Linking Immersive Virtual Field Trips with an Adaptive Learning Platform

NASA Astrophysics Data System (ADS)

Bruce, G.; Taylor, W.; Anbar, A. D.; Semken, S. C.; Buxner, S.; Mead, C.; El-Moujaber, E.; Summons, R. E.; Oliver, C.

2016-12-01

The use of virtual environments in science education has been constrained by the difficulty of guiding a learner's actions within the those environments. In this work, we demonstrate how advances in education software technology allow educators to create interactive learning experiences that respond and adapt intelligently to learner input within the virtual environment. This innovative technology provides a far greater capacity for delivering authentic inquiry-driven educational experiences in unique settings from around the world. Our immersive virtual field trips (iVFT) bring students virtually to geologically significant but inaccessible environments, where they learn through authentic practices of scientific inquiry. In one recent example, students explore the fossil beds in Nilpena, South Australia to learn about the Ediacaran fauna. Students interactively engage in 360° recreations of the environment, uncover the nature of the historical ecosystem by identifying fossils with a dichotomous key, explore actual fossil beds in high resolution imagery, and reconstruct what an ecosystem might have looked like millions of years ago in an interactive simulation. With the new capacity to connect actions within the iVFT to an intelligent tutoring system, these learning experiences can be tracked, guided, and tailored individually to the immediate actions of the student. This new capacity also has great potential for learning designers to take a data-driven approach to lesson improvement and for education researchers to study learning in virtual environments. Thus, we expect iVFT will be fertile ground for novel research. Such iVFT are currently in use in several introductory classes offered online at Arizona State University in anthropology, introductory biology, and astrobiology, reaching thousands of students to date. Drawing from these experiences, we are designing a curriculum for historical geology that will be built around iVFT-based exploration of Earth history.

Digital Hardware Design Teaching: An Alternative Approach

ERIC Educational Resources Information Center

Benkrid, Khaled; Clayton, Thomas

2012-01-01

This article presents the design and implementation of a complete review of undergraduate digital hardware design teaching in the School of Engineering at the University of Edinburgh. Four guiding principles have been used in this exercise: learning-outcome driven teaching, deep learning, affordability, and flexibility. This has identified…

Contextual cueing: implicit learning and memory of visual context guides spatial attention.

PubMed

Chun, M M; Jiang, Y

1998-06-01

Global context plays an important, but poorly understood, role in visual tasks. This study demonstrates that a robust memory for visual context exists to guide spatial attention. Global context was operationalized as the spatial layout of objects in visual search displays. Half of the configurations were repeated across blocks throughout the entire session, and targets appeared within consistent locations in these arrays. Targets appearing in learned configurations were detected more quickly. This newly discovered form of search facilitation is termed contextual cueing. Contextual cueing is driven by incidentally learned associations between spatial configurations (context) and target locations. This benefit was obtained despite chance performance for recognizing the configurations, suggesting that the memory for context was implicit. The results show how implicit learning and memory of visual context can guide spatial attention towards task-relevant aspects of a scene.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

2017-12-01

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Learning through Geography. Pathways in Geography Series, Title No. 7.

ERIC Educational Resources Information Center

Slater, Frances

This teacher's guide is to enable the teacher to promote thinking through the use of geography. The book lays out the rationale in learning theory for an issues-based, question-driven inquiry method and proceeds through a simple model of progression from identifying key questions to developing generalizations. Students study issues of geographic…

The Fusion of Learning Theory and Technology in an Online Music History Course Redesign

ERIC Educational Resources Information Center

Scarnati, Blase; Garcia, Paula

2008-01-01

Teaching today's students requires an integration of learner-centered pedagogy with innovative technological resources. In this article, Blase Scarnati and Paula Garcia describe the redesign of a junior-level music history course guided by learner-centered principles and driven by a fusion of stimulating technology-based learning tools and…

Deep learning guided stroke management: a review of clinical applications.

PubMed

Feng, Rui; Badgeley, Marcus; Mocco, J; Oermann, Eric K

2018-04-01

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

How Evolution May Work Through Curiosity-Driven Developmental Process.

PubMed

Oudeyer, Pierre-Yves; Smith, Linda B

2016-04-01

Infants' own activities create and actively select their learning experiences. Here we review recent models of embodied information seeking and curiosity-driven learning and show that these mechanisms have deep implications for development and evolution. We discuss how these mechanisms yield self-organized epigenesis with emergent ordered behavioral and cognitive developmental stages. We describe a robotic experiment that explored the hypothesis that progress in learning, in and for itself, generates intrinsic rewards: The robot learners probabilistically selected experiences according to their potential for reducing uncertainty. In these experiments, curiosity-driven learning led the robot learner to successively discover object affordances and vocal interaction with its peers. We explain how a learning curriculum adapted to the current constraints of the learning system automatically formed, constraining learning and shaping the developmental trajectory. The observed trajectories in the robot experiment share many properties with those in infant development, including a mixture of regularities and diversities in the developmental patterns. Finally, we argue that such emergent developmental structures can guide and constrain evolution, in particular with regard to the origins of language. Copyright © 2016 Cognitive Science Society, Inc.

Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.

PubMed

Park, Seong-Cheol; Chung, Chun Kee

2018-06-01

The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13-44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4-25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4-25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17-36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert's manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.

The Effect of Cognitive- and Metacognitive-Based Instruction on Problem Solving by Elementary Students with Mathematical Learning Difficulties

ERIC Educational Resources Information Center

Grizzle-Martin, Tamieka

2014-01-01

Children who struggle in mathematics may also lack cognitive awareness in mathematical problem solving. The cognitively-driven program IMPROVE, a multidimensional method for teaching mathematics, has been shown to be helpful for students with mathematical learning difficulties (MLD). Guided by cognitive theory, the purpose of this…

Pedagogically-Driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

ERIC Educational Resources Information Center

Romero, Lucila; North, Matthew; Gutiérrez, Milagros; Caliusco, Laura

2015-01-01

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to…

A guide to using case-based learning in biochemistry education.

PubMed

Kulak, Verena; Newton, Genevieve

2014-01-01

Studies indicate that the majority of students in undergraduate biochemistry take a surface approach to learning, associated with rote memorization of material, rather than a deep approach, which implies higher cognitive processing. This behavior relates to poorer outcomes, including impaired course performance and reduced knowledge retention. The use of case-based learning (CBL) into biochemistry teaching may facilitate deep learning by increasing student engagement and interest. Abundant literature on CBL exists but clear guidance on how to design and implement case studies is not readily available. This guide provides a representative review of CBL uses in science and describes the process of developing CBL modules to be used in biochemistry. Included is a framework to implement a directed CBL assisted with lectures in a content-driven biochemistry course regardless of class size. Moreover, this guide can facilitate adopting CBL to other courses. Consequently, the information presented herein will be of value to undergraduate science educators with an interest in active learning pedagogies. © 2014 The International Union of Biochemistry and Molecular Biology.

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

Guide to a Student-Family-School-Community Partnership: Using a Student & Data Driven Process to Improve School Environments & Promote Student Success

ERIC Educational Resources Information Center

Burgoa, Carol; Izu, Jo Ann

2010-01-01

This guide presents a data-driven, research-based process--referred to as the "school-community forum process"--for increasing youth voice, promoting resilience, strengthening adult-youth connections, and ultimately, for improving schools. It uses a "student listening circle"--a special type of focus group involving eight to…

A practical guide to big data research in psychology.

PubMed

Chen, Eric Evan; Wojcik, Sean P

2016-12-01

The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Learning in and about Rural Places: Connections and Tensions between Students' Everyday Experiences and Environmental Quality Issues in Their Community

ERIC Educational Resources Information Center

Zimmerman, Heather Toomey; Weible, Jennifer L.

2017-01-01

Guided by sociocultural perspectives on the importance of place as a resource for learning, we investigated 14- and 15-year old students' understandings of their community and water quality during a school-based watershed unit. Methods included a theory-driven thematic analysis of field notes and video transcripts from four biology classrooms, a…

Educating Through Exploration: Emerging Evidence for Improved Learning Outcomes Using a New Theory of Digital Learning Design

NASA Astrophysics Data System (ADS)

Anbar, Ariel; Center for Education Through eXploration

2018-01-01

Advances in scientific visualization and public access to data have transformed science outreach and communication, but have yet to realize their potential impacts in the realm of education. Computer-based learning is a clear bridge between visualization and education that benefits students through adaptative personalization and enhanced access. Building this bridge requires close partnerships among scientists, technologists, and educators.The Infiniscope project fosters such partnerships to produce exploration-driven online learning experiences that teach basic science concepts using a combination of authentic space science narratives, data, and images, and a personalized guided inquiry approach. Infiniscope includes a web portal to host these digital learning experiences, as well as a teaching network of educators using and modifying these experiences. Infiniscope experiences are built around a new theory of digital learning design that we call “education through exploration” (ETX) developed during the creation of successful online, interactive science courses offered at ASU and other institutions. ETX builds on the research-based practices of active learning and guided inquiry to provide a set of design principles that aim to develop higher order thinking skills in addition to understanding of content. It is employed in these experiences by asking students to solve problems and actively discover relationships, supported by an intelligent tutoring system which provides immediate, personalized feedback and scaffolds scientific thinking and methods. The project is led by ASU’s School of Earth and Space Exploration working with learning designers in the Center for Education Through eXploration, with support from NASA’s Science Mission Directorate as part of the NASA Exploration Connection program.We will present an overview of ETX design, the Infinscope project, and emerging evidence of effectiveness.

Data-Driven Learning of Speech Acts Based on Corpora of DVD Subtitles

ERIC Educational Resources Information Center

Kitao, S. Kathleen; Kitao, Kenji

2013-01-01

Data-driven learning (DDL) is an inductive approach to language learning in which students study examples of authentic language and use them to find patterns of language use. This inductive approach to learning has the advantages of being learner-centered, encouraging hypothesis testing and learner autonomy, and helping develop learning skills.…

Frequency-specific hippocampal-prefrontal interactions during associative learning

PubMed Central

Brincat, Scott L.; Miller, Earl K.

2015-01-01

Much of our knowledge of the world depends on learning associations (e.g., face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive/mnemonic abilities are akin to humans. Here, we show functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object-pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, while HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences, and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC, and increased with learning. Rapid object associative learning may occur in PFC, while HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. PMID:25706471

Biography-Driven Culturally Responsive Teaching

ERIC Educational Resources Information Center

Herrera, Socorro

2010-01-01

Nationally known literacy expert Socorro Herrera provides a practical guide for teachers serving culturally and linguistically diverse (CLD) populations. Teachers will learn how to plan and implement more successful culturally responsive instruction using student biographies as the point of departure. The author provides tools for tapping into the…

The Structural Consequences of Big Data-Driven Education.

PubMed

Zeide, Elana

2017-06-01

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.

Improving Mathematics Achievement of Indonesian 5th Grade Students through Guided Discovery Learning

ERIC Educational Resources Information Center

Yurniwati; Hanum, Latipa

2017-01-01

This research aims to find information about the improvement of mathematics achievement of grade five student through guided discovery learning. This research method is classroom action research using Kemmis and Taggart model consists of three cycles. Data used in this study is learning process and learning results. Learning process data is…

Analyzing the Discourse of Chais Conferences for the Study of Innovation and Learning Technologies via a Data-Driven Approach

ERIC Educational Resources Information Center

Silber-Varod, Vered; Eshet-Alkalai, Yoram; Geri, Nitza

2016-01-01

The current rapid technological changes confront researchers of learning technologies with the challenge of evaluating them, predicting trends, and improving their adoption and diffusion. This study utilizes a data-driven discourse analysis approach, namely culturomics, to investigate changes over time in the research of learning technologies. The…

Data-Driven Learning for Beginners: The Case of German Verb-Preposition Collocations

ERIC Educational Resources Information Center

Vyatkina, Nina

2016-01-01

Research on data-driven learning (DDL), or teaching and learning languages with the help of electronic corpora, has shown that it is both effective and efficient. Nevertheless, DDL is still far from common pedagogical practice, not least because the empirical research on it is still limited and narrowly focused. This study addresses some gaps in…

Corpus of High School Academic Texts (COHAT): Data-Driven, Computer Assisted Discovery in Learning Academic English

ERIC Educational Resources Information Center

Bohát, Róbert; Rödlingová, Beata; Horáková, Nina

2015-01-01

Corpus of High School Academic Texts (COHAT), currently of 150,000+ words, aims to make academic language instruction a more data-driven and student-centered discovery learning as a special type of Computer-Assisted Language Learning (CALL), emphasizing students' critical thinking and metacognition. Since 2013, high school English as an additional…

The Effects of Data-Driven Learning upon Vocabulary Acquisition for Secondary International School Students in Vietnam

ERIC Educational Resources Information Center

Karras, Jacob Nolen

2016-01-01

Within the field of computer assisted language learning (CALL), scant literature exists regarding the effectiveness and practicality for secondary students to utilize data-driven learning (DDL) for vocabulary acquisition. In this study, there were 100 participants, who had a mean age of thirteen years, and were attending an international school in…

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

Blocks: A Versatile Learning Tool for Yesterday, Today, and Tomorrow

ERIC Educational Resources Information Center

Anderson, Charlotte

2010-01-01

In today's standards-driven climate, some teachers feel that incorporating content standards in the curriculum leads to a non-developmentally appropriate approach to working with young children. In her work as a preschool teacher trainer, the author shows students how something as common as blocks can guide them through each of the curriculum…

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

A Resource Guide for the Determination of Learning Disabilities. Revised.

ERIC Educational Resources Information Center

Texas Education Agency, Austin. Div. of Special Education.

Intended for special education personnel, the resource guide is designed to clarify the concept of learning disabilities, determine the nature of the data which must be collected to determine the presence or absence of learning disability, develop procedures for collecting the data, and interpret the data. Initial sections address P.L. 94-142 (the…

Enhancement of Self Efficacy of Vocational School Students in Buffer Solution Topics through Guided Inquiry Learning

NASA Astrophysics Data System (ADS)

M, Ardiany; W, Wahyu; A, Supriatna

2017-09-01

The more students who feel less confident in learning, so doing things that are less responsible, such as brawl, drunkenness and others. So researchers need to do research related to student self efficacy in learning, in order to reduce unwanted things. This study aims to determine the effect of guided inquiry learning on improving self-efficacy of learners in the buffer solution topics. The method used is the mixed method which is the two group pretest postest design. The subjects of the study are 60 students of class XI AK in one of the SMKN in Bandung, consisting of 30 experimental class students and 30 control class students. The instruments used in this study mix method consist of self-efficacy questionnaire of pretest and posttest learners, interview guides, and observation sheet. Data analysis using t test with significant α = 0,05. Based on the result of inquiry of guided inquiry study, there is a significant improvement in self efficacy aspect of students in the topic of buffer solution. Data of pretest and posttest interview, observation, questionnaire showed significant result, that is improvement of experimental class with conventionally guided inquiry learning. The mean of self-efficacy of student learning there is significant difference of experiment class than control class equal to 0,047. There is a significant relationship between guided inquiry learning with self efficacy and guided inquiry learning. Each correlation value is 0.737. The learning process with guided inquiry is fun and challenging so that students can expose their ideas and opinions without being forced. From the results of questionnaires students showed an attitude of interest, sincerity and a good response of learning. While the results of questionnaires teachers showed that guided inquiry learning can make students learn actively, increased self-efficacy.

Learning from Learners: A Non-Standard Direct Approach to the Teaching of Writing Skills in EFL in a University Context

ERIC Educational Resources Information Center

Fuster-Márquez, Miguel; Gregori-Signes, Carmen

2018-01-01

Corpora have been used in English as a foreign language materials for decades, and native corpora have been present in the classroom by means of direct approaches such as Data-Driven Learning (Johns, T., and P. King 1991. "'Should you be Persuaded'- Two Samples of Data-Driven Learning Materials." In "Classroom Concordancing,"…

The Effect of Data-Driven Approach to Teaching Vocabulary on Iranian Students' Learning of English Vocabulary

ERIC Educational Resources Information Center

Barabadi, Elyas; Khajavi, Yaser

2017-01-01

Corpus-based data-driven learning (DDL) is an innovation in teaching and learning new vocabulary for EFL students. Using teacher-prepared materials obtained from COCA corpus, the goal of the present study is to compare DDL and traditional methods of teaching vocabulary like consultation of dictionary or a grammar book. As such, two intact classes…

Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification.

PubMed

d'Alessandro, Brian; O'Neil, Cathy; LaGatta, Tom

2017-06-01

Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.

What Data for Data-Driven Learning?

ERIC Educational Resources Information Center

Boulton, Alex

2012-01-01

Corpora have multiple affordances, not least for use by teachers and learners of a foreign language (L2) in what has come to be known as "data-driven learning" or DDL. The corpus and concordance interface were originally conceived by and for linguists, so other users need to adopt the role of "language researcher" to make the most of them. Despite…

Lo que los educadores necesitan saber sobre...El agrupamiento por habilidad [y] La compactacion del curriculum [y] Los alumnos dotados y el aprendizaje cooperativo [y] La actividad tutoral. Guias practica (What Educators Need To Know about...Ability Grouping [and] Curriculum Compacting [and] Gifted Students and Cooperative Learning [and] Mentoring. Practitioners' Guides).

ERIC Educational Resources Information Center

Siegle, Del, Ed.

These four pamphlets in Spanish offer guidelines supported by theory-driven quality research that is problem-based, practice-relevant, and consumer-oriented. Each pamphlet has a section summarizing research from the literature or topic notes as well as implications for the classroom. The first guide offers principles for teachers concerning the…

Using Learning Analytics to Enhance Student Learning in Online Courses Based on Quality Matters Standards

ERIC Educational Resources Information Center

Martin, Florence; Ndoye, Abdou; Wilkins, Patricia

2016-01-01

Quality Matters is recognized as a rigorous set of standards that guide the designer or instructor to design quality online courses. We explore how Quality Matters standards guide the identification and analysis of learning analytics data to monitor and improve online learning. Descriptive data were collected for frequency of use, time spent, and…

Enhancing Extensive Reading with Data-Driven Learning

ERIC Educational Resources Information Center

Hadley, Gregory; Charles, Maggie

2017-01-01

This paper investigates using data-driven learning (DDL) as a means of stimulating greater lexicogrammatical knowledge and reading speed among lower proficiency learners in an extensive reading program. For 16 weekly 90-minute sessions, an experimental group (12 students) used DDL materials created from a corpus developed from the Oxford Bookworms…

Modulation of spatial attention by goals, statistical learning, and monetary reward.

PubMed

Jiang, Yuhong V; Sha, Li Z; Remington, Roger W

2015-10-01

This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention.

Modulation of spatial attention by goals, statistical learning, and monetary reward

PubMed Central

Sha, Li Z.; Remington, Roger W.

2015-01-01

This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention. PMID:26105657

Assessment Data-Driven Inquiry: A Review of How to Use Assessment Results to Inform Chemistry Teaching

ERIC Educational Resources Information Center

Harshman, Jordan; Yezierski, Ellen

2017-01-01

With abundant access to assessments of all kinds, many high school chemistry teachers have the opportunity to gather data from their students on a daily basis. This data can serve multiple purposes, such as informing teachers of students' content difficulties and guiding instruction in a process of data-driven inquiry. In this paper, 83 resources…

Examining Data Driven Decision Making via Formative Assessment: A Confluence of Technology, Data Interpretation Heuristics and Curricular Policy

ERIC Educational Resources Information Center

Swan, Gerry; Mazur, Joan

2011-01-01

Although the term data-driven decision making (DDDM) is relatively new (Moss, 2007), the underlying concept of DDDM is not. For example, the practices of formative assessment and computer-managed instruction have historically involved the use of student performance data to guide what happens next in the instructional sequence (Morrison, Kemp, &…

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

Students concept understanding of fluid static based on the types of teaching

NASA Astrophysics Data System (ADS)

Rahmawati, I. D.; Suparmi; Sunarno, W.

2018-03-01

This research aims to know the concept understanding of student are taught by guided inquiry based learning and conventional based learning. Subjects in this study are high school students as much as 2 classes and each class consists of 32 students, both classes are homogen. The data was collected by conceptual test in the multiple choice form with the students argumentation of the answer. The data analysis used is qualitative descriptive method. The results of the study showed that the average of class that was using guided inquiry based learning is 78.44 while the class with use conventional based learning is 65.16. Based on these data, the guided inquiry model is an effective learning model used to improve students concept understanding.

An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

PubMed Central

Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

2015-01-01

The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

USER'S GUIDE FOR GLOED VERSION 1.0 - THE GLOBAL EMISSIONS DATABASE

EPA Science Inventory

The document is a user's guide for the EPA-developed, powerful software package, Global Emissions Database (GloED). GloED is a user-friendly, menu-driven tool for storing and retrieving emissions factors and activity data on a country-specific basis. Data can be selected from dat...

Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

PubMed Central

Chalmers, Eric; Luczak, Artur; Gruber, Aaron J.

2016-01-01

The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide “goal-directed” behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, “forward sweeps” through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort) required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks. PMID:28018203

Investigating Users' Requirements

PubMed Central

Walker, Deborah S.; Lee, Wen-Yu; Skov, Neil M.; Berger, Carl F.; Athley, Brian D.

2002-01-01

Objective: User data and information about anatomy education were used to guide development of a learning environment that is efficient and effective. The research question focused on how to design instructional software suitable for the educational goals of different groups of users of the Visible Human data set. The ultimate goal of the study was to provide options for students and teachers to use different anatomy learning modules corresponding to key topics, for course work and professional training. Design: The research used both qualitative and quantitative methods. It was driven by the belief that good instructional design must address learning context information and pedagogic content information. The data collection emphasized measurement of users' perspectives, experience, and demands in anatomy learning. Measurement: Users' requirements elicited from 12 focus groups were combined and rated by 11 researchers. Collective data were sorted and analyzed by use of multidimensional scaling and cluster analysis. Results: A set of functions and features in high demand across all groups of users was suggested by the results. However, several subgroups of users shared distinct demands. The design of the learning modules will encompass both unified core components and user-specific applications. The design templates will allow sufficient flexibility for dynamic insertion of different learning applications for different users. Conclusion: This study describes how users' requirements, associated with users' learning experiences, were systematically collected and analyzed and then transformed into guidelines informing the iterative design of multiple learning modules. Information about learning challenges and processes was gathered to define essential anatomy teaching strategies. A prototype instrument to design and polish the Visible Human user interface system is currently being developed using ideas and feedback from users. PMID:12087112

Integrating Cognitive Linguistics Insights into Data-Driven Learning: Teaching Vertical Prepositions

ERIC Educational Resources Information Center

Kilimci, Abdurrahman

2017-01-01

The present study investigates the impact of the integration of the Cognitive Linguistics (CL) pedagogy into Data-driven learning (DDL) on the learners' acquisition of two sets of English spatial prepositions of verticality, "over/under" and "above/below." The study followed a quasi-experimental design with a control and an…

Developing Annotation Solutions for Online Data Driven Learning

ERIC Educational Resources Information Center

Perez-Paredes, Pascual; Alcaraz-Calero, Jose M.

2009-01-01

Although "annotation" is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation…

Data driven modeling of plastic deformation

DOE PAGES

Versino, Daniele; Tonda, Alberto; Bronkhorst, Curt A.

2017-05-01

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 –4 to 10 12 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessedmore » by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.« less

Data driven modeling of plastic deformation

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

Versino, Daniele; Tonda, Alberto; Bronkhorst, Curt A.

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 –4 to 10 12 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessedmore » by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.« less

Biological Inquiry: A New Course and Assessment Plan in Response to the Call to Transform Undergraduate Biology

ERIC Educational Resources Information Center

Goldey, Ellen S.; Abercrombie, Clarence L.; Ivy, Tracie M.; Kusher, Dave I.; Moeller, John F.; Rayner, Doug A.; Smith, Charles F.; Spivey, Natalie W.

2012-01-01

We transformed our first-year curriculum in biology with a new course, Biological Inquiry, in which greater than 50% of all incoming, first-year students enroll. The course replaced a traditional, content-driven course that relied on outdated approaches to teaching and learning. We diversified pedagogical practices by adopting guided inquiry in…

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

The ACR BI-RADS® Experience: Learning From History

PubMed Central

Burnside, Elizabeth S.; Sickles, Edward A.; Bassett, Lawrence W.; Rubin, Daniel L.; Lee, Carol H.; Ikeda, Debra M.; Mendelson, Ellen B.; Wilcox, Pamela A.; Butler, Priscilla F.; D’Orsi, Carl J.

2011-01-01

The Breast Imaging Reporting and Data System® (BI-RADS®) initiative, instituted by the ACR, was begun in the late 1980s to address a lack of standardization and uniformity in mammography practice reporting. An important component of the BI-RADS initiative is the lexicon, a dictionary of descriptors of specific imaging features. The BI-RADS lexicon has always been data driven, using descriptors that previously had been shown in the literature to be predictive of benign and malignant disease. Once established, the BI-RADS lexicon provided new opportunities for quality assurance, communication, research, and improved patient care. The history of this lexicon illustrates a series of challenges and instructive successes that provide a valuable guide for other groups that aspire to develop similar lexicons in the future. PMID:19945040

Development of a Scale to Measure Learners' Perceived Preferences and Benefits of Data-Driven Learning

ERIC Educational Resources Information Center

Mizumoto, Atsushi; Chujo, Kiyomi; Yokota, Kenji

2016-01-01

In spite of researchers' and practitioners' increasing attention to data-driven learning (DDL) and increasing numbers of DDL studies, a multi-item scale to measure learners' attitude toward DDL has not been developed thus far. In the present study, we developed and validated a psychometric scale to measure learners' perceived preferences and…

Retesting the Limits of Data-Driven Learning: Feedback and Error Correction

ERIC Educational Resources Information Center

Crosthwaite, Peter

2017-01-01

An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…

Lexical Awareness and Development through Data Driven Learning: Attitudes and Beliefs of EFL Learners

ERIC Educational Resources Information Center

Asik, Asuman; Vural, Arzu Sarlanoglu; Akpinar, Kadriye Dilek

2016-01-01

Data-driven learning (DDL) has become an innovative approach developed from corpus linguistics. It plays a significant role in the progression of foreign language pedagogy, since it offers learners plentiful authentic corpora examples that make them analyze language rules with the help of online corpora and concordancers. The present study…

Data-Driven Learning: Taking the Computer out of the Equation

ERIC Educational Resources Information Center

Boulton, Alex

2010-01-01

Despite considerable research interest, data-driven learning (DDL) has not become part of mainstream teaching practice. It may be that technical aspects are too daunting for teachers and students, but there seems to be no reason why DDL in its early stages should not eliminate the computer from the equation by using prepared materials on…

On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\\infty}$ Control.

PubMed

Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

2018-04-01

In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

Age differences in learning emerge from an insufficient representation of uncertainty in older adults

PubMed Central

Nassar, Matthew R.; Bruckner, Rasmus; Gold, Joshua I.; Li, Shu-Chen; Heekeren, Hauke R.; Eppinger, Ben

2016-01-01

Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surprising (surprise) or contingencies are more likely to change (hazard rate). In this study, we combine computational modelling with an age-comparative behavioural study to test whether age-related learning deficits emerge from a failure to optimize learning according to the three factors mentioned above. Our results suggest that learning deficits observed in healthy older adults are driven by a diminished capacity to represent and use uncertainty to guide learning. These findings provide insight into age-related cognitive changes and demonstrate how learning deficits can emerge from a failure to accurately assess how much should be learned. PMID:27282467

Development of a Pilot Learning Module on Water Energy Nexus Using a Data-Analytic and Hypothesis-Driven Approach

NASA Astrophysics Data System (ADS)

Eldardiry, H. A.; Unruh, H. G., Sr.; Habib, E. H.; Tidwell, V. C.

2016-12-01

Recent community initiatives have identified key foundational knowledge gaps that need to be addressed before transformative solutions can be made in the area of Food, Energy and Water (FEW) nexus. In addition, knowledge gaps also exist in the area of FEW education and needs to be addressed before we can make an impact on building the next generation FEW workforces. This study reports on the development of a pilot learning-module that focuses on two elements of the FEW nexus, Energy and Water. The module follows an active-learning approach to develop a set of student-centered learning activities using FEW datasets situated in real-world settings in the contiguous US. The module is based on data-driven learning exercises that incorporate different geospatial layers and manipulate datasets at a watershed scale representing the eight-digit Hydrologic Unit Code (HUC8). Examples of such datasets include water usage by different demand sectors (available from the US Geological Survey, USGS), and power plants stratified by energy source, cooling technology, and plant capacity (available from the US Energy Information Administration, EIA). The module is structured in three sections: (1) introduction to the water and energy systems, (2) quantifying stresses on water system at a catchment scale, and (3) scenario-based analysis on the interdependencies in the water-energy systems. Following a data-analytic framework, the module guides students to make different assumptions about water use growth rates and see how these new water demands will impinge on freshwater supplies. The module engages students in analysis that examines how thermoelectric water use would depend on assumptions about future demand for electricity, power plant fuel source, cooling type, and carbon sequestration. Students vary the input parameters, observe and assess the effect on water use, and address gaps via non-potable water resources (e.g., municipal wastewater). The module is implemented using a web-based platform where datasets, lesson contents, and student learning activities are presented within a geo-spatial context. The presentation will share insight on how the dynamics of FEW systems can be taught using meaningful educational experiences that promote students' understanding of FEW systems and their complex inter-connections.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics.

PubMed

Cheng, Sen; Sabes, Philip N

2007-04-01

The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for > or =20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.

Student-Centered Modules to Support Active Learning in Hydrology: Development Experiences and Users' Perspectives

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Habib, E. H.; Deshotel, M.; Merck, M. F.; Lall, U.; Farnham, D. J.

2016-12-01

Traditional approaches to undergraduate hydrology and water resource education are textbook based, adopt unit processes and rely on idealized examples of specific applications, rather than examining the contextual relations in the processes and the dynamics connecting climate and ecosystems. The overarching goal of this project is to address the needed paradigm shift in undergraduate education of engineering hydrology and water resources education to reflect parallel advances in hydrologic research and technology, mainly in the areas of new observational settings, data and modeling resources and web-based technologies. This study presents efforts to develop a set of learning modules that are case-based, data and simulation driven and delivered via a web user interface. The modules are based on real-world case studies from three regional hydrologic settings: Coastal Louisiana, Utah Rocky Mountains and Florida Everglades. These three systems provide unique learning opportunities on topics such as: regional-scale budget analysis, hydrologic effects of human and natural changes, flashflood protection, climate-hydrology teleconnections and water resource management scenarios. The technical design and contents of the modules aim to support students' ability for transforming their learning outcomes and skills to hydrologic systems other than those used by the specific activity. To promote active learning, the modules take students through a set of highly engaging learning activities that are based on analysis of hydrologic data and model simulations. The modules include user support in the form of feedback and self-assessment mechanisms that are integrated within the online modules. Module effectiveness is assessed through an improvement-focused evaluation model using a mixed-method research approach guiding collection and analysis of evaluation data. Both qualitative and quantitative data are collected through student learning data, product analysis, and staff interviews. The presentation shares with the audience lessons learned from the development and implementation of the modules, students' feedback, guidelines on design and content attributes that support active learning in hydrology, and challenges encountered during the class implementation and evaluation of the modules.

Modeling Gas and Gas Hydrate Accumulation in Marine Sediments Using a K-Nearest Neighbor Machine-Learning Technique

NASA Astrophysics Data System (ADS)

Wood, W. T.; Runyan, T. E.; Palmsten, M.; Dale, J.; Crawford, C.

2016-12-01

Natural Gas (primarily methane) and gas hydrate accumulations require certain bio-geochemical, as well as physical conditions, some of which are poorly sampled and/or poorly understood. We exploit recent advances in the prediction of seafloor porosity and heat flux via machine learning techniques (e.g. Random forests and Bayesian networks) to predict the occurrence of gas and subsequently gas hydrate in marine sediments. The prediction (actually guided interpolation) of key parameters we use in this study is a K-nearest neighbor technique. KNN requires only minimal pre-processing of the data and predictors, and requires minimal run-time input so the results are almost entirely data-driven. Specifically we use new estimates of sedimentation rate and sediment type, along with recently derived compaction modeling to estimate profiles of porosity and age. We combined the compaction with seafloor heat flux to estimate temperature with depth and geologic age, which, with estimates of organic carbon, and models of methanogenesis yield limits on the production of methane. Results include geospatial predictions of gas (and gas hydrate) accumulations, with quantitative estimates of uncertainty. The Generic Earth Modeling System (GEMS) we have developed to derive the machine learning estimates is modular and easily updated with new algorithms or data.

Precision global health in the digital age.

