 Elsevier Ltd. All rights reserved.

The effect of brain based learning with contextual approach viewed from adversity quotient

NASA Astrophysics Data System (ADS)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi, R.

2018-05-01

The aim of this research was to find out the effect of Brain Based Learning (BBL) with contextual approach viewed from adversity quotient (AQ) on mathematics achievement. BBL-contextual is the model to optimize the brain in the new concept learning and real life problem solving by making the good environment. Adversity Quotient is the ability to response and faces the problems. In addition, it is also about how to turn the difficulties into chances. This AQ classified into quitters, campers, and climbers. The research method used in this research was quasi experiment by using 2x3 factorial designs. The sample was chosen by using stratified cluster random sampling. The instruments were test and questionnaire for the data of AQ. The results showed that (1) BBL-contextual is better than direct learning on mathematics achievement, (2) there is no significant difference between each types of AQ on mathematics achievement, and (3) there is no interaction between learning model and AQ on mathematics achievement.

Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

Real-Time Analytics for the Healthcare Industry: Arrhythmia Detection.

PubMed

Agneeswaran, Vijay Srinivas; Mukherjee, Joydeb; Gupta, Ashutosh; Tonpay, Pranay; Tiwari, Jayati; Agarwal, Nitin

2013-09-01

It is time for the healthcare industry to move from the era of "analyzing our health history" to the age of "managing the future of our health." In this article, we illustrate the importance of real-time analytics across the healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data scientists use R to write the analytics and are not clear on how to make the data work in real-time and on high-velocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and accuracy trade-off in real-time analytics.

Effects of Integrating an Active Learning-Promoting Mechanism into Location-Based Real-World Learning Environments on Students' Learning Performances and Behaviors

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chang, Shao-Chen; Chen, Pei-Ying; Chen, Xiang-Ya

2018-01-01

Engaging students in real-world learning contexts has been identified by educators as being an important way of helping them learn to apply what they have learned from textbooks to practical problems. The advancements in mobile and image-processing technologies have enabled students to access learning resources and receive learning guidance in…

Science Spots AR: A Platform for Science Learning Games with Augmented Reality

ERIC Educational Resources Information Center

Laine, Teemu H.; Nygren, Eeva; Dirin, Amir; Suk, Hae-Jung

2016-01-01

Lack of motivation and of real-world relevance have been identified as reasons for low interest in science among children. Game-based learning and storytelling are prominent methods for generating intrinsic motivation in learning. Real-world relevance requires connecting abstract scientific concepts with the real world. This can be done by…

SkyDOT: a publicly accessible variability database, containing multiple sky surveys and real-time data

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

Starr, D. L.; Wozniak, P. R.; Vestrand, W. T.

2002-01-01

SkyDOT (Sky Database for Objects in Time-Domain) is a Virtual Observatory currently comprised of data from the RAPTOR, ROTSE I, and OGLE I1 survey projects. This makes it a very large time domain database. In addition, the RAPTOR project provides SkyDOT with real-time variability data as well as stereoscopic information. With its web interface, we believe SkyDOT will be a very useful tool for both astronomers, and the public. Our main task has been to construct an efficient relational database containing all existing data, while handling a real-time inflow of data. We also provide a useful web interface allowing easymore » access to both astronomers and the public. Initially, this server will allow common searches, specific queries, and access to light curves. In the future we will include machine learning classification tools and access to spectral information.« less

Non-traditional approaches to teaching GPS online

NASA Astrophysics Data System (ADS)

Matias, A.; Wolf, D. F., II

2009-12-01

Students are increasingly turning to the web for quality education that fits into their lives. Nonetheless, online learning brings challenges as well as a fresh opportunity for exploring pedagogical practices not present on traditional higher education programs, particularly in the sciences. A team of two dozen Empire State College-State University of New York instructional designers, faculty, and other staff are working on making science relevant to non-majors who may initially have anxiety about general education science courses. One of these courses, GPS and the New Geography, focuses on how Global Positioning System (GPS) technology provides a base for inquiry and scientific discovery from a range of environmental issues with local, regional, and global scope. GPS and the New Geography is an introductory level course developed under a grant supported by the Charitable Leadership Foundation. Taking advantage of the proliferation of tools currently available for online learning management systems, we explore current trends in Web 2.0 applications to aggregate and leverage data to create a nontraditional, interactive learning environment. Using our best practices to promote on-line discussion and interaction, these tools help engage students and foster deep learning. During the 15-week term students learn through case studies, problem-based exercises, and the use of scientific data; thus, expanding their spatial literacy and gain experience using real spatial technology tools to enhance their understanding of real-world issues. In particular, we present how the use of Mapblogs an in-house developed blogging platform that uses GIS interplaying with GPS units, interactive data presentations, intuitive visual working environments, harnessing RSS feeds, and other nontraditional Web 2.0 technology has successfully promoted active learning in the virtual learning environment.

New Media Learning: Student Podcasting and Blogging in an Intro to Meteorology Course

NASA Astrophysics Data System (ADS)

Small, J. D.

2013-12-01

Current weather events and climate change are hot media topics discussed on television, the internet, and through social media. In this world of 'Tweets', 'Texts' and constant multi-media bombardment it is becoming increasingly difficult to engage students in the learning process by simply standing at a podium and lecturing in a darkened classroom. Educational research has found that lectures place students in a passive role, preventing them from actively engaging in the learning process. Through the innovative use of multi-media platforms this study assesses the potential to create active learning opportunities (podcasting and blogging) that connect theoretical 'textbook' atmospheric science with the 'real world.' This work focuses on students enrolled in the Introduction to Meteorology course (MET 101) at the University of Hawaii at Manoa. This study summarizes the impact of the 'course-casting' technique which utilizes podcasts of lectures and supplemental material. Lecture Podcasts are used mainly as a revision tool for students by providing on-demand portable (MP3) course content that supports independent student learning. Students also produced their own podcasts (research projects) to share with classmates throughout the course relating atmospheric science content to personal 'real world' experiences. Along with podcasting, students blogged about designated topics related to weather and climate, making their knowledge and understanding accessible to other students in the course and the general internet community. Student surveys, journals, and final exit interviews are used to assess the impact of the blogging and podcasting exercises on the student learning experience. The number of times each lecture podcast was downloaded is recorded to determine the interest level in using audio lectures as a review tool. Student blogs and podcasts are evaluated based on science content accuracy and student survey evaluations of the learning experience.

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A focus group study of chiropractic students following international service learning experiences

PubMed Central

Boysen, James C.; Salsbury, Stacie A.; Derby, Dustin; Lawrence, Dana J.

2016-01-01

Objective: One objective of chiropractic education is to cultivate clinical confidence in novice practitioners. The purpose of this qualitative study was to describe how participation in a short-term international service learning experience changed perceptions of clinical confidence in senior chiropractic students. Methods: Seventeen senior chiropractic students participated in 4 moderated focus group sessions within 4 months after a clinical educational opportunity held in international settings. Participants answered standard questions on how this educational experience may have changed their clinical confidence. Two investigators performed qualitative thematic analysis of the verbatim transcripts to identify core concepts and supporting themes. Results: The core concept was transformation from an unsure student to a confident doctor. The service learning experience allowed students to deliver chiropractic treatment to patients in a real-world setting, engage in frequent repetitions of technical skills, perform clinical decision-making and care coordination, and communicate with patients and other health professionals. Students described increased clinical confidence in 9 competency areas organized within 3 domains: (1) chiropractic competencies including observation, palpation, and manipulation; (2) clinical competencies including problem solving, clinic flow, and decision-making; and (3) communication competencies, including patient communication, interprofessional communication, and doctor–patient relationship. Students recommended that future service learning programs include debriefing sessions similar to the experience offered by these focus groups to enhance student learning. Conclusion: Senior chiropractic students who participated in an international service learning program gained confidence and valuable practical experience in integrating their chiropractic, clinical, and communication skills for their future practices. PMID:27258817

Reinforcement learning and episodic memory in humans and animals: an integrative framework

PubMed Central

Gershman, Samuel J.; Daw, Nathaniel D.

2018-01-01

We review the psychology and neuroscience of reinforcement learning (RL), which has witnessed significant progress in the last two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, the simplicity of these tasks misses important aspects of reinforcement learning in the real world: (i) State spaces are high-dimensional, continuous, and partially observable; this implies that (ii) data are relatively sparse: indeed precisely the same situation may never be encountered twice; and also that (iii) rewards depend on long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, these theories have largely connected with procedural and semantic memory: how knowledge about action values or world models extracted gradually from many experiences can drive choice. This misses many aspects of memory related to traces of individual events, such as episodic memory. We suggest that these two gaps are related. In particular, the computational challenges can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (i) efficiently approximate value functions over complex state spaces, (ii) learn with very little data, and (iii) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system. PMID:27618944

Video streaming into the mainstream.

PubMed

Garrison, W

2001-12-01

Changes in Internet technology are making possible the delivery of a richer mixture of media through data streaming. High-quality, dynamic content, such as video and audio, can be incorporated into Websites simply, flexibly and interactively. Technologies such as G3 mobile communication, ADSL, cable and satellites enable new ways of delivering medical services, information and learning. Systems such as Quicktime, Windows Media and Real Video provide reliable data streams as video-on-demand and users can tailor the experience to their own interests. The Learning Development Centre at the University of Portsmouth have used streaming technologies together with e-learning tools such as dynamic HTML, Flash, 3D objects and online assessment successfully to deliver on-line course content in economics and earth science. The Lifesign project--to develop, catalogue and stream health sciences media for teaching--is described and future medical applications are discussed.

Real-Time Global Nonlinear Aerodynamic Modeling for Learn-To-Fly

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2016-01-01

Flight testing and modeling techniques were developed to accurately identify global nonlinear aerodynamic models for aircraft in real time. The techniques were developed and demonstrated during flight testing of a remotely-piloted subscale propeller-driven fixed-wing aircraft using flight test maneuvers designed to simulate a Learn-To-Fly scenario. Prediction testing was used to evaluate the quality of the global models identified in real time. The real-time global nonlinear aerodynamic modeling algorithm will be integrated and further tested with learning adaptive control and guidance for NASA Learn-To-Fly concept flight demonstrations.

Building healthy communities: establishing health and wellness metrics for use within the real estate industry.

PubMed

Trowbridge, Matthew J; Pickell, Sarah Gauche; Pyke, Christopher R; Jutte, Douglas P

2014-11-01

It is increasingly well recognized that the design and operation of the communities in which people live, work, learn, and play significantly influence their health. However, within the real estate industry, the health impacts of transportation, community development, and other construction projects, both positive and negative, continue to operate largely as economic externalities: unmeasured, unregulated, and for the most part unconsidered. This lack of transparency limits communities' ability to efficiently advocate for real estate investment that best promotes their health and well-being. It also limits market incentives for innovation within the real estate industry by making it more difficult for developers that successfully target health behaviors and outcomes in their projects to differentiate themselves competitively. In this article we outline the need for actionable, community-relevant, practical, and valuable metrics jointly developed by the health care and real estate sectors to better evaluate and optimize the "performance" of real estate development projects from a population health perspective. Potential templates for implementation, including the successful introduction of sustainability metrics by the green building movement, and preliminary data from selected case-study projects are also discussed. Project HOPE—The People-to-People Health Foundation, Inc.

Benefits of Sign Language Interpreting and Text Alternatives for Deaf Students' Classroom Learning

ERIC Educational Resources Information Center

Marschark, Marc; Leigh, Greg; Sapere, Patricia; Burnham, Denis; Convertino, Carol; Stinson, Michael; Knoors, Harry; Vervloed, Mathijs P. J.; Noble, William

2006-01-01

Four experiments examined the utility of real-time text in supporting deaf students' learning from lectures in postsecondary (Experiments 1 and 2) and secondary classrooms (Experiments 3 and 4). Experiment 1 compared the effects on learning of sign language interpreting, real-time text (C-Print), and both. Real-time text alone led to significantly…

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

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.

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

PubMed

Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

2016-06-01

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

An Adaptive Navigation Support System for Conducting Context-Aware Ubiquitous Learning in Museums

ERIC Educational Resources Information Center

Chiou, Chuang-Kai; Tseng, Judy C. R.; Hwang, Gwo-Jen; Heller, Shelly

2010-01-01

In context-aware ubiquitous learning, students are guided to learn in the real world with personalized supports from the learning system. As the learning resources are realistic objects in the real world, certain physical constraints, such as the limitation of stream of people who visit the same learning object, the time for moving from one object…

Analytical framework and tool kit for SEA follow-up

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

Nilsson, Mans; Wiklund, Hans; Finnveden, Goeran

2009-04-15

Most Strategic Environmental Assessment (SEA) research and applications have so far neglected the ex post stages of the process, also called SEA follow-up. Tool kits and methodological frameworks for engaging effectively with SEA follow-up have been conspicuously missing. In particular, little has so far been learned from the much more mature evaluation literature although many aspects are similar. This paper provides an analytical framework and tool kit for SEA follow-up. It is based on insights and tools developed within programme evaluation and environmental systems analysis. It is also grounded in empirical studies into real planning and programming practices at themore » regional level, but should have relevance for SEA processes at all levels. The purpose of the framework is to promote a learning-oriented and integrated use of SEA follow-up in strategic decision making. It helps to identify appropriate tools and their use in the process, and to systematise the use of available data and knowledge across the planning organization and process. It distinguishes three stages in follow-up: scoping, analysis and learning, identifies the key functions and demonstrates the informational linkages to the strategic decision-making process. The associated tool kit includes specific analytical and deliberative tools. Many of these are applicable also ex ante, but are then used in a predictive mode rather than on the basis of real data. The analytical element of the framework is organized on the basis of programme theory and 'DPSIR' tools. The paper discusses three issues in the application of the framework: understanding the integration of organizations and knowledge; understanding planners' questions and analytical requirements; and understanding interests, incentives and reluctance to evaluate.« less

The child brain computes and utilizes internalized maternal choices

PubMed Central

Lim, Seung-Lark; Cherry, J. Bradley C.; Davis, Ann M.; Balakrishnan, S. N.; Ha, Oh-Ryeong; Bruce, Jared M.; Bruce, Amanda S.

2016-01-01

As children grow, they gradually learn how to make decisions independently. However, decisions like choosing healthy but less-tasty foods can be challenging for children whose self-regulation and executive cognitive functions are still maturing. We propose a computational decision-making process in which children estimate their mother's choices for them as well as their individual food preferences. By employing functional magnetic resonance imaging during real food choices, we find that the ventromedial prefrontal cortex (vmPFC) encodes children's own preferences and the left dorsolateral prefrontal cortex (dlPFC) encodes the projected mom's choices for them at the time of children's choice. Also, the left dlPFC region shows an inhibitory functional connectivity with the vmPFC at the time of children's own choice. Our study suggests that in part, children utilize their perceived caregiver's choices when making choices for themselves, which may serve as an external regulator of decision-making, leading to optimal healthy decisions. PMID:27218420

Problem based learning: the effect of real time data on the website to student independence

NASA Astrophysics Data System (ADS)

Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

2018-05-01

Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

On the hunt for elusive ``meanings''

NASA Astrophysics Data System (ADS)

Roth, Wolff-Michael

2012-09-01

The feature article discussed in this forum presents an interesting description of how students work in the context of a virtual world, where they design phenomena that they subsequently investigate by analyzing graphical representations. The study is aligned with the current canon of science education interested in understanding the inter-psychological and intra-psychological determinants of learning. Its main focus is on "meaning making." In this contribution to the forum, I articulate some shortcomings inherent in this theoretical notion, which, in essence, hides rather than reveals the real issues in and of learning. I offer some alternative avenues, both theoretical and methodological, for framing pertinent issues; in so doing, I (endeavor to) open up new avenues for research in science education. In essence, therefore, I offer possible avenues in response to the question, "What more can there be done by science education research?" that would allow us to eschew what I perceive to be hidden contradictions that interfere with making theoretical and practical advances in our field.

A role of decision-making competency in science learning utilizing a social valuation framework

NASA Astrophysics Data System (ADS)

Katsuo, Akihito

2005-11-01

The role of decision-making in learning performance has been an occasional topic in the research literature in science education, but rarely has it been a central issue in the field. Nonetheless, recent studies regarding the topic in several fields other than education, such as cognitive neuroscience and social choice theory, indicate the fundamental importance(s) of the topic. This study focuses on a possible role of decision-making in science learning. Initially the study was designed to probe the decision-making ability of elementary school children with a modified version of the Iowa Gambling Task (IGT). The experiment involved six Montessori 3rd and 4th grade students as the experimental group and eight public school 3rd and 4th grade students as the control group. The result of the modified IGT revealed a tendency in choice trajectories favoring children at the Montessori school. However, the probabilistic value went below the statistically significant level set by the U test. A further study focused on the impact of better decision-making ability revealed in the first experiment on performances with a science learning module that emphasized collective reasoning. The instruction was based on a set of worksheets with multiple choices on which students were asked to make predictions with and to provide supportive arguments regarding outcomes of experiments introduced in the worksheet. Then the whole class was involved with a real experiment to see which choice was correct. The findings in the study indicated that the Montessori students often obtained higher scores than non-Montessori students in making decision with a tendency of consistency in terms of their choices of the alternatives on the worksheets. The findings of the experiments were supported by a correlational analysis that was performed at the end of study. Although no statistically significant correlations were found, there was a tendency for positively associative shifts between the scores of the modified IGT and the scores for the performances on the science module for the Montessori students.

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning

PubMed Central

Wise, Richard J.S.; Geranmayeh, Fatemeh; Hampshire, Adam

2017-01-01

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword–object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary. SIGNIFICANCE STATEMENT Learning a task involves the brain system within which that specific task becomes established. Therefore, successfully learning a new vocabulary establishes the novel words in the language system. However, there is evidence that in the early stages of learning, networks within multiple-demand cortex (MDC), which control higher cognitive functions, such as working memory, attention, and monitoring of performance, become active. This activity declines once the task is learnt. The present study demonstrated that a node within MDC, located in midline frontal cortex, becomes active during the early stage of learning a novel vocabulary. Importantly, noninvasive brain stimulation of this node improved performance during this stage of learning. This observation demonstrated that MDC activity is important for learning. PMID:28676576

From Real Life to Real Life: Bringing "Double Awareness" from Action Learning Programmes into Organisational Reality

ERIC Educational Resources Information Center

Svalgaard, Lotte

2017-01-01

In Action Learning programmes, it is held central to work on real business challenges (task) while learning about team and self (process); staying mindful aware of the process is referred to in this paper as "double awareness", and emphasises noticing and acting on process cues while working on the task. As business challenges within…

Empirical Evidence of Priming, Transfer, Reinforcement, and Learning in the Real and Virtual Trillium Trails

ERIC Educational Resources Information Center

Harrington, M. C. R.

2011-01-01

Over the past 20 years, there has been a debate on the effectiveness of virtual reality used for learning with young children, producing many ideas but little empirical proof. This empirical study compared learning activity in situ of a real environment (Real) and a desktop virtual reality (Virtual) environment, built with video game technology,…

Rapid classification of hippocampal replay content for real-time applications

PubMed Central

Liu, Daniel F.; Karlsson, Mattias P.; Frank, Loren M.; Eden, Uri T.

