Analysis student self efficacy in terms of using Discovery Learning model with SAVI approach

NASA Astrophysics Data System (ADS)

Sahara, Rifki; Mardiyana, S., Dewi Retno Sari

2017-12-01

Often students are unable to prove their academic achievement optimally according to their abilities. One reason is that they often feel unsure that they are capable of completing the tasks assigned to them. For students, such beliefs are necessary. The term belief has called self efficacy. Self efficacy is not something that has brought about by birth or something with permanent quality of an individual, but is the result of cognitive processes, the meaning one's self efficacy will be stimulated through learning activities. Self efficacy has developed and enhanced by a learning model that can stimulate students to foster confidence in their capabilities. One of them is by using Discovery Learning model with SAVI approach. Discovery Learning model with SAVI approach is one of learning models that involves the active participation of students in exploring and discovering their own knowledge and using it in problem solving by utilizing all the sensory devices they have. This naturalistic qualitative research aims to analyze student self efficacy in terms of use the Discovery Learning model with SAVI approach. The subjects of this study are 30 students focused on eight students who have high, medium, and low self efficacy obtained through purposive sampling technique. The data analysis of this research used three stages, that were reducing, displaying, and getting conclusion of the data. Based on the results of data analysis, it was concluded that the self efficacy appeared dominantly on the learning by using Discovery Learning model with SAVI approach is magnitude dimension.

GeoGebra Assist Discovery Learning Model for Problem Solving Ability and Attitude toward Mathematics

NASA Astrophysics Data System (ADS)

Murni, V.; Sariyasa, S.; Ardana, I. M.

2017-09-01

This study aims to describe the effet of GeoGebra utilization in the discovery learning model on mathematical problem solving ability and students’ attitude toward mathematics. This research was quasi experimental and post-test only control group design was used in this study. The population in this study was 181 of students. The sampling technique used was cluster random sampling, so the sample in this study was 120 students divided into 4 classes, 2 classes for the experimental class and 2 classes for the control class. Data were analyzed by using one way MANOVA. The results of data analysis showed that the utilization of GeoGebra in discovery learning can lead to solving problems and attitudes towards mathematics are better. This is because the presentation of problems using geogebra can assist students in identifying and solving problems and attracting students’ interest because geogebra provides an immediate response process to students. The results of the research are the utilization of geogebra in the discovery learning can be applied in learning and teaching wider subject matter, beside subject matter in this study.

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Understanding Fluorescence Measurements through a Guided-Inquiry and Discovery Experiment in Advanced Analytical Laboratory

ERIC Educational Resources Information Center

Wilczek-Vera, Grazyna; Salin, Eric Dunbar

2011-01-01

An experiment on fluorescence spectroscopy suitable for an advanced analytical laboratory is presented. Its conceptual development used a combination of the expository and discovery styles. The "learn-as-you-go" and direct "hands-on" methodology applied ensures an active role for a student in the process of visualization and discovery of concepts.…

Knowledge Discovery Process: Case Study of RNAV Adherence of Radar Track Data

NASA Technical Reports Server (NTRS)

Matthews, Bryan

2018-01-01

This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.

Big Rock Candy Mountain. Resources for Our Education. A Learning to Learn Catalog. Winter 1970.

ERIC Educational Resources Information Center

Portola Inst., Inc., Menlo Park, CA.

Imaginative learning resources of various types are reported in this catalog under the subject headings of process learning, education environments, classroom materials and methods, home learning, and self discovery. Books reviewed are on the subjects of superstition, Eastern religions, fairy tales, philosophy, creativity, poetry, child care,…

50 CFR 221.42 - When must a party supplement or amend information it has previously provided?

Code of Federal Regulations, 2010 CFR

2010-10-01

... PRESCRIPTIONS IN FERC HYDROPOWER LICENSES Hearing Process Prehearing Conferences and Discovery § 221.42 When... promptly supplement or amend any prior response to a discovery request if it learns that the response: (1...