PubMed

Flahault, Antoine; Geissbuhler, Antoine; Guessous, Idris; Guérin, Philippe; Bolon, Isabelle; Salathé, Marcel; Escher, Gérard

2017-04-19

Precision global health is an approach similar to precision medicine, which facilitates, through innovation and technology, better targeting of public health interventions on a global scale, for the purpose of maximising their effectiveness and relevance. Illustrative examples include: the use of remote sensing data to fight vector-borne diseases; large databases of genomic sequences of foodborne pathogens helping to identify origins of outbreaks; social networks and internet search engines for tracking communicable diseases; cell phone data in humanitarian actions; drones to deliver healthcare services in remote and secluded areas. Open science and data sharing platforms are proposed for fostering international research programmes under fair, ethical and respectful conditions. Innovative education, such as massive open online courses or serious games, can promote wider access to training in public health and improving health literacy. The world is moving towards learning healthcare systems. Professionals are equipped with data collection and decision support devices. They share information, which are complemented by external sources, and analysed in real time using machine learning techniques. They allow for the early detection of anomalies, and eventually guide appropriate public health interventions. This article shows how information-driven approaches, enabled by digital technologies, can help improving global health with greater equity.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

The Financial and Non-Financial Aspects of Developing a Data-Driven Decision-Making Mindset in an Undergraduate Business Curriculum

ERIC Educational Resources Information Center

Bohler, Jeffrey; Krishnamoorthy, Anand; Larson, Benjamin

2017-01-01

Making data-driven decisions is becoming more important for organizations faced with confusing and often contradictory information available to them from their operating environment. This article examines one college of business' journey of developing a data-driven decision-making mindset within its undergraduate curriculum. Lessons learned may be…

The Use of Linking Adverbials in Academic Essays by Non-Native Writers: How Data-Driven Learning Can Help

ERIC Educational Resources Information Center

Garner, James Robert

2013-01-01

Over the past several decades, the TESOL community has seen an increased interest in the use of data-driven learning (DDL) approaches. Most studies of DDL have focused on the acquisition of vocabulary items, including a wide range of information necessary for their correct usage. One type of vocabulary that has yet to be properly investigated has…

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.

Data Wise: A Step by Step Guide to Using Assessment Results to Improve Teaching and Learning. Revised and Expanded Edition

ERIC Educational Resources Information Center

Boudett, Kathryn Parker, Ed.; City, Elizabeth A., Ed.; Murnane, Richard J., Ed.

2013-01-01

"Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning" presents a clear and carefully tested blueprint for school leaders. It shows how examining test scores and other classroom data can become a catalyst for important schoolwide conversations that will enhance schools' abilities to capture…

Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning. Revised and Expanded Edition

ERIC Educational Resources Information Center

Boudett, Kathryn Parker, Ed.; City, Elizabeth A., Ed.; Murnane, Richard J., Ed.

2013-01-01

"Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning" presents a clear and carefully tested blueprint for school leaders. It shows how examining test scores and other classroom data can become a catalyst for important schoolwide conversations that will enhance schools' abilities to capture…

Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning

ERIC Educational Resources Information Center

Boudett, Kathryn Parker, Ed.; City, Elizabeth, Ed.; Murnane, Richard, Ed.

2005-01-01

In the wake of the accountability movement, school administrators are inundated with data about their students. How can they use this information to support student achievement? "Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning" presents a clear and carefully tested blueprint for school leaders. It shows…

Awareness in contextual cueing of visual search as measured with concurrent access- and phenomenal-consciousness tasks.

PubMed

Schlagbauer, Bernhard; Müller, Hermann J; Zehetleitner, Michael; Geyer, Thomas

2012-10-25

In visual search, context information can serve as a cue to guide attention to the target location. When observers repeatedly encounter displays with identical target-distractor arrangements, reaction times (RTs) are faster for repeated relative to nonrepeated displays, the latter containing novel configurations. This effect has been termed "contextual cueing." The present study asked whether information about the target location in repeated displays is "explicit" (or "conscious") in nature. To examine this issue, observers performed a test session (after an initial training phase in which RTs to repeated and nonrepeated displays were measured) in which the search stimuli were presented briefly and terminated by visual masks; following this, observers had to make a target localization response (with accuracy as the dependent measure) and indicate their visual experience and confidence associated with the localization response. The data were examined at the level of individual displays, i.e., in terms of whether or not a repeated display actually produced contextual cueing. The results were that (a) contextual cueing was driven by only a very small number of about four actually learned configurations; (b) localization accuracy was increased for learned relative to nonrepeated displays; and (c) both consciousness measures were enhanced for learned compared to nonrepeated displays. It is concluded that contextual cueing is driven by only a few repeated displays and the ability to locate the target in these displays is associated with increased visual experience.

Biological inquiry: a new course and assessment plan in response to the call to transform undergraduate biology.

PubMed

Goldey, Ellen S; Abercrombie, Clarence L; Ivy, Tracie M; Kusher, Dave I; Moeller, John F; Rayner, Doug A; Smith, Charles F; Spivey, Natalie W

2012-01-01

We transformed our first-year curriculum in biology with a new course, Biological Inquiry, in which >50% of all incoming, first-year students enroll. The course replaced a traditional, content-driven course that relied on outdated approaches to teaching and learning. We diversified pedagogical practices by adopting guided inquiry in class and in labs, which are devoted to building authentic research skills through open-ended experiments. Students develop core biological knowledge, from the ecosystem to molecular level, and core skills through regular practice in hypothesis testing, reading primary literature, analyzing data, interpreting results, writing in disciplinary style, and working in teams. Assignments and exams require higher-order cognitive processes, and students build new knowledge and skills through investigation of real-world problems (e.g., malaria), which engages students' interest. Evidence from direct and indirect assessment has guided continuous course revision and has revealed that compared with the course it replaced, Biological Inquiry produces significant learning gains in all targeted areas. It also retains 94% of students (both BA and BS track) compared with 79% in the majors-only course it replaced. The project has had broad impact across the entire college and reflects the input of numerous constituencies and close collaboration among biology professors and students.

Biological Inquiry: A New Course and Assessment Plan in Response to the Call to Transform Undergraduate Biology

PubMed Central

Goldey, Ellen S.; Abercrombie, Clarence L.; Ivy, Tracie M.; Kusher, Dave I.; Moeller, John F.; Rayner, Doug A.; Smith, Charles F.; Spivey, Natalie W.

2012-01-01

We transformed our first-year curriculum in biology with a new course, Biological Inquiry, in which >50% of all incoming, first-year students enroll. The course replaced a traditional, content-driven course that relied on outdated approaches to teaching and learning. We diversified pedagogical practices by adopting guided inquiry in class and in labs, which are devoted to building authentic research skills through open-ended experiments. Students develop core biological knowledge, from the ecosystem to molecular level, and core skills through regular practice in hypothesis testing, reading primary literature, analyzing data, interpreting results, writing in disciplinary style, and working in teams. Assignments and exams require higher-order cognitive processes, and students build new knowledge and skills through investigation of real-world problems (e.g., malaria), which engages students’ interest. Evidence from direct and indirect assessment has guided continuous course revision and has revealed that compared with the course it replaced, Biological Inquiry produces significant learning gains in all targeted areas. It also retains 94% of students (both BA and BS track) compared with 79% in the majors-only course it replaced. The project has had broad impact across the entire college and reflects the input of numerous constituencies and close collaboration among biology professors and students. PMID:23222831

Applying a Theory-Driven Framework to Guide Quality Improvement Efforts in Nursing Homes: The LOCK Model.

PubMed

Mills, Whitney L; Pimentel, Camilla B; Palmer, Jennifer A; Snow, A Lynn; Wewiorski, Nancy J; Allen, Rebecca S; Hartmann, Christine W

2018-05-08

Implementing quality improvement (QI) programs in nursing homes continues to encounter significant challenges, despite recognized need. QI approaches provide nursing home staff with opportunities to collaborate on developing and testing strategies for improving care delivery. We present a theory-driven and user-friendly adaptable framework and facilitation package to overcome existing challenges and guide QI efforts in nursing homes. The framework is grounded in the foundational concepts of strengths-based learning, observation, relationship-based teams, efficiency, and organizational learning. We adapted these concepts to QI in the nursing home setting, creating the "LOCK" framework. The LOCK framework is currently being disseminated across the Veterans Health Administration. The LOCK framework has five tenets: (a) Look for the bright spots, (b) Observe, (c) Collaborate in huddles, (d) Keep it bite-sized, and (e) facilitation. Each tenet is described. We also present a case study documenting how a fictional nursing home can implement the LOCK framework as part of a QI effort to improve engagement between staff and residents. The case study describes sample observations, processes, and outcomes. We also discuss practical applications for nursing home staff, the adaptability of LOCK for different QI projects, the specific role of facilitation, and lessons learned. The proposed framework complements national efforts to improve quality of care and quality of life for nursing home residents and may be valuable across long-term care settings and QI project types.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

NASA Astrophysics Data System (ADS)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

PubMed

Bissonette, Gregory B; Roesch, Matthew R

2016-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action

PubMed Central

Roesch, Matthew R.

2017-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Exploring the Extreme: High Performance Learning Activities in Mathematics, Science and Technology. An Educator's Guide. EG-2002-10-001-DFRC

ERIC Educational Resources Information Center

Dana, Judi; Kock, Meri; Lewis, Mike; Peterson, Bruce; Stowe, Steve

2010-01-01

The many activities contained in this teaching guide emphasize hands-on involvement, prediction, data collection and interpretation, teamwork, and problem solving. The guide also contains background information about aeronautical research that can help students learn how airplanes fly. Following the background sections are a series of activities…

Computer-Guided Diagnosis of Learning Disabilities: A Prototype.

ERIC Educational Resources Information Center

Colbourn, Marlene Jones

A computer based diagnostic system to assist educators in the assessment of learning disabled children aged 8 to 10 years in the area of reading is described and evaluated. The system is intended to guide the diagnosis of reading problems through step by step analysis of available data and requests for additional data. The system provides a…

Materials discovery guided by data-driven insights

NASA Astrophysics Data System (ADS)

Klintenberg, Mattias

As the computational power continues to grow systematic computational exploration has become an important tool for materials discovery. In this presentation the Electronic Structure Project (ESP/ELSA) will be discussed and a number of examples presented that show some of the capabilities of a data-driven methodology for guiding materials discovery. These examples include topological insulators, detector materials and 2D materials. ESP/ELSA is an initiative that dates back to 2001 and today contain many tens of thousands of materials that have been investigated using a robust and high accuracy electronic structure method (all-electron FP-LMTO) thus providing basic materials first-principles data for most inorganic compounds that have been structurally characterized. The web-site containing the ESP/ELSA data has as of today been accessed from more than 4,000 unique computers from all around the world.

Key Elements of Observing Practice: A Data Wise DVD and Facilitator's Guide

ERIC Educational Resources Information Center

Boudett, Kathryn Parker; City, Elizabeth A.; Russell, Marcia K.

2010-01-01

Based on the bestselling book "Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning", and its companion volume, "Data Wise in Action", this DVD and Facilitator's Guide offer insight into one of the most challenging steps in capturing data about school performance: observing and analyzing instructional…

Using Data to Improve Student Outcomes: Learning from Leading Colleges. Education Trust Higher Education Practice Guide #2

ERIC Educational Resources Information Center

Education Trust, 2016

2016-01-01

All across the country, leaders in colleges and universities are asking the same question: What can we do to improve student success, especially for the low-income students and students of color whose graduation rates often lag behind? This second practice guide: "Using Data to Improve Student Outcomes: Learning from Leading Colleges"…

e-Research and Learning Theory: What Do Sequence and Process Mining Methods Contribute?

ERIC Educational Resources Information Center

Reimann, Peter; Markauskaite, Lina; Bannert, Maria

2014-01-01

This paper discusses the fundamental question of how data-intensive e-research methods could contribute to the development of learning theories. Using methodological developments in research on self-regulated learning as an example, it argues that current applications of data-driven analytical techniques, such as educational data mining and its…

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

PubMed

Ding, Michael Q; Chen, Lujia; Cooper, Gregory F; Young, Jonathan D; Lu, Xinghua

2018-02-01

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR . ©2017 American Association for Cancer Research.

Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

ERIC Educational Resources Information Center

Liu, Ran; Koedinger, Kenneth R.

2017-01-01

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…

A Guide to Fundraising at Historically Black Colleges and Universities: An All Campus Approach

ERIC Educational Resources Information Center

Gasman, Marybeth; Bowman, Nelson, III

2011-01-01

"A Guide to Fundraising at Historically Black Colleges and Universities" is a comprehensive, research-based work that brings the best practices and expertise of seminal professionals to the larger Black college environment and beyond. Drawing on data-driven advice from interviews with successful Black college fundraisers and private sector…

Secondary adaptation of memory-guided saccades

PubMed Central

Srimal, Riju; Curtis, Clayton E.

2011-01-01

Adaptation of saccade gains in response to errors keeps vision and action co-registered in the absence of awareness or effort. Timing is key, as the visual error must be available shortly after the saccade is generated or adaptation does not occur. Here, we tested the hypothesis that when feedback is delayed, learning still occurs, but does so through small secondary corrective saccades. Using a memory-guided saccade task, we gave feedback about the accuracy of saccades that was falsely displaced by a consistent amount, but only after long delays. Despite the delayed feedback, over time subjects improved in accuracy toward the false feedback. They did so not by adjusting their primary saccades, but via directed corrective saccades made before feedback was given. We propose that saccade learning may be driven by different types of feedback teaching signals. One teaching signal relies upon a tight temporal relation with the saccade and contributes to obligatory learning independent of awareness. When this signal is ineffective due to delayed error feedback, a second compensatory teaching signal enables flexible adjustments to the spatial goal of saccades and helps maintain sensorimotor accuracy. PMID:20803135

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR). Version 3.5, Quick Reference Guide

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

Gilbert, B.G.; Richards, R.E.; Reece, W.J.

1992-10-01

This Reference Guide contains instructions on how to install and use Version 3.5 of the NRC-sponsored Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR). The NUCLARR data management system is contained in compressed files on the floppy diskettes that accompany this Reference Guide. NUCLARR is comprised of hardware component failure data (HCFD) and human error probability (HEP) data, both of which are available via a user-friendly, menu driven retrieval system. The data may be saved to a file in a format compatible with IRRAS 3.0 and commercially available statistical packages, or used to formulate log-plots and reports of data retrievalmore » and aggregation findings.« less

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Active machine learning-driven experimentation to determine compound effects on protein patterns.

PubMed

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

The effect of conceptual metaphors through guided inquiry on student's conceptual change

NASA Astrophysics Data System (ADS)

Menia, Meli; Mudzakir, Ahmad; Rochintaniawati, Diana

2017-05-01

The purpose of this study was to identify student's conceptual change of global warming after integrated science learning based guided inquiry through conceptual metaphors. This study used a quasi-experimental with a nonequivalent control group design. The subject was students of two classes of one of MTsN Salido. Data was collected using conceptual change test (pretest and posttest), observation sheet to observe the learning processes, questionnaire sheet to identify students responses, and interview to identifyteacher'srespons of science learning with conceptual metaphors. The results showed that science learning based guided inquiry with conceptual metaphors is better than science learning without conceptual metaphors. The average of posttest experimental class was 79,40 and control class was 66,09. The student's conceptual change for two classes changed significantly byusing mann whitney U testwith P= 0,003(P less than sig. value, P< 0,05). This means that there was differenceson student's conceptual changebeetwen integrated science learning based guided inquiry with conceptual metaphors class and integrated science learning without conceptual metaphors class. The study also showed that teachers and studentsgive positive responsesto implementation of integrated science learning based guided inquiry with conceptual metaphors.

Divulging Personal Information within Learning Analytics Systems

ERIC Educational Resources Information Center

Ifenthaler, Dirk; Schumacher, Clara

2015-01-01

The purpose of this study was to investigate if students are prepared to release any personal data in order to inform learning analytics systems. Besides the well-documented benefits of learning analytics, serious concerns and challenges are associated with the application of these data driven systems. Most notably, empirical evidence regarding…

Student Analysis of Handout Development based on Guided Discovery Method in Process Evaluation and Learning Outcomes of Biology

NASA Astrophysics Data System (ADS)

Nerita, S.; Maizeli, A.; Afza, A.

2017-09-01

Process Evaluation and Learning Outcomes of Biology subjects discusses the evaluation process in learning and application of designed and processed learning outcomes. Some problems found during this subject was the student difficult to understand the subject and the subject unavailability of learning resources that can guide and make students independent study. So, it necessary to develop a learning resource that can make active students to think and to make decisions with the guidance of the lecturer. The purpose of this study is to produce handout based on guided discovery method that match the needs of students. The research was done by using 4-D models and limited to define phase that is student requirement analysis. Data obtained from the questionnaire and analyzed descriptively. The results showed that the average requirement of students was 91,43%. Can be concluded that students need a handout based on guided discovery method in the learning process.

E-Labs - Learning with Authentic Data

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

Bardeen, Marjorie G.; Wayne, Mitchell

the success teachers have had providing an opportunity for students to: • Organize and conduct authentic research. • Experience the environment of scientific collaborations. • Possibly make real contributions to a burgeoning scientific field. We've created projects that are problem-based, student driven and technology dependent. Students reach beyond classroom walls to explore data with other students and experts and share results, publishing original work to a worldwide audience. Students can discover and extend the research of other students, modeling the processes of modern, large-scale research projects. From start to finish e-Labs are student-led, teacher-guided projects. Students need only a Webmore » browser to access computing techniques employed by professional researchers. A Project Map with milestones allows students to set the research plan rather than follow a step-by-step process common in other online projects. Most importantly, e-Labs build the learning experience around the students' own questions and let them use the very tools that scientists use. Students contribute to and access shared data, most derived from professional research databases. They use common analysis tools, store their work and use metadata to discover, replicate and confirm the research of others. This is where real scientific collaboration begins. Using online tools, students correspond with other research groups, post comments and questions, prepare summary reports, and in general participate in the part of scientific research that is often left out of classroom experiments. Teaching tools such as student and teacher logbooks, pre- and post-tests and an assessment rubric aligned with learner outcomes help teachers guide student work. Constraints on interface designs and administrative tools such as registration databases give teachers the "one-stop-shopping" they seek for multiple e-Labs. Teaching and administrative tools also allow us to track usage and assess the impact on student learning.« less

Irrelevant learned reward associations disrupt voluntary spatial attention.

PubMed

MacLean, Mary H; Diaz, Gisella K; Giesbrecht, Barry

2016-10-01

Attention can be guided involuntarily by physical salience and by non-salient, previously learned reward associations that are currently task-irrelevant. Attention can be guided voluntarily by current goals and expectations. The current study examined, in two experiments, whether irrelevant reward associations could disrupt current, goal-driven, voluntary attention. In a letter-search task, attention was directed voluntarily (i.e., cued) on half the trials by a cue stimulus indicating the hemifield in which the target letter would appear with 100 % accuracy. On the other half of the trials, a cue stimulus was presented, but it did not provide information about the target hemifield (i.e., uncued). On both cued and uncued trials, attention could be involuntarily captured by the presence of a task-irrelevant, and physically non-salient, color, either within the cued or the uncued hemifield. Importantly, one week prior to the letter search task, the irrelevant color had served as a target feature that was predictive of reward in a separate training task. Target identification accuracy was better on cued compared to uncued trials. However, this effect was reduced when the irrelevant, and physically non-salient, reward-associated feature was present in the uncued hemifield. This effect was not observed in a second, control experiment in which the irrelevant color was not predictive of reward during training. Our results indicate that involuntary, value-driven capture can disrupt the voluntary control of spatial attention.

The performance of the upgraded Los Alamos Neutron Source

NASA Astrophysics Data System (ADS)

Ito, Takeyasu; LANL UCN Source Collaboration

2017-09-01

Los Alamos National Laboratory has been operating an ultracold (UCN) source based on a solid deuterium (SD2) UCN converter driven by spallation neutrons for over 10 years. It has recently been successfully upgraded, by replacing the cryostat that contains the cold neutron moderator, SD2 volume, and vertical UCN guide. The horizontal UCN guide that transports UCN out of the radiation shield was also replaced. The new design reflects lessons learned from the 10+ year long operation of the previous version of the UCN source and is optimized to maximize the cold neutron flux at the SD2 volume, featuring a close coupled cold neutron moderator, and maximize the transport of the UCN to experiments. During the commissioning of the upgraded UCN source, data were collected to measure its performance, including cold neutron spectra as a function of the cold moderator temperature, and the UCN density in a vessel outside the source. In this talk, after a brief overview of the design of the upgraded source, the results of the performance tests and comparison to prediction will be presented. This work was funded by LANL LDRD.

Reducing cognitive load in the chemistry laboratory by using technology-driven guided inquiry experiments

NASA Astrophysics Data System (ADS)

Hubacz, Frank, Jr.

The chemistry laboratory is an integral component of the learning experience for students enrolled in college-level general chemistry courses. Science education research has shown that guided inquiry investigations provide students with an optimum learning environment within the laboratory. These investigations reflect the basic tenets of constructivism by engaging students in a learning environment that allows them to experience what they learn and to then construct, in their own minds, a meaningful understanding of the ideas and concepts investigated. However, educational research also indicates that the physical plant of the laboratory environment combined with the procedural requirements of the investigation itself often produces a great demand upon a student's working memory. This demand, which is often superfluous to the chemical concept under investigation, creates a sensory overload or extraneous cognitive load within the working memory and becomes a significant obstacle to student learning. Extraneous cognitive load inhibits necessary schema formation within the learner's working memory thereby impeding the transfer of ideas to the learner's long-term memory. Cognitive Load Theory suggests that instructional material developed to reduce extraneous cognitive load leads to an improved learning environment for the student which better allows for schema formation. This study first compared the cognitive load demand, as measured by mental effort, experienced by 33 participants enrolled in a first-year general chemistry course in which the treatment group, using technology based investigations, and the non-treatment group, using traditional labware, investigated identical chemical concepts on five different exercises. Mental effort was measured via a mental effort survey, a statistical comparison of individual survey results to a procedural step count, and an analysis of fourteen post-treatment interviews. Next, a statistical analysis of achievement was completed by comparing lab grade averages, final exam averages, and final course grade averages between the two groups. Participant mental effort survey results showed significant positive effects of technology in reducing cognitive load for two laboratory investigations. One investigation revealed a significant difference in achievement measured by lab grade average comparisons. Although results of this study are inconclusive as to the usefulness of technology-driven investigations to affect learning, recommendations for further study are discussed.

Problem-based learning: Using students' questions to drive knowledge construction

NASA Astrophysics Data System (ADS)

Chin, Christine; Chia, Li-Gek

2004-09-01

This study employed problem-based learning for project work in a year 9 biology class. The purpose of the study was to investigate (a) students' inspirations for their self-generated problems and questions, (b) the kinds of questions that students asked individually and collaboratively, and (c) how students' questions guided them in knowledge construction. Data sources included observation and field notes, students' written documents, audiotapes and videotapes of students working in groups, and student interviews. Sources of inspiration for students' problems and questions included cultural beliefs and folklore; wonderment about information propagated by advertisements and the media; curiosity arising from personal encounters, family members' concerns, or observations of others; and issues arising from previous lessons in the school curriculum. Questions asked individually pertained to validation of common beliefs and misconceptions, basic information, explanations, and imagined scenarios. The findings regarding questions asked collaboratively are presented as two assertions. Assertion 1 maintained that students' course of learning were driven by their questions. Assertion 2 was that the ability to ask the right'' questions and the extent to which these could be answered, were important in sustaining students' interest in the project. Implications of the findings for instructional practice are discussed.

The Effectiveness of Guided Inquiry-based Learning Material on Students’ Science Literacy Skills

NASA Astrophysics Data System (ADS)

Aulia, E. V.; Poedjiastoeti, S.; Agustini, R.

2018-01-01

The purpose of this research is to describe the effectiveness of guided inquiry-based learning material to improve students’ science literacy skills on solubility and solubility product concepts. This study used Research and Development (R&D) design and was implemented to the 11th graders of Muhammadiyah 4 Senior High School Surabaya in 2016/2017 academic year with one group pre-test and post-test design. The data collection techniques used were validation, observation, test, and questionnaire. The results of this research showed that the students’ science literacy skills are different after implementation of guided inquiry-based learning material. The guided inquiry-based learning material is effective to improve students’ science literacy skills on solubility and solubility product concepts by getting N-gain score with medium and high category. This improvement caused by the developed learning material such as lesson plan, student worksheet, and science literacy skill tests were categorized as valid and very valid. In addition, each of the learning phases in lesson plan has been well implemented. Therefore, it can be concluded that the guided inquiry-based learning material are effective to improve students’ science literacy skills on solubility and solubility product concepts in senior high school.

Utilising database-driven interactive software to enhance independent home-study in a flipped classroom setting: going beyond visualising engineering concepts to ensuring formative assessment

NASA Astrophysics Data System (ADS)

Comerford, Liam; Mannis, Adam; DeAngelis, Marco; Kougioumtzoglou, Ioannis A.; Beer, Michael

2018-07-01

The concept of formative assessment is considered by many to play an important role in enhancing teaching in higher engineering education. In this paper, the concept of the flipped classroom as part of a blended learning curriculum is highlighted as an ideal medium through which formative assessment practices arise. Whilst the advantages of greater interaction between students and lecturers in classes are numerous, there are often clear disadvantages associated with the independent home-study component that complements timetabled sessions in a flipped classroom setting, specifically, the popular method of replacing traditional classroom teaching with video lectures. This leads to a clear lack of assurances that the cited benefits of a flipped classroom approach are echoed in the home-study arena. Over the past three years, the authors have sought to address identified deficiencies in this area of blended learning through the development of database-driven e-learning software with the capability of introducing formative assessment practices to independent home-study. This paper maps out aspects of two specific evolving practices at separate institutions, from which guiding principles of incorporating formative assessment aspects into e-learning software are identified and highlighted in the context of independent home-study as part of a flipped classroom approach.

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Data Driven Decision Making in the Social Studies

ERIC Educational Resources Information Center

Ediger, Marlow

2010-01-01

Data driven decision making emphasizes the importance of the teacher using objective sources of information in developing the social studies curriculum. Too frequently, decisions of teachers have been made based on routine and outdated methods of teaching. Valid and reliable tests used to secure results from pupil learning make for better…

Supporting Interoperability and Context-Awareness in E-Learning through Situation-Driven Learning Processes

ERIC Educational Resources Information Center

Dietze, Stefan; Gugliotta, Alessio; Domingue, John

2009-01-01

Current E-Learning technologies primarily follow a data and metadata-centric paradigm by providing the learner with composite content containing the learning resources and the learning process description, usually based on specific metadata standards such as ADL SCORM or IMS Learning Design. Due to the design-time binding of learning resources,…

Guided Inquiry with Cognitive Conflict Strategy: Drilling Indonesian High School Students’ Creative Thinking Skills

NASA Astrophysics Data System (ADS)

Syadzili, A. F.; Soetjipto; Tukiran

2018-01-01

This research aims to produce physics learning materials in Indonesian high school using guided inquiry with cognitive conflict strategy to drill students’ creative thinking skills in a static fluid learning. This development research used 4D model with one group pre-test and post-test design implemented in the eleventh grade students in the second semester of 2016/2017 academic year. The data were collected by validation sheets, questionnaires, tests and observations, while data analysis techniques is descriptive quantitative analysis. This research obtained several findings, they are : the learning material developed had an average validity score with very valid category. The lesson plan can be implemented very well. The students’ responses toward the learning process were very possitive with the students’ interest to follow the learning. Creative thinking skills of student before the implementation of product was inadequate, then it is very creative after product was implemented. The impacts of the research suggest that guided inquiry may stimulate the students to think creatifly.

Vicarious reinforcement learning signals when instructing others.

PubMed

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

Cutting through the Hype: The Essential Guide to School Reform. Revised, Expanded, and Updated Edition

ERIC Educational Resources Information Center

David, Jane L.; Cuban, Larry

2010-01-01

"Cutting Through the Hype: The Essential Guide to School Reform" is a revised, expanded, and updated version of the classic work by Jane L. David and Larry Cuban. It offers balanced analyses of 23 currently popular school reform strategies, from teacher performance pay and putting mayors in charge to turnaround schools and data-driven instruction.…

The Effectiveness of Process-Oriented Guided Inquiry Learning to Reduce Alternative Conceptions in Secondary Chemistry

ERIC Educational Resources Information Center

Barthlow, Michelle J.; Watson, Scott B.

2014-01-01

A nonequivalent, control group design was used to investigate student achievement in secondary chemistry. This study investigated the effect of process-oriented guided inquiry learning (POGIL) in high school chemistry to reduce alternate conceptions related to the particulate nature of matter versus traditional lecture pedagogy. Data were…

Writing-to-learn in undergraduate science education: a community-based, conceptually driven approach.

PubMed

Reynolds, Julie A; Thaiss, Christopher; Katkin, Wendy; Thompson, Robert J

2012-01-01

Despite substantial evidence that writing can be an effective tool to promote student learning and engagement, writing-to-learn (WTL) practices are still not widely implemented in science, technology, engineering, and mathematics (STEM) disciplines, particularly at research universities. Two major deterrents to progress are the lack of a community of science faculty committed to undertaking and applying the necessary pedagogical research, and the absence of a conceptual framework to systematically guide study designs and integrate findings. To address these issues, we undertook an initiative, supported by the National Science Foundation and sponsored by the Reinvention Center, to build a community of WTL/STEM educators who would undertake a heuristic review of the literature and formulate a conceptual framework. In addition to generating a searchable database of empirically validated and promising WTL practices, our work lays the foundation for multi-university empirical studies of the effectiveness of WTL practices in advancing student learning and engagement.

Active machine learning-driven experimentation to determine compound effects on protein patterns

PubMed Central

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-01-01

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance. DOI: http://dx.doi.org/10.7554/eLife.10047.001 PMID:26840049

Forum Guide to Taking Action with Education Data. NFES 2013-801

ERIC Educational Resources Information Center

National Forum on Education Statistics, 2012

2012-01-01

Education data are growing in quantity, quality, and value. When appropriately used to guide action, data can be a powerful tool for improving school operations, teaching, and learning. Education stakeholders who possess the knowledge, skills, and abilities to appropriately access, analyze, and interpret data will be able to use data to take…

Game-Based Learning Engagement: A Theory- and Data-Driven Exploration

ERIC Educational Resources Information Center

Ke, Fengfeng; Xie, Kui; Xie, Ying

2016-01-01

The promise of using games for learning is that play- and learning-engagement would occur cohesively as a whole to compose a highly motivated learning experience. Yet the conceptualization of such an integrative process in the development of play-based learning engagement is lacking. In this analytical paper, we explored and conceptualized the…

Internet Activities Using Scientific Data. A Self-Guided Exploration.

ERIC Educational Resources Information Center

Froseth, Stan; Poppe, Barbara

This guide is intended for the secondary school teacher (especially math or science) or the student who wants to access and learn about scientific data on the Internet. It is organized as a self-guided exploration. Nine exercises enable the user to access and analyze on-line information from the National Oceanic and Atmospheric Administration…

Effects of DDL Technology on Genre Learning

ERIC Educational Resources Information Center

Cotos, Elena; Link, Stephanie; Huffman, Sarah

2017-01-01

To better understand the promising effects of data-driven learning (DDL) on language learning processes and outcomes, this study explored DDL learning events enabled by the Research Writing Tutor (RWT), a web-based platform containing an English language corpus annotated to enhance rhetorical input, a concordancer that was searchable for…

The development of CERDAS learning strategy guide for science education students of distance education

NASA Astrophysics Data System (ADS)

Rahayu, U.; Darmayanti, T.; Widodo, A.; Redjeki, S.

2017-02-01

Self-regulated learning (SRL) is a part of students’ skills in which they manage, regulate, and monitor their learning process so they can reach their study goal. Students of distance education should comprise this skill. The aim of this research is to describe the development of distance students learning guide, namely “CEDAS strategy” designed for science students. The students’ guidance consists of seven principles, they are; selecting and applying learning strategy appropriately, managing time effectively, planning of learning realistically and accurately, achieving study goal, and doing self-evaluation continuously. The research method was qualitative descriptive. The research involved the students of Universitas Terbuka’ Biology education who participated in Animal Embryology course. The data were collected using a questionnaire and interview. Furthermore, it was analyzed by descriptive analyses. Research finding showed that during try out, most of the students stated that the learning guide was easy to understand, concise, interesting and encouraging for students to continue reading and learning. In the implementation stage, most students commented that the guide is easy to understand, long enough, and helpful so it can be used as a reference to study independently and to apply it in the daily basis.

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Digital Workflow for Computer-Guided Implant Surgery in Edentulous Patients: A Case Report.