2016-01-01

Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments. PMID:27535369

Feedback Blunting: Total Sleep Deprivation Impairs Decision Making that Requires Updating Based on Feedback.

PubMed

Whitney, Paul; Hinson, John M; Jackson, Melinda L; Van Dongen, Hans P A

2015-05-01

To better understand the sometimes catastrophic effects of sleep loss on naturalistic decision making, we investigated effects of sleep deprivation on decision making in a reversal learning paradigm requiring acquisition and updating of information based on outcome feedback. Subjects were randomized to a sleep deprivation or control condition, with performance testing at baseline, after 2 nights of total sleep deprivation (or rested control), and following 2 nights of recovery sleep. Subjects performed a decision task involving initial learning of go and no go response sets followed by unannounced reversal of contingencies, requiring use of outcome feedback for decisions. A working memory scanning task and psychomotor vigilance test were also administered. Six consecutive days and nights in a controlled laboratory environment with continuous behavioral monitoring. Twenty-six subjects (22-40 y of age; 10 women). Thirteen subjects were randomized to a 62-h total sleep deprivation condition; the others were controls. Unlike controls, sleep deprived subjects had difficulty with initial learning of go and no go stimuli sets and had profound impairment adapting to reversal. Skin conductance responses to outcome feedback were diminished, indicating blunted affective reactions to feedback accompanying sleep deprivation. Working memory scanning performance was not significantly affected by sleep deprivation. And although sleep deprived subjects showed expected attentional lapses, these could not account for impairments in reversal learning decision making. Sleep deprivation is particularly problematic for decision making involving uncertainty and unexpected change. Blunted reactions to feedback while sleep deprived underlie failures to adapt to uncertainty and changing contingencies. Thus, an error may register, but with diminished effect because of reduced affective valence of the feedback or because the feedback is not cognitively bound with the choice. This has important implications for understanding and managing sleep loss-induced cognitive impairment in emergency response, disaster management, military operations, and other dynamic real-world settings with uncertain outcomes and imperfect information. © 2015 Associated Professional Sleep Societies, LLC.

A MATLAB Library for Rapid Prototyping of Wireless Communications Algorithms with the Universal Software Radio Peripheral (USRP) Radio Family

DTIC Science & Technology

2013-06-01

Radio is a software development toolkit that provides signal processing blocks to drive the SDR. GNU Radio has many strong points – it is actively...maintained with a large user base, new capabilities are constantly being added, and compiled C code is fast for many real-time applications such as...programming interface (API) makes learning the architecture a daunting task, even for the experienced software developer. This requirement poses many

Learning Reverse Engineering and Simulation with Design Visualization

NASA Technical Reports Server (NTRS)

Hemsworth, Paul J.

2018-01-01

The Design Visualization (DV) group supports work at the Kennedy Space Center by utilizing metrology data with Computer-Aided Design (CAD) models and simulations to provide accurate visual representations that aid in decision-making. The capability to measure and simulate objects in real time helps to predict and avoid potential problems before they become expensive in addition to facilitating the planning of operations. I had the opportunity to work on existing and new models and simulations in support of DV and NASA’s Exploration Ground Systems (EGS).

A Heuristic Algorithm for Planning Personalized Learning Paths for Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Kuo, Fan-Ray; Yin, Peng-Yeng; Chuang, Kuo-Hsien

2010-01-01

In a context-aware ubiquitous learning environment, learning systems can detect students' learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that…

Using What's Learned in the Game for Use in Real Life.

PubMed

Baranowski, Moderator Tom; Fiellin, Participants Lynn E; Gay, Geri; Thompson, Deborah I

2014-02-01

A player can learn many things from playing a game for health. Some of these learnings were deliberately designed for the player to use in his or her real life, outside of any game. The effective ways to enable players to generalize what they learn in the game to their real lives (and thereby benefit from playing the game) are not clear. We have convened a group of expert health game designers and researchers to discuss this important issue.

Real Clients, Real Management, Real Failure: The Risks and Rewards of Service Learning

ERIC Educational Resources Information Center

Cyphert, Dale

2006-01-01

There are multiple advantages to service-learning projects across the business curriculum, but in communication classes the author has found their biggest value to be authenticity. A "real-world" assignment requires the flexible, creative integration of communication skills in an environment where, "unlike exams and other typical university…

Teaching Real-World Applications of Business Statistics Using Communication to Scaffold Learning

ERIC Educational Resources Information Center

Green, Gareth P.; Jones, Stacey; Bean, John C.

2015-01-01

Our assessment research suggests that quantitative business courses that rely primarily on algorithmic problem solving may not produce the deep learning required for addressing real-world business problems. This article illustrates a strategy, supported by recent learning theory, for promoting deep learning by moving students gradually from…

An interactive three-dimensional virtual body structures system for anatomical training over the internet.

PubMed

Temkin, Bharti; Acosta, Eric; Malvankar, Ameya; Vaidyanath, Sreeram

2006-04-01

The Visible Human digital datasets make it possible to develop computer-based anatomical training systems that use virtual anatomical models (virtual body structures-VBS). Medical schools are combining these virtual training systems and classical anatomy teaching methods that use labeled images and cadaver dissection. In this paper we present a customizable web-based three-dimensional anatomy training system, W3D-VBS. W3D-VBS uses National Library of Medicine's (NLM) Visible Human Male datasets to interactively locate, explore, select, extract, highlight, label, and visualize, realistic 2D (using axial, coronal, and sagittal views) and 3D virtual structures. A real-time self-guided virtual tour of the entire body is designed to provide detailed anatomical information about structures, substructures, and proximal structures. The system thus facilitates learning of visuospatial relationships at a level of detail that may not be possible by any other means. The use of volumetric structures allows for repeated real-time virtual dissections, from any angle, at the convenience of the user. Volumetric (3D) virtual dissections are performed by adding, removing, highlighting, and labeling individual structures (and/or entire anatomical systems). The resultant virtual explorations (consisting of anatomical 2D/3D illustrations and animations), with user selected highlighting colors and label positions, can be saved and used for generating lesson plans and evaluation systems. Tracking users' progress using the evaluation system helps customize the curriculum, making W3D-VBS a powerful learning tool. Our plan is to incorporate other Visible Human segmented datasets, especially datasets with higher resolutions, that make it possible to include finer anatomical structures such as nerves and small vessels. (c) 2006 Wiley-Liss, Inc.

The utility of observational studies in clinical decision making: lessons learned from statin trials.

PubMed

Foody, JoAnne M; Mendys, Phillip M; Liu, Larry Z; Simpson, Ross J

2010-05-01

Contemporary clinical decision making is well supported by a wide variety of information sources, including clinical practice guidelines, position papers, and insights from randomized controlled trials (RCTs). Much of our fundamental understanding of cardiovascular risk factors is based on multiple observations from major epidemiologic studies, such as The Seven Country Studies and the US-based Framingham Heart Study. These studies provided the framework for the development of clinical practice guidelines, including the National Cholesterol Education Program Adult Treatment Panel series. The objective of this article is to highlight the value of observational studies as a complement to clinical trial data for clinical decision making in real-world practice. Although RCTs are still the benchmark for assessing clinical efficacy and safety of a specific therapeutic approach, they may be of limited utility to practitioners who must then adapt the lessons learned from the trial into the patient care environment. The use of well-structured observational studies can improve our understanding of the translation of clinical trials into clinical practice, as demonstrated here with the example of statins. Although such studies have their own limitations, improved techniques for design and analysis have reduced the impact of bias and confounders. The introduction of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines has provided more uniformity for such studies. When used together with RCTs, observational studies can enhance our understanding of effectiveness and utility in real-world clinical practice. In the examples of statin observational studies, the results suggest that relative effectiveness of different statins and potential impact of switching statins should be carefully considered in treating individual patients by practicing physicians.

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.

Practice-based small group learning (PBSGL) for CPD: a pilot with general practice trainees to support the transition to independent practice.

PubMed

Rial, Jonathan; Scallan, Samantha

2013-05-01

The paper describes a small-scale enquiry with UK-based general practice specialty trainees (GPSTs) at the time of transition from training to independent practice. It aimed to identify whether they were supported in making this transition through attending practice-based small group learning (PBSGL) sessions. Participants in the study reported that the sessions helped them to consolidate their learning from their third year of training (GPST3), improved their ability to identify and use evidence in practice, and shifted the focus of their learning needs away from the two UK general practice postgraduate exams (applied Knowledge Test or aKT; and Clinical Skills assessment or CSa) and towards 'real world' practice. The two pilot groups have become established as means of peer support and continue to meet, with small changes in composition. The work has led to the wider roll out of PBSGL for newly qualified GPs across Wessex.

[Virtual microscopy in pathology teaching and postgraduate training (continuing education)].

PubMed

Sinn, H P; Andrulis, M; Mogler, C; Schirmacher, P

2008-11-01

As with conventional microscopy, virtual microscopy permits histological tissue sections to be viewed on a computer screen with a free choice of viewing areas and a wide range of magnifications. This, combined with the possibility of linking virtual microscopy to E-Learning courses, make virtual microscopy an ideal tool for teaching and postgraduate training in pathology. Uses of virtual microscopy in pathology teaching include blended learning with the presentation of digital teaching slides in the internet parallel to presentation in the histology lab, extending student access to histology slides beyond the lab. Other uses are student self-learning in the Internet, as well as the presentation of virtual slides in the classroom with or without replacing real microscopes. Successful integration of virtual microscopy depends on its embedding in the virtual classroom and the creation of interactive E-learning content. Applications derived from this include the use of virtual microscopy in video clips, podcasts, SCORM modules and the presentation of virtual microscopy using interactive whiteboards in the classroom.

An Approach to Building a Learning Management System that Emphasizes on Incorporating Individualized Dissemination with Intelligent Tutoring

NASA Astrophysics Data System (ADS)

Ghosh, Sreya

2017-02-01

This article proposes a new six-model architecture for an intelligent tutoring system to be incorporated in a learning management system with domain-independence feature and individualized dissemination. The present six model architecture aims to simulate a human tutor. Some recent extensions of using intelligent tutoring system (ITS) explores learning management systems to behave as a real teacher during a teaching-learning process, by taking care of, mainly, the dynamic response system. However, the present paper argues that to mimic a human teacher it needs not only the dynamic response but also the incorporation of the teacher's dynamic review of students' performance and keeping track of their current level of understanding. Here, the term individualization has been used to refer to tailor making of contents and its dissemination fitting to the individual needs and capabilities of learners who is taking a course online and is subjected to teaching in absentia. This paper describes how the individual models of the proposed architecture achieves the features of ITS.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

PubMed

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Evolution of cooperation driven by incremental learning

NASA Astrophysics Data System (ADS)

Li, Pei; Duan, Haibin

2015-02-01

It has been shown that the details of microscopic rules in structured populations can have a crucial impact on the ultimate outcome in evolutionary games. So alternative formulations of strategies and their revision processes exploring how strategies are actually adopted and spread within the interaction network need to be studied. In the present work, we formulate the strategy update rule as an incremental learning process, wherein knowledge is refreshed according to one's own experience learned from the past (self-learning) and that gained from social interaction (social-learning). More precisely, we propose a continuous version of strategy update rules, by introducing the willingness to cooperate W, to better capture the flexibility of decision making behavior. Importantly, the newly gained knowledge including self-learning and social learning is weighted by the parameter ω, establishing a strategy update rule involving innovative element. Moreover, we quantify the macroscopic features of the emerging patterns to inspect the underlying mechanisms of the evolutionary process using six cluster characteristics. In order to further support our results, we examine the time evolution course for these characteristics. Our results might provide insights for understanding cooperative behaviors and have several important implications for understanding how individuals adjust their strategies under real-life conditions.

Design for learning - a case study of blended learning in a science unit.

PubMed

Gleadow, Roslyn; Macfarlan, Barbara; Honeydew, Melissa

2015-01-01

Making material available through learning management systems is standard practice in most universities, but this is generally seen as an adjunct to the 'real' teaching, that takes place in face-to-face classes. Lecture attendance is poor, and it is becoming increasingly difficult to engage students, both in the material being taught and campus life. This paper describes the redevelopment of a large course in scientific practice and communication that is compulsory for all science students studying at our Melbourne and Malaysian campuses, or by distance education. Working with an educational designer, a blended learning methodology was developed, converting the environment provided by the learning management system into a teaching space, rather than a filing system. To ensure focus, topics are clustered into themes with a 'question of the week', a pre-class stimulus and follow up activities. The content of the course did not change, but by restructuring the delivery using educationally relevant design techniques, the content was contextualised resulting in an integrated learning experience. Students are more engaged intellectually, and lecture attendance has improved. The approach we describe here is a simple and effective approach to bringing this university's teaching and learning into the 21 (st) century.

Learning implicit brain MRI manifolds with deep learning

NASA Astrophysics Data System (ADS)

Bermudez, Camilo; Plassard, Andrew J.; Davis, Larry T.; Newton, Allen T.; Resnick, Susan M.; Landman, Bennett A.

2018-03-01

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a cross-correlation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Inferring Facts From Fiction: Reading Correct and Incorrect Information Affects Memory for Related Information

PubMed Central

Butler, Andrew C.; Dennis, Nancy A.; Marsh, Elizabeth J.

2012-01-01

People can acquire both true and false knowledge about the world from fictional stories (Marsh & Fazio, 2007). The present study explored whether the benefits and costs of learning about the world from fictional stories extend beyond memory for directly stated pieces of information. Of interest was whether readers would use correct and incorrect story references to make deductive inferences about related information in the story, and then integrate those inferences into their knowledge bases. Subjects read stories containing correct, neutral, and misleading references to facts about the world; each reference could be combined with another reference that occurred in a later sentence to make a deductive inference. Later, they answered general knowledge questions that tested for these deductive inferences. The results showed that subjects generated and retained the deductive inferences regardless of whether the inferences were consistent or inconsistent with world knowledge, and irrespective of whether the references were placed consecutively in the text or separated by many sentences. Readers learn more than what is directly stated in stories; they use references to the real world to make both correct and incorrect inferences that are integrated into their knowledge bases. PMID:22640369

Comparison of three problem-based learning conditions (real patients, digital and paper) with lecture-based learning in a dermatology course: a prospective randomized study from China.

PubMed

Li, Jie; Li, Qing Ling; Li, Ji; Chen, Ming Liang; Xie, Hong Fu; Li, Ya Ping; Chen, Xiang

2013-01-01

The precise effect and the quality of different cases used in dermatology problem-based learning (PBL) curricula are yet unclear. To prospectively compare the impact of real patients, digital, paper PBL (PPBL) and traditional lecture-based learning (LBL) on academic results and student perceptions. A total of 120 students were randomly allocated into either real-patients PBL (RPBL) group studied via real-patient cases, digital PBL (DPBL) group studied via digital-form cases, PPBL group studied via paper-form cases, or conventional group who received didactic lectures. Academic results were assessed through review of written examination, objective structured clinical examination and student performance scores. A five-point Likert scale questionnaire was used to evaluate student perceptions. Compared to those receiving lectures only, all PBL participants had better results for written examination, clinical examination and overall performance. Students in RPBL group exhibited better overall performance than those in the other two PBL groups. Real-patient cases were more effective in helping develop students' self-directed learning skills, improving their confidence in future patient encounters and encouraging them to learn more about the discussed condition, compared to digital and paper cases. Both real patient and digital triggers are helpful in improving students' clinical problem-handling skills. However, real patients provide greater benefits to students.

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Creating interactive physics simulations using the power of GeoGebra

NASA Astrophysics Data System (ADS)

Walsh, Tom

2017-05-01

I have long incorporated physics simulations in my physics teaching, and truly appreciate those who have made their simulations available to the public. I often would think of an idea for a simulation I would love to be able to use, but with no real programming background I did not know how I could make my own. That was the case until I discovered GeoGebra, which is an open source software offering "Dynamic Mathematics for Teaching and Learning." GeoGebra is freely available for non-commercial users. It is powerful, easy to learn, and versatile. There are versions for Windows, Mac, and Linux, as well as tablet and phone versions. It can also be run directly from a Chrome browser.

Parallel Online Temporal Difference Learning for Motor Control.

PubMed

Caarls, Wouter; Schuitema, Erik

2016-07-01

Temporal difference (TD) learning, a key concept in reinforcement learning, is a popular method for solving simulated control problems. However, in real systems, this method is often avoided in favor of policy search methods because of its long learning time. But policy search suffers from its own drawbacks, such as the necessity of informed policy parameterization and initialization. In this paper, we show that TD learning can work effectively in real robotic systems as well, using parallel model learning and planning. Using locally weighted linear regression and trajectory sampled planning with 14 concurrent threads, we can achieve a speedup of almost two orders of magnitude over regular TD control on simulated control benchmarks. For a real-world pendulum swing-up task and a two-link manipulator movement task, we report a speedup of 20× to 60× , with a real-time learning speed of less than half a minute. The results are competitive with state-of-the-art policy search.

Making intelligent systems team players. A guide to developing intelligent monitoring systems

NASA Technical Reports Server (NTRS)

Land, Sherry A.; Malin, Jane T.; Thronesberry, Carroll; Schreckenghost, Debra L.

1995-01-01

This reference guide for developers of intelligent monitoring systems is based on lessons learned by developers of the DEcision Support SYstem (DESSY), an expert system that monitors Space Shuttle telemetry data in real time. DESSY makes inferences about commands, state transitions, and simple failures. It performs failure detection rather than in-depth failure diagnostics. A listing of rules from DESSY and cue cards from DESSY subsystems are included to give the development community a better understanding of the selected model system. The G-2 programming tool used in developing DESSY provides an object-oriented, rule-based environment, but many of the principles in use here can be applied to any type of monitoring intelligent system. The step-by-step instructions and examples given for each stage of development are in G-2, but can be used with other development tools. This guide first defines the authors' concept of real-time monitoring systems, then tells prospective developers how to determine system requirements, how to build the system through a combined design/development process, and how to solve problems involved in working with real-time data. It explains the relationships among operational prototyping, software evolution, and the user interface. It also explains methods of testing, verification, and validation. It includes suggestions for preparing reference documentation and training users.

An analysis of intergroup rivalry using Ising model and reinforcement learning

NASA Astrophysics Data System (ADS)

Zhao, Feng-Fei; Qin, Zheng; Shao, Zhuo

2014-01-01

Modeling of intergroup rivalry can help us better understand economic competitions, political elections and other similar activities. The result of intergroup rivalry depends on the co-evolution of individual behavior within one group and the impact from the rival group. In this paper, we model the rivalry behavior using Ising model. Different from other simulation studies using Ising model, the evolution rules of each individual in our model are not static, but have the ability to learn from historical experience using reinforcement learning technique, which makes the simulation more close to real human behavior. We studied the phase transition in intergroup rivalry and focused on the impact of the degree of social freedom, the personality of group members and the social experience of individuals. The results of computer simulation show that a society with a low degree of social freedom and highly educated, experienced individuals is more likely to be one-sided in intergroup rivalry.

Ecological literacy and beyond: Problem-based learning for future professionals.