Process for Discovery

ERIC Educational Resources Information Center

Miller, Andrew

2017-01-01

Project-based learning is a successful way to engage students in learning in the classroom, and research reports increases in student achievement data. This article asks: If both students and teachers are more engaged when project-based learning is used, why aren't the elements of project-based learning being used to engage teachers in…

Learning Processes and Learning Outcomes

DTIC Science & Technology

1992-06-01

establish and maintain activation levels) may process information faster because the relevant traces in long - term memory are already activated...drill and practice, and discovery. Finally, implications for the design of computerized instructional environments are indicated. 14. SUBJECT TERMS lI...outcome. This impact may be direct, or may interact with characteristics of the learner to effect learning outcome. INITIAL STATES Conative and cognitive

Teaching the Fundamentals of Biological Research with Primary Literature: Learning from the Discovery of the Gastric Proton Pump

ERIC Educational Resources Information Center

Zhu, Lixin

2011-01-01

For the purpose of teaching collegians the fundamentals of biological research, literature explaining the discovery of the gastric proton pump was presented in a 50-min lecture. The presentation included detailed information pertaining to the discovery process. This study was chosen because it demonstrates the importance of having a broad range of…

Cache-Cache Comparison for Supporting Meaningful Learning

ERIC Educational Resources Information Center

Wang, Jingyun; Fujino, Seiji

2015-01-01

The paper presents a meaningful discovery learning environment called "cache-cache comparison" for a personalized learning support system. The processing of seeking hidden relations or concepts in "cache-cache comparison" is intended to encourage learners to actively locate new knowledge in their knowledge framework and check…

Student Responses Toward Student Worksheets Based on Discovery Learning for Students with Intrapersonal and Interpersonal Intelligence

NASA Astrophysics Data System (ADS)

Yerizon, Y.; Putra, A. A.; Subhan, M.

2018-04-01

Students have a low mathematical ability because they are used to learning to hear the teacher's explanation. For that students are given activities to sharpen his ability in math. One way to do that is to create discovery learning based work sheet. The development of this worksheet took into account specific student learning styles including in schools that have classified students based on multiple intelligences. The dominant learning styles in the classroom were intrapersonal and interpersonal. The purpose of this study was to discover students’ responses to the mathematics work sheets of the junior high school with a discovery learning approach suitable for students with Intrapersonal and Interpersonal Intelligence. This tool was developed using a development model adapted from the Plomp model. The development process of this tools consists of 3 phases: front-end analysis/preliminary research, development/prototype phase and assessment phase. From the results of the research, it is found that students have good response to the resulting work sheet. The worksheet was understood well by students and its helps student in understanding the concept learned.

A behavioral task for investigating action discovery, selection and switching: comparison between types of reinforcer

PubMed Central

Fisher, Simon D.; Gray, Jason P.; Black, Melony J.; Davies, Jennifer R.; Bednark, Jeffery G.; Redgrave, Peter; Franz, Elizabeth A.; Abraham, Wickliffe C.; Reynolds, John N. J.

2014-01-01

Action discovery and selection are critical cognitive processes that are understudied at the cellular and systems neuroscience levels. Presented here is a new rodent joystick task suitable to test these processes due to the range of action possibilities that can be learnt while performing the task. Rats learned to manipulate a joystick while progressing through task milestones that required increasing degrees of movement accuracy. In a switching phase designed to measure action discovery, rats were repeatedly required to discover new target positions to meet changing task demands. Behavior was compared using both food and electrical brain stimulation reward (BSR) of the substantia nigra as reinforcement. Rats reinforced with food and those with BSR performed similarly overall, although BSR-treated rats exhibited greater vigor in responding. In the switching phase, rats learnt new actions to adapt to changing task demands, reflecting action discovery processes. Because subjects are required to learn different goal-directed actions, this task could be employed in further investigations of the cellular mechanisms of action discovery and selection. Additionally, this task could be used to assess the behavioral flexibility impairments seen in conditions such as Parkinson's disease and obsessive-compulsive disorder. The versatility of the task will enable cross-species investigations of these impairments. PMID:25477795

Career Activity File K-12: School-Based Enterprise.

ERIC Educational Resources Information Center

2000

School-Based Enterprises or SBEs provide work-based learning opportunities to students in communities lacking business and industry involvement. SBEs promote discovery learning and student responsibility in the learning process. They expose students to creative thinking, problem solving, planning and organizational skills, and teamwork. SBEs help…

INDEPENDENT AND GROUP LEARNING.