PubMed

Oh, Ji-Hyeon; An, Xueyin; Jeong, Seung-Mi; Choi, Byung-Ho

2017-12-01

The purpose of this article was to describe a fully digital workflow used to perform computer-guided flapless implant placement in an edentulous patient without the use of conventional impressions, models, or a radiographic guide. Digital data for the workflow were acquired using an intraoral scanner and cone-beam computed tomography (CBCT). The image fusion of the intraoral scan data and CBCT data was performed by matching resin markers placed in the patient's mouth. The definitive digital data were used to design a prosthetically driven implant position, surgical template, and computer-aided design and computer-aided manufacturing fabricated fixed dental prosthesis. The authors believe this is the first published case describing such a technique in computer-guided flapless implant surgery for edentulous patients. Copyright © 2017 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

NASA Astrophysics Data System (ADS)

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

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

From the field and lab to data wrangling and facilitating interagency collaboration- how I ended up in the data science world.

NASA Astrophysics Data System (ADS)

Kreft, J.

2015-12-01

I work to build systems that make environmental data more accessible and usable for others—a role that I love and, ten years ago, would not have guessed I would play. I transitioned from conducting pure research to learning more about data curation and information science, and eventually, to combining knowledge of both the research and data science worlds in my current position at the U.S. Geological Survey Center for Integrated Data Analytics (USGS CIDA). At the USGS, I primarily work on the Water Quality Portal, an interagency tool for providing high performance, standards driven access to water quality data, and the USGS Publications Warehouse, which plays a key and ever expanding role in providing access to USGS Publications and their associated data sets. Both projects require an overarching focus on building services to make science data more visible and accessible to users. In addition, listening to the needs of the research scientists who are both collecting and using the data to improve the tools I guide the development of. Concepts that I learned at the University Of Illinois at Urbana-Champaign Graduate School of Library and Information Science Data Curation Education Program were critical to a successful transition from the research world to the data science world. Data curation and data science are playing an ever-larger role in surmounting current and future data challenges at the USGS, and the need for people with interests in both research and data science will continue to grow.

Ryan King | NREL

Science.gov Websites

research focuses on optimization and machine learning applied to complex energy systems and turbulent flows techniques to improve wind plant design and controls and developed a new data-driven machine learning closure

Guided discovery learning in geometry learning

NASA Astrophysics Data System (ADS)

Khasanah, V. N.; Usodo, B.; Subanti, S.

2018-03-01

Geometry is a part of the mathematics that must be learned in school. The purpose of this research was to determine the effect of Guided Discovery Learning (GDL) toward geometry learning achievement. This research had conducted at junior high school in Sukoharjo on academic years 2016/2017. Data collection was done based on student’s work test and documentation. Hypothesis testing used two ways analysis of variance (ANOVA) with unequal cells. The results of this research that GDL gave positive effect towards mathematics learning achievement. GDL gave better mathematics learning achievement than direct learning. There was no difference of mathematics learning achievement between male and female. There was no an interaction between sex differences and learning models toward student’s mathematics learning achievement. GDL can be used to improve students’ mathematics learning achievement in geometry.

Deep learning for single-molecule science

NASA Astrophysics Data System (ADS)

Albrecht, Tim; Slabaugh, Gregory; Alonso, Eduardo; Al-Arif, SM Masudur R.

2017-10-01

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in machine learning (ML), so-called deep learning (DL) offer interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional ML strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the ‘internal workings’ of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a convolutional neural network (CNN), may be used for base calling in DNA sequencing applications. We compare it with a SVM as a more conventional ML method, and discuss some of the strengths and weaknesses of the approach. In particular, a ‘deep’ neural network has many features of a ‘black box’, which has important implications on how we look at and interpret data.

A Mixed Methods Bounded Case Study: Data-Driven Decision Making within Professional Learning Communities for Response to Intervention

ERIC Educational Resources Information Center

Rodriguez, Gabriel R.

2017-01-01

A growing number of schools are implementing PLCs to address school improvement, staff engage with data to identify student needs and determine instructional interventions. This is a starting point for engaging in the iterative process of learning for the teach in order to increase student learning (Hord & Sommers, 2008). The iterative process…

Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice

ERIC Educational Resources Information Center

Wolf, Mary Ann; Jones, Rachel; Hall, Sara; Wise, Bob

2014-01-01

The field of learning analytics is being discussed in many circles as an emerging concept in education. In many districts and states, the core philosophy behind learning analytics is not entirely new; for more than a decade, discussions of data-driven decision making and the use of data to drive instruction have been common. Still, the U.S.…

Social competence and collaborative guided inquiry science activities: Experiences of students with learning disabilities

NASA Astrophysics Data System (ADS)

Taylor, Jennifer Anne

This thesis presents a qualitative investigation of the effects of social competence on the participation of students with learning disabilities (LD) in the science learning processes associated with collaborative, guided inquiry learning. An inclusive Grade 2 classroom provided the setting for the study. Detailed classroom observations were the primary source of data. In addition, the researcher conducted two interviews with the teacher, and collected samples of students' written work. The purpose of the research was to investigate: (a) How do teachers and peers mediate the participation of students with LD in collaborative, guided inquiry science activities, (b) What learning processes do students with LD participate in during collaborative, guided inquiry science activities, and (c) What components of social competence support and constrain the participation of students with LD during collaborative, guided inquiry science activities? The findings of the study suggest five key ideas for research and teaching in collaborative, guided inquiry science in inclusive classrooms. First, using a variety of collaborative learning formats (whole-class, small-group, and pairs) creates more opportunities for the successful participation of diverse students with LD. Second, creating an inclusive community where students feel accepted and valued may enhance the academic and social success of students with LD. Third, careful selection of partners for students with LD is important for a positive learning experience. Students with LD should be partnered with academically successful, socially competent peers; also, this study suggested that students with LD experience more success working collaboratively in pairs rather than in small groups. Fourth, a variety of strategies are needed to promote active participation and positive social interactions for students with and without LD during collaborative, guided inquiry learning. Fifth, adopting a general approach to teaching collaborative inquiry that crosses curriculum borders may enhance success of inclusive teaching practices.

Ways with Data: Understanding Coding as Writing

ERIC Educational Resources Information Center

Lindgren, Chris

2017-01-01

In this dissertation, I report findings from an exploratory case-study about Ray, a web developer, who works on a data-driven news team that finds and tells compelling stories with large sets of data. I implicate this case of Ray's coding on a data team in a writing studies epistemology, which is guided by the following question: "What might…

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

NASA Astrophysics Data System (ADS)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

On the Quality Assessment of Advanced E-Learning Services

ERIC Educational Resources Information Center

Stefani, Antonia; Vassiliadis, Bill; Xenos, Michalis

2006-01-01

Distance learning has been widely researched the past few years, nevertheless the focus has been more on its technological dimension. Designing, developing and supporting a large scale e-learning application for Higher Education is still a challenging task in many ways. E-learning is data-intensive, user-driven, and has increasing needs for…

Developing Guided Inquiry-Based Student Lab Worksheet for Laboratory Knowledge Course

NASA Astrophysics Data System (ADS)

Rahmi, Y. L.; Novriyanti, E.; Ardi, A.; Rifandi, R.

2018-04-01

The course of laboratory knowledge is an introductory course for biology students to follow various lectures practicing in the biology laboratory. Learning activities of laboratory knowledge course at this time in the Biology Department, Universitas Negeri Padang has not been completed by supporting learning media such as student lab worksheet. Guided inquiry learning model is one of the learning models that can be integrated into laboratory activity. The study aimed to produce student lab worksheet based on guided inquiry for laboratory knowledge course and to determine the validity of lab worksheet. The research was conducted using research and developmet (R&D) model. The instruments used in data collection in this research were questionnaire for student needed analysis and questionnaire to measure the student lab worksheet validity. The data obtained was quantitative from several validators. The validators consist of three lecturers. The percentage of a student lab worksheet validity was 94.18 which can be categorized was very good.

Combat Wound Initiative program.

PubMed

Stojadinovic, Alexander; Elster, Eric; Potter, Benjamin K; Davis, Thomas A; Tadaki, Doug K; Brown, Trevor S; Ahlers, Stephen; Attinger, Christopher E; Andersen, Romney C; Burris, David; Centeno, Jose; Champion, Hunter; Crumbley, David R; Denobile, John; Duga, Michael; Dunne, James R; Eberhardt, John; Ennis, William J; Forsberg, Jonathan A; Hawksworth, Jason; Helling, Thomas S; Lazarus, Gerald S; Milner, Stephen M; Mullick, Florabel G; Owner, Christopher R; Pasquina, Paul F; Patel, Chirag R; Peoples, George E; Nissan, Aviram; Ring, Michael; Sandberg, Glenn D; Schaden, Wolfgang; Schultz, Gregory S; Scofield, Tom; Shawen, Scott B; Sheppard, Forest R; Stannard, James P; Weina, Peter J; Zenilman, Jonathan M

2010-07-01

The Combat Wound Initiative (CWI) program is a collaborative, multidisciplinary, and interservice public-private partnership that provides personalized, state-of-the-art, and complex wound care via targeted clinical and translational research. The CWI uses a bench-to-bedside approach to translational research, including the rapid development of a human extracorporeal shock wave therapy (ESWT) study in complex wounds after establishing the potential efficacy, biologic mechanisms, and safety of this treatment modality in a murine model. Additional clinical trials include the prospective use of clinical data, serum and wound biomarkers, and wound gene expression profiles to predict wound healing/failure and additional clinical patient outcomes following combat-related trauma. These clinical research data are analyzed using machine-based learning algorithms to develop predictive treatment models to guide clinical decision-making. Future CWI directions include additional clinical trials and study centers and the refinement and deployment of our genetically driven, personalized medicine initiative to provide patient-specific care across multiple medical disciplines, with an emphasis on combat casualty care.

Data Literacy: Real-World Learning through Problem-Solving with Data Sets

ERIC Educational Resources Information Center

Erwin, Robin W., Jr.

2015-01-01

The achievement of deep learning by secondary students requires teaching approaches that draw students into task commitment, integrated curricula, and analytical thinking. By using real-world data sets in project based instructional units, teachers can guide students in analyzing, interpreting, and reporting quantitative data. Working with…

Learning in and about rural places: Connections and tensions between students' everyday experiences and environmental quality issues in their community

NASA Astrophysics Data System (ADS)

Zimmerman, Heather Toomey; Weible, Jennifer L.

2017-03-01

Guided by sociocultural perspectives on the importance of place as a resource for learning, we investigated 14- and 15-year old students' understandings of their community and water quality during a school-based watershed unit. Methods included a theory-driven thematic analysis of field notes and video transcripts from four biology classrooms, a qualitative and quantitative analysis of 67 pairs of matched pre- and post-intervention mindmaps, and a content analysis of 73 student reflections. As they learned about water quality, learners recognized the relevance of the watershed's health to the health of their community. Students acknowledged the impacts of local economically driven activities (e.g., natural gas wells, application of agrichemicals) and leisure activities (e.g., boating, fishing) on the watershed's environmental health. As students learned in and about their watershed, they experienced both connections and tensions between their everyday experiences and the environmental problems in their community. The students suggested individual sustainability actions needed to address water quality issues; however, the students struggled to understand how to act collectively. Implications of rural experiences as assets to future environmental sciences learning are discussed as well as the implications of educational experiences that do not include an advocacy component when students uncover environmental health issues. We suggest further consideration is needed on how to help young people develop action-oriented science knowledge, not just inert knowledge of environmental problems, during place-based education units.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Virtual Preoperative Planning and Intraoperative Navigation in Facial Prosthetic Reconstruction: A Technical Note.

PubMed

Verma, Suzanne; Gonzalez, Marianela; Schow, Sterling R; Triplett, R Gilbert

This technical protocol outlines the use of computer-assisted image-guided technology for the preoperative planning and intraoperative procedures involved in implant-retained facial prosthetic treatment. A contributing factor for a successful prosthetic restoration is accurate preoperative planning to identify prosthetically driven implant locations that maximize bone contact and enhance cosmetic outcomes. Navigational systems virtually transfer precise digital planning into the operative field for placing implants to support prosthetic restorations. In this protocol, there is no need to construct a physical, and sometimes inaccurate, surgical guide. The report addresses treatment workflow, radiologic data specifications, and special considerations in data acquisition, virtual preoperative planning, and intraoperative navigation for the prosthetic reconstruction of unilateral, bilateral, and midface defects. Utilization of this protocol for the planning and surgical placement of craniofacial bone-anchored implants allows positioning of implants to be prosthetically driven, accurate, precise, and efficient, and leads to a more predictable treatment outcome.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Big data analytics in hyperspectral imaging for detection of microbial colonies on agar plates (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yoon, Seung-Chul; Park, Bosoon; Lawrence, Kurt C.

2017-05-01

Various types of optical imaging techniques measuring light reflectivity and scattering can detect microbial colonies of foodborne pathogens on agar plates. Until recently, these techniques were developed to provide solutions for hypothesis-driven studies, which focused on developing tools and batch/offline machine learning methods with well defined sets of data. These have relatively high accuracy and rapid response time because the tools and methods are often optimized for the collected data. However, they often need to be retrained or recalibrated when new untrained data and/or features are added. A big-data driven technique is more suitable for online learning of new/ambiguous samples and for mining unknown or hidden features. Although big data research in hyperspectral imaging is emerging in remote sensing and many tools and methods have been developed so far in many other applications such as bioinformatics, the tools and methods still need to be evaluated and adjusted in applications where the conventional batch machine learning algorithms were dominant. The primary objective of this study is to evaluate appropriate big data analytic tools and methods for online learning and mining of foodborne pathogens on agar plates. After the tools and methods are successfully identified, they will be applied to rapidly search big color and hyperspectral image data of microbial colonies collected over the past 5 years in house and find the most probable colony or a group of colonies in the collected big data. The meta-data, such as collection time and any unstructured data (e.g. comments), will also be analyzed and presented with output results. The expected results will be novel, big data-driven technology to correctly detect and recognize microbial colonies of various foodborne pathogens on agar plates.

Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools

ERIC Educational Resources Information Center

García, Olga Arranz; Secades, Vidal Alonso

2013-01-01

In the information age, one of the most influential institutions is education. The recent emergence of MOOCS [Massively Open Online Courses] is a sample of the new expectations that are offered to university students. Basing decisions on data and evidence seems obvious, and indeed, research indicates that data-driven decision-making improves…

Neonatal Informatics: Transforming Neonatal Care Through Translational Bioinformatics

PubMed Central

Palma, Jonathan P.; Benitz, William E.; Tarczy-Hornoch, Peter; Butte, Atul J.; Longhurst, Christopher A.

2012-01-01

The future of neonatal informatics will be driven by the availability of increasingly vast amounts of clinical and genetic data. The field of translational bioinformatics is concerned with linking and learning from these data and applying new findings to clinical care to transform the data into proactive, predictive, preventive, and participatory health. As a result of advances in translational informatics, the care of neonates will become more data driven, evidence based, and personalized. PMID:22924023

Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model.

PubMed

Baldassarre, Gianluca; Mannella, Francesco; Fiore, Vincenzo G; Redgrave, Peter; Gurney, Kevin; Mirolli, Marco

2013-05-01

Reinforcement (trial-and-error) learning in animals is driven by a multitude of processes. Most animals have evolved several sophisticated systems of 'extrinsic motivations' (EMs) that guide them to acquire behaviours allowing them to maintain their bodies, defend against threat, and reproduce. Animals have also evolved various systems of 'intrinsic motivations' (IMs) that allow them to acquire actions in the absence of extrinsic rewards. These actions are used later to pursue such rewards when they become available. Intrinsic motivations have been studied in Psychology for many decades and their biological substrates are now being elucidated by neuroscientists. In the last two decades, investigators in computational modelling, robotics and machine learning have proposed various mechanisms that capture certain aspects of IMs. However, we still lack models of IMs that attempt to integrate all key aspects of intrinsically motivated learning and behaviour while taking into account the relevant neurobiological constraints. This paper proposes a bio-constrained system-level model that contributes a major step towards this integration. The model focusses on three processes related to IMs and on the neural mechanisms underlying them: (a) the acquisition of action-outcome associations (internal models of the agent-environment interaction) driven by phasic dopamine signals caused by sudden, unexpected changes in the environment; (b) the transient focussing of visual gaze and actions on salient portions of the environment; (c) the subsequent recall of actions to pursue extrinsic rewards based on goal-directed reactivation of the representations of their outcomes. The tests of the model, including a series of selective lesions, show how the focussing processes lead to a faster learning of action-outcome associations, and how these associations can be recruited for accomplishing goal-directed behaviours. The model, together with the background knowledge reviewed in the paper, represents a framework that can be used to guide the design and interpretation of empirical experiments on IMs, and to computationally validate and further develop theories on them. Copyright © 2012 Elsevier Ltd. All rights reserved.

Building effective learning experiences around visualizations: NASA Eyes on the Solar System and Infiniscope

NASA Astrophysics Data System (ADS)

Tamer, A. J. J.; Anbar, A. D.; Elkins-Tanton, L. T.; Klug Boonstra, S.; Mead, C.; Swann, J. L.; Hunsley, D.

2017-12-01

Advances in scientific visualization and public access to data have transformed science outreach and communication, but have yet to realize their potential impacts in the realm of education. Computer-based learning is a clear bridge between visualization and education, but creating high-quality learning experiences that leverage existing visualizations requires close partnerships among scientists, technologists, and educators. The Infiniscope project is working to foster such partnerships in order to produce exploration-driven learning experiences around NASA SMD data and images, leveraging the principles of ETX (Education Through eXploration). The visualizations inspire curiosity, while the learning design promotes improved reasoning skills and increases understanding of space science concepts. Infiniscope includes both a web portal to host these digital learning experiences, as well as a teaching network of educators using and modifying these experiences. Our initial efforts to enable student discovery through active exploration of the concepts associated with Small Worlds, Kepler's Laws, and Exoplanets led us to develop our own visualizations at Arizona State University. Other projects focused on Astrobiology and Mars geology led us to incorporate an immersive Virtual Field Trip platform into the Infiniscope portal in support of virtual exploration of scientifically significant locations. Looking to apply ETX design practices with other visualizations, our team at Arizona State partnered with the Jet Propulsion Lab to integrate the web-based version of NASA Eyes on the Eclipse within Smart Sparrow's digital learning platform in a proof-of-concept focused on the 2017 Eclipse. This goes a step beyond the standard features of "Eyes" by wrapping guided exploration, focused on a specific learning goal into standards-aligned lesson built around the visualization, as well as its distribution through Infiniscope and it's digital teaching network. Experience from this development effort has laid the groundwork to explore future integrations with JPL and other NASA partners.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

2016 New Horizons Lecture: Beyond Imaging-Radiology of Tomorrow.

PubMed

Hricak, Hedvig

2018-03-01

This article is based on the New Horizons lecture delivered at the 2016 Radiological Society of North America Annual Meeting. It addresses looming changes for radiology, many of which stem from the disruptive effects of the Fourth Industrial Revolution. This is an emerging era of unprecedented rapid innovation marked by the integration of diverse disciplines and technologies, including data science, machine learning, and artificial intelligence-technologies that narrow the gap between man and machine. Technologic advances and the convergence of life sciences, physical sciences, and bioengineering are creating extraordinary opportunities in diagnostic radiology, image-guided therapy, targeted radionuclide therapy, and radiology informatics, including radiologic image analysis. This article uses the example of oncology to make the case that, if members in the field of radiology continue to be innovative and continuously reinvent themselves, radiology can play an ever-increasing role in both precision medicine and value-driven health care. © RSNA, 2018.

Using Theory to Design Evaluations of Communication Campaigns: The Case of the National Youth Anti-Drug Media Campaign.

PubMed

Hornik, Robert C; Yanovitzky, Itzhak

2003-05-01

We present a general theory about how campaigns can have effects and suggest that the evaluation of communication campaigns must be driven by a theory of effects. The National Youth Anti-Drug Media Campaign illustrates both the theory of campaign effects and implications that theory has for the evaluation design. Often models of effect assume that individual exposure affects cognitions that continue to affect behavior over a short term. Contrarily, effects may operate through social or institutional paths as well as through individual learning, require substantial levels of exposure achieved through multiple channels over time, take time to accumulate detectable change, and affect some members of the audience but not others. Responsive evaluations will choose appropriate units of analysis and comparison groups, data collection schedules sensitive to lagged effects, samples able to detect subgroup effects, and analytic strategies consistent with the theory of effects that guides the campaign.

Establishing & Sustaining Learning-Centered Community Colleges

ERIC Educational Resources Information Center

McPhail, Christine Johnson, Ed

2005-01-01

In its broadest terms, the learning paradigm calls for institutional change and institutional responsibility for learning outcomes. Leaders have to develop structures and processes that allow for more flexibility and creativity. Decisions have to become more data-driven. Barriers to student success have to be identified and removed. This book…

Corpus Use in Language Learning: A Meta-Analysis

ERIC Educational Resources Information Center

Boulton, Alex; Cobb, Tom

2017-01-01

This study applied systematic meta-analytic procedures to summarize findings from experimental and quasi-experimental investigations into the effectiveness of using the tools and techniques of corpus linguistics for second language learning or use, here referred to as data-driven learning (DDL). Analysis of 64 separate studies representing 88…

Developing and Evaluating a Chinese Collocation Retrieval Tool for CFL Students and Teachers

ERIC Educational Resources Information Center

Chen, Howard Hao-Jan; Wu, Jian-Cheng; Yang, Christine Ting-Yu; Pan, Iting

2016-01-01

The development of collocational knowledge is important for foreign language learners; unfortunately, learners often have difficulties producing proper collocations in the target language. Among the various ways of collocation learning, the DDL (data-driven learning) approach encourages the independent learning of collocations and allows learners…

Data Mining in Finance: Using Counterfactuals To Generate Knowledge from Organizational Information Systems.

ERIC Educational Resources Information Center

Dhar, Vasant

1998-01-01

Shows how counterfactuals and machine learning methods can be used to guide exploration of large databases that addresses some of the fundamental problems that organizations face in learning from data. Discusses data mining, particularly in the financial arena; generating useful knowledge from data; and the evaluation of counterfactuals. (LRW)

Embedded performance validity testing in neuropsychological assessment: Potential clinical tools.

PubMed

Rickards, Tyler A; Cranston, Christopher C; Touradji, Pegah; Bechtold, Kathleen T

2018-01-01

The article aims to suggest clinically-useful tools in neuropsychological assessment for efficient use of embedded measures of performance validity. To accomplish this, we integrated available validity-related and statistical research from the literature, consensus statements, and survey-based data from practicing neuropsychologists. We provide recommendations for use of 1) Cutoffs for embedded performance validity tests including Reliable Digit Span, California Verbal Learning Test (Second Edition) Forced Choice Recognition, Rey-Osterrieth Complex Figure Test Combination Score, Wisconsin Card Sorting Test Failure to Maintain Set, and the Finger Tapping Test; 2) Selecting number of performance validity measures to administer in an assessment; and 3) Hypothetical clinical decision-making models for use of performance validity testing in a neuropsychological assessment collectively considering behavior, patient reporting, and data indicating invalid or noncredible performance. Performance validity testing helps inform the clinician about an individual's general approach to tasks: response to failure, task engagement and persistence, compliance with task demands. Data-driven clinical suggestions provide a resource to clinicians and to instigate conversation within the field to make more uniform, testable decisions to further the discussion, and guide future research in this area.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

Global Quantitative Modeling of Chromatin Factor Interactions

PubMed Central

Zhou, Jian; Troyanskaya, Olga G.

2014-01-01

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions. PMID:24675896

Informatics and machine learning to define the phenotype.

PubMed

Basile, Anna Okula; Ritchie, Marylyn DeRiggi

2018-03-01

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Impediments to Research among Students of Institutions of Higher Learning in Southern Nigeria

ERIC Educational Resources Information Center

Asiyai, Romina Ifeoma

2014-01-01

This study examined impediments to research among students of institutions of higher learning in Nigeria. The study was guided by one research question and three hypotheses. Data were collected from 600 final year students randomly selected from institutions of higher learning in Nigeria. Data were analyzed using descriptive statistics of mean to…

Determining E-Learning Competencies: Using Centra[TM] to Collect Focus Group Data

ERIC Educational Resources Information Center

Murphrey, Theresa Pesl; Dooley, Kim E.

2006-01-01

This article shares the results of a needs assessment conducted to guide the development of an e-learning certificate program for implementation at Texas A&M University. Participants were asked to provide input regarding the knowledge, skills, and abilities necessary to work as an e-learning specialist. The qualitative data was collected using…

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Teaching communication and supporting autonomy with a team-based operative simulator.

PubMed

Cook, Mackenzie R; Deal, Shanley B; Scott, Jessica M; Moren, Alexis M; Kiraly, Laszlo N

2016-09-01

Changing residency structure emphasizes the need for formal instruction on team leadership and intraoperative teaching skills. A high fidelity, multi-learner surgical simulation may offer opportunities for senior learners (SLs) to learn these skills while teaching technical skills to junior learners (JLs). We designed and optimized a low-cost inguinal hernia model that paired JLs and SLs as an operative team. This was tested in 3 pilot simulations. Participants' feedback was analyzed using qualitative methods. JL feedback to SLs included the themes "guiding and instructing" and "allowing autonomy." Senior Learner feedback to JLs focused on "mechanics," "knowledge," and "perspective/flow." Both groups focused on "communication" and "professionalism." A multi-learner simulation can successfully meet the technical learning needs of JLs and the teaching and communication learning needs of SLs. This model of resident-driven simulation may illustrate future opportunities for operative simulation. Copyright © 2016 Elsevier Inc. All rights reserved.

Of Elastic Clouds and Treebanks: New Opportunities for Content-Based and Data-Driven Language Learning

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2008-01-01

Creating effective electronic tools for language learning frequently requires large data sets containing extensive examples of actual human language use. Collections of authentic language in spoken and written forms provide developers the means to enrich their applications with real world examples. As the Internet continues to expand…

Social Learning Networks: From Data Analytics to Active Sensing

DTIC Science & Technology

2017-10-13

time updating of user models that in turn dictate the learning path of each student . In particular, we have designed , implemented, and evaluated our...decision, unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box...social network that exists between students , instructors, and modules of learning. Between 2015 and 2017, we completed a variety of data-driven

Effect of Similarity-Based Guided Discovery Learning on Conceptual Performance

ERIC Educational Resources Information Center

Mandrin, Pierre-A; Preckel, Daniel

2009-01-01

Analogies are known to foster concept learning, whereas discovery learning is effective for transfer. By combining discovery learning and analogies or similarities of concepts, attractive new arrangements emerge, but do they maintain both concept and transfer effects? Unfortunately, there is a lack of data confirming such combined effectiveness.…

Lifelong Learning: Capabilities and Aspirations

ERIC Educational Resources Information Center

Ilieva-Trichkova, Petya

2016-01-01

The present paper discusses the potential of the capability approach in conceptualizing and understanding lifelong learning as an agency process, and explores its capacity to guide empirical studies on lifelong learning. It uses data for 20 countries from the Adult Education Survey (2007; 2011) and focuses on aspirations for lifelong learning. The…

An Agent Allocation System for the West Virginia University Extension Service

ERIC Educational Resources Information Center

Dougherty, Michael John; Eades, Daniel

2015-01-01

Extension recognizes the importance of data in guiding programming decisions at the local level. However, allocating personnel resources and specializations at the state level is a more complex process. The West Virginia University Extension Service has adopted a data-driven process to determine the number, location, and specializations of county…

Data on the interaction between thermal comfort and building control research.

PubMed

Park, June Young; Nagy, Zoltan

2018-04-01

This dataset contains bibliography information regarding thermal comfort and building control research. In addition, the instruction of a data-driven literature survey method guides readers to reproduce their own literature survey on related bibliography datasets. Based on specific search terms, all relevant bibliographic datasets are downloaded. We explain the keyword co-occurrences of historical developments and recent trends, and the citation network which represents the interaction between thermal comfort and building control research. Results and discussions are described in the research article entitled "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review" (Park and Nagy, 2018).

Writing-to-Learn in Undergraduate Science Education: A Community-Based, Conceptually Driven Approach

PubMed Central

Reynolds, Julie A.; Thaiss, Christopher; Katkin, Wendy; Thompson, Robert J.

2012-01-01

Despite substantial evidence that writing can be an effective tool to promote student learning and engagement, writing-to-learn (WTL) practices are still not widely implemented in science, technology, engineering, and mathematics (STEM) disciplines, particularly at research universities. Two major deterrents to progress are the lack of a community of science faculty committed to undertaking and applying the necessary pedagogical research, and the absence of a conceptual framework to systematically guide study designs and integrate findings. To address these issues, we undertook an initiative, supported by the National Science Foundation and sponsored by the Reinvention Center, to build a community of WTL/STEM educators who would undertake a heuristic review of the literature and formulate a conceptual framework. In addition to generating a searchable database of empirically validated and promising WTL practices, our work lays the foundation for multi-university empirical studies of the effectiveness of WTL practices in advancing student learning and engagement. PMID:22383613

Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR)

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

Gilbert, B.G.; Richards, R.E.; Reece, W.J.

1992-10-01

This Reference Guide contains instructions on how to install and use Version 3.5 of the NRC-sponsored Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR). The NUCLARR data management system is contained in compressed files on the floppy diskettes that accompany this Reference Guide. NUCLARR is comprised of hardware component failure data (HCFD) and human error probability (HEP) data, both of which are available via a user-friendly, menu driven retrieval system. The data may be saved to a file in a format compatible with IRRAS 3.0 and commercially available statistical packages, or used to formulate log-plots and reports of data retrievalmore » and aggregation findings.« less

Intravascular ultrasound guided directional atherectomy versus directional atherectomy guided by angiography for the treatment of femoropopliteal in-stent restenosis.

PubMed

Krishnan, Prakash; Tarricone, Arthur; K-Raman, Purushothaman; Majeed, Farhan; Kapur, Vishal; Gujja, Karthik; Wiley, Jose; Vasquez, Miguel; Lascano, Rheoneil A; Quiles, Katherine G; Distin, Tashanne; Fontenelle, Ran; Atallah-Lajam, Farah; Kini, Annapoorna; Sharma, Samin

2018-01-01

The aim of this study was to compare 1-year outcomes for patients with femoropopliteal in-stent restenosis using directional atherectomy guided by intravascular ultrasound (IVUS) versus directional atherectomy guided by angiography. This was a retrospective analysis for patients with femoropopliteal in-stent restenosis treated with IVUS-guided directional atherectomy versus directional atherectomy guided by angiography from a single center between March 2012 and February 2016. Clinically driven target lesion revascularization was the primary endpoint and was evaluated through medical chart review as well as phone call follow up. Directional atherectomy guided by IVUS reduces clinically driven target lesion revascularization for patients with femoropopliteal in-stent restenosis.

Toward a more data-driven supervision of collegiate counseling centers.

PubMed

Varlotta, Lori E

2012-01-01

Hearing the national call for higher education accountability, the author of this tripartite article urges university administrators to move towards a more data-driven approach to counseling center supervision. Toward that end, the author first examines a key factor--perceived increase in student pathology--that appears to shape budget and staffing decisions in many university centers. Second, she reviews the emerging but conflicting research of clinician-scholars who are trying to empirically verify or refute that perception; their conflicting results suggest that no study alone should be used as the "final word" in evidence-based decision-making. Third, the author delineates the campus-specific data that should be gathered to guide staffing and budgeting decisions on each campus. She concludes by reminding readers that data-driven decisions can and should foster high-quality care that is concurrently efficient, effective, and in sync with the needs of a particular university and student body.

Developing PK-12 Preservice Teachers' Skills for Understanding Data-Driven Instruction through Inquiry Learning

ERIC Educational Resources Information Center

Odom, Arthur Louis; Bell, Clare Valerie

2017-01-01

This article offers a description of how empirical experiences through the use of procedural knowledge can serve as the stage for the development of hypothetical concepts using the learning cycle, an inquiry teaching and learning method with a long history in science education. The learning cycle brings a unique epistemology by way of using…

Functionally dissociable influences on learning rate in a dynamic environment

PubMed Central

McGuire, Joseph T.; Nassar, Matthew R.; Gold, Joshua I.; Kable, Joseph W.

2015-01-01

Summary Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data, but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals. PMID:25459409

Alcohol Abuse Curriculum Guide for Nurse Practitioner Faculty. Health Professions Education Curriculum Resources Series. Nursing 3.

ERIC Educational Resources Information Center

Hasselblad, Judith

The format for this curriculum guide, written for nurse practitioner faculty, consists of learning objectives, content outline, teaching methodology suggestions, references and recommended readings. Part 1 of the guide, Recognition of Early and Chronic Alcoholism, deals with features of alcoholism such as epidemiological data and theories,…

Digital Suicide Prevention: Can Technology Become a Game-changer?