PubMed

Lewinsohn, Thomas M; Attayde, José Luiz; Fonseca, Carlos Roberto; Ganade, Gislene; Jorge, Leonardo Ré; Kollmann, Johannes; Overbeck, Gerhard E; Prado, Paulo Inácio; Pillar, Valério D; Popp, Daniela; da Rocha, Pedro L B; Silva, Wesley Rodrigues; Spiekermann, Annette; Weisser, Wolfgang W

2015-03-01

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

A Virtual Reality Simulator Prototype for Learning and Assessing Phaco-sculpting Skills

NASA Astrophysics Data System (ADS)

Choi, Kup-Sze

This paper presents a virtual reality based simulator prototype for learning phacoemulsification in cataract surgery, with focus on the skills required for making a cross-shape trench in cataractous lens by an ultrasound probe during the phaco-sculpting procedure. An immersive virtual environment is created with 3D models of the lens and surgical tools. Haptic device is also used as 3D user interface. Phaco-sculpting is simulated by interactively deleting the constituting tetrahedrons of the lens model. Collisions between the virtual probe and the lens are effectively identified by partitioning the space containing the lens hierarchically with an octree. The simulator can be programmed to collect real-time quantitative user data for reviewing and assessing trainee's performance in an objective manner. A game-based learning environment can be created on top of the simulator by incorporating gaming elements based on the quantifiable performance metrics.

Implicit Statistical Learning in Real-World Environments Leads to Ecologically Rational Decision Making.

PubMed

Perkovic, Sonja; Orquin, Jacob Lund

2018-01-01

Ecological rationality results from matching decision strategies to appropriate environmental structures, but how does the matching happen? We propose that people learn the statistical structure of the environment through observation and use this learned structure to guide ecologically rational behavior. We tested this hypothesis in the context of organic foods. In Study 1, we found that products from healthful food categories are more likely to be organic than products from nonhealthful food categories. In Study 2, we found that consumers' perceptions of the healthfulness and prevalence of organic products in many food categories are accurate. Finally, in Study 3, we found that people perceive organic products as more healthful than nonorganic products when the statistical structure justifies this inference. Our findings suggest that people believe organic foods are more healthful than nonorganic foods and use an organic-food cue to guide their behavior because organic foods are, on average, 30% more healthful.

Probability Learning: Changes in Behavior Across Time and Development.

PubMed

Plate, Rista C; Fulvio, Jacqueline M; Shutts, Kristin; Green, C Shawn; Pollak, Seth D

2018-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience-within a learning session and over development-influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Kids Making Sense of Air Quality Around Them Through a Hands-On, STEM-Based Program

NASA Astrophysics Data System (ADS)

Dye, T.

2015-12-01

Air pollution in many parts of the world is harming millions of people, shortening lives, and taking a toll on our ecosystem. Cities in India, China, and even the United States frequently exceed air quality standards. The use of localized data is a powerful enhancement to regulatory monitoring site data. Learning about air quality at a local level is a powerful driver for change. The Kids Making Sense program unites Science, Technology, Engineering, and Mathematics (STEM) education with a complete measurement and environmental education system that teaches youth about air pollution and empowers them to drive positive change in their communities. With this program, youth learn about particle pollution, its sources, and health effects. A half-day lecture is followed by hands-on activity using handheld air sensors paired with an app on smartphones. Students make measurements around schools to discover pollution sources and cleaner areas. Next, the data they collect are crowdsourced on a website for guided discussion and data interpretation. This program meets Next Generation Science Standards, encourages project-based learning and deep understanding of applied science, and allows students to practice science like real scientists. The program has been successfully implemented in several schools in the United States and Asia, including New York City, San Francisco, Los Angeles, and Sacramento in the United States, and Taipei and Taichung in Taiwan. During this talk, we'll provide an overview of the program, discuss some of the challenges, and lay out the next steps for Kids Making Sense.

The revolution of personalized psychiatry: will technology make it happen sooner?

PubMed

Perna, G; Grassi, M; Caldirola, D; Nemeroff, C B

2018-04-01

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Comparison of case-based and lecture-based learning in dental education using the SOLO taxonomy.

PubMed

Ilgüy, Mehmet; Ilgüy, Dilhan; Fişekçioğlu, Erdoğan; Oktay, Inci

2014-11-01

The aim of this study was to compare the impact of case-based learning (CBL) and lecture-based learning (LBL) on fourth-year dental students' clinical decision making by using the Structure of Observed Learning Outcome (SOLO) taxonomy. Participants in the study were fourth-year dental students (n=55) in academic year 2012-13 taught in a large-group LBL context and fourth-year dental students (n=54) in academic year 2013-14 taught with the CBL methodology; both took place in the oral diseases course at Yeditepe University Faculty of Dentistry, Istanbul, Turkey. All eligible students participated, for a 100 percent response rate. A real case was presented to the students in both groups to assess their clinical decision making on the topic of oral diseases. Their performance was evaluated with the SOLO taxonomy. Student t-test was used for statistical evaluation, and significance was set at the p<0.05 level. A statistically significant difference was found between the mean scores of the relational and extended abstract categories of the CBL and LBL groups (p<0.05). Students who were taught with CBL had higher scores at the top two levels of the SOLO taxonomy than students taught with LBL. These findings suggest that an integrated case-based curriculum may be effective in promoting students' deep learning and it holds promise for better integration of clinical cases likely to be encountered during independent practice.

The Best of All Worlds: Immersive Interfaces for Art Education in Virtual and Real World Teaching and Learning Environments

ERIC Educational Resources Information Center

Grenfell, Janette

2013-01-01

Selected ubiquitous technologies encourage collaborative participation between higher education students and educators within a virtual socially networked e-learning landscape. Multiple modes of teaching and learning, ranging from real world experiences, to text and digital images accessed within the Deakin studies online learning management…

The Implementation of Service-Learning in Graduate Instructional Design Coursework

ERIC Educational Resources Information Center

Stefaniak, Jill E.

2015-01-01

This paper describes the design of service-learning experiences with a graduate-level instructional design course. Service-learning provides students with real-life experiences in a situated-learning environment. Students were tasked with working on an instructional design project in a real-world setting to gain consultative experience. This paper…

Uniting Community and University through Service Learning

ERIC Educational Resources Information Center

Arney, Janna B.; Jones, Irma

2006-01-01

At its core, service-learning is about creating opportunities for students to apply theory they learn in the classroom to real-world problems and real-world needs. A service-learning project was initiated with the CEO of the Brownsville Chamber of Commerce. The project required 2nd-year business communication students to interview community…

Features: Real-Time Adaptive Feature and Document Learning for Web Search.

ERIC Educational Resources Information Center

Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

2001-01-01

Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.

PubMed

Gershman, Samuel J; Daw, Nathaniel D

2017-01-03

We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, one challenge in the study of RL is computational: The simplicity of these tasks ignores important aspects of reinforcement learning in the real world: (a) State spaces are high-dimensional, continuous, and partially observable; this implies that (b) data are relatively sparse and, indeed, precisely the same situation may never be encountered twice; furthermore, (c) rewards depend on the long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, theories of RL have largely involved procedural and semantic memory, the way in which knowledge about action values or world models extracted gradually from many experiences can drive choice. This focus on semantic memory leaves out many aspects of memory, such as episodic memory, related to the traces of individual events. We suggest that these two challenges are related. The computational challenge can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (a) efficiently approximate value functions over complex state spaces, (b) learn with very little data, and (c) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system.

Kaiser Permanente's performance improvement system, Part 4: Creating a learning organization.

PubMed

Schilling, Lisa; Dearing, James W; Staley, Paul; Harvey, Patti; Fahey, Linda; Kuruppu, Francesca

2011-12-01

In 2006, recognizing variations in performance in quality, safety, service, and efficiency, Kaiser Permanente leaders initiated the development of a performance improvement (PI) system. Kaiser Permanente has implemented a strategy for creating the systemic capacity for continuous improvement that characterizes a learning organization. Six "building blocks" were identified to enable Kaiser Permanente to make the transition to becoming a learning organization: real-time sharing of meaningful performance data; formal training in problem-solving methodology; workforce engagement and informal knowledge sharing; leadership structures, beliefs, and behaviors; internal and external benchmarking; and technical knowledge sharing. Putting each building block into place required multiple complex strategies combining top-down and bottom-up approaches. Although the strategies have largely been successful, challenges remain. The demand for real-time meaningful performance data can conflict with prioritized changes to health information systems. It is an ongoing challenge to teach PI, change management, innovation, and project management to all managers and staff without consuming too much training time. Challenges with workforce engagement include low initial use of tools intended to disseminate information through virtual social networking. Uptake of knowledge-sharing technologies is still primarily by innovators and early adopters. Leaders adopt new behaviors at varying speeds and have a range of abilities to foster an environment that is psychologically safe and stimulates inquiry. A learning organization has the capability to improve, and it develops structures and processes that facilitate the acquisition and sharing of knowledge.

Parallel-distributed mobile robot simulator

NASA Astrophysics Data System (ADS)

Okada, Hiroyuki; Sekiguchi, Minoru; Watanabe, Nobuo

1996-06-01

The aim of this project is to achieve an autonomous learning and growth function based on active interaction with the real world. It should also be able to autonomically acquire knowledge about the context in which jobs take place, and how the jobs are executed. This article describes a parallel distributed movable robot system simulator with an autonomous learning and growth function. The autonomous learning and growth function which we are proposing is characterized by its ability to learn and grow through interaction with the real world. When the movable robot interacts with the real world, the system compares the virtual environment simulation with the interaction result in the real world. The system then improves the virtual environment to match the real-world result more closely. This the system learns and grows. It is very important that such a simulation is time- realistic. The parallel distributed movable robot simulator was developed to simulate the space of a movable robot system with an autonomous learning and growth function. The simulator constructs a virtual space faithful to the real world and also integrates the interfaces between the user, the actual movable robot and the virtual movable robot. Using an ultrafast CG (computer graphics) system (FUJITSU AG series), time-realistic 3D CG is displayed.

Big data: the management revolution.

PubMed

McAfee, Andrew; Brynjolfsson, Erik

2012-10-01

Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.

GEE-WIS Anchored Problem Solving Using Real-Time Authentic Water Quality Data

NASA Astrophysics Data System (ADS)

Young, M.; Wlodarczyk, M. S.; Branco, B.; Torgersen, T.

2002-05-01

GEE-WIS scientific problem solving consists of observing, hypothesizing, synthesis, argument building and reasoning, in the context of analysis, representation, modeling and sense-making of real-time authentic water quality data. Geoscience Environmental Education - Web-accessible Instrumented Systems, or GEE-WIS, an NSF Geoscience Education grant, has established a set of companion websites that stream real-time data from two campus retention ponds for research and use in secondary and undergraduate water quality lessons. We have targeted scientific problem solving skills because of the nature of the GEE-WIS environment, but further because they are central to state and federal efforts to establish science education curriculum standards and are at the core of performance-based testing. We have used a design experiment process to create and test two Anchored Instruction scenario problems. Customization such as that done through a design process, is acknowledged to be a fundamental component of educational research from an ecological psychology perspective. Our efforts have shared core design elements with other NSF water quality projects. Our method involves the analysis of student written scenario responses for level of scientific problem solving using a qualitative scoring rubric designed from participation in a related NSF project, SCALE (Synergy Communities: Aggregating Learning about Education). Student solutions of GEE-WIS anchor problems from Fall 2001 and Spring 2002 will be summarized. Implications are drawn for those interested in making secondary and high education geoscience more realistic and more motivating for students through the use of real-time authentic data via Internet.

On-chip learning of hyper-spectral data for real time target recognition

NASA Technical Reports Server (NTRS)

Duong, T. A.; Daud, T.; Thakoor, A.

2000-01-01

As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.

Optimizing students' motivation in inquiry-based learning environments: The role of instructional practices

NASA Astrophysics Data System (ADS)

Kempler, Toni M.

The influence of inquiry science instruction on the motivation of 1360 minority inner-city seventh graders was examined. The project-based curriculum incorporates motivating features like real world questions, collaboration, technology, and lesson variety. Students design investigations, collect and analyze data, and create artifacts; challenging tasks require extensive use of learning and metacognitive strategies. Study 1 used Structural Equation Modeling to investigate student perceptions of the prevalence of project-based features, including real world connections, collaboration, academic press, and work norms, and their relation to interest, efficacy, cognitive engagement, and achievement. Perceptions of features related to different motivational outcomes, indicating the importance of using differentiated rather than single measures to study motivation in context. Cognitive engagement was enhanced by interest and efficacy but did not influence achievement, perhaps because students were not proficient strategy users and were new to inquiry. Study 2 examined the relationship between instructional practices and motivation. The 23 teachers in study 1 were observed six times during one unit. Observations focused on curriculum congruence, content accuracy, contextualization, sense making, and management and climate. A majority of teacher enactment was congruent with the curriculum, indicating that students experienced motivating features of project-based science. Hierarchical Linear Modeling showed that contextualization accounted for between-teacher variance in student interest, efficacy, and cognitive engagement; Teachers encouraged motivation through extended real world examples that related material to students' experiences. Cluster analysis was used to determine how patterns of practice affected motivation. Unexpectedly these patterns did not differentially relate to cognitive engagement. Findings showed that interest and efficacy were enhanced when teachers used particular sense making practices. These teachers provided explicit scaffolding for accomplishing complex tasks with questioning and feedback that highlighted key points. Teachers also used effective management practices and maintained a positive classroom climate. In contrast, a pattern of practice where teachers used questioning and feedback to press students to make connections and synthesize concepts without scaffolding support diminished motivation, because students may have needed more help to deal with challenge. Implications from both studies suggest inquiry teachers need to use explicit scaffolding and academic press together, with effective management practices, to support motivation.

Feedback Blunting: Total Sleep Deprivation Impairs Decision Making that Requires Updating Based on Feedback

PubMed Central

Whitney, Paul; Hinson, John M.; Jackson, Melinda L.; Van Dongen, Hans P.A.

2015-01-01

Study Objectives: To better understand the sometimes catastrophic effects of sleep loss on naturalistic decision making, we investigated effects of sleep deprivation on decision making in a reversal learning paradigm requiring acquisition and updating of information based on outcome feedback. Design: Subjects were randomized to a sleep deprivation or control condition, with performance testing at baseline, after 2 nights of total sleep deprivation (or rested control), and following 2 nights of recovery sleep. Subjects performed a decision task involving initial learning of go and no go response sets followed by unannounced reversal of contingencies, requiring use of outcome feedback for decisions. A working memory scanning task and psychomotor vigilance test were also administered. Setting: Six consecutive days and nights in a controlled laboratory environment with continuous behavioral monitoring. Subjects: Twenty-six subjects (22–40 y of age; 10 women). Interventions: Thirteen subjects were randomized to a 62-h total sleep deprivation condition; the others were controls. Results: Unlike controls, sleep deprived subjects had difficulty with initial learning of go and no go stimuli sets and had profound impairment adapting to reversal. Skin conductance responses to outcome feedback were diminished, indicating blunted affective reactions to feedback accompanying sleep deprivation. Working memory scanning performance was not significantly affected by sleep deprivation. And although sleep deprived subjects showed expected attentional lapses, these could not account for impairments in reversal learning decision making. Conclusions: Sleep deprivation is particularly problematic for decision making involving uncertainty and unexpected change. Blunted reactions to feedback while sleep deprived underlie failures to adapt to uncertainty and changing contingencies. Thus, an error may register, but with diminished effect because of reduced affective valence of the feedback or because the feedback is not cognitively bound with the choice. This has important implications for understanding and managing sleep loss-induced cognitive impairment in emergency response, disaster management, military operations, and other dynamic real-world settings with uncertain outcomes and imperfect information. Citation: Whitney P, Hinson JM, Jackson ML, Van Dongen HPA. Feedback blunting: total sleep deprivation impairs decision making that requires updating based on feedback. SLEEP 2015;38(5):745–754. PMID:25515105

Placement education pedagogy as social participation: what are students really learning?

PubMed

Kell, Clare

2014-03-01

This paper draws on empirical fieldwork data of naturally occurring UK physiotherapy placement education to make visible how education is actually carried out and suggest what students may be learning through their placement interactions. The data challenge everyone involved in placement education design and practice to consider the values and practices students are learning to perpetuate through placement education experiences. The researcher undertook an ethnomethodologically informed ethnographic observation of naturally occurring physiotherapy placement education in two UK NHS placement sites. This study adopted a social perspective of learning to focus on the minutiae of placement educator, student and patient interaction practices during student-present therapeutic activities. Two days of placement for each of six senior students were densely recorded in real-time focussing specifically on the verbal, kinesics and proxemics-based elements of the participants' interaction practices. Repeated cycles of data analysis suggested consistent practices irrespective of the placement, educators, students or patients. The data suggest that placement education is a powerful situated learning environment in which students see, experience and learn to reproduce the physiotherapy practices valued by the local placement. Consistently, placement educators and students co-produced patient-facing activities as spectacles of physiotherapy-as-science. In each setting, patients were used as person-absent audiovisual teaching aids from which students learnt to make a case for physiotherapy intervention. The paper challenges physiotherapists and other professions using work-placement education to look behind the rhetoric of their placement documentation and explore the reality of students' learning in the field. The UK-based physiotherapy profession may wish to consider further the possible implications of its self-definition as a 'science-based healthcare profession' on its in-the-presence-of-students interactions with patients. Copyright © 2013 John Wiley & Sons, Ltd.

Effects of Pedagogical Agent Gestures on Social Acceptance and Learning: Virtual Real Relationships in an Elementary Foreign Language Classroom

ERIC Educational Resources Information Center

Davis, Robert; Antonenko, Pavlo

2017-01-01

Pedagogical agents (PAs) are lifelike characters in virtual environments that help facilitate learning through social interactions and the virtual real relationships with the learners. This study explored whether and how PA gesture design impacts learning and agent social acceptance when used with elementary students learning foreign language…

Development of a Ubiquitous Learning Platform Based on a Real-Time Help-Seeking Mechanism

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Wu, Chih-Hsiang; Tseng, Judy C. R.; Huang, Iwen

2011-01-01

The popularity of mobile devices has encouraged the advance of ubiquitous learning, in which students are situated in a real-world learning environment with support from the digital world via the use of mobile, wireless communications, or even sensing technologies. Most of the ubiquitous learning systems are implemented with high-cost sensing…

Effects of ICT Assisted Real and Virtual Learning on the Performance of Secondary School Students

ERIC Educational Resources Information Center

Deka, Monisha; Jena, Ananta Kumar

2017-01-01

The study aimed to assess the effect of ICT assisted real and virtual learning performance over the traditional approach of secondary school students. Non-Equivalent Pretest-Posttest Quasi Experimental Design used to assess and relate the effects of independent variables virtual learning on dependent variables (i.e. learning performance).…

Making clinical case-based learning in veterinary medicine visible: analysis of collaborative concept-mapping processes and reflections.

PubMed

Khosa, Deep K; Volet, Simone E; Bolton, John R

2014-01-01

The value of collaborative concept mapping in assisting students to develop an understanding of complex concepts across a broad range of basic and applied science subjects is well documented. Less is known about students' learning processes that occur during the construction of a concept map, especially in the context of clinical cases in veterinary medicine. This study investigated the unfolding collaborative learning processes that took place in real-time concept mapping of a clinical case by veterinary medical students and explored students' and their teacher's reflections on the value of this activity. This study had two parts. The first part investigated the cognitive and metacognitive learning processes of two groups of students who displayed divergent learning outcomes in a concept mapping task. Meaningful group differences were found in their level of learning engagement in terms of the extent to which they spent time understanding and co-constructing knowledge along with completing the task at hand. The second part explored students' and their teacher's views on the value of concept mapping as a learning and teaching tool. The students' and their teacher's perceptions revealed congruent and contrasting notions about the usefulness of concept mapping. The relevance of concept mapping to clinical case-based learning in veterinary medicine is discussed, along with directions for future research.