ERIC Educational Resources Information Center

DICKINSON, MARIE B.

IN CONTRAST TO THE TRADITIONAL EMPHASES ON ROTE LEARNING AND FACT ACCUMULATION, RECENT TRENDS EMERGING FROM EDUCATIONAL RESEARCH STRESS THE DEVELOPMENT OF THINKING PROCESSES SUCH AS THE ABILITY TO REASON ABSTRACTLY AND TO SYNTHESIZE. CHILDREN WORKING INDEPENDENTLY OR IN GROUPS MOVE THROUGH A DISCOVERY LEARNING CURRICULUM IN WHICH THE TEACHER…

Doors to Discovery [TM]. WWC Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2009

2009-01-01

Doors to Discovery[TM], an early childhood curriculum, focuses on the development of children's vocabulary and expressive and receptive language through a learning process called "shared literacy," where adults and children work together to develop literacy-related skills. Literacy activities, organized into thematic units, encourage children's…

The Discovery of Personal Meaning: Affective Factors in Learning.

ERIC Educational Resources Information Center

Gorrell, Jeffrey

Learner-centered principles espoused by the American Psychological Association (APA) built on research of the last three decades suggest that learning does not simply entail coordinated cognitive processes. These 12 principles portray factors associated with learning as essential parts of the portrayal of learners as active creators of their own…

The rise of deep learning in drug discovery.

PubMed

Chen, Hongming; Engkvist, Ola; Wang, Yinhai; Olivecrona, Marcus; Blaschke, Thomas

2018-06-01

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Discovery of Action Patterns and User Correlations in Task-Oriented Processes for Goal-Driven Learning Recommendation

ERIC Educational Resources Information Center

Zhou, Xiaokang; Chen, Jian; Wu, Bo; Jin, Qun

2014-01-01

With the high development of social networks, collaborations in a socialized web-based learning environment has become increasing important, which means people can learn through interactions and collaborations in communities across social networks. In this study, in order to support the enhanced collaborative learning, two important factors, user…

Discovery learning with SAVI approach in geometry learning

NASA Astrophysics Data System (ADS)

Sahara, R.; Mardiyana; Saputro, D. R. S.

2018-05-01

Geometry is one branch of mathematics that an important role in learning mathematics in the schools. This research aims to find out about Discovery Learning with SAVI approach to achievement of learning geometry. This research was conducted at Junior High School in Surakarta city. Research data were obtained through test and questionnaire. Furthermore, the data was analyzed by using two-way Anova. The results showed that Discovery Learning with SAVI approach gives a positive influence on mathematics learning achievement. Discovery Learning with SAVI approach provides better mathematics learning outcomes than direct learning. In addition, students with high self-efficacy categories have better mathematics learning achievement than those with moderate and low self-efficacy categories, while student with moderate self-efficacy categories are better mathematics learning achievers than students with low self-efficacy categories. There is an interaction between Discovery Learning with SAVI approach and self-efficacy toward student's mathematics learning achievement. Therefore, Discovery Learning with SAVI approach can improve mathematics learning achievement.

The Effect of Simulation Games on the Learning of Computational Problem Solving

ERIC Educational Resources Information Center

Liu, Chen-Chung; Cheng, Yuan-Bang; Huang, Chia-Wen

2011-01-01

Simulation games are now increasingly applied to many subject domains as they allow students to engage in discovery processes, and may facilitate a flow learning experience. However, the relationship between learning experiences and problem solving strategies in simulation games still remains unclear in the literature. This study, thus, analyzed…

The Science of Learning Meets the Art of Teaching

ERIC Educational Resources Information Center

Park, Beverley

2006-01-01

Through the discoveries of neuroscience, educators have moved beyond the intuitive knowledge of how and when learning occurs to a demonstrated scientific understanding of the learning process itself. These new understandings have a two-fold appeal to educators: they allow them to design better, research-based teaching practices, and they help them…

Active Learning Strategies and Assessment in World Geography Classes

ERIC Educational Resources Information Center

Klein, Phil

2003-01-01

Active learning strategies include a variety of methods, such as inquiry and discovery, in which students are actively engaged in the learning process. This article describes several strategies that can be used in secondary-or college-level world geography courses. The goal of these activities is to foster development of a spatial perspective in…

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

University Research Initiative Research Program Summaries

DTIC Science & Technology

1987-06-01

application to intelligent tutoring systems (John Anderson), o Autonomous learning systems (Jaime Carbonell), o Learning algorithms for parallel processing...test them. The primary project will be: o Learning mechanisms in scientific discovery (Herbert Simon). Tutoring systems. These projects are aimed at...near-term results. They 19 will produce tutors for training specific subject matter areas. These projects will push theories of learning forward by

Does Discovery-Based Instruction Enhance Learning?