PubMed

Vahabzadeh, Arshya; Sahin, Ned; Kalali, Amir

2016-01-01

Suicide continues to be a leading cause of death and has been recognized as a significant public health issue. Rapid advances in data science can provide us with useful tools for suicide prevention, and help to dynamically assess suicide risk in quantitative data-driven ways. In this article, the authors highlight the most current international research in digital suicide prevention, including the use of machine learning, smartphone applications, and wearable sensor driven systems. The authors also discuss future opportunities for digital suicide prevention, and propose a novel Sensor-driven Mental State Assessment System.

Science Pipes: A World of Data at Your Fingertips--Exploring Biodiversity with Online Visualization and Analysis Tools

ERIC Educational Resources Information Center

Wilson, Courtney R.; Trautmann, Nancy M.; MaKinster, James G.; Barker, Barbara J.

2010-01-01

A new online tool called "Science Pipes" allows students to conduct biodiversity investigations. With this free tool, students create and run analyses that would otherwise require access to unwieldy data sets and the ability to write computer code. Using these data, students can conduct guided inquiries or hypothesis-driven research to…

Implementing Service Learning into a Graduate Social Work Course: A Step-by-Step Guide

ERIC Educational Resources Information Center

Campbell, Evelyn Marie

2012-01-01

Service learning is a powerful pedagogical tool linking community service to academic learning. Several steps are necessary to implement service learning effectively into the curriculum. This study uses a case example as an exploratory study to pilot-test data on how service learning impacts student outcomes. The paper will (1) provide an overview…

Teachers Harness the Power of Assessment: Collaborative Use of Student Data Gauges Performance and Guides Instruction

ERIC Educational Resources Information Center

Herman, Phillip; Wardrip, Peter; Hall, Ashley; Chimino, Amy

2012-01-01

Improving systematic use of student data to inform the work of teachers, schools, and districts has become a hot topic in education reform. Learning Forward's Standards for Professional Learning stress better use of data, and particularly student performance data, within an integrated approach to improving practice. While better use of data by…

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…

Improving Performance During Image-Guided Procedures

PubMed Central

Duncan, James R.; Tabriz, David

2015-01-01

Objective Image-guided procedures have become a mainstay of modern health care. This article reviews how human operators process imaging data and use it to plan procedures and make intraprocedural decisions. Methods A series of models from human factors research, communication theory, and organizational learning were applied to the human-machine interface that occupies the center stage during image-guided procedures. Results Together, these models suggest several opportunities for improving performance as follows: 1. Performance will depend not only on the operator’s skill but also on the knowledge embedded in the imaging technology, available tools, and existing protocols. 2. Voluntary movements consist of planning and execution phases. Performance subscores should be developed that assess quality and efficiency during each phase. For procedures involving ionizing radiation (fluoroscopy and computed tomography), radiation metrics can be used to assess performance. 3. At a basic level, these procedures consist of advancing a tool to a specific location within a patient and using the tool. Paradigms from mapping and navigation should be applied to image-guided procedures. 4. Recording the content of the imaging system allows one to reconstruct the stimulus/response cycles that occur during image-guided procedures. Conclusions When compared with traditional “open” procedures, the technology used during image-guided procedures places an imaging system and long thin tools between the operator and the patient. Taking a step back and reexamining how information flows through an imaging system and how actions are conveyed through human-machine interfaces suggest that much can be learned from studying system failures. In the same way that flight data recorders revolutionized accident investigations in aviation, much could be learned from recording video data during image-guided procedures. PMID:24921628

An Interactive Platform to Visualize Data-Driven Clinical Pathways for the Management of Multiple Chronic Conditions.

PubMed

Zhang, Yiye; Padman, Rema

2017-01-01

Patients with multiple chronic conditions (MCC) pose an increasingly complex health management challenge worldwide, particularly due to the significant gap in our understanding of how to provide coordinated care. Drawing on our prior research on learning data-driven clinical pathways from actual practice data, this paper describes a prototype, interactive platform for visualizing the pathways of MCC to support shared decision making. Created using Python web framework, JavaScript library and our clinical pathway learning algorithm, the visualization platform allows clinicians and patients to learn the dominant patterns of co-progression of multiple clinical events from their own data, and interactively explore and interpret the pathways. We demonstrate functionalities of the platform using a cluster of 36 patients, identified from a dataset of 1,084 patients, who are diagnosed with at least chronic kidney disease, hypertension, and diabetes. Future evaluation studies will explore the use of this platform to better understand and manage MCC.

Timber sale planning and analysis system: A user`s guide to the TSPAS sale program. Forest Service general technical report

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

Schuster, E.G.; Jones, J.G.; Meacham, M.L.

1995-08-01

Presents a guide to operation and interpretation of TSPAS Sale Program (TSPAS SP), a menu-driven computer program that is one of two programs in the Timber Sale Planning and Analysis System. TSPAS SP is intended to help field teams design and evaluate timber sale alternatives. TSPAS SP evaluate current and long-term timber implications along with associated nontimber outputs. Features include multiple entries and products, real value change, and graphical input. Guide includes user instructions, a glossary, a listing of data needs, and an explanation of error messages.

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

ERIC Educational Resources Information Center

Knight, Simon; Littleton, Karen

2015-01-01

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances…

Reward-Guided Learning with and without Causal Attribution

PubMed Central

Jocham, Gerhard; Brodersen, Kay H.; Constantinescu, Alexandra O.; Kahn, Martin C.; Ianni, Angela M.; Walton, Mark E.; Rushworth, Matthew F.S.; Behrens, Timothy E.J.

2016-01-01

Summary When an organism receives a reward, it is crucial to know which of many candidate actions caused this reward. However, recent work suggests that learning is possible even when this most fundamental assumption is not met. We used novel reward-guided learning paradigms in two fMRI studies to show that humans deploy separable learning mechanisms that operate in parallel. While behavior was dominated by precise contingent learning, it also revealed hallmarks of noncontingent learning strategies. These learning mechanisms were separable behaviorally and neurally. Lateral orbitofrontal cortex supported contingent learning and reflected contingencies between outcomes and their causal choices. Amygdala responses around reward times related to statistical patterns of learning. Time-based heuristic mechanisms were related to activity in sensorimotor corticostriatal circuitry. Our data point to the existence of several learning mechanisms in the human brain, of which only one relies on applying known rules about the causal structure of the task. PMID:26971947

Comparing self-guided learning and educator-guided learning formats for simulation-based clinical training.

PubMed

Brydges, Ryan; Carnahan, Heather; Rose, Don; Dubrowski, Adam

2010-08-01

In this paper, we tested the over-arching hypothesis that progressive self-guided learning offers equivalent learning benefit vs. proficiency-based training while limiting the need to set proficiency standards. We have shown that self-guided learning is enhanced when students learn on simulators that progressively increase in fidelity during practice. Proficiency-based training, a current gold-standard training approach, requires achievement of a criterion score before students advance to the next learning level. Baccalaureate nursing students (n = 15/group) practised intravenous catheterization using simulators that differed in fidelity (i.e. students' perceived realism). Data were collected in 2008. Proficiency-based students advanced from low- to mid- to high-fidelity after achieving a proficiency criterion at each level. Progressive students self-guided their progression from low- to mid- to high-fidelity. Yoked control students followed an experimenter-defined progressive practice schedule. Open-ended students moved freely between the simulators. One week after practice, blinded experts evaluated students' skill transfer on a standardized patient simulation. Group differences were examined using analyses of variance. Proficiency-based students scored highest on the high-fidelity post-test (effect size = 1.22). An interaction effect showed that the Progressive and Open-ended groups maintained their performance from post-test to transfer test, whereas the Proficiency-based and Yoked control groups experienced a significant decrease (P < 0.05). Surprisingly, most Open-ended students (73%) chose the progressive practice schedule. Progressive training and proficiency-based training resulted in equivalent transfer test performance, suggesting that progressive students effectively self-guided when to transition between simulators. Students' preference for the progressive practice schedule indicates that educators should consider this sequence for simulation-based training.

Big data need big theory too

PubMed Central

Dougherty, Edward R.; Highfield, Roger R.

2016-01-01

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’. PMID:27698035

Big data need big theory too.

PubMed

Coveney, Peter V; Dougherty, Edward R; Highfield, Roger R

2016-11-13

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2015 The Authors.

Intravascular ultrasound guided directional atherectomy versus directional atherectomy guided by angiography for the treatment of femoropopliteal in-stent restenosis

PubMed Central

Krishnan, Prakash; Tarricone, Arthur; K-Raman, Purushothaman; Majeed, Farhan; Kapur, Vishal; Gujja, Karthik; Wiley, Jose; Vasquez, Miguel; Lascano, Rheoneil A.; Quiles, Katherine G.; Distin, Tashanne; Fontenelle, Ran; Atallah-Lajam, Farah; Kini, Annapoorna; Sharma, Samin

2017-01-01

Background: The aim of this study was to compare 1-year outcomes for patients with femoropopliteal in-stent restenosis using directional atherectomy guided by intravascular ultrasound (IVUS) versus directional atherectomy guided by angiography. Methods and results: This was a retrospective analysis for patients with femoropopliteal in-stent restenosis treated with IVUS-guided directional atherectomy versus directional atherectomy guided by angiography from a single center between March 2012 and February 2016. Clinically driven target lesion revascularization was the primary endpoint and was evaluated through medical chart review as well as phone call follow up. Conclusions: Directional atherectomy guided by IVUS reduces clinically driven target lesion revascularization for patients with femoropopliteal in-stent restenosis. PMID:29265002

Teacher Use of Data to Guide Instructional Practice in Elementary Schools

ERIC Educational Resources Information Center

Burrows, Debra C.

2011-01-01

This descriptive study focused on the degree to which data-driven decision making as envisioned by the NCLB legislation was actually occurring in the elementary schools studied. A multi-stage random sample of six Pennsylvania school districts out of 19 located within the service area of Pennsylvania Intermediate Unit #17, one of 29 regional…

Effects-Driven Participatory Design: Learning from Sampling Interruptions.

PubMed

Brandrup, Morten; Østergaard, Kija Lin; Hertzum, Morten; Karasti, Helena; Simonsen, Jesper

2017-01-01

Participatory design (PD) can play an important role in obtaining benefits from healthcare information technologies, but we contend that to fulfil this role PD must incorporate feedback from real use of the technologies. In this paper we describe an effects-driven PD approach that revolves around a sustained focus on pursued effects and uses the experience sampling method (ESM) to collect real-use feedback. To illustrate the use of the method we analyze a case that involves the organizational implementation of electronic whiteboards at a Danish hospital to support the clinicians' intra- and interdepartmental coordination. The hospital aimed to reduce the number of phone calls involved in coordinating work because many phone calls were seen as unnecessary interruptions. To learn about the interruptions we introduced an app for capturing quantitative data and qualitative feedback about the phone calls. The investigation showed that the electronic whiteboards had little potential for reducing the number of phone calls at the operating ward. The combination of quantitative data and qualitative feedback worked both as a basis for aligning assumptions to data and showed ESM as an instrument for triggering in-situ reflection. The participant-driven design and redesign of the way data were captured by means of ESM is a central contribution to the understanding of how to conduct effects-driven PD.

Competency Based Education Curriculum for Data Processing.

ERIC Educational Resources Information Center

West Virginia State Vocational Curriculum Lab., Cedar Lakes.

This curriculum for data processing is organized into four learning modules. Each module is comprised of four to seven competencies. A student competency sheet provided for each competency is organized into this format: module and competency number and name, performance guide, learning activities, and an evaluation. Where appropriate, student…

Structure, Content, Delivery, Service, and Outcomes: Quality e-Learning in Higher Education

ERIC Educational Resources Information Center

MacDonald, Colla J.; Thompson, Terrie Lynn

2005-01-01

This paper addresses the need for quality e-Learning experiences. We used the Demand-Driven Learning Model (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001) to evaluate an online Masters in Education course. Multiple data collection methods were used to understand the experiences of stakeholders in this case study: the learners, design…

Guide to Academic Research Career Development

PubMed Central

Smith, Richard J.; Graboyes, Evan M.; Paniello, Randal C.; Paul Gubbels, Samuel

2016-01-01

Objectives/Hypothesis Development of an academic career easily follows a clinical course for which there are multiple role models; however, development of an academic research career involves few role models, and rarely do instructional guides reach out to the new faculty. The purpose of this article is to present the cumulative experiences of previously and currently funded authors to serve as a guide to young as well as older faculty for developing their research careers. Study Design Cumulative experiences of research‐dedicated faculty. Methods This article is the result of lessons learned from developing a Triological Society National Physician‐Scientist Program and Network, as well as the cumulative experiences of the authors. Results Table I illustrates key elements in developing a serious research career. Table II records the career courses of five surgeon‐scientists, highlighting the continued theme focus with theme‐specific publications and progressive grants. These cumulative experiences have face validity but have not been objectively tested. The value added is a composite of 50 years of experiences from authors committed to research career development for themselves and others. Conclusion Crucial elements in developing a research career are a desire for and commitment to high‐quality research, a focus on an overall theme of progressive hypothesis‐driven investigations, research guidance, a willingness to spend the time required, and an ability to learn from and withstand failure. Level of Evidence 5. PMID:28894799

Supporting Inquiry in Science Classrooms with the Web

ERIC Educational Resources Information Center

Simons, Krista; Clark, Doug

2005-01-01

This paper focuses on Web-based science inquiry and five representative science learning environments. The discussion centers around features that sustain science inquiry, namely, data-driven investigation, modeling, collaboration, and scaffolding. From the perspective of these features five science learning environments are detailed: Whyville,…

Recent CESAR (Center for Engineering Systems Advanced Research) research activities in sensor based reasoning for autonomous machines

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

Pin, F.G.; de Saussure, G.; Spelt, P.F.

1988-01-01

This paper describes recent research activities at the Center for Engineering Systems Advanced Research (CESAR) in the area of sensor based reasoning, with emphasis being given to their application and implementation on our HERMIES-IIB autonomous mobile vehicle. These activities, including navigation and exploration in a-priori unknown and dynamic environments, goal recognition, vision-guided manipulation and sensor-driven machine learning, are discussed within the framework of a scenario in which an autonomous robot is asked to navigate through an unknown dynamic environment, explore, find and dock at the panel, read and understand the status of the panel's meters and dials, learn the functioningmore » of a process control panel, and successfully manipulate the control devices of the panel to solve a maintenance emergency problems. A demonstration of the successful implementation of the algorithms on our HERMIES-IIB autonomous robot for resolution of this scenario is presented. Conclusions are drawn concerning the applicability of the methodologies to more general classes of problems and implications for future work on sensor-driven reasoning for autonomous robots are discussed. 8 refs., 3 figs.« less

The Effects of Utilizing Corpus Resources to Correct Collocation Errors in L2 Writing--Students' Performance, Corpus Use and Perceptions

ERIC Educational Resources Information Center

Wu, Yi-ju

2016-01-01

Data-Driven Learning (DDL), in which learners "confront [themselves] directly with the corpus data" (Johns, 2002, p. 108), has shown to be effective in collocation learning in L2 writing. Nevertheless, there have been only few research studies of this type examining the relationship between English proficiency and corpus consultation.…

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

NASA Astrophysics Data System (ADS)

Xu, Jun; Zhang, Lei; Zhang, David

2018-06-01

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.

Eyes for Learning: Preventing and Curing Vision-Related Learning Problems

ERIC Educational Resources Information Center

Orfield, Antonia

2007-01-01

Dr. Orfield's highly readable guide on vision development presents ground-breaking solutions to common learning problems and is supported by substantial data. This holistic common sense--that most people do not know--is not just about vision but also how vision is interrelated with learning. It teaches how to care for a child's vision as well as…

Factors That Affect a School District's Ability to Successfully Implement the Use of Data Warehouse Applications in the Data Driven Decision Making Process

ERIC Educational Resources Information Center

DeLoach, Robin

2012-01-01

The purpose of this study was to explore the factors that influence the ability of teachers and administrators to use data obtained from a data warehouse to inform instruction. The mixed methods study was guided by the following questions: 1) What data warehouse application features affect the ability of an educator to effectively use the…

Adventure Learning and Learner-Engagement: Frameworks for Designers and Educators

ERIC Educational Resources Information Center

Henrickson, Jeni; Doering, Aaron

2013-01-01

There is a recognized need for theoretical frameworks that can guide designers and educators in the development of engagement-rich learning experiences that incorporate emerging technologies in pedagogically sound ways. This study investigated one such promising framework, adventure learning (AL). Data were gathered via surveys, interviews, direct…

Guiding Early and Often: Using Curricular and Learning Analytics to Shape Teaching, Learning, and Student Success in Gateway Courses

ERIC Educational Resources Information Center

Pistilli, Matthew D.; Heileman, Gregory L.

2017-01-01

This chapter provides information on how the promise of analytics can be realized in gateway courses through a combination of good data science and the thoughtful application of outcomes to teaching and learning improvement efforts--especially with and among instructors.

Learning Styles and Self-Regulation.

ERIC Educational Resources Information Center

Vermunt, Jan D. H. M.

Data from 211 adult students (ages 20 to 75) at the Open University, The Netherlands, were used to construct and test an instrument to measure learning styles and regulation processes. Test development was guided by a three-tiered model of self-regulation encompassing: (1) cognitive learning processes (deep, surface, elaborative); (2) regulation…

The effects of data-driven learning activities on EFL learners' writing development.

PubMed

Luo, Qinqin

2016-01-01

Data-driven learning has been proved as an effective approach in helping learners solve various writing problems such as correcting lexical or grammatical errors, improving the use of collocations and generating ideas in writing, etc. This article reports on an empirical study in which data-driven learning was accomplished with the assistance of the user-friendly BNCweb, and presents the evaluation of the outcome by comparing the effectiveness of BNCweb and a search engine Baidu which is most commonly used as reference resource by Chinese learners of English as a foreign language. The quantitative results about 48 Chinese college students revealed that the experimental group which used BNCweb performed significantly better in the post-test in terms of writing fluency and accuracy, as compared with the control group which used the search engine Baidu. However, no significant difference was found between the two groups in terms of writing complexity. The qualitative results about the interview revealed that learners generally showed a positive attitude toward the use of BNCweb but there were still some problems of using corpora in the writing process, thus the combined use of corpora and other types of reference resource was suggested as a possible way to counter the potential barriers for Chinese learners of English.

Digital Suicide Prevention: Can Technology Become a Game-changer?

PubMed Central

Sahin, Ned; Kalali, Amir

2016-01-01

Suicide continues to be a leading cause of death and has been recognized as a significant public health issue. Rapid advances in data science can provide us with useful tools for suicide prevention, and help to dynamically assess suicide risk in quantitative data-driven ways. In this article, the authors highlight the most current international research in digital suicide prevention, including the use of machine learning, smartphone applications, and wearable sensor driven systems. The authors also discuss future opportunities for digital suicide prevention, and propose a novel Sensor-driven Mental State Assessment System. PMID:27800282

Using machine learning algorithms to guide rehabilitation planning for home care clients.

PubMed

Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

2007-12-20

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

Effects of Corpus-Aided Language Learning in the EFL Grammar Classroom: A Case Study of Students' Learning Attitudes and Teachers' Perceptions in Taiwan

ERIC Educational Resources Information Center

Lin, Ming Huei

2016-01-01

This study employed a blended approach to form an extensive assessment of the pedagogical suitability of data-driven learning (DDL) in Taiwan's EFL grammar classrooms. On the one hand, the study quantitatively investigated the effects of DDL compared with that of a traditional deductive approach on the learning motivation and self-efficacy of…

Using Technology and Assessment to Personalize Instruction: Preventing Reading Problems.

PubMed

Connor, Carol McDonald

2017-09-15

Children who fail to learn to read proficiently are at serious risk of referral to special education, grade retention, dropping out of high school, and entering the juvenile justice system. Accumulating research suggests that instruction regimes that rely on assessment to inform instruction are effective in improving the implementation of personalized instruction and, in turn, student learning. However, teachers find it difficult to interpret assessment results in a way that optimizes learning opportunities for all of the students in their classrooms. This article focuses on the use of language, decoding, and comprehension assessments to develop personalized plans of literacy instruction for students from kindergarten through third grade, and A2i technology designed to support teachers' use of assessment to guide instruction. Results of seven randomized controlled trials demonstrate that personalized literacy instruction is more effective than traditional instruction, and that sustained implementation of personalized literacy instruction first through third grade may prevent the development of serious reading problems. We found effect sizes from .2 to .4 per school year, which translates into about a 2-month advantage. These effects accumulated from first through third grade with a large effect size (d = .7) equivalent to a full grade-equivalent advantage on standardize tests of literacy. These results demonstrate the efficacy of technology-supported personalized data-driven literacy instruction to prevent serious reading difficulties. Implications for translational prevention research in education and healthcare are discussed.

Attack of the Killer Fungus: A Hypothesis-Driven Lab Module †

PubMed Central

Sato, Brian K.

2013-01-01

Discovery-driven experiments in undergraduate laboratory courses have been shown to increase student learning and critical thinking abilities. To this end, a lab module involving worm capture by a nematophagous fungus was developed. The goals of this module are to enhance scientific understanding of the regulation of worm capture by soil-dwelling fungi and for students to attain a set of established learning goals, including the ability to develop a testable hypothesis and search for primary literature for data analysis, among others. Students in a ten-week majors lab course completed the lab module and generated novel data as well as data that agrees with the published literature. In addition, learning gains were achieved as seen through a pre-module and post-module test, student self-assessment, class exam, and lab report. Overall, this lab module enables students to become active participants in the scientific method while contributing to the understanding of an ecologically relevant model organism. PMID:24358387

Communication modality sampling for a toddler with Angelman syndrome.

PubMed

Hyppa Martin, Jolene; Reichle, Joe; Dimian, Adele; Chen, Mo

2013-10-01

Vocal, gestural, and graphic communication modes were implemented concurrently with a toddler with Angelman syndrome to identify the most efficiently learned communication mode to emphasize in an initial augmentative communication system. Symbols representing preferred objects were introduced in vocal, gestural, and graphic communication modes using an alternating treatment single-subject experimental design. Conventionally accepted prompting strategies were used to teach symbols in each communication mode. Because the learner did not vocally imitate, vocal mode intervention focused on increasing vocal frequency as an initial step. When graphic and gestural mode performances were compared, the learner most accurately produced requests in graphic mode (percentage of nonoverlapping data = 96). Given the lack of success in prompting vocal productions, a comparison between vocal and the other two communication modes was not made. A growing body of evidence suggests that concurrent modality sampling is a promising low-inference, data-driven procedure that can be used to inform selection of a communication mode(s) for initial emphasis with young children. Concurrent modality sampling can guide clinical decisions regarding the allocation of treatment resources to promote success in building an initial communicative repertoire.

Predicting the stochastic guiding of kinesin-driven microtubules in microfabricated tracks: a statistical-mechanics-based modeling approach.

PubMed

Lin, Chih-Tin; Meyhofer, Edgar; Kurabayashi, Katsuo

2010-01-01

Directional control of microtubule shuttles via microfabricated tracks is key to the development of controlled nanoscale mass transport by kinesin motor molecules. Here we develop and test a model to quantitatively predict the stochastic behavior of microtubule guiding when they mechanically collide with the sidewalls of lithographically patterned tracks. By taking into account appropriate probability distributions of microscopic states of the microtubule system, the model allows us to theoretically analyze the roles of collision conditions and kinesin surface densities in determining how the motion of microtubule shuttles is controlled. In addition, we experimentally observe the statistics of microtubule collision events and compare our theoretical prediction with experimental data to validate our model. The model will direct the design of future hybrid nanotechnology devices that integrate nanoscale transport systems powered by kinesin-driven molecular shuttles.

Subgroup Discovery with User Interaction Data: An Empirically Guided Approach to Improving Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Poitras, Eric G.; Lajoie, Susanne P.; Doleck, Tenzin; Jarrell, Amanda

2016-01-01

Learner modeling, a challenging and complex endeavor, is an important and oft-studied research theme in computer-supported education. From this perspective, Educational Data Mining (EDM) research has focused on modeling and comprehending various dimensions of learning in computer-based learning environments (CBLE). Researchers and designers are…

Visible Machine Learning for Biomedicine.

PubMed

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

Machine Learning for Education: Learning to Teach

DTIC Science & Technology

2016-12-01

such as commercial aviation, healthcare, and military operations. In the context of military applications, serious gaming – the training warfighters...problems. Playing these games not only allowed the warfighter to discover and learn new tactics, techniques, and procedures, but also allowed the...collecting information across relevant sample sizes have motivated a data-driven, game - based simulation approach. For example, industry and academia alike

Using Google as a Super Corpus to Drive Written Language Learning: A Comparison with the British National Corpus

ERIC Educational Resources Information Center

Sha, Guoquan

2010-01-01

Data-driven learning (DDL), or corpus-based language learning, involves the learner in an exploratory task to discover appropriate expressions or collocates regarding his writing. However, the problematic units of meaning in each learner's writing are so diverse that conventional corpora often prove futile. The search engine Google with the…

Engaging Underrepresented High School Students in Data Driven Storytelling: An Examination of Learning Experiences and Outcomes for a Cohort of Rising Seniors Enrolled in the Gaining Early Awareness and Readiness for Undergraduate Program (GEAR UP)

ERIC Educational Resources Information Center

Dierker, Lisa; Ward, Nadia; Alexander, Jalen; Donate, Emmanuel

2017-01-01

Background: Upward trends in data-oriented careers threaten to further increase the underrepresentation of both females and individuals from racial minority groups in programs focused on data analysis and applied statistics. To begin to develop the necessary skills for a data-oriented career, project-based learning seems the most promising given…

Authoring Data-Driven Videos with DataClips.

PubMed

Amini, Fereshteh; Riche, Nathalie Henry; Lee, Bongshin; Monroy-Hernandez, Andres; Irani, Pourang

2017-01-01

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

Computational intelligence in earth sciences and environmental applications: issues and challenges.

PubMed

Cherkassky, V; Krasnopolsky, V; Solomatine, D P; Valdes, J

2006-03-01

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.

The development of guided inquiry-based learning devices on photosynthesis and respiration matter to train science literacy skills

NASA Astrophysics Data System (ADS)

Choirunnisak; Ibrahim, M.; Yuliani

2018-01-01

The purpose of this research was to develop a guided inquiry-based learning devices on photosynthesis and respiration matter that are feasible (valid, practical, and effective) to train students’ science literacy. This research used 4D development model and tested on 15 students of biology education 2016 the State University of Surabaya with using one group pretest-posttest design. Learning devices developed include (a) Semester Lesson Plan (b) Lecture Schedule, (c) Student Activity Sheet, (d) Student Textbook, and (e) testability of science literacy. Research data obtained through validation method, observation, test, and questionnaire. The results were analyzed descriptively quantitative and qualitative. The ability of science literacy was analyzed by n-gain. The results of this research showed that (a) learning devices that developed was categorically very valid, (b) learning activities performed very well, (c) student’s science literacy skills improved that was a category as moderate, and (d) students responses were very positively to the learning that already held. Based on the results of the analysis and discussion, it is concluded that the development of guided inquiry-based learning devices on photosynthesis and respiration matter was feasible to train students literacy science skills.

Integrating Resources into Curriculum with the Systems Connect Planning Guide

NASA Astrophysics Data System (ADS)

Oshry, A.; Bean, J. R.

2017-12-01

A broadly applicable and guided approach for planning curriculum and instruction around new academic standards or initiatives is critical for implementation success. Curriculum and assessment differs across schools and districts, so built-in adaptability is important for maximal adoption and ease of use by educators. The Systems Connect Planning Guide directs the flow of instruction for building conceptual links between topics in a unit/curriculum through critical vetting and integration of relevant resources. This curricular template is flexible for use in any setting or subject area, and ensures applicability, high impact and responsiveness to academic standards while providing inquiry-based, real-world investigations and action that incorporate authentic research and data. These needs are what informed the creation of the three components of the planning guide:• Curriculum Anchor: alignment with academic standards & learning outcomes and setting the context of the topic• Issues Investigations: informing how students explore topics, and incorporate authentic research and data into learning progressions• Civic Action: development of how students could apply their knowledgeThe Planning Guide also incorporates criteria from transdisciplinary practices, cross-cutting concepts, and organizational charts for outlining guiding questions and conceptual links embedded in the guide. Integration of experiential learning and real-world connections into curricula is important for proficiency and deeper understanding of content, replacing discrete, stand-alone experiences which are not explicitly connected. Rather than information being dispelled through individual activities, relying on students to make the connections, intentionally documenting explicit connections provides opportunities to foster deeper understanding by building conceptual links between topics, which is how fundamental knowledge about earth and living systems is gained. Through the critical vetting and sequencing of these resources, educators establish cohesive learning progressions that explicitly build conceptual links between topics, enabling students to use these activities to develop evidence-based explanations of the natural world.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy.

PubMed

Azizi, Shekoofeh; Van Woudenberg, Nathan; Sojoudi, Samira; Li, Ming; Xu, Sheng; Abu Anas, Emran M; Yan, Pingkun; Tahmasebi, Amir; Kwak, Jin Tae; Turkbey, Baris; Choyke, Peter; Pinto, Peter; Wood, Bradford; Mousavi, Parvin; Abolmaesumi, Purang

2018-03-27

We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation. In this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client-server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution. The system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85. Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Privacy-Driven Design of Learning Analytics Applications: Exploring the Design Space of Solutions for Data Sharing and Interoperability

ERIC Educational Resources Information Center

Hoel, Tore; Chen, Weiqin

2016-01-01

Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately, systems will tend to collapse due to a legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their…

Data-Driven Learning and the Acquisition of Italian Collocations: From Design to Student Evaluation

ERIC Educational Resources Information Center

Forti, Luciana

2017-01-01

This paper looks at how corpus data was used to design an Italian as an L2 language learning programme and how it was evaluated by students. The study focuses on the acquisition of Italian verb-noun collocations by Chinese native students attending a ten month long Italian language course before enrolling at an Italian university. It describes how…

Organizational Learning for Library Enhancements: A Collaborative, Research-Driven Analysis of Academic Department Needs

ERIC Educational Resources Information Center

Loo, Jeffery L.; Dupuis, Elizabeth A.

2015-01-01

This article presents a qualitative evaluation methodology of academic departments for library organizational learning and library enhancement planning. This evaluation used campus units' academic program review reports as a data source and employed collaborative content analysis by library liaisons to extract departmental strengths, weaknesses,…

Surveying Professionals' Views of Positive Behavior Support and Behavior Analysis

ERIC Educational Resources Information Center

Filter, Kevin J.; Tincani, Matt; Fung, Daniel

2009-01-01

Positive behavior support (PBS) is an empirically driven approach to improve quality of life influenced by the science of behavior analysis. Recent discussions have evolved around PBS, behavior analysis, and their relationship within education and human services fields. To date, few data have been offered to guide behaviorally oriented…

Basic Electricity/Electronics. Learning Guides.

ERIC Educational Resources Information Center

Eggett, A. J.

This packet consists of 22 student learning guides for high school vocational education students in Illinois. The guides contain tasks for a course in electricity/electronics. Each task guide identifies the task and its purpose and provides a learning contract for the student and teacher to sign. Information on the learning contract consists of a…

Using Students' Performance to Improve Ontologies for Intelligent E-Learning System

ERIC Educational Resources Information Center

Icoz, Kutay; Sanalan, Vehbi A.; Ozdemir, Esra Benli; Kaya, Sukru; Cakar, Mehmet Akif

2015-01-01

Ontologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by…

Competency-based achievement system: using formative feedback to teach and assess family medicine residents' skills.

PubMed

Ross, Shelley; Poth, Cheryl N; Donoff, Michel; Humphries, Paul; Steiner, Ivan; Schipper, Shirley; Janke, Fred; Nichols, Darren

2011-09-01

Family medicine residency programs require innovative means to assess residents' competence in "soft" skills (eg, patient-centred care, communication, and professionalism) and to identify residents who are having difficulty early enough in their residency to provide remedial training. To develop a method to assess residents' competence in various skills and to identify residents who are having difficulty. The Competency-Based Achievement System (CBAS) was designed to measure competence using 3 main principles: formative feedback, guided self-assessment, and regular face-to-face meetings. The CBAS is resident driven and provides a framework for meaningful interactions between residents and advisors. Residents use the CBAS to organize and review their feedback, to guide their own assessment of their progress, and to discern their future learning needs. Advisors use the CBAS to monitor, guide, and verify residents' knowledge of and competence in important skills. By focusing on specific skills and behaviour, the CBAS enables residents and advisors to make formative assessments and to communicate their findings. Feedback indicates that the CBAS is a user-friendly and helpful system to assess competence.