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

Thumann, A.

This book presents the detailed guidance on how to effectively purchase deregulated energy, based on first-hand reports from many of the nation`s most knowledgeable experts. It is designed to provide the kind of practical advice needed by professionals who are responsible for making energy purchasing decisions. The book gives a ten-step program to guide building owners in purchasing decision making, a state-by-state retail competition update, and guidelines for buying electricity and natural gas over the worldwide web. Other topics include contract renegotiation strategies, an assessment of power pools, the role of aggregators in the energy market, real time pricing issues,more » where cogeneration fits within today`s marketplace, and lessons learned from deregulation experiences in Scandinavia and England.« less

The Power of Real-World Application

ERIC Educational Resources Information Center

Stam, Brad

2011-01-01

Linked learning transforms students' high school experience by linking a college preparatory course sequence with demanding technical education, and linking real-world experiences with classroom learning to help students gain an advantage in high school, postsecondary education, and careers. With linked learning, students follow industry-themed…

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

Measuring societal effects of transdisciplinary research projects: design and application of an evaluation method.

PubMed

Walter, Alexander I; Helgenberger, Sebastian; Wiek, Arnim; Scholz, Roland W

2007-11-01

Most Transdisciplinary Research (TdR) projects combine scientific research with the building of decision making capacity for the involved stakeholders. These projects usually deal with complex, societally relevant, real-world problems. This paper focuses on TdR projects, which integrate the knowledge of researchers and stakeholders in a collaborative transdisciplinary process through structured methods of mutual learning. Previous research on the evaluation of TdR has insufficiently explored the intended effects of transdisciplinary processes on the real world (societal effects). We developed an evaluation framework for assessing the societal effects of transdisciplinary processes. Outputs (measured as procedural and product-related involvement of the stakeholders), impacts (intermediate effects connecting outputs and outcomes) and outcomes (enhanced decision making capacity) are distinguished as three types of societal effects. Our model links outputs and outcomes of transdisciplinary processes via the impacts using a mediating variables approach. We applied this model in an ex post evaluation of a transdisciplinary process. 84 out of 188 agents participated in a survey. The results show significant mediation effects of the two impacts "network building" and "transformation knowledge". These results indicate an influence of a transdisciplinary process on the decision making capacity of stakeholders, especially through social network building and the generation of knowledge relevant for action.

Exposure to Unsolvable Anagrams Impairs Performance on the Iowa Gambling Task

PubMed Central

Starcke, Katrin; Agorku, Janet D.; Brand, Matthias

2017-01-01

Recent research indicates that external manipulations, such as stress or mood induction, can affect decision-making abilities. In the current study, we investigated whether the exposure to an unsolvable task affected subsequent performance on the Iowa Gambling Task. Participants were randomly assigned to a condition in which they were exposed to unsolvable anagrams (n = 20), or a condition in which they worked on solvable anagrams (n = 22). Afterwards, all participants played the Iowa Gambling Task, a prominent task that measures decision making under uncertain conditions with no explicit rules for gains and losses. In this task, it is essential to process feedback from previous decisions. The results demonstrated that participants who worked on unsolvable anagrams made more disadvantageous decisions on the Iowa Gambling Task than the other participants. In addition, a significant gender effect was observed: Males who worked on unsolvable anagrams made a more disadvantageous decisions than the other male participants. Females who worked on unsolvable anagrams also made more disadvantageous decision than the other female participants, but differences were small and not significant. We conclude that the exposure to unsolvable anagrams induced the experience of uncontrollability which can elicit stress and learned helplessness. Stress and learned helplessness might have reduced the ability to learn from the given feedback, particularly in male participants. We assume that in real life, uncontrollable challenges that last longer than a single experimental manipulation can affect decision making severely, at least in males. PMID:28642693

Citizen Science as a REAL Environment for Authentic Scientific Inquiry

ERIC Educational Resources Information Center

Meyer, Nathan J.; Scott, Siri; Strauss, Andrea Lorek; Nippolt, Pamela L.; Oberhauser, Karen S.; Blair, Robert B.

2014-01-01

Citizen science projects can serve as constructivist learning environments for programming focused on science, technology, engineering, and math (STEM) for youth. Attributes of "rich environments for active learning" (REALs) provide a framework for design of Extension STEM learning environments. Guiding principles and design strategies…

Linking Project-Based Interdisciplinary Learning and Recommended Professional Competencies with Business Management, Digital Media, Distance Learning, Engineering Technology, and English

ERIC Educational Resources Information Center

Bender, Melinda; Fulwider, Miles; Stemkoski, Michael J.

2008-01-01

This paper encourages the investigation of real world problems by students and faculty and links recommended student competencies with project based learning. In addition to the traditional course objectives, project-based learning (PBL) uses real world problems for classroom instruction and fieldwork to connect students, instructors, and industry…

What You Learn is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

PubMed Central

Smith, Linda

2016-01-01

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps to use eye movement data to infer underlying real-time processes in statistical word learning. PMID:22213894

Sound-symbolism boosts novel word learning.

PubMed

Lockwood, Gwilym; Dingemanse, Mark; Hagoort, Peter

2016-08-01

The existence of sound-symbolism (or a non-arbitrary link between form and meaning) is well-attested. However, sound-symbolism has mostly been investigated with nonwords in forced choice tasks, neither of which are representative of natural language. This study uses ideophones, which are naturally occurring sound-symbolic words that depict sensory information, to investigate how sensitive Dutch speakers are to sound-symbolism in Japanese in a learning task. Participants were taught 2 sets of Japanese ideophones; 1 set with the ideophones' real meanings in Dutch, the other set with their opposite meanings. In Experiment 1, participants learned the ideophones and their real meanings much better than the ideophones with their opposite meanings. Moreover, despite the learning rounds, participants were still able to guess the real meanings of the ideophones in a 2-alternative forced-choice test after they were informed of the manipulation. This shows that natural language sound-symbolism is robust beyond 2-alternative forced-choice paradigms and affects broader language processes such as word learning. In Experiment 2, participants learned regular Japanese adjectives with the same manipulation, and there was no difference between real and opposite conditions. This shows that natural language sound-symbolism is especially strong in ideophones, and that people learn words better when form and meaning match. The highlights of this study are as follows: (a) Dutch speakers learn real meanings of Japanese ideophones better than opposite meanings, (b) Dutch speakers accurately guess meanings of Japanese ideophones, (c) this sensitivity happens despite learning some opposite pairings, (d) no such learning effect exists for regular Japanese adjectives, and (e) this shows the importance of sound-symbolism in scaffolding language learning. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Integrating Real-time Earthquakes into Natural Hazard Courses

NASA Astrophysics Data System (ADS)

Furlong, K. P.; Benz, H. M.; Whitlock, J. S.; Bittenbinder, A. N.; Bogaert, B. B.

2001-12-01

Natural hazard courses are playing an increasingly important role in college and university earth science curricula. Students' intrinsic curiosity about the subject and the potential to make the course relevant to the interests of both science and non-science students make natural hazards courses popular additions to a department's offerings. However, one vital aspect of "real-life" natural hazard management that has not translated well into the classroom is the real-time nature of both events and response. The lack of a way to entrain students into the event/response mode has made implementing such real-time activities into classroom activities problematic. Although a variety of web sites provide near real-time postings of natural hazards, students essentially learn of the event after the fact. This is particularly true for earthquakes and other events with few precursors. As a result, the "time factor" and personal responsibility associated with natural hazard response is lost to the students. We have integrated the real-time aspects of earthquake response into two natural hazard courses at Penn State (a 'general education' course for non-science majors, and an upper-level course for science majors) by implementing a modification of the USGS Earthworm system. The Earthworm Database Management System (E-DBMS) catalogs current global seismic activity. It provides earthquake professionals with real-time email/cell phone alerts of global seismic activity and access to the data for review/revision purposes. We have modified this system so that real-time response can be used to address specific scientific, policy, and social questions in our classes. As a prototype of using the E-DBMS in courses, we have established an Earthworm server at Penn State. This server receives national and global seismic network data and, in turn, transmits the tailored alerts to "on-duty" students (e-mail, pager/cell phone notification). These students are responsible to react to the alarm real-time, consulting other members of their class and accessing the E-DBMS server and other links to glean information that they will then use to make decisions. Students wrestle with the complications in interpreting natural hazard data, evaluating whether a response is needed, and problems such as those associated with communication between media and the public through these focused exercises. Although earthquakes are targeted at present, similar DBMS systems are envisioned for other natural hazards like flooding, volcanoes, and severe weather. We are testing this system as a prototype intended to be expanded to provide web-based access to classes at both the middle/high school and college/university levels.

Connected Traveler

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

2016-06-01

The Connected Traveler framework seeks to boost the energy efficiency of personal travel and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. It is anticipated that this approach will establish a feedback loop that 'learns' traveler preferences and customizes incentives to meet or exceed energy efficiency targets by empowering individual travelers with information needed to make energy-efficient choices and reducing the complexity required to validate transportation system energy savings. This handout provides an overview of NREL's Connected Traveler project, including graphics, milestones, and contact information.

The Digital Board in a University Setting: Two Real Cases in Europe and East Africa

NASA Astrophysics Data System (ADS)

Bertarelli, Fabio; Corradini, Matteo; Guaraldi, Giacomo; Genovese, Elisabetta; Kilwake, Juma; Mutua, Stephen

Usually the digital board is thought of as a tool that can only be used beneficially in the context of primary school, secondary school or in a situation of learning handicap. In this case study we want to highlight how the new tools can be used in more broad settings such as teaching in scientific and technical universities. The easy adoption of all useful software on the market to the use of these tools makes them an innovative element in the teaching techniques of the future.

Endovascular Neurosurgery: Personal Experience and Future Perspectives.

PubMed

Raymond, Jean

2016-09-01

From Luessenhop's early clinical experience until the present day, experimental methods have been introduced to make progress in endovascular neurosurgery. A personal historical narrative, spanning the 1980s to 2010s, with a review of past opportunities, current problems, and future perspectives. Although the technology has significantly improved, our clinical culture remains a barrier to methodologically sound and safe innovative care and progress. We must learn how to safely practice endovascular neurosurgery in the presence of uncertainty and verify patient outcomes in real time. Copyright © 2016 Elsevier Inc. All rights reserved.

Simulating the Rayleigh-Taylor instability with the Ising model

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

Ball, Justin R.; Elliott, James B.

2011-08-26

The Ising model, implemented with the Metropolis algorithm and Kawasaki dynamics, makes a system with its own physics, distinct from the real world. These physics are sophisticated enough to model behavior similar to the Rayleigh-Taylor instability and by better understanding these physics, we can learn how to modify the system to better re ect reality. For example, we could add a v x and a v y to each spin and modify the exchange rules to incorporate them, possibly using two body scattering laws to construct a more realistic system.

Using Oceanography to Support Active Learning

NASA Astrophysics Data System (ADS)

Byfield, V.

2012-04-01

Teachers are always on the lookout for material to give their brightest students, in order to keep them occupied, stimulated and challenged, while the teacher gets on with helping the rest. They are also looking for material that can inspire and enthuse those who think that school is 'just boring!' Oceanography, well presented, has the capacity to do both. As a relatively young science, oceanography is not a core curriculum subject (possibly an advantage), but it draws on the traditional sciences of biology, chemistry, physic and geology, and can provide wonderful examples for teaching concepts in school sciences. It can also give good reasons for learning science, maths and technology. Exciting expeditions (research cruises) to far-flung places; opportunities to explore new worlds, a different angle on topical debates such as climate change, pollution, or conservation can bring a new life to old subjects. Access to 'real' data from satellites or Argo floats can be used to develop analytical and problem solving skills. The challenge is to make all this available in a form that can easily be used by teachers and students to enhance the learning experience. We learn by doing. Active teaching methods require students to develop their own concepts of what they are learning. This stimulates new neural connections in the brain - the physical manifestation of learning. There is a large body of evidence to show that active learning is much better remembered and understood. Active learning develops thinking skills through analysis, problem solving, and evaluation. It helps learners to use their knowledge in realistic and useful ways, and see its importance and relevance. Most importantly, properly used, active learning is fun. This paper presents experiences from a number of education outreach projects that have involved the National Oceanography Centre in Southampton, UK. All contain some element of active learning - from quizzes and puzzles to analysis of real data from satellites and Argo floats - all combined with background information about the Ocean. Many also aim to inspire and enthuse, by bringing in the human and personal, for example through blogs and Q/A sessions. This presentation takes a look at what has worked, and what may perhaps have been a little less successful.

STS Case Study Development Support

NASA Technical Reports Server (NTRS)

Rosa de Jesus, Dan A.; Johnson, Grace K.

2013-01-01

The Shuttle Case Study Collection (SCSC) has been developed using lessons learned documented by NASA engineers, analysts, and contractors. The SCSC provides educators with a new tool to teach real-world engineering processes with the goal of providing unique educational materials that enhance critical thinking, decision-making and problem-solving skills. During this third phase of the project, responsibilities included: the revision of the Hyper Text Markup Language (HTML) source code to ensure all pages follow World Wide Web Consortium (W3C) standards, and the addition and edition of website content, including text, documents, and images. Basic HTML knowledge was required, as was basic knowledge of photo editing software, and training to learn how to use NASA's Content Management System for website design. The outcome of this project was its release to the public.

Establishing a Presence

NASA Technical Reports Server (NTRS)

McCandless, Jeffrey

2005-01-01

The basis for this successful collaboration was face-to-face communication. Though it was sometimes stressful being on the road so much, I really learned the importance of being present to work together and ask questions in person. Another measure of success was that in the midst of this project and traveling, my wife and I managed to start a family. My oldest boy got a real kick out of visiting Space Center Houston when he was two to learn all about the "face futtle" which goes way up in the sky. When practical, collocation and face-to-face communication on a project eliminate misunderstandings, establish relationships, make information more easily accessible, and promote a team atmosphere. Compromise is key to balancing both family and career goals. Knowing when to prioritize each is important to success in both aspects.

Oxytocin decreases aversion to angry faces in an associative learning task.

PubMed

Evans, Simon; Shergill, Sukhwinder S; Averbeck, Bruno B

2010-12-01

Social and financial considerations are often integrated when real life decisions are made, and recent studies have provided evidence that similar brain networks are engaged when either social or financial information is integrated. Other studies, however, have suggested that the neuropeptide oxytocin can specifically affect social behaviors, which would suggest separable mechanisms at the pharmacological level. Thus, we examined the hypothesis that oxytocin would specifically affect social and not financial information in a decision making task, in which participants learned which of the two faces, one smiling and the other angry or sad, was most often being rewarded. We found that oxytocin specifically decreased aversion to angry faces, without affecting integration of positive or negative financial feedback or choices related to happy vs sad faces.

Bringing the Real World in: Reflection on Building a Virtual Learning Environment

ERIC Educational Resources Information Center

Mundkur, Anuradha; Ellickson, Cara

2012-01-01

We reflect on translating participatory and experiential learning methodologies into an online teaching environment through a Virtual Learning Environment (VLE) that simulates the "real-world" contexts of international development in order to develop an applied critical understanding of gender analysis and gender mainstreaming. Rather than being…

Investigating Functions Using Real-World Data

ERIC Educational Resources Information Center

Arnold, Stephen

2006-01-01

The possibilities for using graphic calculators to enhance the teaching and learning of mathematics are great. However, the boundaries explode when these powerful tools for learning are connected to data logging devices: a whole new approach to mathematics learning becomes possible. Using real world data to introduce the main functions (which are…

Towards Real-Time Speech Emotion Recognition for Affective E-Learning

ERIC Educational Resources Information Center

Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

2016-01-01

This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Monocular depth perception using image processing and machine learning

NASA Astrophysics Data System (ADS)

Hombali, Apoorv; Gorde, Vaibhav; Deshpande, Abhishek

2011-10-01

This paper primarily exploits some of the more obscure, but inherent properties of camera and image to propose a simpler and more efficient way of perceiving depth. The proposed method involves the use of a single stationary camera at an unknown perspective and an unknown height to determine depth of an object on unknown terrain. In achieving so a direct correlation between a pixel in an image and the corresponding location in real space has to be formulated. First, a calibration step is undertaken whereby the equation of the plane visible in the field of view is calculated along with the relative distance between camera and plane by using a set of derived spatial geometrical relations coupled with a few intrinsic properties of the system. The depth of an unknown object is then perceived by first extracting the object under observation using a series of image processing steps followed by exploiting the aforementioned mapping of pixel and real space coordinate. The performance of the algorithm is greatly enhanced by the introduction of reinforced learning making the system independent of hardware and environment. Furthermore the depth calculation function is modified with a supervised learning algorithm giving consistent improvement in results. Thus, the system uses the experience in past and optimizes the current run successively. Using the above procedure a series of experiments and trials are carried out to prove the concept and its efficacy.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

PubMed Central

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS.

PubMed

Noack, Marko; Partzsch, Johannes; Mayr, Christian G; Hänzsche, Stefan; Scholze, Stefan; Höppner, Sebastian; Ellguth, Georg; Schüffny, Rene

2015-01-01

Synaptic dynamics, such as long- and short-term plasticity, play an important role in the complexity and biological realism achievable when running neural networks on a neuromorphic IC. For example, they endow the IC with an ability to adapt and learn from its environment. In order to achieve the millisecond to second time constants required for these synaptic dynamics, analog subthreshold circuits are usually employed. However, due to process variation and leakage problems, it is almost impossible to port these types of circuits to modern sub-100nm technologies. In contrast, we present a neuromorphic system in a 28 nm CMOS process that employs switched capacitor (SC) circuits to implement 128 short term plasticity presynapses as well as 8192 stop-learning synapses. The neuromorphic system consumes an area of 0.36 mm(2) and runs at a power consumption of 1.9 mW. The circuit makes use of a technique for minimizing leakage effects allowing for real-time operation with time constants up to several seconds. Since we rely on SC techniques for all calculations, the system is composed of only generic mixed-signal building blocks. These generic building blocks make the system easy to port between technologies and the large digital circuit part inherent in an SC system benefits fully from technology scaling.

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.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

PubMed

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

Understanding of anesthesia machine function is enhanced with a transparent reality simulation.