ERIC Educational Resources Information Center

Alfieri, Louis; Brooks, Patricia J.; Aldrich, Naomi J.; Tenenbaum, Harriet R.

2011-01-01

Discovery learning approaches to education have recently come under scrutiny (Tobias & Duffy, 2009), with many studies indicating limitations to discovery learning practices. Therefore, 2 meta-analyses were conducted using a sample of 164 studies: The 1st examined the effects of unassisted discovery learning versus explicit instruction, and the…

Linking teaching and research in an undergraduate course and exploring student learning experiences

NASA Astrophysics Data System (ADS)

Wallin, Patric; Adawi, Tom; Gold, Julie

2017-01-01

In this case study, we first describe how teaching and research are linked in a master's course on tissue engineering. A central component of the course is an authentic research project that the students carry out in smaller groups and in collaboration with faculty. We then explore how the students experience learning in this kind of discovery-oriented environment. Data were collected through a survey, reflective writing, and interviews. Using a general inductive approach for qualitative analysis, we identified three themes related to the students' learning experiences: learning to navigate the field, learning to do real research, and learning to work with others. Overall, the students strongly valued learning in a discovery-oriented environment and three aspects of the course contributed to much of its success: taking a holistic approach to linking teaching and research, engaging students in the whole inquiry process, and situating authentic problems in an authentic physical and social context.

Perceptual learning modules in mathematics: enhancing students' pattern recognition, structure extraction, and fluency.

PubMed

Kellman, Philip J; Massey, Christine M; Son, Ji Y

2010-04-01

Learning in educational settings emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other crucial components of learning, especially improvements produced by experience in the extraction of information: perceptual learning (PL). We suggest that such improvements characterize both simple sensory and complex cognitive, even symbolic, tasks through common processes of discovery and selection. We apply these ideas in the form of perceptual learning modules (PLMs) to mathematics learning. We tested three PLMs, each emphasizing different aspects of complex task performance, in middle and high school mathematics. In the MultiRep PLM, practice in matching function information across multiple representations improved students' abilities to generate correct graphs and equations from word problems. In the Algebraic Transformations PLM, practice in seeing equation structure across transformations (but not solving equations) led to dramatic improvements in the speed of equation solving. In the Linear Measurement PLM, interactive trials involving extraction of information about units and lengths produced successful transfer to novel measurement problems and fraction problem solving. Taken together, these results suggest (a) that PL techniques have the potential to address crucial, neglected dimensions of learning, including discovery and fluent processing of relations; (b) PL effects apply even to complex tasks that involve symbolic processing; and (c) appropriately designed PL technology can produce rapid and enduring advances in learning. Copyright © 2009 Cognitive Science Society, Inc.

Discovery Learning Strategies in English

ERIC Educational Resources Information Center

Singaravelu, G.

2012-01-01

The study substantiates that the effectiveness of Discovery Learning method in learning English Grammar for the learners at standard V. Discovery Learning is particularly beneficial for any student learning a second language. It promotes peer interaction and development of the language and the learning of concepts with content. Reichert and…

Designing Instruction for the Web: Incorporating New Conceptions of the Learning Process.

ERIC Educational Resources Information Center

Hunt, Nancy P.

New technologies such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) have led to recent discoveries about how the brain works and how people learn. The interactive capabilities of World Wide Web-based instructional strategies can be employed to better match how we teach with how we know students learn. This paper…

Image Processing: A State-of-the-Art Way to Learn Science.