Competency-Based Achievement System

PubMed Central

Ross, Shelley; Poth, Cheryl N.; Donoff, Michel; Humphries, Paul; Steiner, Ivan; Schipper, Shirley; Janke, Fred; Nichols, Darren

2011-01-01

Abstract Problem addressed Family medicine residency programs require innovative means to assess residents’ competence in “soft” skills (eg, patient-centred care, communication, and professionalism) and to identify residents who are having difficulty early enough in their residency to provide remedial training. Objective of program To develop a method to assess residents’ competence in various skills and to identify residents who are having difficulty. Program description The Competency-Based Achievement System (CBAS) was designed to measure competence using 3 main principles: formative feedback, guided self-assessment, and regular face-to-face meetings. The CBAS is resident driven and provides a framework for meaningful interactions between residents and advisors. Residents use the CBAS to organize and review their feedback, to guide their own assessment of their progress, and to discern their future learning needs. Advisors use the CBAS to monitor, guide, and verify residents’ knowledge of and competence in important skills. Conclusion By focusing on specific skills and behaviour, the CBAS enables residents and advisors to make formative assessments and to communicate their findings. Feedback indicates that the CBAS is a user-friendly and helpful system to assess competence. PMID:21918129

A program wide framework for evaluating data driven teaching and learning - earth analytics approaches, results and lessons learned

NASA Astrophysics Data System (ADS)

Wasser, L. A.; Gold, A. U.

2017-12-01

There is a deluge of earth systems data available to address cutting edge science problems yet specific skills are required to work with these data. The Earth analytics education program, a core component of Earth Lab at the University of Colorado - Boulder - is building a data intensive program that provides training in realms including 1) interdisciplinary communication and collaboration 2) earth science domain knowledge including geospatial science and remote sensing and 3) reproducible, open science workflows ("earth analytics"). The earth analytics program includes an undergraduate internship, undergraduate and graduate level courses and a professional certificate / degree program. All programs share the goals of preparing a STEM workforce for successful earth analytics driven careers. We are developing an program-wide evaluation framework that assesses the effectiveness of data intensive instruction combined with domain science learning to better understand and improve data-intensive teaching approaches using blends of online, in situ, asynchronous and synchronous learning. We are using targeted online search engine optimization (SEO) to increase visibility and in turn program reach. Finally our design targets longitudinal program impacts on participant career tracts over time.. Here we present results from evaluation of both an interdisciplinary undergrad / graduate level earth analytics course and and undergraduate internship. Early results suggest that a blended approach to learning and teaching that includes both synchronous in-person teaching and active classroom hands-on learning combined with asynchronous learning in the form of online materials lead to student success. Further we will present our model for longitudinal tracking of participant's career focus overtime to better understand long-term program impacts. We also demonstrate the impact of SEO optimization on online content reach and program visibility.

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

PubMed

Tonutti, Michele; Gras, Gauthier; Yang, Guang-Zhong

2017-07-01

Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models. A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh. The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm. The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. Copyright © 2017 Elsevier B.V. All rights reserved.

A Learning Framework for Control-Oriented Modeling of Buildings

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

Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and bigmore » data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.« less

A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.

PubMed

Wan, Tao; Bloch, B Nicolas; Danish, Shabbar; Madabhushi, Anant

2014-11-20

In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality - laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM), epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.

Data Based Instruction in Reading

ERIC Educational Resources Information Center

Ediger, Marlow

2010-01-01

Data based instruction has received much attention in educational literature. It relates well to measurement driven teaching and learning. Data may come from several sources including mandated tests, district wide testing, formative and summative evaluations, as well as teacher written tests. Objective information is intended for use in data based…

School-Based Coaches Plant Seeds of Learning: A Districtwide Approach to Data Analysis Promotes Job-Embedded Learning and Improved Teacher Practice

ERIC Educational Resources Information Center

Hill, Rachelle; Rapp, Lori

2012-01-01

Schools and districts are inundated with data from a variety of sources. As a result, using data to guide instructional planning can be daunting for teachers and schools. While schools and districts are dealing with shrinking budgets and growing demands for high student achievement, an investment in school-based coaching can provide exponential…

Quick-look guide to the crustal dynamics project's data information system

NASA Technical Reports Server (NTRS)

Noll, Carey E.; Behnke, Jeanne M.; Linder, Henry G.

1987-01-01

Described are the contents of the Crustal Dynamics Project Data Information System (DIS) and instructions on the use of this facility. The main purpose of the DIS is to store all geodetic data products acquired by the Project in a central data bank and to maintain information about the archive of all Project-related data. Access and use of the DIS menu-driven system is described as well as procedures for contacting DIS staff and submitting data requests.

Using Theory to Design Evaluations of Communication Campaigns: The Case of the National Youth Anti-Drug Media Campaign

PubMed Central

Hornik, Robert C.; Yanovitzky, Itzhak

2014-01-01

We present a general theory about how campaigns can have effects and suggest that the evaluation of communication campaigns must be driven by a theory of effects. The National Youth Anti-Drug Media Campaign illustrates both the theory of campaign effects and implications that theory has for the evaluation design. Often models of effect assume that individual exposure affects cognitions that continue to affect behavior over a short term. Contrarily, effects may operate through social or institutional paths as well as through individual learning, require substantial levels of exposure achieved through multiple channels over time, take time to accumulate detectable change, and affect some members of the audience but not others. Responsive evaluations will choose appropriate units of analysis and comparison groups, data collection schedules sensitive to lagged effects, samples able to detect subgroup effects, and analytic strategies consistent with the theory of effects that guides the campaign. PMID:25525317

Feature and Statistical Model Development in Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Kim, Inho

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

KNMI DataLab experiences in serving data-driven innovations

NASA Astrophysics Data System (ADS)

Noteboom, Jan Willem; Sluiter, Raymond

2016-04-01

Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Apprentissage naturel et apprentissage guide (Natural Learning and Guided Learning).

ERIC Educational Resources Information Center

Veronique, Daniel

1984-01-01

Although second language pedagogy has tended increasingly toward simulation, role-playing, and natural communication, it has not profited from existing research on natural learning in second languages. The emphasis should be on understanding how the processes of guided learning and natural learning differ, psychologically and sociologically, and…

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Data extraction for complex meta-analysis (DECiMAL) guide.

PubMed

Pedder, Hugo; Sarri, Grammati; Keeney, Edna; Nunes, Vanessa; Dias, Sofia

2016-12-13

As more complex meta-analytical techniques such as network and multivariate meta-analyses become increasingly common, further pressures are placed on reviewers to extract data in a systematic and consistent manner. Failing to do this appropriately wastes time, resources and jeopardises accuracy. This guide (data extraction for complex meta-analysis (DECiMAL)) suggests a number of points to consider when collecting data, primarily aimed at systematic reviewers preparing data for meta-analysis. Network meta-analysis (NMA), multiple outcomes analysis and analysis combining different types of data are considered in a manner that can be useful across a range of data collection programmes. The guide has been shown to be both easy to learn and useful in a small pilot study.

BEHAVIOR OF MEALWORMS, TEACHER'S GUIDE.

ERIC Educational Resources Information Center

Elementary Science Study, Newton, MA.

THIS TEACHER'S GUIDE IS DESIGNED FOR USE WITH AN ELEMENTARY SCIENCE STUDY UNIT, THE "BEHAVIOR OF MEALWORMS." BY MAKING CAREFUL OBSERVATIONS AND PERFORMING SIMPLE EXPERIMENTS, THE CHILDREN LEARN HOW TO APPROACH A PROBLEM, HOW TO INTERPRET AND EVALUATE DATA, AND, IN GENERAL, HOW TO CONDUCT A SCIENTIFIC INVESTIGATION. THE MATERIALS HAVE…

Supplementary Teaching Materials for Business Courses.

ERIC Educational Resources Information Center

Boulden, Alfred W., Ed.

This teaching guide for business education contains supplementary instructional materials for the subjects of accounting, business English, business mathematics, career education, consumer education, data processing, and office procedures. The units differ in format and in types of learning activities presented. The learning activity package for…

Theoretically-Driven Infrastructure for Supporting Healthcare Teams Training at a Military Treatment Facility

NASA Technical Reports Server (NTRS)

Turner, Robert T.; Parodi, Andrea V.

2011-01-01

The Team Resource Center (TRC) at Naval Medical Center Portsmouth (NMCP) currently hosts a tri-service healthcare teams training course three times annually . The course consists of didactic learning coupled with simulation exercises to provide an interactive educational experience for healthcare professionals. The course is also the foundation of a research program designed to explore the use of simulation technologies for enhancing team training and evaluation. The TRC has adopted theoretical frameworks for evaluating training readiness and efficacy, and is using these frameworks to guide a systematic reconfiguration of the infrastructure supporting healthcare teams training and research initiatives at NMCP.

From Actions to Habits

PubMed Central

Yin, Henry H.

2008-01-01

Recent work on the role of overlapping cerebral networks in action selection and habit formation has important implications for alcohol addiction research. As reviewed below, (1) these networks, which all involve a group of deep-brain structures called the basal ganglia, are associated with distinct behavioral control processes, such as reward-guided Pavlovian conditional responses, goal-directed instrumental actions, and stimulus-driven habits; (2) different stages of action learning are associated with different networks, which have the ability to change (i.e., plasticity); and (3) exposure to alcohol and other addictive drugs can have profound effects on these networks by influencing the mechanisms underlying neural plasticity. PMID:23584008

Predicting the trajectories and intensities of hurricanes by applying machine learning techniques

NASA Astrophysics Data System (ADS)

Sujithkumar, A.; King, A. W.; Kovilakam, M.; Graves, D.

2017-12-01

The world has witnessed an escalation of devastating hurricanes and tropical cyclones over the last three decades. Hurricanes and tropical cyclones of very high magnitude will likely be even more frequent in a warmer world. Thus, precise forecasting of the track and intensity of hurricane/tropical cyclones remains one of the meteorological community's top priorities. However, comprehensive prediction of hurricane/ tropical cyclone is a difficult problem due to the many complexities of underlying physical processes with many variables and complex relations. The availability of global meteorological and hurricane/tropical storm climatological data opens new opportunities for data-driven approaches to hurricane/tropical cyclone modeling. Here we report initial results from two data-driven machine learning techniques, specifically, random forest (RF) and Bayesian learning (BL) to predict the trajectory and intensity of hurricanes and tropical cyclones. We used International Best Track Archive for Climate Stewardship (IBTrACS) data along with weather data from NOAA in a 50 km buffer surrounding each of the reported hurricane and tropical cyclone tracts to train the model. Initial results reveal that both RF and BL are skillful in predicting storm intensity. We will also present results for the more complicated trajectory prediction.

Virtual screening of inorganic materials synthesis parameters with deep learning

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Kevin; Jegelka, Stefanie; Olivetti, Elsa

2017-12-01

Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO3 synthesis parameter sets, propose driving factors for brookite TiO2 formation, and identify correlations between alkali-ion intercalation and MnO2 polymorph selection.

Guided Learning at Work.

ERIC Educational Resources Information Center

Billett, Stephen

2000-01-01

Guided learning (questioning, diagrams/analogies, modeling, coaching) was studied through critical incident interviews in five workplaces. Participation in everyday work activities was the most effective contributor to workplace learning. Organizational readiness and the efficacy of guided learning in resolving novel tasks were also important. (SK)

As above, so below? Towards understanding inverse models in BCI

NASA Astrophysics Data System (ADS)

Lindgren, Jussi T.

2018-02-01

Objective. In brain-computer interfaces (BCI), measurements of the user’s brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. We investigate if more accurate BCIs can be obtained by reconstructing the source activities in the volume. Approach. We contrast the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning. We explain these approaches in a common linear dictionary framework and review the different ways to obtain the dictionary parameters. We consider the effect of source reconstruction on some major difficulties in BCI classification, namely information loss, feature selection and nonstationarity of the EEG. Main results. Our analysis suggests that the approaches differ mainly in their parameter estimation. Physiological source reconstruction may thus be expected to improve BCI accuracy if machine learning is not used or where it produces less optimal parameters. We argue that the considered difficulties of surface EEG classification can remain in the reconstructed volume and that data-driven techniques are still necessary. Finally, we provide some suggestions for comparing approaches. Significance. The present work illustrates the relationships between source reconstruction and machine learning-based approaches for EEG data representation. The provided analysis and discussion should help in understanding, applying, comparing and improving such techniques in the future.

Abandoned babies and absent policies.

PubMed

Mueller, Joanne; Sherr, Lorraine

2009-12-01

Although infant abandonment is a historical problem, we know remarkably little about the conditions or effects of abandonment to guide evidence driven policies. This paper briefly reviews the existing international evidence base with reference to potential mental health considerations before mapping current UK guidelines and procedures, and available incidence data. Limitations arising from these findings are discussed with reference to international practice, and interpreted in terms of future pathways for UK policy. A systematic approach was utilized to gather available data on policy information and statistics on abandoned babies in the UK. A review of the limited literature indicates that baby abandonment continues to occur, with potentially wide-ranging mental health ramifications for those involved. However, research into such consequences is lacking, and evidence with which to understand risk factors or motives for abandonment is scarce. International approaches to the issue remain controversial with outcomes unclear. Our systematic search identified that no specific UK policy relating to baby abandonment exists, either nationally or institutionally. This is compounded by a lack of accurate of UK abandonment statistics. Data that does exist is not comprehensive and sources are incompatible, resulting in an ambiguous picture of UK baby abandonment. Available literature indicates an absence of clear provision, policy and research on baby abandonment. Based on current understanding of maternal and child mental health issues likely to be involved in abandonment, existing UK strategy could be easily adapted to avoid the 'learning from scratch' approach. National policies on recording and handling of baby abandonments are urgently needed, and future efforts should be concentrated on establishing clear data collection frameworks to inform understanding, guide competent practice and enable successfully targeted interventions.

Guiding Music Students during Workshop-Based On-the-Job Learning

ERIC Educational Resources Information Center

Virkkula, Esa; Kunwar, Jagat Bahadur

2017-01-01

This article explains the realisation and impact of tutoring on learning through a new kind of on-the-job learning method in workshops led by professional musicians. The research is a qualitative case study involving 62 upper secondary Finnish vocational music students who participated in 11 workshops. The research data consist of (a) workshop…

Corpora Processing and Computational Scaffolding for a Web-Based English Learning Environment: The CANDLE Project

ERIC Educational Resources Information Center

Liou, Hsien-Chin; Chang, Jason S; Chen, Hao-Jan; Lin, Chih-Cheng; Liaw, Meei-Ling; Gao, Zhao-Ming; Jang, Jyh-Shing Roger; Yeh, Yuli; Chuang, Thomas C.; You, Geeng-Neng

2006-01-01

This paper describes the development of an innovative web-based environment for English language learning with advanced data-driven and statistical approaches. The project uses various corpora, including a Chinese-English parallel corpus ("Sinorama") and various natural language processing (NLP) tools to construct effective English…

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

An inquiry-based biochemistry laboratory structure emphasizing competency in the scientific process: a guided approach with an electronic notebook format.

PubMed

L Hall, Mona; Vardar-Ulu, Didem

2014-01-01

The laboratory setting is an exciting and gratifying place to teach because you can actively engage the students in the learning process through hands-on activities; it is a dynamic environment amenable to collaborative work, critical thinking, problem-solving and discovery. The guided inquiry-based approach described here guides the students through their laboratory work at a steady pace that encourages them to focus on quality observations, careful data collection and thought processes surrounding the chemistry involved. It motivates students to work in a collaborative manner with frequent opportunities for feedback, reflection, and modification of their ideas. Each laboratory activity has four stages to keep the students' efforts on track: pre-lab work, an in-lab discussion, in-lab work, and a post-lab assignment. Students are guided at each stage by an instructor created template that directs their learning while giving them the opportunity and flexibility to explore new information, ideas, and questions. These templates are easily transferred into an electronic journal (termed the E-notebook) and form the basic structural framework of the final lab reports the students submit electronically, via a learning management system. The guided-inquiry based approach presented here uses a single laboratory activity for undergraduate Introductory Biochemistry as an example. After implementation of this guided learning approach student surveys reported a higher level of course satisfaction and there was a statistically significant improvement in the quality of the student work. Therefore we firmly believe the described format to be highly effective in promoting student learning and engagement. © 2013 by The International Union of Biochemistry and Molecular Biology.

Developing a Theory-Driven Model of Community College Student Engagement

ERIC Educational Resources Information Center

Schuetz, Pam

2008-01-01

This article presents a study that uses iterative searches of literature, guided by data from campus observations, interviews, and surveys, to locate and test a theory that can be used to strengthen institutional leverage over student engagement and outcomes. A review of literature was conducted to frame a preliminary theoretical framework for…

Clinical Reasoning in the Assessment and Intervention Planning for Writing Disorder

ERIC Educational Resources Information Center

Harrison, Gina L.; McManus, Kelly L.

2017-01-01

The incidence of writing disorder is as common as reading disorder, but it is frequently under-identified and rarely targeted for intervention. Increasing clinical understanding on various subtypes of writing disorder through assessment guided by data-driven decision making may alleviate this disparity for students with writing disorders. The…

Internal Consistency and Cross-Informant Agreement of the Lithuanian-Translated Behavioral and Emotional Rating Scale

ERIC Educational Resources Information Center

Sointu, Erkko T.; Geležiniene, Renata; Lambert, Matthew C.; Nordness, Philip D.

2015-01-01

Educational professionals need assessments that yield psychometrically sound scores to assess students' behavioral and emotional functioning in order to guide data-driven decision-making processes. Rating scales have been found to be effective and economical, and often multiple informant perspectives can be obtained. The agreement between multiple…

Accelerating the rate of improvement in cystic fibrosis care: contributions and insights of the learning and leadership collaborative.

PubMed

Godfrey, Marjorie M; Oliver, Brant J

2014-04-01

The Learning and Leadership Collaborative (LLC) supports cystic fibrosis (CF) centres' responses to the variation in CF outcomes in the USA. Between 2002 and 2013, the Cystic Fibrosis Foundation (CFF) designed, tested and modified the LLC to guide front line staff efforts in these efforts. This paper describes the CFF LLC evolution and essential elements that have facilitated increased improvement capability of CF centres and improved CF outcomes. CF centre improvement teams across the USA have participated in 11 LLCs of 12 months' duration since 2002. Based on the Dartmouth Microsystem Improvement Curriculum, the original LLC included face to face meetings, an email listserv, conference calls and completion of between learning session task books. The LLCs evolved over time to include internet based learning, an electronic repository of improvement resources and examples, change ideas driven by evidence based clinical practice guidelines, benchmarking site visits, an applied QI measurement curriculum and team coaching. Over 90% of the CF centres in the USA have participated in the LLCs and have increased their improvement capabilities. Ten essential elements were identified as contributors to the successful LLCs: LLC national leadership and coordination, local leadership, people with CF and families involvement, registry data transparency, standardised improvement curriculum with evidence based change ideas, internet resources with reminders, team coaching, regular progress reporting and tracking, benchmarking site visits and applied improvement measurement. The LLCs have contributed to improved medical and process outcomes over the past 10 years. Ten essential elements of the LLCs may benefit improvement efforts in other chronic care populations and health systems.

Task Rotation: Strategies for Differentiating Activities and Assessments by Learning Style. A Strategic Teacher PLC Guide

ERIC Educational Resources Information Center

Silver, Harvey; Moirao, Daniel; Jackson, Joyce

2011-01-01

One of the hardest jobs in teaching is to differentiate learning activities and assessments to your students' learning styles. But you and your colleagues can learn how to do this together when each of you has this guide to the Task Rotation strategy from our ultimate guide to teaching strategies, "The Strategic Teacher". Use the guide in your…

Endogenously and exogenously driven selective sustained attention: Contributions to learning in kindergarten children.

PubMed

Erickson, Lucy C; Thiessen, Erik D; Godwin, Karrie E; Dickerson, John P; Fisher, Anna V

2015-10-01

Selective sustained attention is vital for higher order cognition. Although endogenous and exogenous factors influence selective sustained attention, assessment of the degree to which these factors influence performance and learning is often challenging. We report findings from the Track-It task, a paradigm that aims to assess the contribution of endogenous and exogenous factors to selective sustained attention within the same task. Behavioral accuracy and eye-tracking data on the Track-It task were correlated with performance on an explicit learning task. Behavioral accuracy and fixations to distractors during the Track-It task did not predict learning when exogenous factors supported selective sustained attention. In contrast, when endogenous factors supported selective sustained attention, fixations to distractors were negatively correlated with learning. Similarly, when endogenous factors supported selective sustained attention, higher behavioral accuracy was correlated with greater learning. These findings suggest that endogenously and exogenously driven selective sustained attention, as measured through different conditions of the Track-It task, may support different kinds of learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Discovering Our Delta: A Learning Guide for Community Research. Teacher Guide [and] Student Community Research Guide.

ERIC Educational Resources Information Center

Smithsonian Institution, Washington, DC. Center for Folklife Programs and Cultural Studies.

This teacher guide and student community research guide unit are intended to help students learn to conduct research in their community and to communicate the results of that research to classmates and others. The unit, which can be used in conjunction with a video, helps students learn about community research, oral history, and folklore…

Science on a Sphere and Data in the Classroom: A Marriage Between Limitless Learning Experiences.

NASA Astrophysics Data System (ADS)

Zepecki, S., III; Dean, A. F.; Pisut, D.

2017-12-01

NOAA and other agencies have contributed significantly to the creation and distribution of educational materials to enhance the public understanding of the interconnectedness of the Earth processes and human activities. Intended for two different learning audiences, Science on a Sphere and Data in the Classroom are both educational tools used to enhance understanding of our world and how human activity influences change. Recently, NOAA has undertaken the task of marrying Data in the Classroom's NGSS aligned curriculum, which includes topics such as El Niño, sea level rise, and coral bleaching, with Science on a Sphere's Earth and space data visualization exhibits. This partnership allows for the fluidity of NOAA's data-driven learning materials, and fosters the homogeneity of formal and informal learning experiences for varied audiences.

Learning and exploration in action-perception loops.

PubMed

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Machine learning based cloud mask algorithm driven by radiative transfer modeling

NASA Astrophysics Data System (ADS)

Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.

2017-12-01

Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Children and their 4-H animal projects: How children use science in agricultural activity

NASA Astrophysics Data System (ADS)

Emo, Kenneth Roy

Many children are introduced to science through informal educational programs. 4-H, an educational youth program, has a history of introducing scientific practices into agriculture. The purpose of this ethnographically-driven case study is to examine how science informs the actions of children raising market animals in a 4-H project. For two years the researcher collected data on 4-H children with market animal projects. Observations, interviews, and artifacts gathered are interpreted using the framework of activity theory. This study provides evidence for how the context of an activity system influences individual actions. Rules developed by the organization guide the actions of children to incorporate physical and psychological tools of science into their project to achieve the object: producing animals of proper weight and quality to be competitive in the county fair. Children learn the necessary actions from a community of practitioners through which expertise is distributed. Children's learning is demonstrated by the way their participation in their project changes with time, from receiving assistance from others to developing expertise in which they provide assistance to others. The strength of this educational experience is how children apply specific tools of science in ways that provide meaning and relevancy to their 4-H activity.

Medical Assisting. A Learning Guide.

ERIC Educational Resources Information Center

Meyer, Rosemarie

This competency-based, individualized learning package, consisting of 50 learning guides, is designed for use by students who are studying to become medical assistants. Included among the topics addressed in the individual learning guides are the following: using and caring for microscopes, understanding medical ethics and law, developing…

Diesel Equipment Department. Student Learning Guide.

ERIC Educational Resources Information Center

Palm Beach County Board of Public Instruction, West Palm Beach, FL.

Eleven student learning guides are provided for the duty entitled "completing core curriculum" of the diesel equipment program. Each learning guide concerns one of the tasks that comprise the duty. Introductory materials for each guide include the purpose and performance and enabling objectives. For each enabling objective, these materials are…

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

PubMed Central

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

PubMed

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

PubMed Central

Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-01-01

Background The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. PMID:28903894

Learning partial differential equations via data discovery and sparse optimization

NASA Astrophysics Data System (ADS)

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

Learning partial differential equations via data discovery and sparse optimization.

PubMed

Schaeffer, Hayden

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

Learning partial differential equations via data discovery and sparse optimization

PubMed Central

2017-01-01

We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection. PMID:28265183

Mobile Guide System Using Problem-Solving Strategy for Museum Learning: A Sequential Learning Behavioural Pattern Analysis

ERIC Educational Resources Information Center

Sung, Y.-T.; Hou, H.-T.; Liu, C.-K.; Chang, K.-E.

2010-01-01

Mobile devices have been increasingly utilized in informal learning because of their high degree of portability; mobile guide systems (or electronic guidebooks) have also been adopted in museum learning, including those that combine learning strategies and the general audio-visual guide systems. To gain a deeper understanding of the features and…

Data-Driven Learning of Total and Local Energies in Elemental Boron

NASA Astrophysics Data System (ADS)

Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor

2018-04-01

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β -rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

Data-Driven Learning of Total and Local Energies in Elemental Boron.

PubMed

Deringer, Volker L; Pickard, Chris J; Csányi, Gábor

2018-04-13

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

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.

Medical Assisting Learning Guides.

ERIC Educational Resources Information Center

Meyer, Rose

Eight student learning guides are provided for a medical assisting program at the secondary, postsecondary, or adult level. Each learning guide is composed of these component parts: a title page that states the task, purpose, program and task numbers, estimated time, and prerequisites; an optional learning contract that includes terminal…

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Deep Change: Cases and Commentary on Schools and Programs of Successful Reform in High Stakes States. Research in Curriculum and Instruction

ERIC Educational Resources Information Center

Ponder, Gerald, Ed.; Strahan, David, Ed.

2005-01-01

This book presents cases of schools (Part One) and programs at the district level and beyond (Part Two) in which reform, while driven by high-stakes accountability, became larger and deeper through data-driven dialogue, culture change, organizational learning, and other elements of high performing cultures. Commentaries on cross-case patterns by…

The Development of Teaching and Learning in Bright-Field Microscopy Technique

ERIC Educational Resources Information Center

Iskandar, Yulita Hanum P.; Mahmud, Nurul Ethika; Wahab, Wan Nor Amilah Wan Abdul; Jamil, Noor Izani Noor; Basir, Nurlida

2013-01-01

E-learning should be pedagogically-driven rather than technologically-driven. The objectives of this study are to develop an interactive learning system in bright-field microscopy technique in order to support students' achievement of their intended learning outcomes. An interactive learning system on bright-field microscopy technique was…

Segmentation of thalamus from MR images via task-driven dictionary learning

NASA Astrophysics Data System (ADS)

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D.; Prince, Jerry L.

2016-03-01

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.

Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

PubMed

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D; Prince, Jerry L

2016-02-27

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.

A Novel Data-Driven Learning Method for Radar Target Detection in Nonstationary Environments

DTIC Science & Technology

2016-05-01

Classifier ensembles for changing environments,” in Multiple Classifier Systems, vol. 3077, F. Roli, J. Kittler and T. Windeatt, Eds. New York, NY...Dec. 2006, pp. 1113–1118. [21] J. Z. Kolter and M. A. Maloof, “Dynamic weighted majority: An ensemble method for drifting concepts,” J. Mach. Learn...Trans. Neural Netw., vol. 22, no. 10, pp. 1517–1531, Oct. 2011. [23] R. Polikar, “ Ensemble learning,” in Ensemble Machine Learning: Methods and

Data-driven modeling, control and tools for cyber-physical energy systems

NASA Astrophysics Data System (ADS)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Confirmatory factor analysis of teaching and learning guiding principles instrument among teacher educators in higher education institutions

NASA Astrophysics Data System (ADS)

Masuwai, Azwani; Tajudin, Nor'ain Mohd; Saad, Noor Shah

2017-05-01

The purpose of this study is to develop and establish the validity and reliability of an instrument to generate teaching and learning guiding principles using Teaching and Learning Guiding Principles Instrument (TLGPI). Participants consisted of 171 Malaysian teacher educators. It is an essential instrument to reflect in generating the teaching and learning guiding principles in higher education level in Malaysia. Confirmatory Factor Analysis has validated all 19 items of TLGPI whereby all items indicated high reliability and internal consistency. A Confirmatory Factor Analysis also confirmed that a single factor model was used to generate teaching and learning guiding principles.

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering.

PubMed

Sangavai, C; Chellapandi, P

2017-12-01

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n -butanol, n -butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

Invention activities as preparation for learning laboratory data handling skills

NASA Astrophysics Data System (ADS)

Day, James

2012-10-01

Undergraduate physics laboratories are often driven by a mix of goals, and usually enough of them to cause cognitive overload for the student. Our recent findings align well with studies indicating that students often exit a physics lab without having properly learned how to handle real data. The value of having students explore the underlying structure of a problem before being able to solve it has been shown as an effective way to ready students for learning. Borrowing on findings from the fields of education and cognitive psychology, we use ``invention activities'' to precede direct instruction and bolster learning. In this talk I will show some of what we have learned about students' data handling skills, explain how an invention activity works, and share some observations of successful transfer.

Simulated Students and Classroom Use of Model-Based Intelligent Tutoring

NASA Technical Reports Server (NTRS)

Koedinger, Kenneth R.

2008-01-01

Two educational uses of models and simulations: 1) Students create models and use simulations ; and 2) Researchers create models of learners to guide development of reliably effective materials. Cognitive tutors simulate and support tutoring - data is crucial to create effective model. Pittsburgh Science of Learning Center: Resources for modeling, authoring, experimentation. Repository of data and theory. Examples of advanced modeling efforts: SimStudent learns rule-based model. Help-seeking model: Tutors metacognition. Scooter uses machine learning detectors of student engagement.

Teacher Discourse Strategies Used in Kindergarten Inquiry-Based Science Learning

ERIC Educational Resources Information Center

Harris, Karleah; Crabbe, Jordan Jimmy; Harris, Charlene

2017-01-01

This study examines teacher discourse strategies used in kindergarten inquiry-based science learning as part of the Scientific Literacy Project (SLP) (Mantzicopoulos, Patrick & Samarapungavan, 2005). Four public kindergarten science classrooms were chosen to implement science teaching strategies using a guided-inquiry approach. Data were…

A Case Study about Communication Strategies

ERIC Educational Resources Information Center

Lin, Grace Hui Chin

2011-01-01

The primary purpose of this case study was to identify what were Taiwanese University English as a Foreign Language (EFL) learners' perceptions about learning communication strategies. This study collected qualitative data about students' beliefs and attitudes as they learned communication strategies. The research question guiding the study was:…

Who Owns Educational Theory? Big Data, Algorithms and the Expert Power of Education Data Science

ERIC Educational Resources Information Center

Williamson, Ben

2017-01-01

"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…

Using Student Achievement Data Effectively to Inform Instruction

ERIC Educational Resources Information Center

Bunns, Sandra D.

2012-01-01

The use of student achievement data to improve teaching and learning is a national concern driven by accountability requirements of the No Child Left Behind Act of 2002. Research studies that examine how schools use student achievement data document the need for teachers to connect data to instructional practices. Bruner's social constructivist…

Process-Driven Culture Learning in American KFL Classroom Settings

ERIC Educational Resources Information Center

Byon, Andrew Sangpil

2007-01-01

Teaching second language (L2) culture can be either content- or process-driven. The content-driven approach refers to explicit instruction of L2 cultural information. On the other hand, the process-driven approach focuses on students' active participation in cultural learning processes. In this approach, teachers are not only information…

Comparison of performance due to guided hyperlearning, unguided hyperlearning, and conventional learning in mathematics: an empirical study

NASA Astrophysics Data System (ADS)

Fathurrohman, Maman; Porter, Anne; Worthy, Annette L.

2014-07-01

In this paper, the use of guided hyperlearning, unguided hyperlearning, and conventional learning methods in mathematics are compared. The design of the research involved a quasi-experiment with a modified single-factor multiple treatment design comparing the three learning methods, guided hyperlearning, unguided hyperlearning, and conventional learning. The participants were from three first-year university classes, numbering 115 students in total. Each group received guided, unguided, or conventional learning methods in one of the three different topics, namely number systems, functions, and graphing. The students' academic performance differed according to the type of learning. Evaluation of the three methods revealed that only guided hyperlearning and conventional learning were appropriate methods for the psychomotor aspects of drawing in the graphing topic. There was no significant difference between the methods when learning the cognitive aspects involved in the number systems topic and the functions topic.