PubMed

Fischler, Ira S; Kaschub, Cynthia E; Lizdas, David E; Lampotang, Samsun

2008-01-01

Photorealistic simulations may provide efficient transfer of certain skills to the real system, but by being opaque may fail to encourage deeper learning of the structure and function of the system. Schematic simulations that are more abstract, with less visual fidelity but make system structure and function transparent, may enhance deeper learning and optimize retention and transfer of learning. We compared learning effectiveness of these 2 modes of externalizing the output of a common simulation engine (the Virtual Anesthesia Machine, VAM) that models machine function and dynamics and responds in real time to user interventions such as changes in gas flow or ventilation. Undergraduate students (n = 39) and medical students (n = 35) were given a single, 1-hour guided learning session with either a Transparent or an Opaque version of the VAM simulation. The following day, the learners' knowledge of machine components, function, and dynamics was tested. The Transparent-VAM groups scored higher than the Opaque-VAM groups on a set of multiple-choice questions concerning conceptual knowledge about anesthesia machines (P = 0.009), provided better and more complete explanations of component function (P = 0.003), and were more accurate in remembering and inferring cause-and-effect dynamics of the machine and relations among components (P = 0.003). Although the medical students outperformed undergraduates on all measures, a similar pattern of benefits for the Transparent VAM was observed for these 2 groups. Schematic simulations that transparently allow learners to visualize, and explore, underlying system dynamics and relations among components may provide a more effective mental model for certain systems. This may lead to a deeper understanding of how the system works, and therefore, we believe, how to detect and respond to potentially adverse situations.

Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation

DTIC Science & Technology

2016-03-17

ARL-TR-7629 ● MAR 2016 US Army Research Laboratory Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time...ARL-TR-7629 ● MAR 2016 US Army Research Laboratory Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation...SUBTITLE Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

New strategies to strengthen the soil science knowledge of student during field activities

NASA Astrophysics Data System (ADS)

Benito, Marta; Hontoria, Chiquinquirá; Masaguer, Alberto; Diéguez, Carmen; Almorox, Javier; Pérez, Juana; Santano, Jesús; Mariscal, Ignacio; Gutiérrez, Jesús; Moliner, Ana

2013-04-01

Soil Science can be considered a discipline that serves as a fundamental base for other disciplines such as ecology, agronomy, plant production, etc. In order to demonstrate the relevance and connection to real world it is important to develop field and practical activities. Field activities help student to comprehend soil as part of the landscape and the natural ecosystems. These activities also help them to realize the importance of historical soil use on the quality of todaýs soil and landscapes. It is well known that fieldwork practices are essential to strengthen the soil science knowledge of students and their learning process. These fieldwork practices involve doing a physical activity rather than passively attending lectures or watching demonstrations. The simple visual and tactile observations in the field could be used to predict soil behavior and these direct observations are best made in the field. Students who learned in the field using an active work are more motivated, have more positive attitudes, and place more value in their work than those that learn passively. Therefore, when scheduling the coursework an important time is assigned to field work, which sometimes is not sufficiently profited from the standpoint of student learning taking into consideration the economic effort involved. We are aware that part of the students are simple spectators in the field so we encourage their participation by making them responsible for obtaining part of the information about the place and the types of soils that will be visited. On the other hand, we will invite the students to do some game based exercises, which are fun and force them to work in groups and to pay attention to explanations. Our objective is to present the information in a more attractive way, making the learning of soil profile description and easier task. The exercises that we propose are both field and problem-based learning to make sure that the knowledge is more memorable (non-stop learning). Fieldwork is amenable to different strategies for enhancing feedback and for providing assessments and this work presents several of them.

Learning through Real-World Problem Solving: The Power of Integrative Teaching.

ERIC Educational Resources Information Center

Nagel, Nancy G.

This book is based on the idea that curriculum development projects focused on integrated or interdisciplinary teaching within the context of real-world problem solving creates dynamics and meaningful learning experiences for students. The real-world, problem-solving units presented in this book were created by four intern teachers, their mentor…

GoNorth! - An Adventure Learning Case Study

NASA Astrophysics Data System (ADS)

Porsild, M.; Doering, A.; Pregont, P.

2008-12-01

GoNorth! is an adventure learning series developed at the University of Minnesota in collaboration with NOMADS Online Expeditions. GoNorth! uses real-time experiences of dogsled expeditions on a multimedia saturated website at http://www.PolarHusky.com to motivate and engage millions of K-12 students and teachers. The program is free and research (Doering & Veletsianos, 2007) shows that it can be adopted by any teacher who signs up to use the program. It is currently utilized in 3400+ classrooms across the 50 US States and in 29 countries worldwide. Research (Doering & Veletsianos, 2007; 2008) notes that students working with GoNorth! are excited, motivated, and eager to engage with authentic tasks, solve real-world problems, collaborate with colleagues and experts, and initiate actions in their own community. Our team of educators, scientists and explorers circumnavigate the Arctic traveling by dog team to a new Arctic locale every year. Driven by an environmental question of particular relevance to the given Arctic region, each year a comprehensive natural and social science GoNorth! Curriculum & Activity Guide (450+ pages) is developed reflecting the expedition's current Arctic locale and its indigenous culture. The associated online learning environment delivers comprehensive resources about the region of travel, collaborative opportunities, live field updates and field research findings synched real-time to the curriculum. Field research relevant to understanding patterns of climate change and polar science is conducted with independent researchers featured as "Cool GoNorth! Scientists." Collaborations span from scientists at NASA and the United States Department of Agriculture to student observers in pan-Arctic communities as part of the NSF-supported initiative "What Is Climate Change to You?." This scientific research and fieldwork in turn coincides with the curriculum. The result is a community of learners on the Internet gaining knowledge from Arctic peoples, subject matter experts, scientists and from each other. As we profile GoNorth! this presentation is your opportunity to experience the implementation of the principles that make up an adventure learning program-highlighting both challenges and rewards of using the adventure learning framework.

Experiment-o-mania

NASA Astrophysics Data System (ADS)

Drndarski, Marina

2015-04-01

Every 21st century student is expected to develop science literacy skills. As this is not part of Serbian national curriculum yet, we decided to introduce it with this project. Experiment-o-mania provides students to experience science in different and exciting way. It makes opportunity for personalized learning offering space and time to ask (why, where, how, what if) and to try. Therefore, we empower young people with skills of experimenting, and they love science back. They ask questions, make hypothesis, make problems and solve them, make mistakes, discuss about the results. Subsequently this raises the students' interest for school curriculum. This vision of science teaching is associated with inquiry-based learning. Experiment-o-mania is the unique and recognizable teaching methodology for the elementary school Drinka Pavlović, Belgrade, Serbia. Experiment-o-mania implies activities throughout the school year. They are held on extra class sessions, through science experiments, science projects or preparations for School's Days of science. Students learn to ask questions, make observations, classify data, communicate ideas, conduct experiments, analyse results and make conclusions. All science teachers participate in designing activities and experiments for students in Experiment-o-mania teaching method. But they are not alone. Teacher of fine arts, English teachers and others also take part. Students have their representatives in this team, too. This is a good way to blend knowledge among different school subject and popularize science in general. All the experiments are age appropriate and related to real life situations, local community, society and the world. We explore Fibonacci's arrays, saving energy, solar power, climate change, environmental problems, pollution, daily life situations in the country or worldwide. We introduce great scientists as Nikola Tesla, Milutin Milanković and sir Isaac Newton. We celebrate all relevant international days, weeks, months or years (this year, 2015. the students will prepare opera science for celebrate the International Year of Light and International Year of Soils). Experiment-o-mania makes science teaching and learning exciting for teachers as well as for students. The acquisition of this kind of teaching method (and its frequency) empowers students and become self-regulated learners, independent, to creatively solve problems, to innovate, to truly understand and appreciate science and to better understand themselves and the world around them.

Flexible functional regression methods for estimating individualized treatment regimes.

PubMed

Ciarleglio, Adam; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd

2016-01-01

A major focus of personalized medicine is on the development of individualized treatment rules. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pre-treatment functional data to guide in decision-making. Furthermore, those methods that do allow for the incorporation of functional pre-treatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with Q -learning or A -learning in order to obtain treatment decision rules. Properties of the corresponding estimators are discussed. Our approaches are evaluated in several realistic settings using synthetic data and are applied to real data arising from a clinical trial comparing two treatments for major depressive disorder in which baseline imaging data are available for subjects who are subsequently treated.

Interacting Learning Processes during Skill Acquisition: Learning to control with gradually changing system dynamics.

PubMed

Ludolph, Nicolas; Giese, Martin A; Ilg, Winfried

2017-10-16

There is increasing evidence that sensorimotor learning under real-life conditions relies on a composition of several learning processes. Nevertheless, most studies examine learning behaviour in relation to one specific learning mechanism. In this study, we examined the interaction between reward-based skill acquisition and motor adaptation to changes of object dynamics. Thirty healthy subjects, split into two groups, acquired the skill of balancing a pole on a cart in virtual reality. In one group, we gradually increased the gravity, making the task easier in the beginning and more difficult towards the end. In the second group, subjects had to acquire the skill on the maximum, most difficult gravity level. We hypothesized that the gradual increase in gravity during skill acquisition supports learning despite the necessary adjustments to changes in cart-pole dynamics. We found that the gradual group benefits from the slow increment, although overall improvement was interrupted by the changes in gravity and resulting system dynamics, which caused short-term degradations in performance and timing of actions. In conclusion, our results deliver evidence for an interaction of reward-based skill acquisition and motor adaptation processes, which indicates the importance of both processes for the development of optimized skill acquisition schedules.

Going Beyond the Lecture Class - Is it Worth it?

NASA Astrophysics Data System (ADS)

Funaro, G. M.; Paytan, A.; Arrigo, K. R.; Chandran, R.; Schindell, J.

2001-12-01

Lectures, labs, and seminars dominate the course offerings at most American universities. Students have been learning successfully from these teaching methods for hundreds of years. Alternatively, in order to provide a more personally meaningful learning experience, educational researchers espouse a constructivist approach to learning. To demonstrate this approach, we will describe a case study of two courses, marine chemistry and biological oceanography, that were taught as a single interdisciplinary experience by Stanford University faculty. The courses incorporated an innovative curriculum using active learning methodologies including problem-based learning and teamwork, a set of interactive and facilitative teaching practices, customized technology that worked in the background to make the course effective and efficient, and a goal to reveal the interdisciplinary nature of the content in the two courses. Videotapes of group problem solving revealed that students displayed higher order thinking skills. Students indicated in focus groups that teamwork provided a motivating, rich, learning environment. The communication technology supported both the faculty in the delivery and assessment of the course and the students in communicating with their teams. The technology was the glue that made the course work effectively and efficiently. The overall learning experience can be best expressed by the students themselves who said they felt like they were participating in "real science" for the first time.

Immersive simulated reality scenarios for enhancing students' experience of people with learning disabilities across all fields of nurse education.

PubMed

Saunder, Lorna; Berridge, Emma-Jane

2015-11-01

Poor preparation of nurses, regarding learning disabilities can have devastating consequences. High-profile reports and the Nursing and Midwifery Council requirements led this University to introduce Shareville into the undergraduate and postgraduate nursing curriculum. Shareville is a virtual environment developed at Birmingham City University, in which student nurses learn from realistic, problem-based scenarios featuring people with learning disabilities. Following the implementation of the resource an evaluation of both staff and student experience was undertaken. Students reported that problem-based scenarios were sufficiently real and immersive. Scenarios presented previously unanticipated considerations, offering new insights, and giving students the opportunity to practise decision-making in challenging scenarios before encountering them in practice. The interface and the quality of the graphics were criticised, but, this did not interfere with learning. Nine lecturers were interviewed, they generally felt positively towards the resource and identified strengths in terms of blended learning and collaborative teaching. The evaluation contributes to understandings of learning via simulated reality, and identifies process issues that will inform the development of further resources and their roll-out locally, and may guide other education providers in developing and implementing resources of this nature. There was significant parity between lecturers' expectations of students' experience of Shareville. Copyright © 2015 Elsevier Ltd. All rights reserved.

Holistic science: An understanding of science education encompassing ethical and social issues

NASA Astrophysics Data System (ADS)

Malekpour, Susan

Science has often been viewed, by the majority of our educators and the general public, as being objective and emotionless. Based on this view, our educators teach science in the same manner, objectively and in an abstract form. This manner of teaching has hindered our learners' ability for active learning and distanced them from the subject matter. In this action research, I have examined holistic science pedagogy in conjunction with a constructivism theory. In holistic science pedagogy, scientific knowledge is combined with subjective personal experiences and social issues. There is an interaction between student and scientific data when the student's context, relationships, and lived experiences that play a role in the scientific recognition of the world were incorporated into the learning process. In this pedagogical model, the factual content was viewed from the context of social and ethical implications. By empowering learners with this ability, science knowledge will no longer be exclusive to a select group. This process empowers the general population with the ability to understand scientific knowledge and therefore the ability to make informed decisions based on this knowledge. The goal was to make curriculum developers more conscious of factors that can positively influence the learning process and increase student engagement and understanding within the science classroom. The holistic approach to science pedagogy has enlightened and empowered our adult learners more effectively. Learners became more actively engaged in their own process of learning. Teachers must be willing to listen and implement student suggestions on improving the teaching/learning process. Teachers should be willing to make the effort in connecting with their students by structuring courses so the topics would be relevant to the students in relation to real world and social/ethical and political issues. Holistic science pedagogy strives for social change through the empowerment of adult learners with scientific knowledge. This research has demonstrated that learners can better understand the decision-making process and more easily relate their experiences, and therefore their knowledge, to social/political and ethical issues.

Investigating the Relationship between Instructors’ Use of Active-Learning Strategies and Students’ Conceptual Understanding and Affective Changes in Introductory Biology: A Comparison of Two Active-Learning Environments

PubMed Central

Cleveland, Lacy M.; Olimpo, Jeffrey T.; DeChenne-Peters, Sue Ellen

2017-01-01

In response to calls for reform in undergraduate biology education, we conducted research examining how varying active-learning strategies impacted students’ conceptual understanding, attitudes, and motivation in two sections of a large-lecture introductory cell and molecular biology course. Using a quasi-experimental design, we collected quantitative data to compare participants’ conceptual understanding, attitudes, and motivation in the biological sciences across two contexts that employed different active-learning strategies and that were facilitated by unique instructors. Students participated in either graphic organizer/worksheet activities or clicker-based case studies. After controlling for demographic and presemester affective differences, we found that students in both active-learning environments displayed similar and significant learning gains. In terms of attitudinal and motivational data, significant differences were observed for two attitudinal measures. Specifically, those students who had participated in graphic organizer/worksheet activities demonstrated more expert-like attitudes related to their enjoyment of biology and ability to make real-world connections. However, all motivational and most attitudinal data were not significantly different between the students in the two learning environments. These data reinforce the notion that active learning is associated with conceptual change and suggests that more research is needed to examine the differential effects of varying active-learning strategies on students’ attitudes and motivation in the domain. PMID:28389428

Real-time GMAW quality classification using an artificial neural network with airborne acoustic signals as inputs

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

Matteson, A.; Morris, R.; Tate, R.

1993-12-31

The acoustic signal produced by the gas metal arc welding (GMAW) arc contains information about the behavior of the arc column, the molten pool and droplet transfer. It is possible to detect some defect producing conditions from the acoustic signal from the GMAW arc. An intelligent sensor, called the Weld Acoustic Monitor (WAM) has been developed to take advantage of this acoustic information in order to provide real-time quality assessment information for process control. The WAM makes use of an Artificial Neural Network (ANN) to classify the characteristic arc acoustic signals of acceptable and unacceptable welds. The ANN used inmore » the Weld Acoustic Monitor developed its own set of rules for this classification problem by learning a data base of known GMAW acoustic signals.« less

Real-Time Interactive Social Environments: A Review of BT's Generic Learning Platform.

ERIC Educational Resources Information Center

Gardner, Michael; Ward, Holly

1999-01-01

Describes the development of a generic learning platform for online distance learning and explains RISE (Real-time Interactive Social Environments), a Web-based system. Reports results of trials at the University of Hull Language Institute in an English as a Foreign Language course that investigated system usability, teacher roles, and student…

Developing Management Student Cultural Fluency for the Real World: A Situated Cultural Learning Approach

ERIC Educational Resources Information Center

Zhu, Yunxia; Okimoto, Tyler G.; Roan, Amanda; Xu, Henry

2017-01-01

Purpose: To connect students with the real world of management practice, the purpose of this paper is to extend and operationalize the situated cultural learning approach (SiCuLA) through five learning processes occurring within communities of practice. These include integration of cultural contexts, authentic activities, reflections,…

The Effects of Videoconferenced Distance-Learning Instruction in a Taiwanese Company

ERIC Educational Resources Information Center

Lin, Chin-Hung; Yang, Shu-Ching

2011-01-01

Distance learning, where instruction is given to students despite wide separations of students and teachers, is increasingly popular. Videoconferencing, which is examined in this study, is a distance learning mode of featuring real-time interaction of students and teachers and provides sequence, real-time, vision, and actual interaction. This…

Learning to Map and Mapping to Learn Our Students' Worlds

ERIC Educational Resources Information Center

Rubel, Laurie H.; Chu, Haiwen; Shookhoff, Lauren

2011-01-01

The National Council of Teachers of Mathematics (NCTM), through its Connections Standard, highlights the importance of "the opportunity for students to experience mathematics in a context." Seeing how mathematics can be used to describe real-world phenomena can motivate students to learn more mathematics. Connecting mathematics to the real world…

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2016-01-01

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

Abstract and proportional myoelectric control for multi-fingered hand prostheses.

PubMed

Pistohl, Tobias; Cipriani, Christian; Jackson, Andrew; Nazarpour, Kianoush

2013-12-01

Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses' full functionality, it is essential to study efficient ways of high dimensional myoelectric control. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally. But can a similar control scheme be translated into real-time operation of a dexterous robotic hand? We found that despite different degrees of freedom in the effector output, the learning process for controlling a robotic hand was surprisingly similar to that for a virtual two-dimensional cursor. Control signals were derived from the EMG in two different ways, with a linear and a Bayesian filter, to test how stable user intentions could be conveyed through them. Our analysis indicates that without visual feedback, control accuracy benefits from filters that reject high EMG amplitudes. In summary, we conclude that findings on myoelectric control principles, studied in abstract, virtual tasks can be transferred to real-life prosthetic applications.

Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

PubMed

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-10-22

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

Trends in extreme learning machines: a review.

PubMed

Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

2015-01-01

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

PubMed Central

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-01-01

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

Stacked Multilayer Self-Organizing Map for Background Modeling.

PubMed

Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

2015-09-01

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

PubMed

Singh, Anita

2017-07-01

Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.

Active Learning in PhysicsTechnology and Research-based Techniques Emphasizing Interactive Lecture Demonstrations

NASA Astrophysics Data System (ADS)

Thornton, Ronald

2010-10-01

Physics education research has shown that learning environments that engage students and allow them to take an active part in their learning can lead to large conceptual gains compared to traditional instruction. Examples of successful curricula and methods include Peer Instruction, Just in Time Teaching, RealTime Physics, Workshop Physics, Scale-Up, and Interactive Lecture Demonstrations (ILDs). An active learning environment is often difficult to achieve in lecture sessions. This presentation will demonstrate the use of sequences of Interactive Lecture Demonstrations (ILDs) that use real experiments often involving real-time data collection and display combined with student interaction to create an active learning environment in large or small lecture classes. Interactive lecture demonstrations will be done in the area of mechanics using real-time motion probes and the Visualizer. A video tape of students involved in interactive lecture demonstrations will be shown. The results of a number of research studies at various institutions (including international) to measure the effectiveness of ILDs and guided inquiry conceptual laboratories will be presented.