ERIC Educational Resources Information Center

Raphael, Jacqueline; Greenberg, Richard

1995-01-01

Teachers participating in the Image Processing for Teaching Process, begun at the University of Arizona's Lunar and Planetary Laboratory in 1989, find this technology ideal for encouraging student discovery, promoting constructivist science or math experiences, and adapting in classrooms. Because image processing is not a computerized text, it…

10 CFR 708.2 - What are the definitions of terms used in this part?

Code of Federal Regulations, 2010 CFR

2010-01-01

... calendar day. Discovery means a process used to enable the parties to learn about each other's evidence... DOE. Mediation means an informal, confidential process in which a neutral third person assists the...

A Constructivist Approach to Studying the Bullwhip Effect by Simulating the Supply Chain

ERIC Educational Resources Information Center

González-Torre, Pilar L.; Adenso-Díaz, B.; Moreno, Plácido

2015-01-01

The Cider Game is a simulator for a supply chain-related learning environment. Its main feature is that it provides support to students in the constructivist discovery process when learning how to make logistics decisions, at the same time as noting the occurrence of the bullwhip phenomenon. This learning environment seeks a balance between direct…

Putting the Laboratory at the Center of Teaching Chemistry

ERIC Educational Resources Information Center

Bopegedera, A. M. R. P.

2011-01-01

This article describes an effective approach to teaching chemistry by bringing the laboratory to the center of teaching, to bring the excitement of discovery to the learning process. The lectures and laboratories are closely integrated to provide a holistic learning experience. The laboratories progress from verification to open-inquiry and…

The effect of discovery learning and problem-based learning on middle school students’ self-regulated learning

NASA Astrophysics Data System (ADS)

Miatun, A.; Muntazhimah

2018-01-01

The aim of this research was to determine the effect of learning models on mathematics achievement viewed from student’s self-regulated learning. The learning model compared were discovery learning and problem-based learning. The population was all students at the grade VIII of Junior High School in Boyolali regency. The samples were students of SMPN 4 Boyolali, SMPN 6 Boyolali, and SMPN 4 Mojosongo. The instruments used were mathematics achievement tests and self-regulated learning questionnaire. The data were analyzed using unbalanced two-ways Anova. The conclusion was as follows: (1) discovery learning gives better achievement than problem-based learning. (2) Achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. (3) For discovery learning, achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. For problem-based learning, students who have high and medium self-regulated learning have the same achievement. (4) For students who have high self-regulated learning, discovery learning gives better achievement than problem-based learning. Students who have medium and low self-regulated learning, both learning models give the same achievement.

Fostering First-Graders' Reasoning Strategies with the Most Basic Sums

ERIC Educational Resources Information Center

Purpura, David J.; Baroody, Arthur J.; Eiland, Michael D.; Reid, Erin E.

2012-01-01

In a meta-analysis of 164 studies, Alfieri, Brooks, Aldrich, and Tenenbaum (2010) found that assisted discovery learning was more effective than explicit instruction or unassisted discovery learning and that explicit instruction resulted in more favorable outcomes than unassisted discovery learning. In other words, "unassisted discovery does…

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

A View of the Science Education Research Literature: Scientific Discovery Learning with Computer Simulations.

ERIC Educational Resources Information Center

Robinson, William R.

2000-01-01

Describes a review of research that addresses the effectiveness of simulations in promoting scientific discovery learning and the problems that learners may encounter when using discovery learning. (WRM)

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.…

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

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.

Blending Problem-Based Learning and Peer-Led Team Learning, in an Open Ended "Home-Grown" Pharmaceutical Chemistry Case Study

ERIC Educational Resources Information Center

Veale, Clinton G. L.; Krause, Rui W. M.; Sewry, Joyce D.

2018-01-01

Pharmaceutical chemistry, medicinal chemistry and the drug discovery process require experienced practitioners to employ reasoned speculation in generating creative ideas, which can be used to evolve promising molecules into drugs. The ever-evolving world of pharmaceutical chemistry requires university curricula that prepare graduates for their…

Current Research on the Relative Effectiveness of Selected Media Characteristics.

ERIC Educational Resources Information Center

Gulliford, Nancy L.