Robustness of waves with a high phase velocity

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

Tajima, T., E-mail: ttajima@uci.edu; Tri Alpha Energy, Inc., P.O. Box 7010, Rancho Santa Margarita, CA 92688; Necas, A., E-mail: anecas@trialphaenergy.com

Norman Rostoker pioneered research of (1) plasma-driven accelerators and (2) beam-driven fusion reactors. The collective acceleration, coined by Veksler, advocates to drive above-ionization plasma waves by an electron beam to accelerate ions. The research on this, among others, by the Rostoker group incubated the idea that eventually led to the birth of the laser wakefield acceleration (LWFA), by which a large and robust accelerating collective fields may be generated in plasma in which plasma remains robust and undisrupted. Besides the emergence of LWFA, the Rostoker research spawned our lessons learned on the importance of adiabatic acceleration of ions in collectivemore » accelerators, including the recent rebirth in laser-driven ion acceleration efforts in a smooth adiabatic fashion by a variety of ingenious methods. Following Rostoker’s research in (2), the beam-driven Field Reversed Configuration (FRC) has accomplished breakthroughs in recent years. The beam-driven kinetic plasma instabilities have been found to drive the reactivity of deuteron-deuteron fusion beyond the thermonuclear yield in C-2U plasma that Rostoker started. This remarkable result in FRCs as well as the above mentioned LWFA may be understood with the aid of the newly introduced idea of the “robustness hypothesis of waves with a high phase velocity”. It posits that when the wave driven by a particle beam (or laser pulse) has a high phase velocity, its amplitude is high without disrupting the supporting bulk plasma. This hypothesis may guide us into more robust and efficient fusion reactors and more compact accelerators.« less

Using Tracker as a Pedagogical Tool for Understanding Projectile Motion

ERIC Educational Resources Information Center

Wee, Loo Kang; Chew, Charles; Goh, Giam Hwee; Tan, Samuel; Lee, Tat Leong

2012-01-01

This article reports on the use of Tracker as a pedagogical tool in the effective learning and teaching of projectile motion in physics. When a computer model building learning process is supported and driven by video analysis data, this free Open Source Physics tool can provide opportunities for students to engage in active enquiry-based…

Learning Systems Biology: Conceptual Considerations toward a Web-Based Learning Platform

ERIC Educational Resources Information Center

Emmert-Streib, Frank; Dehmer, Matthias; Lyardet, Fernando

2013-01-01

Within recent years, there is an increasing need to train students, from biology and beyond, in quantitative methods that are relevant to cope with data-driven biology. Systems Biology is such a field that places a particular focus on the functional aspect of biology and molecular interacting processes. This paper deals with the conceptual design…

An Inquiry-Based Contextual Approach as the Primary Mode of Learning Science with Microcomputer-Based Laboratory Technology

ERIC Educational Resources Information Center

Espinoza, Fernando; Quarless, Duncan

2010-01-01

Science instruction can be designed to be laboratory-data driven. We report on an investigation of the use of thematic inquiry-based tasks with active incorporation of mathematics, science, and microcomputer-based laboratory technology in standards-correlated activities that enhanced learning experiences. Activities involved students in two major…

Problematising the Use of Education to Address Social Inequity: Could Participatory Action Research Be a Step Forwards?

ERIC Educational Resources Information Center

Giannakaki, Marina-Stefania; McMillan, Ian David; Karamichas, John

2018-01-01

This paper critiques international trends towards certain school practices aimed at promoting equity and social justice by closing gaps in specific learning outcomes among students. It argues that even though some of these practices (e.g. individualised student support, data-driven leadership) improve learning outcomes for certain groups…

Teacher Empowerment in the Implementation of Response to Intervention: A Case Study

ERIC Educational Resources Information Center

Barge, Evie Taff

2012-01-01

Response to Intervention (RtI) is a data-driven process that supports the academic needs of students through targeted interventions to address specific identified areas of weakness. When implemented effectively, RtI aids students at the onset of learning concerns and can remediate learning problems which have, in the past, led to students being…

Meaningful Dialogue in Digitally Mediated Learning for In-Service Teacher Development

ERIC Educational Resources Information Center

Cramp, Andy

2015-01-01

This paper considers the role and development of meaningful dialogue in digitally mediated learning (DML) in UK higher education for teachers. It argues that more research is vital in the field of meaningful dialogue if we are to avoid the risk that pedagogic values in DML become increasingly driven by market forces toward "data vending"…

Young Children's Development of Scientific Knowledge Through the Combination of Teacher-Guided Play and Child-Guided Play

NASA Astrophysics Data System (ADS)

Sliogeris, Marija; Almeida, Sylvia Christine

2017-09-01

Play-based approaches to science learning allow children to meaningfully draw on their everyday experiences and activities as they explore science concepts in context. Acknowledging the crucial role of the teacher in facilitating science learning through play, the purpose of this qualitative study was to examine how teacher-guided play, in conjunction with child-guided play, supports children's development of science concepts. While previous research on play-based science learning has mainly focused on preschool settings, this study explores the possibilities of play-based approaches to science in primary school contexts. Using a qualitative methodology grounded in the cultural-historical theoretical perspective, children's learning was examined during a science learning sequence that combined teacher-guided and child-guided play. This study revealed that the teacher-guided play explicitly introduced science concepts which children then used and explored in subsequent child-guided play. However, intentional teaching during the child-guided play continued to be important. Play-based approaches to science allowed children to make sense of the science concepts using familiar, everyday knowledge and activities. It became evident that the expectations and values communicated through classroom practices influenced children's learning through play.

Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

PubMed

Lasko, Thomas A; Denny, Joshua C; Levy, Mia A

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies.

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

PubMed Central

Lasko, Thomas A.; Denny, Joshua C.; Levy, Mia A.

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies. PMID:23826094

The effects of a flexible visual acuity-driven ranibizumab treatment regimen in age-related macular degeneration: outcomes of a drug and disease model.

PubMed

Holz, Frank G; Korobelnik, Jean-François; Lanzetta, Paolo; Mitchell, Paul; Schmidt-Erfurth, Ursula; Wolf, Sebastian; Markabi, Sabri; Schmidli, Heinz; Weichselberger, Andreas

2010-01-01

Differences in treatment responses to ranibizumab injections observed within trials involving monthly (MARINA and ANCHOR studies) and quarterly (PIER study) treatment suggest that an individualized treatment regimen may be effective in neovascular age-related macular degeneration. In the present study, a drug and disease model was used to evaluate the impact of an individualized, flexible treatment regimen on disease progression. For visual acuity (VA), a model was developed on the 12-month data from ANCHOR, MARINA, and PIER. Data from untreated patients were used to model patient-specific disease progression in terms of VA loss. Data from treated patients from the period after the three initial injections were used to model the effect of predicted ranibizumab vitreous concentration on VA loss. The model was checked by comparing simulations of VA outcomes after monthly and quarterly injections during this period with trial data. A flexible VA-guided regimen (after the three initial injections) in which treatment is initiated by loss of >5 letters from best previously observed VA scores was simulated. Simulated monthly and quarterly VA-guided regimens showed good agreement with trial data. Simulation of VA-driven individualized treatment suggests that this regimen, on average, sustains the initial gains in VA seen in clinical trials at month 3. The model predicted that, on average, to maintain initial VA gains, an estimated 5.1 ranibizumab injections are needed during the 9 months after the three initial monthly injections, which amounts to a total of 8.1 injections during the first year. A flexible, individualized VA-guided regimen after the three initial injections may sustain vision improvement with ranibizumab and could improve cost-effectiveness and convenience and reduce drug administration-associated risks.

The guided autobiography method: a learning experience.

PubMed

Thornton, James E

2008-01-01

This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in dynamic, temporary social groups. The article is organized in four sections and summary. The first section provides a brief overview of the guided autobiography method describing the interplay of learning and experiencing in temporary social groups. The second section offers a limited review on learning and experiencing as processes that are essential for development, growth, and change. The third section reviews the small group activities and the emergence of the "developmental exchange" in the guided autobiography method. Two theoretical constructs provide a conceptual foundation for the developmental exchange: a counterpart theory of aging as development and collaborative-situated group learning theory. The summary recaps the main ideas and issues that shape the guided autobiography method as learning and social experience using the theme, "Where to go from here."

Specialized Motor-Driven dusp1 Expression in the Song Systems of Multiple Lineages of Vocal Learning Birds

PubMed Central

Horita, Haruhito; Kobayashi, Masahiko; Liu, Wan-chun; Oka, Kotaro; Jarvis, Erich D.; Wada, Kazuhiro

2012-01-01

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits. PMID:22876306

Optogenetic Inhibition of Ventral Pallidum Neurons Impairs Context-Driven Salt Seeking.

PubMed

Chang, Stephen E; Smedley, Elizabeth B; Stansfield, Katherine J; Stott, Jeffrey J; Smith, Kyle S

2017-06-07

Salt appetite, in which animals can immediately seek out salt when under a novel state of sodium deprivation, is a classic example of how homeostatic systems interface with learned associations to produce an on-the-fly updating of motivated behavior. Neural activity in the ventral pallidum (VP) has been shown to encode changes in the value of salt under such conditions, both the value of salt itself (Tindell et al., 2006) and the motivational value of its predictive cues (Tindell et al., 2009; Robinson and Berridge, 2013). However, it is not known whether the VP is necessary for salt appetite in terms of seeking out salt or consuming salt following sodium depletion. Here, we used a conditioned place-preference procedure to investigate the effects of optogenetically inhibiting the VP on context-driven salt seeking and the consumption of salt following deprivation. Male rats learned to associate one context with sucrose and another context with less-desirable salt. Following sodium depletion, and in the absence of either sucrose or salt, we found that inhibiting the VP selectively reduced the elevation in time spent in the salt-paired context. VP inhibition had minimal effects on the consumption of salt once it was made available. To our knowledge, this is the first evidence that the VP or any brain region is necessary for the ability to use contextual cues to guide salt seeking. These results highlight a dissociation between deficit-driven reward seeking and reward consumption to replenish those deficits, with the former process being particularly sensitive to on-line VP activity. SIGNIFICANCE STATEMENT Salt appetite, in which rats will immediately seek out a once-undesirable concentrated salt solution after being depleted of bodily sodium despite never having tasted salt as a positive reward, is a phenomenon showing how animals can update their motivational goals without any new learning or conditioning. This salt-seeking behavior is also observed when the animal is presented with salt-paired cues. The neural circuitry necessary for context-driven salt-seeking behavior is unknown. We used a novel conditioned place preference procedure to show that optogenetic inhibition of the ventral pallidum (VP), a region known for processing reward, impairs context-driven salt seeking and has minimal effects on the consumption of salt itself following sodium depletion. These results highlight the importance of the VP in context-driven reward-seeking behavior. Copyright © 2017 the authors 0270-6474/17/375670-11$15.00/0.

Response-Guided Community Detection: Application to Climate Index Discovery

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

Bello, Gonzalo; Angus, Michael; Pedemane, Navya

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability.more » Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.« less

Mitigating Mosquito Disease Vectors with Citizen Science: a Review of the GLOBE Observer Mosquito Habitat Mapper Pilot and Implications for Wide-scale Implementation

NASA Astrophysics Data System (ADS)

Riebeek Kohl, H.; Low, R.; Boger, R. A.; Schwerin, T. G.; Janney, D. W.

2017-12-01

The spread of disease vectors, including mosquitoes, is an increasingly significant global environmental issue driven by a warming climate. In 2017, the GLOBE Observer Program launched a new citizen science initiative to map mosquito habitats using the free GLOBE Observer App for smart phones and tablets. The app guides people to identify mosquito larvae and breeding sites, and then once documented, to eliminate or treat the site to prevent further breeding. It also gives citizen scientists the option to identify the mosquito larvae species to determine whether it is one of three genera that potentially could transmit Zika, dengue fever, yellow fever, chikungunya, and other diseases. This data is uploaded to an international database that is freely available to the public and science community. GLOBE Observer piloted the initiative with educators in the United States, Brazil, and Peru, and it is now open for global participation. This presentation will discuss lessons learned in the pilot phase as well as plans to implement the initiative worldwide in partnership with science museums and science centers. GLOBE Observer is the non-student citizen science arm of the Global Learning and Observations to Benefit the Environment (GLOBE) Program, a long-standing, international science and education program that provides students and citizen scientists with the opportunity to participate in data collection and the scientific process, and contribute meaningfully to our understanding of the Earth system and global environment. GLOBE Observer data collection also includes cloud cover and cloud type and land cover/land use (in late 2017).

Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

PubMed Central

Gao, Yaozong; Zhan, Yiqiang

2015-01-01

Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ∼0.89) and fast (∼4 s), which satisfies the real-world clinical requirements of IGRT. PMID:24495983

Use of Data to Support Teaching and Learning: A Case Study of Two School Districts. ACT Research Report Series, 2015 (1)

ERIC Educational Resources Information Center

Dougherty, Chrys

2015-01-01

This report summarizes how school and district leaders and academic coaches in two Texas school districts used assessment and other types of data to assess the quality of teaching and learning, to coach and supervise teachers, and to guide management decisions. The report also describes how district and school leaders supported teachers' use of…

Guidance of spatial attention by incidental learning and endogenous cuing

PubMed Central

Jiang, Yuhong V.; Swallow, Khena M.; Rosenbaum, Gail M.

2012-01-01

Our visual system is highly sensitive to regularities in the environment. Locations that were important in one’s previous experience are often prioritized during search, even though observers may not be aware of the learning. In this study we characterized the guidance of spatial attention by incidental learning of a target’s spatial probability, and examined the interaction between endogenous cuing and probability cuing. Participants searched for a target (T) among distractors (L’s). The target was more often located in one region of the screen than in others. We found that search RT was faster when the target appeared in the high-frequency region rather than the low-frequency regions. This difference increased when there were more items on the display, suggesting that probability cuing guides spatial attention. Additional data indicated that on their own, probability cuing and endogenous cuing (e.g., a central arrow that predicted a target’s location) were similarly effective at guiding attention. However, when both cues were presented at once, probability cuing was largely eliminated. Thus, although both incidental learning and endogenous cuing can effectively guide attention, endogenous cuing takes precedence over incidental learning. PMID:22506784

Use of the cumulative sum method (CUSUM) to assess the learning curves of ultrasound-guided continuous femoral nerve block.

PubMed

Kollmann-Camaiora, A; Brogly, N; Alsina, E; Gilsanz, F

2017-10-01

Although ultrasound is a basic competence for anaesthesia residents (AR) there is few data available on the learning process. This prospective observational study aims to assess the learning process of ultrasound-guided continuous femoral nerve block and to determine the number of procedures that a resident would need to perform in order to reach proficiency using the cumulative sum (CUSUM) method. We recruited 19 AR without previous experience. Learning curves were constructed using the CUSUM method for ultrasound-guided continuous femoral nerve block considering 2 success criteria: a decrease of pain score>2 in a [0-10] scale after 15minutes, and time required to perform it. We analyse data from 17 AR for a total of 237 ultrasound-guided continuous femoral nerve blocks. 8/17 AR became proficient for pain relief, however all the AR who did more than 12 blocks (8/8) became proficient. As for time of performance 5/17 of AR achieved the objective of 12minutes, however all the AR who did more than 20 blocks (4/4) achieved it. The number of procedures needed to achieve proficiency seems to be 12, however it takes more procedures to reduce performance time. The CUSUM methodology could be useful in training programs to allow early interventions in case of repeated failures, and develop competence-based curriculum. Copyright © 2017 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

The Effectiveness of Guided Inquiry Learning for Comparison Topics

NASA Astrophysics Data System (ADS)

Asnidar; Khabibah, S.; Sulaiman, R.

2018-01-01

This research aims at producing a good quality learning device using guided inquiry for comparison topics and describing the effectiveness of guided inquiry learning for comparison topics. This research is a developmental research using 4-D model. The result is learning device consisting of lesson plan, student’s worksheet, and achievement test. The subjects of the study were class VII students, each of which has 46 students. Based on the result in the experimental class, the learning device using guided inquiry for comparison topics has good quality. The learning device has met the valid, practical, and effective aspects. The result, especially in the implementation class, showed that the learning process with guided inquiry has fulfilled the effectiveness indicators. The ability of the teacher to manage the learning process has fulfilled the criteria good. In addition, the students’ activity has fulfilled the criteria of, at least, good. Moreover, the students’ responses to the learning device and the learning activities were positive, and the students were able to complete the classical learning. Based on the result of this research, it is expected that the learning device resulted can be used as an alternative learning device for teachers in implementing mathematic learning for comparison topics.

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

PubMed

Martínez-Martínez, F; Rupérez-Moreno, M J; Martínez-Sober, M; Solves-Llorens, J A; Lorente, D; Serrano-López, A J; Martínez-Sanchis, S; Monserrat, C; Martín-Guerrero, J D

2017-11-01

This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (<0.2 s). Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploring E-Learning. IES Report 376.

ERIC Educational Resources Information Center

Pollard, E.; Hillage, J.

This guide summarizes current research and commentary on e-learning, examining the key issues facing organizations exploring e-learning for employee development. The guide contains six sections. The first section provides an introduction to the issue of e-learning and a summary of the issues discussed in the remainder of the guide. Section 2…

Industrial Electrical Maintenance Learning Guides and Task Listing by Occupational Titles.

ERIC Educational Resources Information Center

Whitmer, Melvin

Seven student learning guides are provided for an industrial electrical maintenance program at the secondary, postsecondary, or adult level. Each learning guide is composed of these component parts: a title page that states the task, purpose, program and task numbers, estimated time, and prerequisites; an optional learning contract that includes…

Validation of "Teaching and Learning Guiding Principles Instrument" for Malaysian Higher Learning Institutions

ERIC Educational Resources Information Center

Rahman, Nurulhuda Abd; Masuwai, Azwani; Tajudin, Nor'ain Mohd; Tek, Ong Eng; Adnan, Mazlini

2016-01-01

Purpose: This study was aimed at establishing, through the validation of the "Teaching and Learning Guiding Principles Instrument" (TLGPI), the validity and reliability of the underlying factor structure of the Teaching and Learning Guiding Principles (TLGP) generated by a previous study. Method: A survey method was used to collect data…

Learning clinically useful information from images: Past, present and future.

PubMed

Rueckert, Daniel; Glocker, Ben; Kainz, Bernhard

2016-10-01

Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work. Copyright © 2016 Elsevier B.V. All rights reserved.

Managing Home and Work Responsibilities. Secondary Learning Guide 9. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on managing home and work responsibilities is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve…

Strengthening Parenting Skills: Teenagers. Secondary Learning Guide 3. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on strengthening parenting skills is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems;…

Progressive Assessment of Student Engagement with Web-Based Guided Learning

ERIC Educational Resources Information Center

Katuk, Norliza

2013-01-01

Purpose: The purpose of this research is to investigate student engagement in guided web-based learning systems. It looks into students' engagement and their behavioral patterns in two types of guided learning systems (i.e. a fully- and a partially-guided). The research also aims to demonstrate how the engagement evolves from the…

Making Consumer Choices. Secondary Learning Guide 6. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on making consumer choices is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems; establish…

Strengthening Parenting Skills: School Age. Secondary Learning Guide 2. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on strengthening parenting skills is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems;…

Conserving Limited Resources. Secondary Learning Guide 14. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on conserving limited resources is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems; establish…

Preventing Teen Pregnancy. Secondary Learning Guide 4. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on preventing teen pregnancy is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems; establish…

Balancing Work and Family. Secondary Learning Guide 5. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on balancing work and family is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems; establish…

Assisting At-Risk Populations. Secondary Learning Guide 11. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on assisting at-risk populations (dropouts and homeless people) is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative…

The effect of inquiry-flipped classroom model toward students' achievement on chemical reaction rate

NASA Astrophysics Data System (ADS)

Paristiowati, Maria; Fitriani, Ella; Aldi, Nurul Hanifah

2017-08-01

The aim of this research is to find out the effect of Inquiry-Flipped Classroom Models toward Students' Achievement on Chemical Reaction Rate topic. This study was conducted at SMA Negeri 3 Tangerang in Eleventh Graders. The Quasi Experimental Method with Non-equivalent Control Group design was implemented in this study. 72 students as the sample was selected by purposive sampling. Students in experimental group were learned through inquiry-flipped classroom model. Meanwhile, in control group, students were learned through guided inquiry learning model. Based on the data analysis, it can be seen that there is significant difference in the result of the average achievement of the students. The average achievement of the students in inquiry-flipped classroom model was 83,44 and the average achievement of the students in guided inquiry learning model was 74,06. It can be concluded that the students' achievement with inquiry-flipped classroom better than guided inquiry. The difference of students' achievement were significant through t-test which is tobs 3.056 > ttable 1.994 (α = 0.005).

The Principal's Role in Supporting Learning Communities

ERIC Educational Resources Information Center

Hord, Shirley M.; Hirsh, Stephanie A.

2009-01-01

In this article, the authors discuss the following approaches which principals have found support strong learning communities: (1) Emphasize to teachers that you know they can succeed--together; (2) Expect teachers to keep knowledge fresh; (3) Guide communities toward self-governance; (4) Make data accessible; (5) Teach discussion and…

Knowledge Creation through User-Guided Data Mining: A Database Case

ERIC Educational Resources Information Center

Steiger, David M.

2008-01-01

This case focuses on learning by applying the four integrating mechanisms of Nonaka's knowledge creation theory: socialization, externalization, combination and internalization. In general, such knowledge creation and internalization (i.e., learning) is critical to database students since they will be expected to apply their specialized database…

An evidence-based approach to nurses week celebrations.

PubMed

Hensinger, Barbara; Parry, Juanita; Calarco, Margaret M; Fuhrmann, Sarah

2008-04-01

It is time to examine nurses week investments. With expenses increasingly scrutinized, healthcare leaders require data-driven decisions. Managing by instinct and intuition is both inadequate and reckless. This survey of 727 registered nurses identifies celebratory options for nurses week that nurses find meaningful. Knowing what registered nurses value will guide approaches to an effective nurses week activity planning.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Cross-modal learning to rank via latent joint representation.

PubMed

Wu, Fei; Jiang, Xinyang; Li, Xi; Tang, Siliang; Lu, Weiming; Zhang, Zhongfei; Zhuang, Yueting

2015-05-01

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.

Predicting psychosis across diagnostic boundaries: Behavioral and computational modeling evidence for impaired reinforcement learning in schizophrenia and bipolar disorder with a history of psychosis.

PubMed

Strauss, Gregory P; Thaler, Nicholas S; Matveeva, Tatyana M; Vogel, Sally J; Sutton, Griffin P; Lee, Bern G; Allen, Daniel N

2015-08-01

There is increasing evidence that schizophrenia (SZ) and bipolar disorder (BD) share a number of cognitive, neurobiological, and genetic markers. Shared features may be most prevalent among SZ and BD with a history of psychosis. This study extended this literature by examining reinforcement learning (RL) performance in individuals with SZ (n = 29), BD with a history of psychosis (BD+; n = 24), BD without a history of psychosis (BD-; n = 23), and healthy controls (HC; n = 24). RL was assessed through a probabilistic stimulus selection task with acquisition and test phases. Computational modeling evaluated competing accounts of the data. Each participant's trial-by-trial decision-making behavior was fit to 3 computational models of RL: (a) a standard actor-critic model simulating pure basal ganglia-dependent learning, (b) a pure Q-learning model simulating action selection as a function of learned expected reward value, and (c) a hybrid model where an actor-critic is "augmented" by a Q-learning component, meant to capture the top-down influence of orbitofrontal cortex value representations on the striatum. The SZ group demonstrated greater reinforcement learning impairments at acquisition and test phases than the BD+, BD-, and HC groups. The BD+ and BD- groups displayed comparable performance at acquisition and test phases. Collapsing across diagnostic categories, greater severity of current psychosis was associated with poorer acquisition of the most rewarding stimuli as well as poor go/no-go learning at test. Model fits revealed that reinforcement learning in SZ was best characterized by a pure actor-critic model where learning is driven by prediction error signaling alone. In contrast, BD-, BD+, and HC were best fit by a hybrid model where prediction errors are influenced by top-down expected value representations that guide decision making. These findings suggest that abnormalities in the reward system are more prominent in SZ than BD; however, current psychotic symptoms may be associated with reinforcement learning deficits regardless of a Diagnostic and Statistical Manual of Mental Disorders (5th Edition; American Psychiatric Association, 2013) diagnosis. (c) 2015 APA, all rights reserved).

Data Driven Professional Development Design for Out-of-School Time Educators Using Planetary Science and Engineering Educational Materials

NASA Astrophysics Data System (ADS)

Clark, J.; Bloom, N.

2017-12-01

Data driven design practices should be the basis for any effective educational product, particularly those used to support STEM learning and literacy. Planetary Learning that Advances the Nexus of Engineering, Technology, and Science (PLANETS) is a five-year NASA-funded (NNX16AC53A) interdisciplinary and cross-institutional partnership to develop and disseminate STEM out-of-school time (OST) curricular and professional development units that integrate planetary science, technology, and engineering. The Center for Science Teaching and Learning at Northern Arizona University, the U.S. Geological Survey Astrogeology Science Center, and the Museum of Science Boston are partners in developing, piloting, and researching the impact of three out of school time units. Two units are for middle grades youth and one is for upper elementary aged youth. The presentation will highlight the data driven development process of the educational products used to provide support for educators teaching these curriculum units. This includes how data from the project needs assessment, curriculum pilot testing, and professional support product field tests are used in the design of products for out of school time educators. Based on data analysis, the project is developing and testing four tiers of professional support for OST educators. Tier 1 meets the immediate needs of OST educators to teach curriculum and include how-to videos and other direct support materials. Tier 2 provides additional content and pedagogical knowledge and includes short content videos designed to specifically address the content of the curriculum. Tier 3 elaborates on best practices in education and gives guidance on methods, for example, to develop cultural relevancy for underrepresented students. Tier 4 helps make connections to other NASA or educational products that support STEM learning in out of school settings. Examples of the tiers of support will be provided.

Non-daily pre-exposure prophylaxis for HIV prevention

PubMed Central

Anderson, Peter L.; García-Lerma, J. Gerardo; Heneine, Walid

2015-01-01

Purpose of review To discuss non-daily pre-exposure prophylaxis (PrEP) modalities that may provide advantages compared with daily PrEP in cost and cumulative toxicity, but may have lower adherence forgiveness. Recent Findings Animal models have informed our understanding of early viral transmission events, which help guide event-driven PrEP dosing strategies. These models indicate early establishment of viral replication in rectal or cervicovaginal tissues, so event-driven PrEP should rapidly deliver high mucosal drug concentrations within hours of the potential exposure event. Macaque models have demonstrated the high biological efficacy for event-driven dosing of oral tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) against both vaginal and rectal virus transmission. In humans, the IPERGAY study demonstrated 86% efficacy for event-driven oral TDF/FTC dosing among men who have sex with men (MSM), while no similar efficacy data are available on women or heterosexual men. The HPTN 067 study showed that certain MSM populations adhere well to non-daily PrEP while other populations of women adhere more poorly to non-daily versus daily regimens. Pharmacokinetic studies following oral TDF/FTC dosing in humans, indicate that TFV-diphosphate (the active form of TFV) accumulates to higher concentrations in rectal versus cervicovaginal tissue but non-adherence in trials complicates the interpretation of differential mucosal drug concentrations. Summary Event-driven dosing for TFV-based PrEP has promise for HIV prevention in MSM. Future research of event-driven PrEP in women and heterosexual men should be guided by a better understanding of the importance of mucosal drug concentrations for PrEP efficacy and its sensitivity to adherence. PMID:26633641

Health Care Transformation: A Strategy Rooted in Data and Analytics.

PubMed

Koster, John; Stewart, Elizabeth; Kolker, Eugene

2016-02-01

Today's consumers purchasing any product or service are armed with information and have high expectations. They expect service providers and payers to know about their unique needs. Data-driven decisions can help organizations meet those expectations and fulfill those needs.Health care, however, is not strictly a retail relationship-the sacred trust between patient and doctor, the clinician-patient relationship, must be preserved. The opportunities and challenges created by the digitization of health care are at the crux of the most crucial strategic decisions for academic medicine. A transformational vision grounded in data and analytics must guide health care decisions and actions.In this Commentary, the authors describe three examples of the transformational force of data and analytics to improve health care in order to focus attention on academic medicine's vital role in guiding the needed changes.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

PubMed

Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

2016-01-01

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

42 CFR 418.58 - Condition of participation: Quality assessment and performance improvement.

Code of Federal Regulations, 2013 CFR

2013-10-01

.... The hospice must develop, implement, and maintain an effective, ongoing, hospice-wide data-driven... learning throughout the hospice. (3) The hospice must take actions aimed at performance improvement and...

42 CFR 418.58 - Condition of participation: Quality assessment and performance improvement.

Code of Federal Regulations, 2014 CFR

2014-10-01

.... The hospice must develop, implement, and maintain an effective, ongoing, hospice-wide data-driven... learning throughout the hospice. (3) The hospice must take actions aimed at performance improvement and...

42 CFR 418.58 - Condition of participation: Quality assessment and performance improvement.

Code of Federal Regulations, 2012 CFR

2012-10-01

.... The hospice must develop, implement, and maintain an effective, ongoing, hospice-wide data-driven... learning throughout the hospice. (3) The hospice must take actions aimed at performance improvement and...

Personally Driven Professional Development: Reflective Self-Study as a Way for Teachers to Take Control of Their Own Professional Development

ERIC Educational Resources Information Center

Attard, Karl

2017-01-01

This article is about personally driven professional development through the use of reflective self-study. The argument that teachers need to take responsibility for their own learning while also taking decisions on how and in what areas to develop is strongly made throughout the article. Data for this article were gathered over a 10-year period…

Data-driven discovery of Koopman eigenfunctions using deep learning

NASA Astrophysics Data System (ADS)

Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

Perspective: Interactive material property databases through aggregation of literature data

NASA Astrophysics Data System (ADS)

Seshadri, Ram; Sparks, Taylor D.

2016-05-01

Searchable, interactive, databases of material properties, particularly those relating to functional materials (magnetics, thermoelectrics, photovoltaics, etc.) are curiously missing from discussions of machine-learning and other data-driven methods for advancing new materials discovery. Here we discuss the manual aggregation of experimental data from the published literature for the creation of interactive databases that allow the original experimental data as well additional metadata to be visualized in an interactive manner. The databases described involve materials for thermoelectric energy conversion, and for the electrodes of Li-ion batteries. The data can be subject to machine-learning, accelerating the discovery of new materials.

Data That Drive: Closing the Loop in the Learning Hospital System

PubMed Central

Liu, Vincent X.; Morehouse, John W.; Baker, Jennifer M.; Greene, John D.; Kipnis, Patricia; Escobar, Gabriel J.

2017-01-01

The learning healthcare system describes a vision of US healthcare that capitalizes on science, information technology, incentives, and care culture to drive improvements in the quality of health care. The inpatient setting, one of the most costly and impactful domains of healthcare, is an ideal setting in which to use data and information technology to foster continuous learning and quality improvement. The rapid digitization of inpatient medicine offers incredible new opportunities to use data from routine care to generate new discovery and thus close the virtuous cycle of learning. We use an object lesson—sepsis care within the 21 hospitals of the Kaiser Permanente Northern California integrated healthcare delivery system—to offer insight into the critical elements necessary for developing a learning hospital system. We then describe how a hospital-wide data-driven approach to inpatient care can facilitate improvements in the quality of hospital care. PMID:27805797

Forum Guide to Facilities Information Management: A Resource for State and Local Education Agencies. NFES 2012-808

ERIC Educational Resources Information Center

National Forum on Education Statistics, 2012

2012-01-01

Safe and secure facilities that foster learning are crucial to providing quality education services, and developing and maintaining these facilities requires considerable resources and organization. Facility information systems allow education organizations to collect and manage data that can be used to inform and guide decisionmaking about the…

Effect of Guided Collaboration on General and Special Educators' Perceptions of Collaboration and Student Achievement

ERIC Educational Resources Information Center

Laine, Sandra

2013-01-01

This study investigated the effects of a guided collaboration approach during professional learning community meetings (PLC's) on the perceptions of general and special educators as well as the effect on student performance as measured by benchmark evaluation. A mixed methodology approach was used to collect data through surveys, weekly…

A common neural circuit mechanism for internally guided and externally reinforced forms of motor learning.

PubMed

Hisey, Erin; Kearney, Matthew Gene; Mooney, Richard

2018-04-01

The complex skills underlying verbal and musical expression can be learned without external punishment or reward, indicating their learning is internally guided. The neural mechanisms that mediate internally guided learning are poorly understood, but a circuit comprising dopamine-releasing neurons in the midbrain ventral tegmental area (VTA) and their targets in the basal ganglia are important to externally reinforced learning. Juvenile zebra finches copy a tutor song in a process that is internally guided and, in adulthood, can learn to modify the fundamental frequency (pitch) of a target syllable in response to external reinforcement with white noise. Here we combined intersectional genetic ablation of VTA neurons, reversible blockade of dopamine receptors in the basal ganglia, and singing-triggered optogenetic stimulation of VTA terminals to establish that a common VTA-basal ganglia circuit enables internally guided song copying and externally reinforced syllable pitch learning.