Problem solving performance and learning strategies of undergraduate students who solved microbiology problems using IMMEX educational software

NASA Astrophysics Data System (ADS)

Ebomoyi, Josephine Itota

The objectives of this study were as follows: (1) Determine the relationship between learning strategies and performance in problem solving, (2) Explore the role of a student's declared major on performance in problem solving, (3) Understand the decision making process of high and low achievers during problem solving. Participants (N = 65) solved problems using the Interactive multimedia exercise (IMMEX) software. All participants not only solved "Microquest," which focuses on cellular processes and mode of action of antibiotics, but also "Creeping Crud," which focuses on the cause, origin and transmission of diseases. Participants also responded to the "Motivated Strategy Learning Questionnaire" (MSLQ). Hierarchical multiple regression was used for analysis with GPA (Gracie point average) as a control. There were 49 (78.6%) that successfully solved "Microquest" while 52 (82.5%) successfully solved "Creeping Crud". Metacognitive self regulation strategy was significantly (p < .10) related to ability to solve "Creeping Crud". Peer learning strategy showed a positive significant (p < .10) relationship with scores obtained from solving "Creeping Crud". Students' declared major made a significant (p < .05) difference on the ability to solve "Microquest". A subset (18) volunteered for a think aloud method to determine decision-making process. High achievers used fewer steps, and had more focused approach than low achievers. Common strategies and attributes included metacognitive skills, writing to keep track, using prior knowledge. Others included elements of frustration/confusion and self-esteem problems. The implications for educational and relevance to real life situations are discussed.

Developing nursing practice through work-based learning.

PubMed

Clarke, David J; Copeland, Lisa

2003-12-01

Developing nursing practice in any area demands skills, knowledge, support and a long term commitment to the achievement of best practice. It is easy to become overwhelmed by the competing demands for client care and service delivery. It is not always easy to see how good ideas, clinical concerns and professionally led objectives, can be realised in practice. Ongoing professional development activities, including formal educational programmes can contribute to individual staff members' ability to take on practice development projects. Too often however, educational programmes are seen as making little real difference to clinical practice. Work-based learning, a relatively new approach in higher education in the United Kingdom, presents opportunities for Universities and healthcare providers to work in partnership to realise the shared aims of developing nursing practice. Specific examples, drawn from the personal experiences of one of the authors, will examine the contribution of a work-based learning approach to integrating learning and developing practice in the field of cancer care. The work-based learning approach can bring about tangible benefits for patients, practitioners and organisations, but only if the organisational and contextual factors which impact on practice and its development are properly considered and managed through effective partnerships.

Using Research-Based Interactive Video Vignettes to Enhance Out-of-Class Learning in Introductory Physics

NASA Astrophysics Data System (ADS)

Laws, Priscilla W.; Willis, Maxine C.; Jackson, David P.; Koenig, Kathleen; Teese, Robert

2015-02-01

Ever since the first generalized computer-assisted instruction system (PLATO1) was introduced over 50 years ago, educators have been adding computer-based materials to their classes. Today many textbooks have complete online versions that include video lectures and other supplements. In the past 25 years the web has fueled an explosion of online homework and course management systems, both as blended learning and online courses. Meanwhile, introductory physics instructors have been implementing new approaches to teaching based on the outcomes of Physics Education Research (PER). A common theme of PER-based instruction has been the use of active-learning strategies designed to help students overcome alternative conceptions that they often bring to the study of physics.2 Unfortunately, while classrooms have become more active, online learning typically relies on passive lecture videos or Kahn-style3 tablet drawings. To bring active learning online, the LivePhoto Physics Group has been developing Interactive Video Vignettes (IVVs) that add interactivity and PER-based elements to short presentations. These vignettes incorporate web-based video activities that contain interactive elements and typically require students to make predictions and analyze real-world phenomena.

Intelligent Image Based Computer Aided Education (IICAE)

NASA Astrophysics Data System (ADS)

David, Amos A.; Thiery, Odile; Crehange, Marion

1989-03-01

Artificial Intelligence (AI) has found its way into Computer Aided Education (CAE), and there are several systems constructed to put in evidence its interesting advantages. We believe that images (graphic or real) play an important role in learning. However, the use of images, outside their use as illustration, makes it necessary to have applications such as AI. We shall develop the application of AI in an image based CAE and briefly present the system under construction to put in evidence our concept. We shall also elaborate a methodology for constructing such a system. Futhermore we shall briefly present the pedagogical and psychological activities in a learning process. Under the pedagogical and psychological aspect of learning, we shall develop areas such as the importance of image in learning both as pedagogical objects as well as means for obtaining psychological information about the learner. We shall develop the learner's model, its use, what to build into it and how. Under the application of AI in an image based CAE, we shall develop the importance of AI in exploiting the knowledge base in the learning environment and its application as a means of implementing pedagogical strategies.

Learning about water resource sharing through game play

NASA Astrophysics Data System (ADS)

Ewen, Tracy; Seibert, Jan

2016-10-01

Games are an optimal way to teach about water resource sharing, as they allow real-world scenarios to be enacted. Both students and professionals learning about water resource management can benefit from playing games, through the process of understanding both the complexity of sharing of resources between different groups and decision outcomes. Here we address how games can be used to teach about water resource sharing, through both playing and developing water games. An evaluation of using the web-based game Irrigania in the classroom setting, supported by feedback from several educators who have used Irrigania to teach about the sustainable use of water resources, and decision making, at university and high school levels, finds Irrigania to be an effective and easy tool to incorporate into a curriculum. The development of two water games in a course for masters students in geography is also presented as a way to teach and communicate about water resource sharing. Through game development, students learned soft skills, including critical thinking, problem solving, team work, and time management, and overall the process was found to be an effective way to learn about water resource decision outcomes. This paper concludes with a discussion of learning outcomes from both playing and developing water games.

Problem-Based Learning for Didactic Presentation to Baccalaureate Nursing Students.

PubMed

Montenery, Susan

2017-05-01

Nursing judgment is an essential component in the delivery of safe, quality patient care. Nurses must have the knowledge and skills to question authority, make judgments, substantiate evidence, and advocate for the patient. Traditional pedagogy in content-laden courses remains primarily lecture based. Incorporating active strategies to strengthen professional practice is essential. A pilot study assessed senior baccalaureate nursing students' perceptions of problem-based learning (PBL) and their readiness for self-directed learning. In addition, the authors analyzed the relationship between readiness for self-directed learning and course content mastery using PBL. Students completed the Self-directed Learning Readiness Scale, the Problem-Based Learning Environment Inventory, and course content mastery exams. Students reported positive experiences with PBL and readiness for self-directed learning. Readiness for self-directed learning and 2 of 5 exam scores were inversely, significantly related. Students' perceptions of their readiness for self-directed learning did not always correspond with course content mastery. Specifically, some students who perceived themselves as ready for self-directed learning did not perform well on course content exams. This inverse relationship has not been reported by other researchers and brings an interesting perspective to student perceptions and actual performance. Four themes emerged from students' narrative responses: Prepared Me for Real Life Professional Situations, Stimulated My Critical Thinking, Promoted Independent Problem Solving, and Supported Learning Retention. PBL as a pedagogical approach provides opportunities for nursing students to explore their professional independence while attempting to master content.

Personal Health—Personalized Science: A new driver for science education?

NASA Astrophysics Data System (ADS)

Roth, Wolff-Michael

2014-06-01

Since the 1950s, originating with and driven by the Sputnik shock, there have been tremendous efforts to improve science education. Over the past two decades, the initial focus on science content has been abandoned, at least among many science education researchers, in favor of socio-scientific issues. Yet even this social turn does not appear to make much difference, as scores of students continue to be turned off in/by school science. In this contribution, based on a first-person (phenomenological) investigation, I suggest why and under which condition health and environment may constitute suitable contexts for (also) teaching science. I conclude that more than content and approach (science) educators need to reconsider the very structure of schooling, which may be the real problem in making learning an authentic endeavor.

Building place-based collaborations to develop high school students' groundwater systems knowledge and decision-making capacity

NASA Astrophysics Data System (ADS)

Podrasky, A.; Covitt, B. A.; Woessner, W.

2017-12-01

The availability of clean water to support human uses and ecological integrity has become an urgent interest for many scientists, decision makers and citizens. Likewise, as computational capabilities increasingly revolutionize and become integral to the practice of science, technology, engineering and math (STEM) disciplines, the STEM+ Computing (STEM+C) Partnerships program seeks to integrate the use of computational approaches in K-12 STEM teaching and learning. The Comp Hydro project, funded by a STEM+C grant from the National Science Foundation, brings together a diverse team of scientists, educators, professionals and citizens at sites in Arizona, Colorado, Maryland and Montana to foster water literacy, as well as computational science literacy, by integrating authentic, place- and data- based learning using physical, mathematical, computational and conceptual models. This multi-state project is currently engaging four teams of six teachers who work during two academic years with educators and scientists at each site. Teams work to develop instructional units specific to their region that integrate hydrologic science and computational modeling. The units, currently being piloted in high school earth and environmental science classes, provide a classroom context to investigate student understanding of how computation is used in Earth systems science. To develop effective science instruction that is rich in place- and data- based learning, effective collaborations between researchers, educators, scientists, professionals and citizens are crucial. In this poster, we focus on project implementation in Montana, where an instructional unit has been developed and is being tested through collaboration among University scientists, researchers and educators, high school teachers and agency and industry scientists and engineers. In particular, we discuss three characteristics of effective collaborative science education design for developing and implementing place- and data- based science education to support students in developing socio-scientific and computational literacy sufficient for making decisions about real world issues such as groundwater contamination. These characteristics include that science education experiences are real, responsive/accessible and rigorous.

CosmoQuest: Galvanizing a Dynamic, Inclusive Professional Learning Network

NASA Astrophysics Data System (ADS)

Cobb, W. H.; Buxner, S.; Bracey, G.; Noel-Storr, J.; Gay, P.; Graff, P. V.

2016-12-01

The CosmoQuest Virtual Research Facility offers experiences to audiences around the nation and globally through pioneering citizen science. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and individuals of all ages—to explore and make sense of our solar system and beyond. Scaffolded by an educational framework that inspires 21stCentury learners, CosmoQuest engages people—you, me!—in analyzing and interpreting real NASA data, inspiring questions and defining problems. Linda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] …; [and] connected to teachers' collaborative work in professional learning community...." (2012). In light of that, what can CosmoQuest offer NASA STEM education as a virtual research facility? CosmoQuest engages scientists with learners, and learners with science. As a virual research facility, its focal point must be its online platform. CosmoQuest empowers and expands community through a variety of social channels, including science and education-focused hangouts, podcasts, virtual star parties, and social media. In addition to creating standards-aligned materials, CosmoQuest channels are a hub for excellent resources throughout NASA and the larger astronomical community. In support of CosmoQuest citizen science opportunities, the process and outcomes of CosmoQuest initiatives will be leveraged and shared. Thus, CosmoQuest will be present and alive in the awareness of its growing community. Finally, to make CosmoQuest truly relevant, partnerships between scientists and educators are encouraged and facilitated, and "just-in-time" opportunities to support constituents exploring emerging NASA STEM education and new NASA data will be offered, engaging audiences ranging from diverse educators to the curious learner of any age.

Earth Systems Field Work: Service Learning at Local and Global Scales

NASA Astrophysics Data System (ADS)

Moore, A.; Derry, L. A.

2016-12-01

The Earth & Environmental Systems (EES) Field Program engages students in hands-on exploration along the boundaries of the living earth, solid earth, ocean, and atmosphere. Based on Hawaíi Island, the semester-length program integrates scientific study with environmental stewardship and service learning. Each year EES students contribute 3000 hours of service to their host community. Throughout the semester students engage in different service activities. Most courses includes a service component - for example - study of the role of invasive species in native ecosystems includes an invasive species removal project. Each student completes a 4-week service internship with a local school, NGO, state or federal agency. Finally, the student group works to offset the carbon footprint of the program in collaboration with local conservation projects. This effort sequesters CO2 emissions while at the same time contributing to reforestation of degraded native ecosystems. Students learn that expertise is not confined to "the academy," and that wisdom and inspiration can be found in unexpected venues. Much of the service learning in the EES Program occurs in collaboration with local partners. Service internships require students to identify a partner and to design a tractable project. Students work daily with their sponsor and make a formal presentation of their project at the end of the internship period. This includes speaking to a non-technical community gathering as well as to a scientific audience. For many students the opportunity to work on a real problem, of interest in the real world, is a highlight of the semester. Beyond working in support of local community groups, the EES Prograḿs C-neutral project engages students with work in service to the global commons. Here the outcome is not measurable within the time frame of a semester, yet the intangible result makes the experience even more powerful. Students take responsibility for an important issue that is not quantified in terms of an end-of-semester grade and without feedback from the academic or local community. By working through the process of calculating and offsetting their carbon footprint - entirely with their own labor - students learn that every individual has the tools and the ability to create change, and that they have the responsibility to do so.

More than just a game: the role of simulation in the teaching of product design and entrepreneurship to mechanical engineering students

NASA Astrophysics Data System (ADS)

Costello, Gabriel J.

2017-11-01

The purpose of this work is to contribute to the debate on the best pedagogical approach to developing undergraduate mechanical engineering skills to meet the requirements of contemporary complex working environments. The paper provides an example of using student-entrepreneur collaboration in the teaching of modules to Mechanical Engineering final-year students. Problem-based learning (PBL) is one of the most significant recent innovations in the area of education for the professions. This work proposes to make an original contribution by simulating a real-life entrepreneur interaction for the students. The current literature largely confines simulation-based learning to computer applications such as games. However, this paper argues that role playing by students interfacing with technology start-ups can also be regarded as 'simulation' in a wider sense. Consequently, the paper proposes the concept of simulation-action learning as an enhancement of PBL and to distinguish it from computer simulation.

Analyzing Hidden Semantics in Social Bookmarking of Open Educational Resources

NASA Astrophysics Data System (ADS)

Minguillón, Julià

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describing them. This is especially interesting in the field of open educational resources, as delicious is a simple way to bridge the institutional point of view (i.e. learning object repositories) with the individual one (i.e. personal collections), thus promoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used to improve descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags create clusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

From Tech Skills to Life Skills: Google Online Marketing Challenge and Experiential Learning

ERIC Educational Resources Information Center

Croes, Jo-Anne V.; Visser, Melina M.

2015-01-01

The Google Online Marketing Challenge (GOMC) is a global, online student competition sponsored by Google. It is a prime example of an experiential learning activity that includes using real money ($250 sponsored by Google) with a real client. The GOMC has yielded compelling results in student engagement and learning objectives related to the…

Developing Situational Learning Events: A Practical Merger of Real-Life Events with Content Instruction.

ERIC Educational Resources Information Center

Salyer, B. Keith; Thyfault, Alberta

This paper discusses the value of merging real-life events with content instruction and provides six sample lessons to illustrate such instruction. A brief review of the literature notes historic recognition of the importance of applied learning, the issue of retention and transfer of learning, the approach of using content relevant experiences…

Preliminary Results of Professional Development Program for School Science Research

ERIC Educational Resources Information Center

Wuttiprom, Sura; Wuttisela, Karntarat; Phonchaiya, Sonthi; Athiwaspong, Wanwalai; Chitaree, Ratchapak; Sharma, Manjula Devi

2016-01-01

Teachers need to design their courses to be as similar to real-life situations as possible as genuine learning emerges in real life as opposed to studying in class. Research-based learning is an innovative approach exploring many critical strategies for success in the twenty-first century. In it, students drive their own learning through inquiry,…

The Influences of the 2D Image-Based Augmented Reality and Virtual Reality on Student Learning

ERIC Educational Resources Information Center

Liou, Hsin-Hun; Yang, Stephen J. H.; Chen, Sherry Y.; Tarng, Wernhuar

2017-01-01

Virtual reality (VR) learning environments can provide students with concepts of the simulated phenomena, but users are not allowed to interact with real elements. Conversely, augmented reality (AR) learning environments blend real-world environments so AR could enhance the effects of computer simulation and promote students' realistic experience.…

Impacts of Integrating the Repertory Grid into an Augmented Reality-Based Learning Design on Students' Learning Achievements, Cognitive Load and Degree of Satisfaction

ERIC Educational Resources Information Center

Wu, Po-Han; Hwang, Gwo-Jen; Yang, Mei-Ling; Chen, Chih-Hung

2018-01-01

Augmented reality (AR) offers potential advantages for intensifying environmental context awareness and augmenting students' experiences in real-world environments by dynamically overlapping digital materials with a real-world environment. However, some challenges to AR learning environments have been described, such as participants' cognitive…

A Context-Aware Knowledge Map to Support Ubiquitous Learning Activities for a u-Botanical Museum

ERIC Educational Resources Information Center

Wang, Shu-Lin; Chen, Chia-Chen; Zhang, Zhe George

2015-01-01

Recent developments in mobile and wireless communication technologies have played a vital role in building the u-learning environment that now combines both real-world and digital learning resources. However, learners still require assistance to control real objects and manage the abundance of available materials; otherwise, their mental workload…

Problem-Based Learning Pedagogies: Psychological Processes and Enhancement of Intelligences

ERIC Educational Resources Information Center

Tan, Oon-Seng

2007-01-01

Education in this 21st century is concerned with developing intelligences. Problem solving in real-world contexts involves multiple ways of knowing and learning. Intelligence in the real world involves not only learning how to do things effectively but also more importantly the ability to deal with novelty and growing our capacity to adapt, select…

Learning and Teaching Mathematics through Real Life Models

ERIC Educational Resources Information Center

Takaci, Djurdjica; Budinski, Natalija

2011-01-01

This paper proposes modelling based learning as a tool for learning and teaching mathematics in high school. We report on an example of modelling real world problems in two high schools in Serbia where students were introduced for the first time to the basic concepts of modelling. Student use of computers and educational software, GeoGebra, was…

Observation versus classification in supervised category learning.

PubMed

Levering, Kimery R; Kurtz, Kenneth J

2015-02-01

The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.

RealTime Physics: Active learning laboratory

NASA Astrophysics Data System (ADS)

Thornton, Ronald K.; Sokoloff, David R.

1997-03-01

Our research shows that student learning of physics concepts in introductory physics courses is enhanced by the use of special guided discovery laboratory curricula which embody the results of educational research and which are supported by the use of the Tools for Scientific Thinking microcomputer-based laboratory (MBL) tools. In this paper we first describe the general characteristics of the research-based RealTime Physics laboratory curricula developed for use in introductory physics classes in colleges, universities and high schools. We then describe RealTime Physics Mechanics in detail. Finally we examine student learning of dynamics in traditional physics courses and in courses using RealTime Physics Mechanics, primarily by the use of correlated questions on the Force and Motion Conceptual Evaluation. We present considerable evidence that students who use the new laboratory curricula demonstrate significantly improved learning and retention of dynamics concepts compared to students taught by traditional methods.

Symbol Grounding Without Direct Experience: Do Words Inherit Sensorimotor Activation From Purely Linguistic Context?

PubMed

Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

2018-05-01

Theories of embodied cognition assume that concepts are grounded in non-linguistic, sensorimotor experience. In support of this assumption, previous studies have shown that upwards response movements are faster than downwards movements after participants have been presented with words whose referents are typically located in the upper vertical space (and vice versa for downwards responses). This is taken as evidence that processing these words reactivates sensorimotor experiential traces. This congruency effect was also found for novel words, after participants learned these words as labels for novel objects that they encountered either in their upper or lower visual field. While this indicates that direct experience with a word's referent is sufficient to evoke said congruency effects, the present study investigates whether this direct experience is also a necessary condition. To this end, we conducted five experiments in which participants learned novel words from purely linguistic input: Novel words were presented in pairs with real up- or down-words (Experiment 1); they were presented in natural sentences where they replaced these real words (Experiment 2); they were presented as new labels for these real words (Experiment 3); and they were presented as labels for novel combined concepts based on these real words (Experiment 4 and 5). In all five experiments, we did not find any congruency effects elicited by the novel words; however, participants were always able to make correct explicit judgements about the vertical dimension associated to the novel words. These results suggest that direct experience is necessary for reactivating experiential traces, but this reactivation is not a necessary condition for understanding (in the sense of storing and accessing) the corresponding aspects of word meaning. Copyright © 2017 Cognitive Science Society, Inc.

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

NASA Astrophysics Data System (ADS)

Sanchez, Christopher A.; Ruddell, Benjamin L.; Schiesser, Roy; Merwade, Venkatesh

2016-03-01

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data-driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower-division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

NASA Astrophysics Data System (ADS)

Sanchez, C. A.; Ruddell, B. L.; Schiesser, R.; Merwade, V.

2015-07-01

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

Functional Contour-following via Haptic Perception and Reinforcement Learning.

PubMed

Hellman, Randall B; Tekin, Cem; van der Schaar, Mihaela; Santos, Veronica J

2018-01-01

Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic perception and decision-making for a haptics-driven, functional contour-following task: the closure of a ziplock bag. This task is challenging for robots because the bag is deformable, transparent, and visually occluded by artificial fingertip sensors that are also compliant. A deep neural net classifier was trained to estimate the state of a zipper within a robot's pinch grasp. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards by balancing exploration versus exploitation of the state-action space. The C-MAB learner outperformed a benchmark Q-learner by more efficiently exploring the state-action space while learning a hard-to-code task. The learned C-MAB policy was tested with novel ziplock bag scenarios and contours (wire, rope). Importantly, this work contributes to the development of reinforcement learning approaches that account for limited resources such as hardware life and researcher time. As robots are used to perform complex, physically interactive tasks in unstructured or unmodeled environments, it becomes important to develop methods that enable efficient and effective learning with physical testbeds.

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

Introductory Biology Courses: A Framework To Support Active Learning in Large Enrollment Introductory Science Courses

PubMed Central

2005-01-01

Active learning and research-oriented activities have been increasingly used in smaller, specialized science courses. Application of this type of scientific teaching to large enrollment introductory courses has been, however, a major challenge. The general microbiology lecture/laboratory course described has been designed to incorporate published active-learning methods. Three major case studies are used as platforms for active learning. Themes from case studies are integrated into lectures and laboratory experiments, and in class and online discussions and assignments. Students are stimulated to apply facts to problem-solving and to learn research skills such as data analysis, writing, and working in teams. This course is feasible only because of its organizational framework that makes use of teaching teams (made up of faculty, graduate assistants, and undergraduate assistants) and Web-based technology. Technology is a mode of communication, but also a system of course management. The relevance of this model to other biology courses led to assessment and evaluation, including an analysis of student responses to the new course, class performance, a university course evaluation, and retention of course learning. The results are indicative of an increase in student engagement in research-oriented activities and an appreciation of real-world context by students. PMID:15917873

Machine Learning Techniques for Stellar Light Curve Classification

NASA Astrophysics Data System (ADS)

Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

Radiology Teacher: a free, Internet-based radiology teaching file server.

PubMed

Talanow, Roland

2009-12-01

Teaching files are an essential ingredient in residency education. The online program Radiology Teacher was developed to allow the creation of interactive and customized teaching files in real time. Online access makes it available anytime and anywhere, and it is free of charge, user tailored, and easy to use. No programming skills, additional plug-ins, or installations are needed, allowing its use even on protected intranets. Special effects for enhancing the learning experience as well as the linking and the source code are created automatically by the program. It may be used in different modes by individuals and institutions to share cases from multiple authors in a single database. Radiology Teacher is an easy-to-use automatic teaching file program that may enhance users' learning experiences by offering different modes of user-defined presentations.

Learning to Select Supplier Portfolios for Service Supply Chain

PubMed Central

Zhang, Rui; Li, Jingfei; Wu, Shaoyu; Meng, Dabin

2016-01-01

The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To address this problem, we propose a novel supplier portfolio selection method based on a well known machine learning approach, i.e., Ranking Neural Network (RankNet). In the proposed method, we regard the problem of supplier portfolio selection as a ranking problem, which integrates a large scale of decision making features into a ranking neural network. Extensive simulation experiments are conducted, which demonstrate the feasibility and effectiveness of the proposed method. The proposed supplier portfolio selection model can be applied in a real corporation easily in the future. PMID:27195756

The Small Helm Project: an academic activity addressing international corruption for undergraduate civil engineering and construction management students.

PubMed

Benzley, Steven E

2006-04-01

This paper presents an academic project that addresses the issue of international corruption in the engineering and construction industry, in a manner that effectively incorporates several learning experiences. The major objectives of the project are to provide the students a learning activity that will 1) make a meaningful contribution within the disciplines being studied; 2) teach by experience a significant principle that can be valuable in numerous situations during an individual's career, and 3) engage the minds, experiences, and enthusiasm of the participants in a real ethical challenge that is prevalent in all of their chosen professional fields. The paper describes the full details of the project, the actual implementation of it during Winter Semester 2005, the experiences gained during the initial trial, and the modifications and improvements incorporated for future implementation.

Mathematical modelling in engineering: an alternative way to teach Linear Algebra

NASA Astrophysics Data System (ADS)

Domínguez-García, S.; García-Planas, M. I.; Taberna, J.

2016-10-01

Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic classroom approach in which students modelled real-world problems and turn gain a deeper knowledge of the Linear Algebra subject. Considering that most students are digital natives, we use the e-portfolio as a tool of communication between students and teachers, besides being a good place making the work visible. In this article, we present an overview of the design and implementation of a project-based learning for a Linear Algebra course taught during the 2014-2015 at the 'ETSEIB'of Universitat Politècnica de Catalunya (UPC).

Shuttle Case Study Collection Website Development

NASA Technical Reports Server (NTRS)

Ransom, Khadijah S.; Johnson, Grace K.

2012-01-01

As a continuation from summer 2012, the Shuttle Case Study Collection has been developed using lessons learned documented by NASA engineers, analysts, and contractors. Decades of information related to processing and launching the Space Shuttle is gathered into a single database to provide educators with an alternative means to teach real-world engineering processes. The goal is to provide additional engineering materials that enhance critical thinking, decision making, and problem solving skills. During this second phase of the project, the Shuttle Case Study Collection website was developed. Extensive HTML coding to link downloadable documents, videos, and images was required, as was training to learn NASA's Content Management System (CMS) for website design. As the final stage of the collection development, the website is designed to allow for distribution of information to the public as well as for case study report submissions from other educators online.

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.

Using videos, apps and hands-on experience in undergraduate hydrology teaching

NASA Astrophysics Data System (ADS)

Van Loon, Anne

2016-04-01

Hydrological sciences teaching always needs to make a link between the classroom and the outside world. This can be done with fieldwork and excursions, but the increasing availability of open educational resources gives more-and-more other options to make theory more understandable and applicable. In the undergraduate teaching of hydrology at the University of Birmingham we make use of a number of tools to enhance the hydrology 'experience' of students. Firstly, we add hydrological science videos available in the public domain to our explanations of theory. These are both visualisations of concepts and recorded demonstrations in the field or the lab. One example is the concept of catchments and travel times which has been excellently visualised by MetEd. Secondly, we use a number of mobile phone apps, which provide virtual reality information and real-time monitoring information. We use the MySoil App (by Natural Environment Research Council (NERC), British Geological Survey (BGS) and Centre for Ecology & Hydrology (CEH)) and iGeology / iGeology3D (by BGS) to let students explore soil properties and hydrogeology of an area of interest. And we use the River Levels App (by OGL based on Environment Agency real time data) for exploring real time river levels and investigating spatial variability. Finally, we developed small hands-on projects for students to apply the theory outside the classroom. We for instance let them do simple infiltration experiments and ask them to them design a measurement plan. Evaluations have shown that students enjoy these activities and that it helps their learning. In this presentation we hope to share our experience so that the options for using open (educational) resources for hydrology teaching become more used in linking the classroom to the outside world.

Cyber Physical Intelligence for Oil Spills (CPI)

NASA Astrophysics Data System (ADS)

Lary, D. J.

2015-12-01

The National Academy of Sciences estimate 1.7 to 8.8 million tons of oil are released into global waters every year. The effects of these spills include dead wildlife, oil covered marshlands and contaminated water. Deepwater horizon cost approximately $50 billion and severely challenged response capabilities. In such large spills optimizing a coordinated response is a particular challenge. This challenge can be met in a revolutionary new way by using an objectively optimized Cyber Physical Decision Making System (CPS) for rapid response products and a framework for objectively optimized decision-making in an uncertain environment. The CPS utilizes machine learning for the processing of the massive real-time streams of Big Data from comprehensive hyperspectral remote sensing acquired by a team of low-cost robotic aerial vehicles, providing a real-time aerial view and stream of hyperspectral imagery from the near UV to the thermal infrared, and a characterization of oil thickness, oil type and oil weathering. The objective decision making paradigm is modeled on the human brain and provides the optimal course trajectory for response vessels to achieve the most expeditious cleanup of oil spills using the available resources. In addition, oil spill cleanups often involve surface oil burns that can lead to air quality issues. The aerial vehicles comprehensively characterize air quality in real-time, streaming location, temperature, pressure, humidity, the abundance of 6 criterion pollutants (O3, CO, NO, NO2, SO2, and H2S) and the full size distribution of airborne particulates. This CPS can be readily applied to other systems in agriculture, water conversation, monitoring of stream quality, air quality, diagnosing risk of wild fires, etc..

Competence and Quality in Real-Life Decision Making.

PubMed

Geisler, Martin; Allwood, Carl Martin

2015-01-01

What distinguishes a competent decision maker and how should the issue of decision quality be approached in a real-life context? These questions were explored in three studies. In Study 1, using a web-based questionnaire and targeting a community sample, we investigated the relationships between objective and subjective indicators of real-life decision-making success. In Study 2 and 3, targeting two different samples of professionals, we explored if the prevalent cognitively oriented definition of decision-making competence could be beneficially expanded by adding aspects of competence in terms of social skills and time-approach. The predictive power for each of these three aspects of decision-making competence was explored for different indicators of real-life decision-making success. Overall, our results suggest that research on decision-making competence would benefit by expanding the definition of competence, by including decision-related abilities in terms of social skills and time-approach. Finally, the results also indicate that individual differences in real-life decision-making success profitably can be approached and measured by different criteria.

CosmoQuest: Creative Engagement & Citizen Science Ignite Authentic Science

NASA Astrophysics Data System (ADS)

Cobb, W. H.; Noel-Storr, J.; Tweed, A.; Asplund, S.; Aiello, M. P.; Lebofsky, L. A.; Chilton, H.; Gay, P.

2016-12-01

The CosmoQuest Virtual Research Facility offers in-depth experiences to diverse audiences nationally and internationally through pioneering citizen science. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and individuals of all ages—to explore and make sense of our solar system and beyond. CosmoQuest creates pathways for engaging diverse audiences in authentic science, encouraging scientists to engage with learners, and learners to engage with scientists. Here is a sequence of activities developed by CosmoQuest, leveraging a NASA Discovery and New Frontiers Programs activity developed for the general STEAM community, that activates STEM learning. The Spark: Igniting Curiosity Art and the Cosmic Connection uses the elements of art—shape, line, color, texture, value—to hone observation skills and inspire questions. Learners explore NASA image data from celestial bodies in our solar system—planets, asteroids, moons. They investigate their geology, analyzing features and engaging in scientific discourse rising from evidence while creating a beautiful piece of art. The Fuel: Making Connections Crater Comparisons explore authentic NASA image data sets, engrossing learners at a deeper level. With skills learned in Art and the Cosmic Connection, learners analyze specific image sets with the feedback of mission team members. The Burn: Evolving Community Become a Solar System Mapper. Investigate and analyze NASA mission image data of Mars, Mercury, the Moon and Vesta through CosmoQuest's citizen science projects. Learners make real-world connections while contributing to NASA science. Scaffolded by an educational framework that inspires 21st century learners, CosmoQuest engages people in analyzing and interpreting real NASA data, inspiring questions, defining problems, and realizing their potential to contribute to genuine scientific results. Through social channels, CosmoQuest empowers and expands its community, including science and education-focused hangouts, virtual star parties, and diverse social media. CosmoQuest offers a hub for excellent resources throughout NASA and the larger astronomy community and fosters the conversations they inspire.

Effects of Cues and Real Objects on Learning in a Mobile Device Supported Environment

ERIC Educational Resources Information Center

Liu, Tzu-Chien; Lin, Yi-Chun; Paas, Fred

2013-01-01

This study investigated whether arrow-line cues can improve the effectiveness and efficiency of learning in a mobile device supported learning environment on leaf morphology of plants, either with or without the use of real plants. A cued and un-cued condition, in which primary school students used text and pictures on a tablet PC, were compared…

Developing Conceptual Understanding in a Statistics Course: Merrill's First Principles and Real Data at Work

ERIC Educational Resources Information Center

Tu, Wendy; Snyder, Martha M.

2017-01-01

Difficulties in learning statistics primarily at the college-level led to a reform movement in statistics education in the early 1990s. Although much work has been done, effective learning designs that facilitate active learning, conceptual understanding of statistics, and the use of real-data in the classroom are needed. Guided by Merrill's First…

Beyond game effectiveness. Part II, a qualitative study of multi-role experiential learning.

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

Willis, Matthew; Tucker, Eilish Marie; Raybourn, Elaine Marie

The present paper is the second in a series published at I/ITSEC that seeks to explain the efficacy of multirole experiential learning employed to create engaging game-based training methods transitioned to the U.S. Army, U.S. Army Special Forces, Civil Affairs, and Psychological Operations teams. The first publication (I/ITSEC 2009) summarized findings from a quantitative study that investigated experiential learning in the multi-player, PC-based game module transitioned to PEO-STRI, DARWARS Ambush! NK (non-kinetic). The 2009 publication reported that participants of multi-role (Player and Reflective Observer/Evaluator) game-based training reported statistically significant learning and engagement. Additionally when the means of the two groupsmore » (Player and Reflective Observer/Evaluator) were compared, they were not statistically significantly different from each other. That is to say that both playing as well as observing/evaluating were engaging learning modalities. The Observer/Evaluator role was designed to provide an opportunity for real-time reflection and meta-cognitive learning during game play. Results indicated that this role was an engaging way to learn about communication, that participants learned something about cultural awareness, and that the skills they learned were helpful in problem solving and decision-making. The present paper seeks to continue to understand what and how users of non-kinetic game-based missions learn by revisiting the 2009 quantitative study with further investigation such as stochastic player performance analysis using latent semantic analyses and graph visualizations to correlate against human coder ratings and pre- and post-test self-analysis. The results are applicable to First-Person game-based learning systems designed to enhance trainee intercultural communication, interpersonal skills, and adaptive thinking. In the full paper, we discuss results obtained from data collected from 78 research participants of diverse backgrounds who trained by engaging in tasks directly, as well as observing and evaluating peer performance in real-time. The goal is two-fold. One is to quantify and visualize detailed player performance data coming from game play transcription to give further understanding to the results in the 2009 I/ITSEC paper. The second is to develop a set of technologies from this quantification and visualization approach into a generalized application tool to be used to aid in future games development of player/learner models and game adaptation algorithms.« less

Is it worth changing pattern recognition methods for structural health monitoring?

NASA Astrophysics Data System (ADS)

Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

2017-05-01

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

40 years of Landsat images: What we learned about science and politics

NASA Astrophysics Data System (ADS)

Dozier, Jeff

2014-03-01

The first Landsat (then called ERTS - Earth Resources Technology Satellite) launched in 1972. Landsat 8 launched in February 2013. The 40 + years of images have yielded a remarkable history of changes in Earth's land surface, and the program has accomplished significant technological achievements. However, the sustained long-term record owes more to luck than careful program planning, and especially benefitted from the remarkable 27-year life of Landsat 5. Recommendations for the future center mainly on making the program a real Program with a commitment to sustaining it, as well as some ideas to reduce cost and improve effectiveness.

Pubface: Celebrity face identification based on deep learning

NASA Astrophysics Data System (ADS)

Ouanan, H.; Ouanan, M.; Aksasse, B.

2018-05-01

In this paper, we describe a new real time application called PubFace, which allows to recognize celebrities in public spaces by employs a new pose invariant face recognition deep neural network algorithm with an extremely low error rate. To build this application, we make the following contributions: firstly, we build a novel dataset with over five million faces labelled. Secondly, we fine tuning the deep convolutional neural network (CNN) VGG-16 architecture on our new dataset that we have built. Finally, we deploy this model on the Raspberry Pi 3 model B using the OpenCv dnn module (OpenCV 3.3).

Using artificial intelligence to bring evidence-based medicine a step closer to making the individual difference.

PubMed

Sissons, B; Gray, W A; Bater, A; Morrey, D

2007-03-01

The vision of evidence-based medicine is that of experienced clinicians systematically using the best research evidence to meet the individual patient's needs. This vision remains distant from clinical reality, as no complete methodology exists to apply objective, population-based research evidence to the needs of an individual real-world patient. We describe an approach, based on techniques from machine learning, to bridge this gap between evidence and individual patients in oncology. We examine existing proposals for tackling this gap and the relative benefits and challenges of our proposed, k-nearest-neighbour-based, approach.

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

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

Alexandrov, Boian; Vesselinov, Velimir Valentinov; Djidjev, Hristo Nikolov

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

The Effect of Teaching Practices with Real Life Content in Light and Sound Learning Areas

ERIC Educational Resources Information Center

Yalçin, Sema Altun; Yalçin, Pasa; Akar, M. Said; Sagirli, Meryem Özturan

2017-01-01

In this present study, it was aimed to investigate the effect of teaching practices with real life content in light and sound learning areas. With this purpose, it was intended to determine the contribution of teaching practices with real life content (TPRLC) to the levels of pre-service teachers' skills to associate the light and sound learning…

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.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

PubMed

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

From bioterrorism exercise to real-life public health crisis: lessons for emergency hotline operations.

PubMed

Uscher-Pines, Lori; Bookbinder, Sylvia H; Miro, Suzanne; Burke, Thomas

2007-01-01

Although public health agencies routinely operate hotlines to communicate key messages to the public, they are rarely evaluated to improve hotline management. Since its creation in 2003, the New Jersey Department of Health & Senior Services' Emergency Communications Center has confronted two large-scale incidents that have tested its capabilities in this area. The influenza vaccine shortage of 2004 and the April 2005 TOPOFF 3 full-scale bioterrorism exercise provided both real-life and simulated crisis situations from which to derive general insights into the strengths and weaknesses of hotline administration. This article identifies problems in the areas of staff and message management by analyzing call volume data and the qualitative observations of group feedback sessions and semistructured interviews with hotline staff. It also makes recommendations based on lessons learned to improve future hotline operations in public health emergencies.