Three pedagogical approaches to introductory physics labs and their effects on student learning outcomes

NASA Astrophysics Data System (ADS)

Chambers, Timothy

This dissertation presents the results of an experiment that measured the learning outcomes associated with three different pedagogical approaches to introductory physics labs. These three pedagogical approaches presented students with the same apparatus and covered the same physics content, but used different lab manuals to guide students through distinct cognitive processes in conducting their laboratory investigations. We administered post-tests containing multiple-choice conceptual questions and free-response quantitative problems one week after students completed these laboratory investigations. In addition, we collected data from the laboratory practical exam taken by students at the end of the semester. Using these data sets, we compared the learning outcomes for the three curricula in three dimensions of ability: conceptual understanding, quantitative problem-solving skill, and laboratory skills. Our three pedagogical approaches are as follows. Guided labs lead students through their investigations via a combination of Socratic-style questioning and direct instruction, while students record their data and answers to written questions in the manual during the experiment. Traditional labs provide detailed written instructions, which students follow to complete the lab objectives. Open labs provide students with a set of apparatus and a question to be answered, and leave students to devise and execute an experiment to answer the question. In general, we find that students performing Guided labs perform better on some conceptual assessment items, and that students performing Open labs perform significantly better on experimental tasks. Combining a classical test theory analysis of post-test results with in-lab classroom observations allows us to identify individual components of the laboratory manuals and investigations that are likely to have influenced the observed differences in learning outcomes associated with the different pedagogical approaches. Due to the novel nature of this research and the large number of item-level results we produced, we recommend additional research to determine the reproducibility of our results. Analyzing the data with item response theory yields additional information about the performance of our students on both conceptual questions and quantitative problems. We find that performing lab activities on a topic does lead to better-than-expected performance on some conceptual questions regardless of pedagogical approach, but that this acquired conceptual understanding is strongly context-dependent. The results also suggest that a single "Newtonian reasoning ability" is inadequate to explain student response patterns to items from the Force Concept Inventory. We develop a framework for applying polytomous item response theory to the analysis of quantitative free-response problems and for analyzing how features of student solutions are influenced by problem-solving ability. Patterns in how students at different abilities approach our post-test problems are revealed, and we find hints as to how features of a free-response problem influence its item parameters. The item-response theory framework we develop provides a foundation for future development of quantitative free-response research instruments. Chapter 1 of the dissertation presents a brief history of physics education research and motivates the present study. Chapter 2 describes our experimental methodology and discusses the treatments applied to students and the instruments used to measure their learning. Chapter 3 provides an introduction to the statistical and analytical methods used in our data analysis. Chapter 4 presents the full data set, analyzed using both classical test theory and item response theory. Chapter 5 contains a discussion of the implications of our results and a data-driven analysis of our experimental methods. Chapter 6 describes the importance of this work to the field and discusses the relevance of our research to curriculum development and to future work in physics education research.

The Development and Validation of an Instrument to Monitor the Implementation of Social Constructivist Learning Environments in Grade 9 Science Classrooms in South Africa

ERIC Educational Resources Information Center

Luckay, Melanie B.; Laugksch, Rudiger C.

2015-01-01

This article describes the development and validation of an instrument that can be used to assess students' perceptions of their learning environment as a means of monitoring and guiding changes toward social constructivist learning environments. The study used a mixed-method approach with priority given to the quantitative data collection. During…

A deep learning method for lincRNA detection using auto-encoder algorithm.

PubMed

Yu, Ning; Yu, Zeng; Pan, Yi

2017-12-06

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.

Peer-Led Guided in Calculus at University of South Florida

ERIC Educational Resources Information Center

Bénéteau, Catherine; Fox, Gordon; Xu, Xiaoying; Lewis, Jennifer E.; Ramachandran, Kandethody; Campbell, Scott; Holcomb, John

2016-01-01

This paper describes the development of a Peer-Led Guided Inquiry (PLGI) program for teaching calculus at the University of South Florida. This approach uses the POGIL (Process Oriented Guided Inquiry Learning) teaching strategy and the small group learning model PLTL (Peer-Led Team Learning). The developed materials used a learning cycle based on…

Nebraska Work Based Learning Manual. Planning and Implementation Guides for Educators, Employers, Policymakers, and Parents.

ERIC Educational Resources Information Center

Nebraska State Dept. of Education, Lincoln.

This manual contains a series of 10 detailed guides for school practitioners who are beginning to create work-based learning programs at their schools. Work-Based Learning Overview defines the different elements of work-based learning and describes the roles of program participants. Program Planning Guide offers suggestions about how to plan…

Education Leaders' Guide to Transforming Student and Learning Supports. A Center Guide

ERIC Educational Resources Information Center

Center for Mental Health in Schools at UCLA, 2014

2014-01-01

New directions for student and learning supports are key to systemically addressing barriers to learning and teaching. The aim is to unify and then develop a comprehensive and equitable system of student/learning supports at every school. This guide incorporates years of research and prototype development and a variety of examples from…

Self-Regulated Learning Strategies of Engineering College Students While Learning Electric Circuit Concepts with Enhanced Guided Notes

ERIC Educational Resources Information Center

Lawanto, Oenardi; Santoso, Harry

2013-01-01

The current study evaluated engineering college students' self-regulated learning (SRL) strategies while learning electric circuit concepts using enhanced guided notes (EGN). Our goal was to describe how students exercise SRL strategies and how their grade performance changes after using EGN. Two research questions guided the study: (1) To what…

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

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

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Improving Responses to Individual and Family Crises. Secondary Learning Guide 10. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on improving responses to crises is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve problems;…

Improving Individual, Child, and Family Nutrition, Health and Wellness. Secondary Learning Guide 8. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on improving individual, child, and family nutrition is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to…

Applying Consumer and Homemaking Skills to Jobs and Careers. Secondary Learning Guide 13. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on applying consumer and homemaking skills to jobs and careers is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative…

Educational interactive multimedia software: The impact of interactivity on learning

NASA Astrophysics Data System (ADS)

Reamon, Derek Trent

This dissertation discusses the design, development, deployment and testing of two versions of educational interactive multimedia software. Both versions of the software are focused on teaching mechanical engineering undergraduates about the fundamentals of direct-current (DC) motor physics and selection. The two versions of Motor Workshop software cover the same basic materials on motors, but differ in the level of interactivity between the students and the software. Here, the level of interactivity refers to the particular role of the computer in the interaction between the user and the software. In one version, the students navigate through information that is organized by topic, reading text, and viewing embedded video clips; this is referred to as "low-level interactivity" software because the computer simply presents the content. In the other version, the students are given a task to accomplish---they must design a small motor-driven 'virtual' vehicle that competes against computer-generated opponents. The interaction is guided by the software which offers advice from 'experts' and provides contextual information; we refer to this as "high-level interactivity" software because the computer is actively participating in the interaction. The software was used in two sets of experiments, where students using the low-level interactivity software served as the 'control group,' and students using the highly interactive software were the 'treatment group.' Data, including pre- and post-performance tests, questionnaire responses, learning style characterizations, activity tracking logs and videotapes were collected for analysis. Statistical and observational research methods were applied to the various data to test the hypothesis that the level of interactivity effects the learning situation, with higher levels of interactivity being more effective for learning. The results show that both the low-level and high-level interactive versions of the software were effective in promoting learning about the subject of motors. The focus of learning varied between users of the two versions, however. The low-level version was more effective for teaching concepts and terminology, while the high-level version seemed to be more effective for teaching engineering applications.

Frontal Theta Reflects Uncertainty and Unexpectedness during Exploration and Exploitation

PubMed Central

Figueroa, Christina M.; Cohen, Michael X; Frank, Michael J.

2012-01-01

In order to understand the exploitation/exploration trade-off in reinforcement learning, previous theoretical and empirical accounts have suggested that increased uncertainty may precede the decision to explore an alternative option. To date, the neural mechanisms that support the strategic application of uncertainty-driven exploration remain underspecified. In this study, electroencephalography (EEG) was used to assess trial-to-trial dynamics relevant to exploration and exploitation. Theta-band activities over middle and lateral frontal areas have previously been implicated in EEG studies of reinforcement learning and strategic control. It was hypothesized that these areas may interact during top-down strategic behavioral control involved in exploratory choices. Here, we used a dynamic reward–learning task and an associated mathematical model that predicted individual response times. This reinforcement-learning model generated value-based prediction errors and trial-by-trial estimates of exploration as a function of uncertainty. Mid-frontal theta power correlated with unsigned prediction error, although negative prediction errors had greater power overall. Trial-to-trial variations in response-locked frontal theta were linearly related to relative uncertainty and were larger in individuals who used uncertainty to guide exploration. This finding suggests that theta-band activities reflect prefrontal-directed strategic control during exploratory choices. PMID:22120491

The Role of Frontal Cortical and Medial-Temporal Lobe Brain Areas in Learning a Bayesian Prior Belief on Reversals

PubMed Central

Jang, Anthony I.; Costa, Vincent D.; Rudebeck, Peter H.; Chudasama, Yogita; Murray, Elisabeth A.

2015-01-01

Reversal learning has been extensively studied across species as a task that indexes the ability to flexibly make and reverse deterministic stimulus–reward associations. Although various brain lesions have been found to affect performance on this task, the behavioral processes affected by these lesions have not yet been determined. This task includes at least two kinds of learning. First, subjects have to learn and reverse stimulus–reward associations in each block of trials. Second, subjects become more proficient at reversing choice preferences as they experience more reversals. We have developed a Bayesian approach to separately characterize these two learning processes. Reversal of choice behavior within each block is driven by a combination of evidence that a reversal has occurred, and a prior belief in reversals that evolves with experience across blocks. We applied the approach to behavior obtained from 89 macaques, comprising 12 lesion groups and a control group. We found that animals from all of the groups reversed more quickly as they experienced more reversals, and correspondingly they updated their prior beliefs about reversals at the same rate. However, the initial values of the priors that the various groups of animals brought to the task differed significantly, and it was these initial priors that led to the differences in behavior. Thus, by taking a Bayesian approach we find that variability in reversal-learning performance attributable to different neural systems is primarily driven by different prior beliefs about reversals that each group brings to the task. SIGNIFICANCE STATEMENT The ability to use prior knowledge to adapt choice behavior is critical for flexible decision making. Reversal learning is often studied as a form of flexible decision making. However, prior studies have not identified which brain regions are important for the formation and use of prior beliefs to guide choice behavior. Here we develop a Bayesian approach that formally characterizes learning set as a concept, and we show that, in macaque monkeys, the amygdala and medial prefrontal cortex have a role in establishing an initial belief about the stability of the reward environment. PMID:26290251

Hands-On Learning: A Problem-Based Approach to Teaching Microsoft Excel

ERIC Educational Resources Information Center

Slayter, Erik; Higgins, Lindsey M.

2018-01-01

The development of a student's ability to make data-driven decisions has become a focus in higher education (Schield 1999; Stephenson and Caravello 2007). Data literacy, the ability to understand and use data to effectively inform decisions, is a fundamental component of information competence (Mandinach and Gummer 2013; Stephenson and Caravello,…

State Education Data Systems That Increase Learning and Improve Accountability. Policy Issues. Number 16

ERIC Educational Resources Information Center

Palaich, Robert M.; Griffin Good, Dixie; van der Ploeg, Arie

2004-01-01

Driven by growing accountability pressures, states and districts have invested in a variety of computerized systems for data storage, analysis, and reporting. As accountability policies demand access to more transparent and accurate data about every aspect of the education process, developing linkages among historically disparate systems is…

Capture the Human Side of Learning: Data Makeover Puts Students Front and Center

ERIC Educational Resources Information Center

Sharratt, Lyn; Fullan, Michael

2013-01-01

Education is overloaded with programs and data. The growth of digital power has aided and abetted the spread of accountability-driven data--Adequate Yearly Progress, test results for every child in every grade, Common Core standards, formative and summative assessments. Technology accelerates the onslaught of data. All this information goes for…

Data Driven Decision-Making in the Era of Accountability: Fostering Faculty Data Cultures for Learning

ERIC Educational Resources Information Center

Hora, Matthew T.; Bouwma-Gearhart, Jana; Park, Hyoung Joon

2017-01-01

In this article the authors report findings from a practice-based study that examines the cultural practices of data use among 59 science and engineering faculty from three large, public research universities. In this exploratory study they documented how faculty use teaching-related data "in the wild" using interviews and classroom…

Data-Driven School Improvement: Linking Data and Learning. Technology, Education--Connections (TEC) Series

ERIC Educational Resources Information Center

Mandinach, Ellen B., Ed.; Honey, Margaret, Ed.

2008-01-01

With federal and local demands for increased accountability, educators at all levels are now expected to acquire the necessary skills and knowledge to be effective data users and decision makers. This book brings together stakeholders representing a variety of perspectives to explore how educators actually use data and technology tools to achieve…

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

PubMed

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

2015-02-01

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

Intertextual learning strategy with guided inquiry on solubility equilibrium concept to improve the student’s scientific processing skills

NASA Astrophysics Data System (ADS)

Wardani, K. U.; Mulyani, S.; Wiji

2018-04-01

The aim of this study was to develop intertextual learning strategy with guided inquiry on solubility equilibrium concept to enhance student’s scientific processing skills. This study was conducted with consideration of some various studies which found that lack of student’s process skills in learning chemistry was caused by learning chemistry is just a concept. The method used in this study is a Research and Development to generate the intertextual learning strategy with guided inquiry. The instruments used in the form of sheets validation are used to determine the congruence of learning activities by step guided inquiry learning and scientific processing skills with aspects of learning activities. Validation results obtained that the learning activities conducted in line with aspects of indicators of the scientific processing skills.

Phraseology and Frequency of Occurrence on the Web: Native Speakers' Perceptions of Google-Informed Second Language Writing

ERIC Educational Resources Information Center

Geluso, Joe

2013-01-01

Usage-based theories of language learning suggest that native speakers of a language are acutely aware of formulaic language due in large part to frequency effects. Corpora and data-driven learning can offer useful insights into frequent patterns of naturally occurring language to second/foreign language learners who, unlike native speakers, are…

"We Live and Learn": English and Ambivalence in a New Capitalist State

ERIC Educational Resources Information Center

Prendergast, Catherine

2008-01-01

This article reports on data drawn from a larger critical ethnographic study of English language use and instruction in Slovakia at the moment of capitalist integration. Slovaks who sought to learn English at the turn of the millennium were driven by the brand new anxiety of job insecurity. English lessons at this time sold themselves as the…

Separating Fact and Fiction: The Real Story of Corpus Use in Language Teaching

ERIC Educational Resources Information Center

Boulton, Alex

2013-01-01

This paper investigates uses of corpora in language learning ("data-driven learning") through analysis of a 600K-word corpus of empirical research papers in the field. The corpus can tell us much--the authors and the countries the studies are conducted in, the types of publication, and so on. The corpus investigation itself starts with…

Retaking the Test

ERIC Educational Resources Information Center

Backer, David Isaac; Lewis, Tyson Edward

2015-01-01

"Data-driven" teaching and learning is common sense in education today, and it is common sense that these data should come from standardized tests. Critiques of standardization either make no constructive suggestions for what to use in place of the tests or they call for better, more scientifically rigorous, reliable, and…

Adult learning theories: implications for learning and teaching in medical education: AMEE Guide No. 83.

PubMed

Taylor, David C M; Hamdy, Hossam

2013-11-01

There are many theories that explain how adults learn and each has its own merits. This Guide explains and explores the more commonly used ones and how they can be used to enhance student and faculty learning. The Guide presents a model that combines many of the theories into a flow diagram which can be followed by anyone planning learning. The schema can be used at curriculum planning level, or at the level of individual learning. At each stage of the model, the Guide identifies the responsibilities of both learner and educator. The role of the institution is to ensure that the time and resources are available to allow effective learning to happen. The Guide is designed for those new to education, in the hope that it can unravel the difficulties in understanding and applying the common learning theories, whilst also creating opportunities for debate as to the best way they should be used.

A manifold learning approach to data-driven computational materials and processes

NASA Astrophysics Data System (ADS)

Ibañez, Ruben; Abisset-Chavanne, Emmanuelle; Aguado, Jose Vicente; Gonzalez, David; Cueto, Elias; Duval, Jean Louis; Chinesta, Francisco

2017-10-01

Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy, …), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ universal laws while minimizing the need of explicit, often phenomenological, models. They are based on manifold learning methodologies.

GeoMapApp Learning Activities: Enabling the democratisation of geoscience learning

NASA Astrophysics Data System (ADS)

Goodwillie, A. M.; Kluge, S.

2011-12-01

GeoMapApp Learning Activities (http://serc.carleton.edu/geomapapp) are step-by-step guided inquiry geoscience education activities that enable students to dictate the pace of learning. They can be used in the classroom or out of class, and their guided nature means that the requirement for teacher intervention is minimised which allows students to spend increased time analysing and understanding a broad range of geoscience data, content and concepts. Based upon GeoMapApp (http://www.geomapapp.org), a free, easy-to-use map-based data exploration and visualisation tool, each activity furnishes the educator with an efficient package of downloadable documents. This includes step-by-step student instructions and answer sheet; a teacher's edition annotated worksheet containing teaching tips, additional content and suggestions for further work; quizzes for use before and after the activity to assess learning; and a multimedia tutorial. The activities can be used by anyone at any time in any place with an internet connection. In essence, GeoMapApp Learning Activities provide students with cutting-edge technology, research-quality geoscience data sets, and inquiry-based learning in a virtual lab-like environment. Examples of activities so far created are student calculation and analysis of the rate of seafloor spreading, and present-day evidence on the seafloor for huge ancient landslides around the Hawaiian islands. The activities are designed primarily for students at the community college, high school and introductory undergraduate levels, exposing students to content and concepts typically found in those settings.

Learner-centered teaching in the college science classroom: a practical guide for teaching assistants, instructors, and professors

NASA Astrophysics Data System (ADS)

Dominguez, Margaret Z.; Vorndran, Shelby

2014-09-01

The Office of Instruction and Assessment at the University of Arizona currently offers a Certificate in College Teaching Program. The objective of this program is to develop the competencies necessary to teach effectively in higher education today, with an emphasis on learner-centered teaching. This type of teaching methodology has repeatedly shown to have superior effects compared to traditional teacher-centered approaches. The success of this approach has been proven in both short term and long term teaching scenarios. Students must actively participate in class, which allows for the development of depth of understanding, acquisition of critical thinking, and problem-solving skills. As optical science graduate students completing the teaching program certificate, we taught a recitation class for OPTI 370: Photonics and Lasers for two consecutive years. The recitation was an optional 1-hour long session to supplement the course lectures. This recitation received positive feedback and learner-centered teaching was shown to be a successful method for engaging students in science, specifically in optical sciences following an inquiry driven format. This paper is intended as a guide for interactive, multifaceted teaching, due to the fact that there are a variety of learning styles found in every classroom. The techniques outlined can be implemented in many formats: a full course, recitation session, office hours and tutoring. This guide is practical and includes only the most effective and efficient strategies learned while also addressing the challenges faced, such as formulating engaging questions, using wait time and encouraging shy students.

Validity And Practicality of Experiment Integrated Guided Inquiry-Based Module on Topic of Colloidal Chemistry for Senior High School Learning

NASA Astrophysics Data System (ADS)

Andromeda, A.; Lufri; Festiyed; Ellizar, E.; Iryani, I.; Guspatni, G.; Fitri, L.

2018-04-01

This Research & Development study aims to produce a valid and practical experiment integrated guided inquiry based module on topic of colloidal chemistry. 4D instructional design model was selected in this study. Limited trial of the product was conducted at SMAN 7 Padang. Instruments used were validity and practicality questionnaires. Validity and practicality data were analyzed using Kappa moment. Analysis of the data shows that Kappa moment for validity was 0.88 indicating a very high degree of validity. Kappa moments for the practicality from students and teachers were 0.89 and 0.95 respectively indicating high degree of practicality. Analysis on the module filled in by students shows that 91.37% students could correctly answer critical thinking, exercise, prelab, postlab and worksheet questions asked in the module. These findings indicate that the integrated guided inquiry based module on topic of colloidal chemistry was valid and practical for chemistry learning in senior high school.

Interaction between Gaming and Multistage Guiding Strategies on Students' Field Trip Mobile Learning Performance and Motivation

ERIC Educational Resources Information Center

Chen, Chih-Hung; Liu, Guan-Zhi; Hwang, Gwo-Jen

2016-01-01

In this study, an integrated gaming and multistage guiding approach was proposed for conducting in-field mobile learning activities. A mobile learning system was developed based on the proposed approach. To investigate the interaction between the gaming and guiding strategies on students' learning performance and motivation, a 2 × 2 experiment was…

Moving to Learn: How Guiding the Hands Can Set the Stage for Learning

ERIC Educational Resources Information Center

Brooks, Neon; Goldin-Meadow, Susan

2016-01-01

Previous work has found that guiding problem-solvers' movements can have an immediate effect on their ability to solve a problem. Here we explore these processes in a learning paradigm. We ask whether guiding a learner's movements can have a delayed effect on learning, setting the stage for change that comes about only after instruction. Children…

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.

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

PubMed

Bhat, Ajaz Ahmad; Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

2016-07-01

Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. © 2016 The Author(s).

Understanding the Impact of New Technology on Life and Work. Secondary Learning Guide 12. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on understanding the impact of new technology is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative approaches to solve…

Active Collaborative Learning through Remote Tutoring

ERIC Educational Resources Information Center

Gehret, Austin U.; Elliot, Lisa B.; MacDonald, Jonathan H. C.

2017-01-01

An exploratory case study approach was used to describe remote tutoring in biochemistry and general chemistry with students who are deaf or hard of hearing (D/HH). Data collected for analysis were based on the observations of the participant tutor. The research questions guiding this study included (1) How is active learning accomplished in…

Hanford Borehole Geologic Information System (HBGIS) Updated User’s Guide for Web-based Data Access and Export

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

Mackley, Rob D.; Last, George V.; Allwardt, Craig H.

2008-09-24

The Hanford Borehole Geologic Information System (HBGIS) is a prototype web-based graphical user interface (GUI) for viewing and downloading borehole geologic data. The HBGIS is being developed as part of the Remediation Decision Support function of the Soil and Groundwater Remediation Project, managed by Fluor Hanford, Inc., Richland, Washington. Recent efforts have focused on improving the functionality of the HBGIS website in order to allow more efficient access and exportation of available data in HBGIS. Users will benefit from enhancements such as a dynamic browsing, user-driven forms, and multi-select options for selecting borehole geologic data for export. The need formore » translating borehole geologic data into electronic form within the HBGIS continues to increase, and efforts to populate the database continue at an increasing rate. These new web-based tools should help the end user quickly visualize what data are available in HBGIS, select from among these data, and download the borehole geologic data into a consistent and reproducible tabular form. This revised user’s guide supersedes the previous user’s guide (PNNL-15362) for viewing and downloading data from HBGIS. It contains an updated data dictionary for tables and fields containing borehole geologic data as well as instructions for viewing and downloading borehole geologic data.« less

The Guided Autobiography Method: A Learning Experience

ERIC Educational Resources Information Center

Thornton, James E.

2008-01-01

This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

Computers and Cooperative Learning. Tech Use Guide: Using Computer Technology.

ERIC Educational Resources Information Center

Council for Exceptional Children, Reston, VA. Center for Special Education Technology.

This guide focuses on the use of computers and cooperative learning techniques in classrooms that include students with disabilities. The guide outlines the characteristics of cooperative learning such as goal interdependence, individual accountability, and heterogeneous groups, emphasizing the value of each group member. Several cooperative…

Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

ERIC Educational Resources Information Center

Westberry, Nicola; Franken, Margaret

2015-01-01

This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

Application-Driven Educational Game to Assist Young Children in Learning English Vocabulary

ERIC Educational Resources Information Center

Chen, Zhi-Hong; Lee, Shu-Yu

2018-01-01

This paper describes the development of an educational game, named My-Pet-Shop, to enhance young children's learning of English vocabulary. The educational game is underpinned by an application-driven model, which consists of three components: application scenario, subject learning, and learning regulation. An empirical study is further conducted…

Hands-Only CPR

MedlinePlus

... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ...

Using Performance Task Data to Improve Instruction

ERIC Educational Resources Information Center

Abbott, Amy L.; Wren, Douglas G.

2016-01-01

Two well-accepted ideas among educators are (a) performance assessment is an effective means of assessing higher-order thinking skills and (b) data-driven instruction planning is a valuable tool for optimizing student learning. This article describes a locally developed performance task (LDPT) designed to measure critical thinking, problem…

Data Driven Program Planning for GIS Instruction

ERIC Educational Resources Information Center

Scarletto, Edith

2013-01-01

This study used both focus groups (qualitative) and survey data (quantitative) to develop and expand an instruction program for GIS services. It examined the needs and preferences faculty and graduate students have for learning about GIS applications for teaching and research. While faculty preferred in person workshops and graduate students…

Energy Dissipation and Dynamics in Large Guide Field Turbulence Driven Reconnection at the Magnetopause

NASA Astrophysics Data System (ADS)

TenBarge, J. M.; Shay, M. A.; Sharma, P.; Juno, J.; Haggerty, C. C.; Drake, J. F.; Bhattacharjee, A.; Hakim, A.

2017-12-01

Turbulence and magnetic reconnection are the primary mechanisms responsible for the conversion of stored magnetic energy into particle energy in many space and astrophysical plasmas. The magnetospheric multiscale mission (MMS) has given us unprecedented access to high cadence particle and field data of turbulence and magnetic reconnection at earth's magnetopause. The observations include large guide field reconnection events generated within the turbulent magnetopause. Motivated by these observations, we present a study of large guide reconnection using the fully kinetic Eulerian Vlasov-Maxwell component of the Gkeyll simulation framework, and we also employ and compare with gyrokinetics to explore the asymptotically large guide field limit. In addition to studying the configuration space dynamics, we leverage the recently developed field-particle correlations to diagnose the dominant sources of dissipation and compare the results of the field-particle correlation to other energy dissipation measures.

Clinical and regulatory considerations in pharmacogenetic testing.

PubMed

Schuck, Robert N; Marek, Elizabeth; Rogers, Hobart; Pacanowski, Michael

2016-12-01

Both regulatory science and clinical practice rely on best available scientific data to guide decision-making. However, changes in clinical practice may be driven by numerous other factors such as cost. In this review, we reexamine noteworthy examples where pharmacogenetic testing information was added to drug labeling to explore how the available evidence, potential public health impact, and predictive utility of each pharmacogenetic biomarker impacts clinical uptake. Advances in the field of pharmacogenetics have led to new discoveries about the genetic basis for variability in drug response. The Food and Drug Administration recognizes the value of pharmacogenetic testing strategies and has been proactive about incorporating pharmacogenetic information into the labeling of both new drugs and drugs already on the market. Although some examples have readily translated to routine clinical practice, clinical uptake of genetic testing for many drugs has been limited. Both regulatory science and clinical practice rely on data-driven approaches to guide decision making; however, additional factors are also important in clinical practice that do not impact regulatory decision making, and these considerations may result in heterogeneity in clinical uptake of pharmacogenetic testing. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Chemistry and the Periodic Table: Teacher's Guide Levels A, B, and C. Preliminary Limited Edition.

ERIC Educational Resources Information Center

Cambridge Physics Outlet, Woburn, MA. Education Programs Dept.

This is a two-part curriculum package for the teaching of chemistry and the periodic table. The first part, the Teacher's Guide, contains information necessary for using the equipment in a typical classroom including learning goals, vocabulary, math skills, and sample data for each activity. The second part of the package consists of photocopy…

The Structure of the Atom: Teacher's Guide Levels A, B, and C. Preliminary Limited Edition.

ERIC Educational Resources Information Center

Cambridge Physics Outlet, Woburn, MA. Education Programs Dept.

This is a two-part curriculum package for teaching the structure of atoms. The first part--the Teacher's Guide--contains information necessary for using the equipment in a typical classroom including learning goals, vocabulary, math skills, and sample data for each activity. The second part of the package consists of photocopy masters for a set of…

Overcoming complexities: Damage detection using dictionary learning framework

NASA Astrophysics Data System (ADS)

Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

2018-04-01

For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

Automotive Mechanics. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These 33 learning guides are self-instructional packets for 33 tasks identified as essential for performance on an entry-level job in automotive mechanics. Each guide is based on a terminal performance objective (task) and 1-9 enabling objectives. For each enabliing objective, some or all of these materials may be presented: learning steps…

Mechanical Drafting. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These four learning guides are self-instructional packets for four tasks identified as essential for performance on an entry-level job in mechanical drafting. Each guide is based on a terminal performance objective (task) and 2-4 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps…

Livestock. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These 25 learning guides are self-instructional packets for 25 tasks identified as essential for performance on an entry-level job in livestock production. Each guide is based on a terminal performance objective (task) and 1-4 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps (outline…

Family Learning Outdoors: Guided Participation on a Nature Walk

ERIC Educational Resources Information Center

Zimmerman, Heather Toomey; McClain, Lucy R.

2016-01-01

This informal learning research project examined how guided participation processes support the use of cultural tools (such as scientific equipment) during a nature walk at one nature center. This paper analyzed family interactions outdoors using microethnographic methods. An informal learning framework based on guided participation and cultural…

Ventral striatal network connectivity reflects reward learning and behavior in patients with Parkinson's disease.

PubMed

Petersen, Kalen; Van Wouwe, Nelleke; Stark, Adam; Lin, Ya-Chen; Kang, Hakmook; Trujillo-Diaz, Paula; Kessler, Robert; Zald, David; Donahue, Manus J; Claassen, Daniel O

2018-01-01

A subgroup of Parkinson's disease (PD) patients treated with dopaminergic therapy develop compulsive reward-driven behaviors, which can result in life-altering morbidity. The mesocorticolimbic dopamine network guides reward-motivated behavior; however, its role in this treatment-related behavioral phenotype is incompletely understood. Here, mesocorticolimbic network function in PD patients who develop impulsive and compulsive behaviors (ICB) in response to dopamine agonists was assessed using BOLD fMRI. The tested hypothesis was that network connectivity between the ventral striatum and the limbic cortex is elevated in patients with ICB and that reward-learning proficiency reflects the extent of mesocorticolimbic network connectivity. To evaluate this hypothesis, 3.0T BOLD-fMRI was applied to measure baseline functional connectivity on and off dopamine agonist therapy in age and sex-matched PD patients with (n = 19) or without (n = 18) ICB. An incentive-based task was administered to a subset of patients (n = 20) to quantify positively or negatively reinforced learning. Whole-brain voxelwise analyses and region-of-interest-based mixed linear effects modeling were performed. Elevated ventral striatal connectivity to the anterior cingulate gyrus (P = 0.013), orbitofrontal cortex (P = 0.034), insula (P = 0.044), putamen (P = 0.014), globus pallidus (P < 0.01), and thalamus (P < 0.01) was observed in patients with ICB. A strong trend for elevated amygdala-to-midbrain connectivity was found in ICB patients on dopamine agonist. Ventral striatum-to-subgenual cingulate connectivity correlated with reward learning (P < 0.01), but not with punishment-avoidance learning. These data indicate that PD-ICB patients have elevated network connectivity in the mesocorticolimbic network. Behaviorally, proficient reward-based learning is related to this enhanced limbic and ventral striatal connectivity. Hum Brain Mapp 39:509-521, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Big(ger) Data as Better Data in Open Distance Learning

ERIC Educational Resources Information Center

Prinsloo, Paul; Archer, Elizabeth; Barnes, Glen; Chetty, Yuraisha; van Zyl, Dion

2015-01-01

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously…

Learning by Doing: A Handbook for Professional Learning Communities at Work™ (Second Edition)-- Action Guide

ERIC Educational Resources Information Center

Solution Tree, 2010

2010-01-01

This action guide is intended to assist in the reading of and reflection upon "Learning by Doing: A Handbook for Professional Learning Communities at Work, Second Edition" by Richard DuFour, Rebecca DuFour, Richard Eaker, and Thomas Many. The guide can be used by an individual, a small group, or an entire faculty to identify key points,…

CPR Facts and Stats

MedlinePlus

... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ...

Mechanisms influencing student understanding on an outdoor guided field trip

NASA Astrophysics Data System (ADS)

Caskey, Nourah Al-Rashid

Field trips are a basic and important, yet often overlooked part of the student experience. They provide the opportunity to integrate real world knowledge with classroom learning and student previous personal experiences. Outdoor guided field trips leave students with an increased understanding, awareness and interest and in science. However, the benefits of this experience are ambiguous at best (Falk and Balling, 1982; Falk and Dierking, 1992; Kisiel, 2006.) Students on an outdoor guided field trip to a local nature park experienced a significant increase in their understanding of the rock cycle. The changes in the pre-field trip test and the post-field trip test as well as their answers in interviews showed a profound change in the students' understanding and in their interest in the subject matter. The use of the "student's voice" (Bamberger and Tal, 2008) was the motivation for data analysis. By using the students' voice, I was able to determine the mechanisms that might influence their understanding of a subject. The central concepts emerging from the data were: the outdoor setting; the students' interest; the social interaction. From these central concepts, a conceptual model was developed. The outdoor setting allows for the freedom to explore, touch, smell and movement. This, in turn, leads to an increased interest in subject matter. As the students are exploring, they are enjoying themselves and become more open to learning. Interest leads to a desire to learn (Dewey, 1975). In addition to allowing the freedom to explore and move, the outdoor setting creates the condition for social interaction. The students talk to each other as they walk; they have in-depth discourse regarding the subject matter---with the teachers, each other and with the guides. The guides have an extremely important role in the students' learning. The more successful guides not only act as experts, but also adjust to the students' needs and act or speak accordingly. The interconnections of these three concepts---the outdoor setting, the students' interest, the social interaction---worked to provide the mechanisms by which the students increased their understanding of the rock cycle.