Teaching clinical reasoning: case-based and coached.

PubMed

Kassirer, Jerome P

2010-07-01

Optimal medical care is critically dependent on clinicians' skills to make the right diagnosis and to recommend the most appropriate therapy, and acquiring such reasoning skills is a key requirement at every level of medical education. Teaching clinical reasoning is grounded in several fundamental principles of educational theory. Adult learning theory posits that learning is best accomplished by repeated, deliberate exposure to real cases, that case examples should be selected for their reflection of multiple aspects of clinical reasoning, and that the participation of a coach augments the value of an educational experience. The theory proposes that memory of clinical medicine and clinical reasoning strategies is enhanced when errors in information, judgment, and reasoning are immediately pointed out and discussed. Rather than using cases artificially constructed from memory, real cases are greatly preferred because they often reflect the false leads, the polymorphisms of actual clinical material, and the misleading test results encountered in everyday practice. These concepts foster the teaching and learning of the diagnostic process, the complex trade-offs between the benefits and risks of diagnostic tests and treatments, and cognitive errors in clinical reasoning. The teaching of clinical reasoning need not and should not be delayed until students gain a full understanding of anatomy and pathophysiology. Concepts such as hypothesis generation, pattern recognition, context formulation, diagnostic test interpretation, differential diagnosis, and diagnostic verification provide both the language and the methods of clinical problem solving. Expertise is attainable even though the precise mechanisms of achieving it are not known.

Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution.

PubMed

Boteanu, Adrian; St Clair, Aaron; Mohseni-Kabir, Anahita; Saldanha, Carl; Chernova, Sonia

2016-12-01

This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives. In this article, we contribute a context-aware algorithm that leverages the linguistic information embedded in the task description to identify candidate substitution objects without reliance on explicit object affordance information. Specifically, we show that the task context provided by the task labels within the action structure of a task plan can be leveraged to disambiguate information within a noisy large-scale semantic network containing hundreds of potential object candidates to identify successful object substitutions with high accuracy. We present two extensive evaluations of our work on both abstract and real-world robot tasks, showing that the substitutions made by our system are valid, accepted by users, and lead to a statistically significant reduction in robot learning time. In addition, we report the outcomes of testing our approach with a large number of crowd workers interacting with a robot in real time.

A novel method to value real options in health care: the case of a multicohort human papillomavirus vaccination strategy.

PubMed

Favato, Giampiero; Baio, Gianluca; Capone, Alessandro; Marcellusi, Andrea; Saverio Mennini, Francesco

2013-07-01

A large number of economic evaluations have already confirmed the cost-effectiveness of different human papillomavirus (HPV) vaccination strategies. Standard analyses might not capture the full economic value of novel vaccination programs because the cost-effectiveness paradigm fails to take into account the value of active management. Management decisions can be seen as real options, a term used to refer to the application of option pricing theory to the valuation of investments in nonfinancial assets in which much of the value is attributable to flexibility and learning over time. The aim of this article was to discuss the potential advantages shown by using the payoff method in the valuation of the cost-effectiveness of competing HPV immunization programs. This was the first study, to the best of our knowledge, to use the payoff method to determine the real option values of 4 different HPV vaccination strategies targeting female subjects aged 12, 15, 18, and 25 years. The payoff method derives the real option value from the triangular payoff distribution of the project's net present value, which is treated as a triangular fuzzy number. To inform the real option model, cost-effectiveness data were derived from an empirically calibrated Bayesian model designed to assess the cost-effectiveness of a multicohort HPV vaccination strategy in the context of the current cervical cancer screening program in Italy. A net health benefit approach was used to calculate the expected fuzzy net present value for each of the 4 vaccination strategies evaluated. Costs per quality-adjusted life-year gained seemed to be related to the number of cohorts targeted: a single cohort of girls aged 12 years (€10,955 [95% CI, -1,021 to 28,212]) revealed the lowest cost among the 4 alternative strategies evaluated. The real option valuation challenged the cost-effectiveness dominance of a single cohort of 12-year-old girls. The simultaneous vaccination of 2 cohorts of girls aged 12 and 15 years yielded a real option value (€17,723) equivalent to that attributed to a single cohort of 12-year-old girls (€17,460). The payoff method showed distinctive advantages in the valuation of the cost-effectiveness of competing health care interventions, essentially determined by the replacement of the nonfuzzy numbers that are commonly used in cost-effectiveness analysis models, with fuzzy numbers as an input to inform the real option pricing method. The real option approach to value uncertainty makes policy making in health care an evolutionary process and creates a new "space" for decision-making choices. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Benefits of Sign Language Interpreting and Text Alternatives for Deaf Students' Classroom Learning

PubMed Central

Marschark, Marc; Leigh, Greg; Sapere, Patricia; Burnham, Denis; Convertino, Carol; Stinson, Michael; Knoors, Harry; Vervloed, Mathijs P. J.; Noble, William

2006-01-01

Four experiments examined the utility of real-time text in supporting deaf students' learning from lectures in postsecondary (Experiments 1 and 2) and secondary classrooms (Experiments 3 and 4). Experiment 1 compared the effects on learning of sign language interpreting, real-time text (C-Print), and both. Real-time text alone led to significantly higher performance by deaf students than the other two conditions, but performance by deaf students in all conditions was significantly below that of hearing peers who saw lectures without any support services. Experiment 2 compared interpreting and two forms of real-time text, C-Print and Communication Access Real-Time Translation, at immediate testing and after a 1-week delay (with study notes). No significant differences among support services were obtained at either testing. Experiment 3 also failed to reveal significant effects at immediate or delayed testing in a comparison of real-time text, direct (signed) instruction, and both. Experiment 4 found no significant differences between interpreting and interpreting plus real-time text on the learning of either new words or the content of television programs. Alternative accounts of the observed pattern of results are considered, but it is concluded that neither sign language interpreting nor real-time text have any inherent, generalized advantage over the other in supporting deaf students in secondary or postsecondary settings. Providing deaf students with both services simultaneously does not appear to provide any generalized benefit, at least for the kinds of materials utilized here. PMID:16928778

Benefits of sign language interpreting and text alternatives for deaf students' classroom learning.

PubMed

Marschark, Marc; Leigh, Greg; Sapere, Patricia; Burnham, Denis; Convertino, Carol; Stinson, Michael; Knoors, Harry; Vervloed, Mathijs P J; Noble, William

2006-01-01

Four experiments examined the utility of real-time text in supporting deaf students' learning from lectures in postsecondary (Experiments 1 and 2) and secondary classrooms (Experiments 3 and 4). Experiment 1 compared the effects on learning of sign language interpreting, real-time text (C-Print), and both. Real-time text alone led to significantly higher performance by deaf students than the other two conditions, but performance by deaf students in all conditions was significantly below that of hearing peers who saw lectures without any support services. Experiment 2 compared interpreting and two forms of real-time text, C-Print and Communication Access Real-Time Translation, at immediate testing and after a 1-week delay (with study notes). No significant differences among support services were obtained at either testing. Experiment 3 also failed to reveal significant effects at immediate or delayed testing in a comparison of real-time text, direct (signed) instruction, and both. Experiment 4 found no significant differences between interpreting and interpreting plus real-time text on the learning of either new words or the content of television programs. Alternative accounts of the observed pattern of results are considered, but it is concluded that neither sign language interpreting nor real-time text have any inherent, generalized advantage over the other in supporting deaf students in secondary or postsecondary settings. Providing deaf students with both services simultaneously does not appear to provide any generalized benefit, at least for the kinds of materials utilized here.

Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS

PubMed Central

Noack, Marko; Partzsch, Johannes; Mayr, Christian G.; Hänzsche, Stefan; Scholze, Stefan; Höppner, Sebastian; Ellguth, Georg; Schüffny, Rene

2015-01-01

Synaptic dynamics, such as long- and short-term plasticity, play an important role in the complexity and biological realism achievable when running neural networks on a neuromorphic IC. For example, they endow the IC with an ability to adapt and learn from its environment. In order to achieve the millisecond to second time constants required for these synaptic dynamics, analog subthreshold circuits are usually employed. However, due to process variation and leakage problems, it is almost impossible to port these types of circuits to modern sub-100nm technologies. In contrast, we present a neuromorphic system in a 28 nm CMOS process that employs switched capacitor (SC) circuits to implement 128 short term plasticity presynapses as well as 8192 stop-learning synapses. The neuromorphic system consumes an area of 0.36 mm2 and runs at a power consumption of 1.9 mW. The circuit makes use of a technique for minimizing leakage effects allowing for real-time operation with time constants up to several seconds. Since we rely on SC techniques for all calculations, the system is composed of only generic mixed-signal building blocks. These generic building blocks make the system easy to port between technologies and the large digital circuit part inherent in an SC system benefits fully from technology scaling. PMID:25698914

Twenty-first century learning in afterschool.

PubMed

Schwarz, Eric; Stolow, David

2006-01-01

Twenty-first century skills increasingly represent the ticket to the middle class. Yet, the authors argue, in-school learning is simply not enough to help students develop these skills. The authors make the case that after-school (or out-of-school) learning programs are emerging as one of the nation's most promising strategies for preparing young people for the workforce and civic life. Most school systems have significant limitations for teaching twenty-first century skills. They have the limits of time: with only six hours per day there is barely enough time to teach even the basic skills, especially for those students starting already behind. They have the limits of structure: typical school buildings and classrooms are not physically set up for innovative learning. They have the limits of inertia and bureaucracy: school systems are notoriously resistant to change. And perhaps most important, they have the limits of priorities: especially with the onset of the No Child Left Behind Act, schools are laserlike in their focus on teaching the basics and therefore have less incentive to incorporate twenty-first century skills. Meanwhile, the authors argue that after-school programs are an untapped resource with three competitive advantages. First, they enable students to work collaboratively in small groups, a setup on which the modern economy will increasingly rely. Second, they are well suited to project-based learning and the development of mastery. Third, they allow students to learn in the real-world contexts that make sense. Yet the after-school sector is fraught with challenges. It lacks focus-Is it child care, public safety, homework tutoring? And it lacks rigorous results. The authors argue that the teaching of twenty-first century skills should become the new organizing principle for afterschool that will propel the field forward and more effectively bridge in-school and out-of-school learning.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

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

Implementing Courseware to Support Learning through Real-World Erroneous Examples: Students' Perceptions of Tertiary Courseware and Obstacles to Implementing Effective Delivery through VLE

ERIC Educational Resources Information Center

Monthienvichienchai, Rachada; Melis, Erica

2006-01-01

This paper presents a study in a UK university that investigated how first-year (freshman) Information Systems undergraduates perceive learning through courseware containing real-world erroneous examples derived from their peers and what obstacles had to be overcome to implement effective e-Learning support for using and creating such courseware.…

Applying Collaborative Learning and Quality Improvement to Public Health: Lessons from the Collaborative Improvement and Innovation Network (CoIIN) to Reduce Infant Mortality.

PubMed

Ghandour, Reem M; Flaherty, Katherine; Hirai, Ashley; Lee, Vanessa; Walker, Deborah Klein; Lu, Michael C

2017-06-01

Infant mortality remains a significant public health problem in the U.S. The Collaborative Improvement & Innovation Network (CoIIN) model is an innovative approach, using the science of quality improvement and collaborative learning, which was applied across 13 Southern states in Public Health Regions IV and VI to reduce infant mortality and improve birth outcomes. We provide an in-depth discussion of the history, development, implementation, and adaptation of the model based on the experience of the original CoIIN organizers and participants. In addition to the political genesis and functional components of the initiative, 8 key lessons related to staffing, planning, and implementing future CoIINs are described in detail. This paper reports the findings from a process evaluation of the model. Data on the states' progress toward reducing infant mortality and improving birth outcomes were collected through a survey in the final months of a 24-month implementation period, as well as through ongoing team communications. The peer-to-peer exchange and platform for collaborative learning, as well as the sharing of data across the states, were major strengths and form the foundation for future CoIIN efforts. A lasting legacy of the initiative is the unique application and sharing of provisional "real time" data to inform "real time" decision-making. The CoIIN model of collaborative learning, QI, and innovation offers a promising approach to strengthening partnerships within and across states, bolstering data systems to inform and track progress more rapidly, and ultimately accelerating improvement toward healthier communities, States, and the Nation as a whole.

Atmosphere Kits: Hands-On Learning Activities with a Foundation in NASA Earth Science Missions.

NASA Astrophysics Data System (ADS)

Teige, V.; McCrea, S.; Damadeo, K.; Taylor, J.; Lewis, P. M., Jr.; Chambers, L. H.

2016-12-01

The Science Directorate (SD) at NASA Langley Research Center provides many opportunities to involve students, faculty, researchers, and the citizen science community in real world science. The SD Education Team collaborates with the education community to bring authentic Earth science practices and real-world data into the classroom, provide the public with unique NASA experiences, engaging activities, and advanced technology, and provide products developed and reviewed by science and education experts. Our goals include inspiring the next generation of Science, Technology, Engineering and Mathematics (STEM) professionals and improving STEM literacy by providing innovative participation pathways for educators, students, and the public. The SD Education Team has developed Atmosphere activity kits featuring cloud and aerosol learning activities with a foundation in NASA Earth Science Missions, the Next Generation Science Standards, and The GLOBE Program's Elementary Storybooks. Through cloud kit activities, students will learn how to make estimates from observations and how to categorize and classify specific cloud properties, including cloud height, cloud cover, and basic cloud types. The purpose of the aerosol kit is to introduce students to aerosols and how they can affect the colors we see in the sky. Students will engage in active observation and reporting, explore properties of light, and model the effects of changing amounts/sizes or aerosols on sky color and visibility. Learning activity extensions include participation in ground data collection of environmental conditions and comparison and analysis to related NASA data sets, including but not limited to CERES, CALIPSO, CloudSat, and SAGE III on ISS. This presentation will provide an overview of multiple K-6 NASA Earth Science hands-on activities and free resources will be available.

Time-domain Surveys and Data Shift: Case Study at the intermediate Palomar Transient Factory

NASA Astrophysics Data System (ADS)

Rebbapragada, Umaa; Bue, Brian; Wozniak, Przemyslaw R.

2015-01-01

Next generation time-domain surveys are susceptible to the problem of data shift that is caused by upgrades to data processing pipelines and instruments. Data shift degrades the performance of automated machine learning classifiers that vet detections and classify source types because fundamental assumptions are violated when classifiers are built in one data regime but are deployed on data from another. This issue is not currently discussed within the astronomical community, but will be increasingly pressing over the next decade with the advent of new time domain surveys.We look at the problem of data shift that was caused by a data pipeline upgrade when the intermediate Palomar Transient Factory (iPTF) succeeded the Palomar Transient Factory (PTF) in January 2013. iPTF relies upon machine-learned Real-Bogus classifiers to vet sources extracted from subtracted images on a scale of zero to one where zero indicates a bogus (image artifact) and one indicates a real astronomical transient, with the overwhelming majority of candidates are scored as bogus. An effective Real-Bogus system filters all but the most promising candidates, which are presented to human scanners who make decisions about triggering follow up assets.The Real-Bogus systems currently in operation at iPTF (RB4 and RB5) solve the data shift problem. The statistical models of RB4 and RB5 were built from the ground up using examples from iPTF alone, whereas an older system, RB2, was built using PTF data, but was deployed after iPTF launched. We discuss the machine learning assumptions that are violated when a system is trained on one domain (PTF) but deployed on another (iPTF) that experiences data shift. We provide illustrative examples of data parameters and statistics that experienced shift. Finally, we show results comparing the three systems in operation, demonstrating that systems that solve domain shift (RB4 and RB5) are superior to those that don't (RB2).Research described in this abstract was carried out at the Jet Propulsion Laboratory under contract with the National Aeronautics and Space Administration. US Government Support Acknowledged.

Flood and Weather Monitoring Using Real-time Twitter Data Streams

NASA Astrophysics Data System (ADS)

Demir, I.; Sit, M. A.; Sermet, M. Y.

2016-12-01

Social media data is a widely used source to making inference within public crisis periods and events in disaster times. Specifically, since Twitter provides large-scale data publicly in real-time, it is one of the most extensive resources with location information. This abstract provides an overview of a real-time Twitter analysis system to support flood preparedness and response using a comprehensive information-centric flood ontology and natural language processing. Within the scope of this project, we deal with acquisition and processing of real-time Twitter data streams. System fetches the tweets with specified keywords and classifies them as related to flooding or heavy weather conditions. The system uses machine learning algorithms to discover patterns using the correlation between tweets and Iowa Flood Information System's (IFIS) extensive resources. The system uses these patterns to forecast the formation and progress of a potential future flood event. While fetching tweets, predefined hashtags are used for filtering and enhancing the relevancy for selected tweets. With this project, tweets can also be used as an alternative data source where other data sources are not sufficient for specific tasks. During the disasters, the photos that people upload alongside their tweets can be collected and placed to appropriate locations on a mapping system. This allows decision making authorities and communities to see the most recent outlook of the disaster interactively. In case of an emergency, concentration of tweets can help the authorities to determine a strategy on how to reach people most efficiently while providing them the supplies they need. Thanks to the extendable nature of the flood ontology and framework, results from this project will be a guide for other natural disasters, and will be shared with the community.

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

PubMed

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

A developmental approach to learning causal models for cyber security

NASA Astrophysics Data System (ADS)

Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

Vernier caliper and micrometer computer models using Easy Java Simulation and its pedagogical design features—ideas for augmenting learning with real instruments

NASA Astrophysics Data System (ADS)

Wee, Loo Kang; Tiang Ning, Hwee

2014-09-01

This paper presents the customization of Easy Java Simulation models, used with actual laboratory instruments, to create active experiential learning for measurements. The laboratory instruments are the vernier caliper and the micrometer. Three computer model design ideas that complement real equipment are discussed. These ideas involve (1) a simple two-dimensional view for learning from pen and paper questions and the real world; (2) hints, answers, different scale options and the inclusion of zero error; (3) assessment for learning feedback. The initial positive feedback from Singaporean students and educators indicates that these tools could be successfully shared and implemented in learning communities. Educators are encouraged to change the source code for these computer models to suit their own purposes; they have creative commons attribution licenses for the benefit of all.

A Policy Representation Using Weighted Multiple Normal Distribution

NASA Astrophysics Data System (ADS)

Kimura, Hajime; Aramaki, Takeshi; Kobayashi, Shigenobu

In this paper, we challenge to solve a reinforcement learning problem for a 5-linked ring robot within a real-time so that the real-robot can stand up to the trial and error. On this robot, incomplete perception problems are caused from noisy sensors and cheap position-control motor systems. This incomplete perception also causes varying optimum actions with the progress of the learning. To cope with this problem, we adopt an actor-critic method, and we propose a new hierarchical policy representation scheme, that consists of discrete action selection on the top level and continuous action selection on the low level of the hierarchy. The proposed hierarchical scheme accelerates learning on continuous action space, and it can pursue the optimum actions varying with the progress of learning on our robotics problem. This paper compares and discusses several learning algorithms through simulations, and demonstrates the proposed method showing application for the real robot.