Computational neuroscience approach to biomarkers and treatments for mental disorders.

PubMed

Yahata, Noriaki; Kasai, Kiyoto; Kawato, Mitsuo

2017-04-01

Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry. © 2016 The Authors. Psychiatry and Clinical Neurosciences © 2016 Japanese Society of Psychiatry and Neurology.

Data-driven system to predict academic grades and dropout.

PubMed

Rovira, Sergi; Puertas, Eloi; Igual, Laura

2017-01-01

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

Lessons Learned from the Implementation of Total Quality Management at the Naval Aviation Depot, North Island, California

DTIC Science & Technology

1988-12-01

Kaoru Ishikawa recognized the potential of statistical process control during one of Dr. Deming’s many instructional visits to Japan. He wrote the Guide...to Quality Control which has been utilized for both self-study and classroom training. In the Guide to Quality Control, Dr. Ishikawa describes...job data are essential for making a proper evaluation.( Ishikawa , p. 14) The gathering of data and its subsequent analysis are the foundation of

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Analogical transfer as guided by an abstraction process: the case of learning by doing in text editing.

PubMed

Sander, E; Richard, J F

1997-11-01

The authors proposed that not only are the first attempts to solve a problem made by analogy but also that progress in learning can be guided by referring to more abstract knowledge, which affords new possibilities. Two experiments investigated this view in a situation of learning how to use a text editor. Experiments 1A to 1C identified the knowledge associated with 3 domains hypothesized as sources of transfer at increasing levels of abstraction (typewriting, writing in general, manipulating objects). Experiment 2 tested whether participants first use their knowledge about typewriting, then about writing in general, and then about manipulating objects. The data showed that the order of acquisition of text editor functions appeared to be strongly related to this hierarchy, supporting the idea that part of learning consists of discovering properties of objects by accessing increasingly general domains.

Guiding without feeling guided: Implicit scaffolding through interactive simulation design

NASA Astrophysics Data System (ADS)

Paul, Ariel; Podolefsky, Noah; Perkins, Katherine

2013-01-01

While PhET interactive simulations (sims) were historically designed for college students, they are used at lower grade levels, and we are currently developing sims targeted at middle school (MS). In studying how MS students interact with and learn from these sims, we have been extracting insights about design for the middle-grade-levels and across K-16. This collection of work has highlighted the importance of implicit scaffolding, a design framework that reduces the amount of explicit instruction needed to facilitate learning. We present a case study of redesigning a sim - Energy Skate Park (ESP) - for effective use in MS. We conducted think-aloud interviews with MS students to identify successful features, sources of confusion or unproductive distraction, as well as features inconsistent with gradeappropriate learning goals. Drawing on these data and the principle of implicit scaffolding, we developed Energy Skate Park Basics (ESPB). Interviews on ESPB demonstrate increased usability and learning for MS students.

The Evolution of Big Data and Learning Analytics in American Higher Education

ERIC Educational Resources Information Center

Picciano, Anthony G.

2012-01-01

Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is…

The cerebellum: a neuronal learning machine?

NASA Technical Reports Server (NTRS)

Raymond, J. L.; Lisberger, S. G.; Mauk, M. D.

1996-01-01

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Strategic Framing: How Leaders Craft the Meaning of Data Use for Equity and Learning

ERIC Educational Resources Information Center

Park, Vicki; Daly, Alan J.; Guerra, Alison Wishard

2013-01-01

Although there is an emerging body of research that examines data-driven decision making (DDDM) in schools, little attention has been paid to how local leaders strategically frame sensemaking around data use. This exploratory case examines how district and school leaders consciously framed the implementation of DDDM in one urban high school.…

Statistically-Driven Visualizations of Student Interactions with a French Online Course Video

ERIC Educational Resources Information Center

Youngs, Bonnie L.; Prakash, Akhil; Nugent, Rebecca

2018-01-01

Logged tracking data for online courses are generally not available to instructors, students, and course designers and developers, and even if these data were available, most content-oriented instructors do not have the skill set to analyze them. Learning analytics, mined from logged course data and usually presented in the form of learning…

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

The ICER Value Framework: Integrating Cost Effectiveness and Affordability in the Assessment of Health Care Value.

PubMed

Pearson, Steven D

2018-03-01

What should be the relationship between the concepts of cost effectiveness and affordability in value assessments for health care interventions? This question has received greater attention in recent years given increasing financial pressures on health systems, leading to different views on how assessment reports and decision-making processes can provide the best structure for considering both elements. In the United States, the advent of explicit value frameworks to guide drug assessments has also focused attention on this issue, driven in part by the prominent inclusion of affordability within the value framework used to guide reports from the Institute for Clinical and Economic Review. After providing a formal definition of affordability for health care systems, this article argues that, even after using empirical estimates of true health system opportunity cost, cost-effectiveness thresholds cannot by themselves be set in a way that subsumes questions about short-term affordability. The article then presents an analysis of different approaches to integrating cost effectiveness and budget impact assessments within information to guide decision making. The evolution and experience with the Institute for Clinical and Economic Review value framework are highlighted, providing lessons learned and guiding principles for future efforts to bring measures of affordability within the scope of value assessment. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The New Learning Market.

ERIC Educational Resources Information Center

Mager, Caroline; Robinson, Peter; Fletcher, Mick; Stanton, Geoff; Perry, Adrian; Westwood, Andy

This document contains seven papers examining the implications of proposed major reforms to the United Kingdom's post-16 sector. "The New Learning Market--Overview" (Caroline Mager) argues for balancing market-driven and planning-driven approaches to planning post-16 education. "Education and Training as a Learning Market"…

Student Needs to Practicum Guidance in Physiology of Animals Based on Guided Inquiry

NASA Astrophysics Data System (ADS)

Widiana, R.; Susanti, S.; Susanti, D.

2017-09-01

The achievement of the subject of animal physiology requires that the students actively and creatively find their knowledge independently in understanding the concepts, theories, physiological processes, decompose, assemble, compare and modify physiological processes in relation to the fluctuation of environmental factors through practicum activities. The achievement of this lesson has not been fully realized because the learning resources used can’t guide, direct and make the independent students achieve their learning achievement and the practical handbook used has not been able to lead the students active and creative in finding their own knowledge. The practical handbook used so far consists only of the introduction of materials, work steps and questions. For that, we need to develop guided inquiry guide based on the needs of students. Objectives this study produces a practical handbook that fits the needs of the students. The research was done by using 4-D models and limited to define stage that is student requirement analysis. Data obtained from the questionnaire and analysed descriptively. The questionnaire obtained an average of 88.16%. So the needs of students will guide guided inquiry based inquiry both to be developed.

How Are We Approaching Data-Informed Practice? Development of the "Survey of Data Use and Professional Learning"

ERIC Educational Resources Information Center

Jimerson, Jo Beth

2016-01-01

As in international schooling contexts, talk about data-driven practice has become ubiquitous in schooling dialogues in the USA, and with the pending reauthorization of the No Child Left Behind Act (the main driver of increased data use in American schools), educators in the USA should expect even greater calls for formalized data use. Yet, the…

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

A Cyber Enabled Collaborative Environment for Creating, Sharing and Using Data and Modeling Driven Curriculum Modules for Hydrology Education

NASA Astrophysics Data System (ADS)

Merwade, V.; Ruddell, B. L.; Fox, S.; Iverson, E. A. R.

2014-12-01

With the access to emerging datasets and computational tools, there is a need to bring these capabilities into hydrology classrooms. However, developing curriculum modules using data and models to augment classroom teaching is hindered by a steep technology learning curve, rapid technology turnover, and lack of an organized community cyberinfrastructure (CI) for the dissemination, publication, and sharing of the latest tools and curriculum material for hydrology and geoscience education. The objective of this project is to overcome some of these limitations by developing a cyber enabled collaborative environment for publishing, sharing and adoption of data and modeling driven curriculum modules in hydrology and geosciences classroom. The CI is based on Carleton College's Science Education Resource Center (SERC) Content Management System. Building on its existing community authoring capabilities the system is being extended to allow assembly of new teaching activities by drawing on a collection of interchangeable building blocks; each of which represents a step in the modeling process. Currently the system hosts more than 30 modules or steps, which can be combined to create multiple learning units. Two specific units: Unit Hydrograph and Rational Method, have been used in undergraduate hydrology class-rooms at Purdue University and Arizona State University. The structure of the CI and the lessons learned from its implementation, including preliminary results from student assessments of learning will be presented.

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

PubMed

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

2011-10-01

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

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

PubMed Central

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

2011-01-01

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

Parts Marketing. A Student Learning Guide.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

This learning guide is a self-instructional packet for one task identified as essential for performance on an entry-level job in parts marketing. The guide is based on a terminal performance objective (task) and two enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps (outline of student…

Welding. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These 23 learning guides are self-instructional packets for 23 tasks identified as essential for performance on an entry-level job in welding. Each guide is based on a terminal performance objective (task) and 1-4 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps (outline of student…

Plumbing and Pipefitting. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These 32 learning guides are self-instructional packets for 32 tasks identified as essential for performance on an entry-level job in plumbing and pipefitting. Each guide is based on a terminal performance objective (task) and 1-4 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps…

Clothing Production. Student Learning Guides.

ERIC Educational Resources Information Center

Ridge Vocational-Technical Center, Winter Haven, FL.

These 59 learning guides are self-instructional packets for 59 tasks identified as essential for performance on an entry-level job in clothing production. Each guide is based on a terminal performance objective (task) and 2-5 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning steps (outline…

Comparison of Performance Due to Guided Hyperlearning, Unguided Hyperlearning, and Conventional Learning in Mathematics: An Empirical Study

ERIC Educational Resources Information Center

Fathurrohman, Maman; Porter, Anne; Worthy, Annette L.

2014-01-01

In this paper, the use of guided hyperlearning, unguided hyperlearning, and conventional learning methods in mathematics are compared. The design of the research involved a quasi-experiment with a modified single-factor multiple treatment design comparing the three learning methods, guided hyperlearning, unguided hyperlearning, and conventional…

Writing To Learn in Science: A Curriculum Guide.

ERIC Educational Resources Information Center

Chatel, Regina G.

This curriculum guide supports and gives structure to engaging students in writing-to-learn activities in science classes by delineating writing outcomes and assessment. The guide is structured according to the beliefs that students need models, revision is the key to successful writing, writing is a tool for demonstrating learning, and writing is…

Evaluation of a cross-cultural training program for Pakistani educators: Lessons learned and implications for program planning.

PubMed

Mazur, Rebecca; Woodland, Rebecca H

2017-06-01

In this paper, we share the results of a summative evaluation of PEILI, a US-based adult professional development/training program for secondary school Pakistani teachers. The evaluation was guided by the theories of cultural competence (American Psychological Association, 2003; Bamberger, 1999; Wadsworth, 2001) and established frameworks for the evaluation of professional development/training and instructional design (Bennett, 1975; Guskey, 2002; King, 2014; Kirkpatrick, 1967). The explicit and implicit stakeholder assumptions about the connections between program resources, activities, outputs, and outcomes are described. Participant knowledge and skills were measured via scores on a pre/posttest of professional knowledge, and a standards-based performance assessment rubric. In addition to measuring short-term program outcomes, we also sought to incorporate theory-driven thinking into the evaluation design. Hence, we examined participant self-efficacy and access to social capital, two evidenced-based determinants or "levers" that theoretically explain the transformative space between an intervention and its outcomes (Chen, 2012). Data about program determinants were collected and analyzed through a pre/posttest of self-efficacy and social network analysis. Key evaluation findings include participant acquisition of new instructional skills, increased self-efficacy, and the formation of a nascent professional support network. Lessons learned and implications for the design and evaluation of cross-cultural teacher professional development programs are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluating children's conservation biology learning at the zoo.

PubMed

Jensen, Eric

2014-08-01

Millions of children visit zoos every year with parents or schools to encounter wildlife firsthand. Public conservation education is a requirement for membership in professional zoo associations. However, in recent years zoos have been criticized for failing to educate the public on conservation issues and related biological concepts, such as animal adaptation to habitats. I used matched pre- and postvisit mixed methods questionnaires to investigate the educational value of zoo visits for children aged 7-15 years. The questionnaires gathered qualitative data from these individuals, including zoo-related thoughts and an annotated drawing of a habitat. A content analysis of these qualitative data produced the quantitative data reported in this article. I evaluated the relative learning outcomes of educator-guided and unguided zoo visits at London Zoo, both in terms of learning about conservation biology (measured by annotated drawings) and changing attitudes toward wildlife conservation (measured using thought-listing data). Forty-one percent of educator-guided visits and 34% of unguided visits resulted in conservation biology-related learning. Negative changes in children's understanding of animals and their habitats were more prevalent in unguided zoo visits. Overall, my results show the potential educational value of visiting zoos for children. However, they also suggest that zoos' standard unguided interpretive materials are insufficient for achieving the best outcomes for visiting children. These results support a theoretical model of conservation biology learning that frames conservation educators as toolmakers who develop conceptual resources to enhance children's understanding of science. © 2014 Society for Conservation Biology.

Learning tactile skills through curious exploration

PubMed Central

Pape, Leo; Oddo, Calogero M.; Controzzi, Marco; Cipriani, Christian; Förster, Alexander; Carrozza, Maria C.; Schmidhuber, Jürgen

2012-01-01

We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots. PMID:22837748

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

PubMed Central

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. PMID:25427176

Toward computational cumulative biology by combining models of biological datasets.

PubMed

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

An Overview to Research on Education Technology Based on Constructivist Learning Approach

ERIC Educational Resources Information Center

Asiksoy, Gulsum; Ozdamli, Fezile

2017-01-01

The aim of this research is to determine the trends of education technology researches on Constructivist Learning Approach, which were published on database of ScienceDirect between 2010 and 2016. It also aims to guide researchers who will do studies in this field. After scanning the database, 81 articles published on ScienceDirect's data base…

Promoting University Students' Collaborative Learning through Instructor-Guided Writing Groups

ERIC Educational Resources Information Center

Mutwarasibo, Faustin

2013-01-01

This paper aims to examine how to promote university students' engagement in learning by means of instructor-initiated EFL writing groups. The research took place in Rwanda and was undertaken as a case study involving 34 second year undergraduate students, divided into 12 small working groups and one instructor. The data were collected by means of…

Researching Students across Spaces and Places: Capturing Digital Data "On the Go"

ERIC Educational Resources Information Center

Falloon, Garry

2018-01-01

Criticisms have been levelled at e-research that limited knowledge has been produced helpful for guiding educators in using digital tools more effectively for teaching and learning. This issue has become more acute with the emergence of mobile devices that enable learners to transition across different learning spaces and times. Traditional data…

Sustainability Factors for E-Learning Initiatives

ERIC Educational Resources Information Center

Gunn, Cathy

2010-01-01

This paper examines the challenges that "grass roots" e-learning initiatives face in trying to become sustainable. A cross-institutional study focused on local, rather than centrally driven, initiatives. A number of successful e-learning innovations were identified that had been driven by capable teachers seeking solutions to real…

Analyzing students' attitudes towards science during inquiry-based lessons

NASA Astrophysics Data System (ADS)

Kostenbader, Tracy C.

Due to the logistics of guided-inquiry lesson, students learn to problem solve and develop critical thinking skills. This mixed-methods study analyzed the students' attitudes towards science during inquiry lessons. My quantitative results from a repeated measures survey showed no significant difference between student attitudes when taught with either structured-inquiry or guided-inquiry lessons. The qualitative results analyzed through a constant-comparative method did show that students generate positive interest, critical thinking and low level stress during guided-inquiry lessons. The qualitative research also gave insight into a teacher's transition to guided-inquiry. This study showed that with my students, their attitudes did not change during this transition according to the qualitative data however, the qualitative data did how high levels of excitement. The results imply that students like guided-inquiry laboratories, even though they require more work, just as much as they like traditional laboratories with less work and less opportunity for creativity.

The effectiveness of module based on guided inquiry method to improve students’ logical thinking ability

NASA Astrophysics Data System (ADS)

Ash-Shiddieqy, M. H.; Suparmi, A.; Sunarno, W.

2018-04-01

The purpose of this research is to understand the effectiveness of module based on guided inquiry method to improve students’ logical thinking ability. This research only evaluate the students’ logical ability after follows the learning activities that used developed physics module based on guided inquiry method. After the learning activities, students This research method uses a test instrument that adapts TOLT instrument. There are samples of 68 students of grade XI taken from SMA Negeri 4 Surakarta.Based on the results of the research can be seen that in the experimental class and control class, the posttest value aspect of probabilistic reasoning has the highest value than other aspects, whereas the posttest value of the proportional reasoning aspect has the lowest value. The average value of N-gain in the experimental class is 0.39, while in the control class is 0.30. Nevertheless, the N-gain values obtained in the experimental class are larger than the control class, so the guided inquiry-based module is considered more effective for improving students’ logical thinking. Based on the data obtained from the research shows the modules available to help teachers and students in learning activities. The developed Physics module is integrated with every syntax present in guided inquiry method, so it can be used to improve students’ logical thinking ability.

AED (Automated External Defibrillator) Programs: Questions and Answers

MedlinePlus

... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ... Training CPR In Schools Training Kits RQI AHA Blended Learning & eLearning Guide AHA Instructors ECC Educational Conferences Programs ...

Train the Trainer. Facilitator Guide Sample. Basic Blueprint Reading (Chapter One).

ERIC Educational Resources Information Center

Saint Louis Community Coll., MO.

This publication consists of three sections: facilitator's guide--train the trainer, facilitator's guide sample--Basic Blueprint Reading (Chapter 1), and participant's guide sample--basic blueprint reading (chapter 1). Section I addresses why the trainer should learn new classroom techniques; lecturing versus facilitating; learning styles…

Big Data Analytics for Prostate Radiotherapy.

PubMed

Coates, James; Souhami, Luis; El Naqa, Issam

2016-01-01

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Big Data Analytics for Prostate Radiotherapy

PubMed Central

Coates, James; Souhami, Luis; El Naqa, Issam

2016-01-01

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose–volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the “RadoncSpace”) in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches. PMID:27379211

Interpretable Deep Models for ICU Outcome Prediction

PubMed Central

Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

2016-01-01

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

Examining the impact of the Guided Constructivist teaching method on students' misconceptions about concepts of Newtonian physics

NASA Astrophysics Data System (ADS)

Ibrahim, Hyatt Abdelhaleem

The effect of Guided Constructivism (Interactivity-Based Learning Environment) and Traditional Expository instructional methods on students' misconceptions about concepts of Newtonian Physics was investigated. Four groups of 79 of University of Central Florida students enrolled in Physics 2048 participated in the study. A quasi-experimental design of nonrandomized, nonequivalent control and experimental groups was employed. The experimental group was exposed to the Guided Constructivist teaching method, while the control group was taught using the Traditional Expository teaching approach. The data collection instruments included the Force Concept Inventory Test (FCI), the Mechanics Baseline Test (MBT), and the Maryland Physics Expectation Survey (MPEX). The Guided Constructivist group had significantly higher means than the Traditional Expository group on the criterion variables of: (1) conceptions of Newtonian Physics, (2) achievement in Newtonian Physics, and (3) beliefs about the content of Physics knowledge, beliefs about the role of Mathematics in learning Physics, and overall beliefs about learning/teaching/appropriate roles of learners and teachers/nature of Physics. Further, significant relationships were found between (1) achievement, conceptual structures, beliefs about the content of Physics knowledge, and beliefs about the role of Mathematics in learning Physics; (2) changes in misconceptions about the physical phenomena, and changes in beliefs about the content of Physics knowledge. No statistically significant difference was found between the two teaching methods on achievement of males and females. These findings suggest that differences in conceptual learning due to the nature of the teaching method used exist. Furthermore, greater conceptual learning is fostered when teachers use interactivity-based teaching strategies to train students to link everyday experience in the real physical world to formal school concepts. The moderate effect size and power of the study suggest that the effect may not be subtle, but reliable. Physics teachers can use these results to inform their decisions about structuring learning environment when conceptual learning is important.

A data driven method for estimation of B(avail) and appK(D) using a single injection protocol with [¹¹C]raclopride in the mouse.

PubMed

Wimberley, Catriona J; Fischer, Kristina; Reilhac, Anthonin; Pichler, Bernd J; Gregoire, Marie Claude

2014-10-01

The partial saturation approach (PSA) is a simple, single injection experimental protocol that will estimate both B(avail) and appK(D) without the use of blood sampling. This makes it ideal for use in longitudinal studies of neurodegenerative diseases in the rodent. The aim of this study was to increase the range and applicability of the PSA by developing a data driven strategy for determining reliable regional estimates of receptor density (B(avail)) and in vivo affinity (1/appK(D)), and validate the strategy using a simulation model. The data driven method uses a time window guided by the dynamic equilibrium state of the system as opposed to using a static time window. To test the method, simulations of partial saturation experiments were generated and validated against experimental data. The experimental conditions simulated included a range of receptor occupancy levels and three different B(avail) and appK(D) values to mimic diseases states. Also the effect of using a reference region and typical PET noise on the stability and accuracy of the estimates was investigated. The investigations showed that the parameter estimates in a simulated healthy mouse, using the data driven method were within 10±30% of the simulated input for the range of occupancy levels simulated. Throughout all experimental conditions simulated, the accuracy and robustness of the estimates using the data driven method were much improved upon the typical method of using a static time window, especially at low receptor occupancy levels. Introducing a reference region caused a bias of approximately 10% over the range of occupancy levels. Based on extensive simulated experimental conditions, it was shown the data driven method provides accurate and precise estimates of B(avail) and appK(D) for a broader range of conditions compared to the original method. Copyright © 2014 Elsevier Inc. All rights reserved.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

Sensitivity to value-driven attention is predicted by how we learn from value.

PubMed

Jahfari, Sara; Theeuwes, Jan

2017-04-01

Reward learning is known to influence the automatic capture of attention. This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. Participants performed an instrumental learning task that was directly followed by an attentional capture task. A hierarchical Bayesian reinforcement model was used to infer individual differences in learning from high or low reward. Results showed a strong relationship between high-reward learning rates (or the weight that is put on learning after a high reward) and the magnitude of attentional capture with high-reward colors. Individual differences in learning from high or low rewards were further related to performance differences when high- or low-value distractors were present. These findings provide novel insight into the development of value-driven attentional capture by showing how information updating after desired or undesired outcomes can influence future deployments of automatic attention.

Machine learning in materials informatics: recent applications and prospects

NASA Astrophysics Data System (ADS)

Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam; Mannodi-Kanakkithodi, Arun; Kim, Chiho

2017-12-01

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as "descriptors", may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven "materials informatics" strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

PubMed

Murphy, Matthew C; Poplawsky, Alexander J; Vazquez, Alberto L; Chan, Kevin C; Kim, Seong-Gi; Fukuda, Mitsuhiro

2016-08-15

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

PubMed

Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-09-13

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. ©Peggy Peissig, Kelsey M Schwei, Christopher Kadolph, Joseph Finamore, Efrain Cancel, Catherine A McCarty, Asha Okorie, Kate L Thomas, Jennifer Allen Pacheco, Jyotishman Pathak, Stephen B Ellis, Joshua C Denny, Luke V Rasmussen, Gerard Tromp, Marc S Williams, Tamara R Vrabec, Murray H Brilliant. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.09.2017.

Using Program Theory-Driven Evaluation Science to Crack the Da Vinci Code

ERIC Educational Resources Information Center

Donaldson, Stewart I.

2005-01-01

Program theory-driven evaluation science uses substantive knowledge, as opposed to method proclivities, to guide program evaluations. It aspires to update, clarify, simplify, and make more accessible the evolving theory of evaluation practice commonly referred to as theory-driven or theory-based evaluation. The evaluator in this chapter provides a…

A Validation Study of the What's My School Mindset? Survey

ERIC Educational Resources Information Center

Hanson, Janet; Bangert, Arthur; Ruff, William

2016-01-01

The What's My School Mindset? (WMSM) survey is purported to operationalize teachers' beliefs of their school's ability to help all children learn and grow. In today's data driven educational climate it is important to select a reliable instrument for collecting teacher perceptions about their school culture. Accurate data is necessary to support…

A Community Publication and Dissemination System for Hydrology Education Materials

NASA Astrophysics Data System (ADS)

Ruddell, B. L.

2015-12-01

Hosted by CUAHSI and the Science Education Resource Center (SERC), federated by the National Science Digital Library (NSDL), and allied with the Water Data Center (WDC), Hydrologic Information System (HIS), and HydroShare projects, a simple cyberinfrastructure has been launched for the publication and dissemination of data and model driven university hydrology education materials. This lightweight system's metadata describes learning content as a data-driven module with defined data inputs and outputs. This structure allows a user to mix and match modules to create sequences of content that teach both hydrology and computer learning outcomes. Importantly, this modular infrastructure allows an instructor to substitute a module based on updated computer methods for one based on outdated computer methods, hopefully solving the problem of rapid obsolescence that has hampered previous community efforts. The prototype system is now available from CUAHSI and SERC, with some example content. The system is designed to catalog, link to, make visible, and make accessible the existing and future contributions of the community; this system does not create content. Submissions from hydrology educators are eagerly solicited, especially for existing content.

Curriculum Guide for Fashion Merchandising (Fashion Salesperson).

ERIC Educational Resources Information Center

Gregory, Margaret R.

This curriculum guide is designed to help teachers teach a course in fashion merchandising to high school students. The guide contains eight performance-based learning modules, each consisting of one to seven units. Each unit teaches a job-relevant task, and includes performance objectives, performance guides, resources, learning activities,…

Data for Renewable Energy Planning, Policy, and Investment

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

Cox, Sarah L

Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerationsmore » to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.« less

Washington State Guide to Planning, Implementing and Improving Work-based Learning. A Guide for Educators at All Levels.

ERIC Educational Resources Information Center

Highline Community Coll., Des Moines, WA.

This guide, which is intended primarily for school and college personnel interested in initiating or improving work-based learning, examines the development and implementation of work-based education programs in Washington. The following topics are discussed: the rationale for work-based learning (legislative and educational change information,…

Improving Responses to Individual and Family Crises. Learning Guide 10. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This learning guide on improving responses to individual and family crises is part of a series of learning guides developed for competency-based adult consumer and homemaking education programs in community colleges, adult education centers, community centers, and the workplace. Focus is on the connections among personal, family, and job…

Business and Office Education: Accounting, Clerk. Instructor's Manual [and] Student Learning Activity Guide. Kit No. 204.

ERIC Educational Resources Information Center

Cliatt, Katherine H.

This learning activity guide and instructor's manual provide information and exercises for an exploratory activity in accounting. Instructional objectives covered in the guide are for the students to learn (1) reasons for studying accounting and related job descriptions, (2) definitions for accounting terms, (3) the accounting equation, (4) how to…

Learning for the Future: Neighborhood Renewal through Adult and Community Learning. A Guide for Local Authorities.

ERIC Educational Resources Information Center

Merton, Bryan; Turner, Cheryl; Ward, Jane; White, Lenford

This guide is intended to assist managers within England's local authority adult and community education services in supporting neighborhood renewal through adult and community learning (ACL). The guide's overall aim is to promote the skills, knowledge, and understanding that underpin the following items: (1) identification and development of…

The Relationship between Nutrition & Learning. A School Employee's Guide to Information and Action.

ERIC Educational Resources Information Center

Parker, Lynn; And Others

The physical, emotional, and intellectual impact of nutrition on children's ability to learn is the subject of this guide for school personnel. The guide is divided into two parts and includes two appendices. Part 1, "What We Know About the Relationship Between Nutrition and Learning," reviews research linking nutrition and academic…

Application of digital diagnostic impression, virtual planning, and computer-guided implant surgery for a CAD/CAM-fabricated, implant-supported fixed dental prosthesis: a clinical report.

PubMed

Stapleton, Brandon M; Lin, Wei-Shao; Ntounis, Athanasios; Harris, Bryan T; Morton, Dean

2014-09-01

This clinical report demonstrated the use of an implant-supported fixed dental prosthesis fabricated with a contemporary digital approach. The digital diagnostic data acquisition was completed with a digital diagnostic impression with an intraoral scanner and cone-beam computed tomography with a prefabricated universal radiographic template to design a virtual prosthetically driven implant surgical plan. A surgical template fabricated with computer-aided design and computer-aided manufacturing (CAD/CAM) was used to perform computer-guided implant surgery. The definitive digital data were then used to design the definitive CAD/CAM-fabricated fixed dental prosthesis. Copyright © 2014 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Investigating the application of Rasch theory in measuring change in middle school student performance in physical science

NASA Astrophysics Data System (ADS)

Cunningham, Jessica D.

Newton's Universe (NU), an innovative teacher training program, strives to obtain measures from rural, middle school science teachers and their students to determine the impact of its distance learning course on understanding of temperature. No consensus exists on the most appropriate and useful method of analysis to measure change in psychological constructs over time. Several item response theory (IRT) models have been deemed useful in measuring change, which makes the choice of an IRT model not obvious. The appropriateness and utility of each model, including a comparison to a traditional analysis of variance approach, was investigated using middle school science student performance on an assessment over an instructional period. Predetermined criteria were outlined to guide model selection based on several factors including research questions, data properties, and meaningful interpretations to determine the most appropriate model for this study. All methods employed in this study reiterated one common interpretation of the data -- specifically, that the students of teachers with any NU course experience had significantly greater gains in performance over the instructional period. However, clear distinctions were made between an analysis of variance and the racked and stacked analysis using the Rasch model. Although limited research exists examining the usefulness of the Rasch model in measuring change in understanding over time, this study applied these methods and detailed plausible implications for data-driven decisions based upon results for NU and others. Being mindful of the advantages and usefulness of each method of analysis may help others make informed decisions about choosing an appropriate model to depict changes to evaluate other programs. Results may encourage other researchers to consider the meaningfulness of using IRT for this purpose. Results have implications for data-driven decisions for future professional development courses, in science education and other disciplines. KEYWORDS: Item Response Theory, Rasch Model, Racking and Stacking, Measuring Change in Student Performance, Newton's Universe teacher training

BrainLiner: A Neuroinformatics Platform for Sharing Time-Aligned Brain-Behavior Data

PubMed Central

Takemiya, Makoto; Majima, Kei; Tsukamoto, Mitsuaki; Kamitani, Yukiyasu

2016-01-01

Data-driven neuroscience aims to find statistical relationships between brain activity and task behavior from large-scale datasets. To facilitate high-throughput data processing and modeling, we created BrainLiner as a web platform for sharing time-aligned, brain-behavior data. Using an HDF5-based data format, BrainLiner treats brain activity and data related to behavior with the same salience, aligning both behavioral and brain activity data on a common time axis. This facilitates learning the relationship between behavior and brain activity. Using a common data file format also simplifies data processing and analyses. Properties describing data are unambiguously defined using a schema, allowing machine-readable definition of data. The BrainLiner platform allows users to upload and download data, as well as to explore and search for data from the web platform. A WebGL-based data explorer can visualize highly detailed neurophysiological data from within the web browser, and a data-driven search feature allows users to search for similar time windows of data. This increases transparency, and allows for visual inspection of neural coding. BrainLiner thus provides an essential set of tools for data sharing and data-driven modeling. PMID:26858636

Data that drive: Closing the loop in the learning hospital system.

PubMed

Liu, Vincent X; Morehouse, John W; Baker, Jennifer M; Greene, John D; Kipnis, Patricia; Escobar, Gabriel J

2016-11-01

The learning healthcare system describes a vision of US healthcare that capitalizes on science, information technology, incentives, and care culture to drive improvements in the quality of health care. The inpatient setting, one of the most costly and impactful domains of healthcare, is an ideal setting in which to use data and information technology to foster continuous learning and quality improvement. The rapid digitization of inpatient medicine offers incredible new opportunities to use data from routine care to generate new discovery and thus close the virtuous cycle of learning. We use an object lesson-sepsis care within the 21 hospitals of the Kaiser Permanente Northern California integrated healthcare delivery system-to offer insight into the critical elements necessary for developing a learning hospital system. We then describe how a hospital-wide data-driven approach to inpatient care can facilitate improvements in the quality of hospital care. Journal of Hospital Medicine 2016;11:S11-S17. © 2016 Society of Hospital Medicine. © 2016 Society of Hospital Medicine.