The literature of research and theory on media, the psychology of learning, and the technology of instruction is reviewed. The focus is on discovering what is currently known about the intersection of these fields. Current thoughts and discoveries about brain structure and processing are discussed. The management of learning as a system is another…

"I'm Just Playing iPad": Comparing Prekindergarteners' and Preservice Teachers' Social Interactions While Using Tablets for Learning

ERIC Educational Resources Information Center

Moore, Holly Carrell; Adair, Jennifer Keys

2015-01-01

In this article we share descriptive findings from two qualitative, grounded theory (Glaser, 1978, 1992, 1998) studies on how two distinct groups of learners--prekindergarteners and preservice teachers in early childhood education coursework--used touch-screen tablets in their playful, discovery-based learning processes. We found similarities…

(A)Historical Science

PubMed Central

Fang, Ferric C.

2015-01-01

In contrast to many other human endeavors, science pays little attention to its history. Fundamental scientific discoveries are often considered to be timeless and independent of how they were made. Science and the history of science are regarded as independent academic disciplines. Although most scientists are aware of great discoveries in their fields and their association with the names of individual scientists, few know the detailed stories behind the discoveries. Indeed, the history of scientific discovery is sometimes recorded only in informal accounts that may be inaccurate or biased for self-serving reasons. Scientific papers are generally written in a formulaic style that bears no relationship to the actual process of discovery. Here we examine why scientists should care more about the history of science. A better understanding of history can illuminate social influences on the scientific process, allow scientists to learn from previous errors, and provide a greater appreciation for the importance of serendipity in scientific discovery. Moreover, history can help to assign credit where it is due and call attention to evolving ethical standards in science. History can make science better. PMID:26371119

(A)Historical science.

PubMed

Casadevall, Arturo; Fang, Ferric C

2015-12-01

In contrast to many other human endeavors, science pays little attention to its history. Fundamental scientific discoveries are often considered to be timeless and independent of how they were made. Science and the history of science are regarded as independent academic disciplines. Although most scientists are aware of great discoveries in their fields and their association with the names of individual scientists, few know the detailed stories behind the discoveries. Indeed, the history of scientific discovery is sometimes recorded only in informal accounts that may be inaccurate or biased for self-serving reasons. Scientific papers are generally written in a formulaic style that bears no relationship to the actual process of discovery. Here we examine why scientists should care more about the history of science. A better understanding of history can illuminate social influences on the scientific process, allow scientists to learn from previous errors, and provide a greater appreciation for the importance of serendipity in scientific discovery. Moreover, history can help to assign credit where it is due and call attention to evolving ethical standards in science. History can make science better. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

The Impact of Students' Exploration Strategies on Discovery Learning Using Computer-Based Simulations

ERIC Educational Resources Information Center

Dalgarno, Barney; Kennedy, Gregor; Bennett, Sue

2014-01-01

Discovery-based learning designs incorporating active exploration are common within instructional software. However, researchers have highlighted empirical evidence showing that "pure" discovery learning is of limited value and strategies which reduce complexity and provide guidance to learners are important if potential learning…

Self Assessment and Discovery Learning

ERIC Educational Resources Information Center

McDonald, Betty

2011-01-01

Discovery learning in higher education has been reported to be effective in assisting learners to understand difficult concepts and retain long term information. This paper seeks to illustrate how one self assessment model may be used to demonstrate discovery learning in a collaborative atmosphere of students sharing and getting to know each…

Effectiveness of discovery learning model on mathematical problem solving

NASA Astrophysics Data System (ADS)

Herdiana, Yunita; Wahyudin, Sispiyati, Ririn

2017-08-01

This research is aimed to describe the effectiveness of discovery learning model on mathematical problem solving. This research investigate the students' problem solving competency before and after learned by using discovery learning model. The population used in this research was student in grade VII in one of junior high school in West Bandung Regency. From nine classes, class VII B were randomly selected as the sample of experiment class, and class VII C as control class, which consist of 35 students every class. The method in this research was quasi experiment. The instrument in this research is pre-test, worksheet and post-test about problem solving of mathematics. Based on the research, it can be conclude that the qualification of problem solving competency of students who gets discovery learning model on level 80%, including in medium category and it show that discovery learning model effective to improve mathematical problem solving.

Discovery Learning in Autonomous Agents Using Genetic Algorithms

DTIC Science & Technology

1993-12-01

Meyer and Wilson (47). 65. Roitblat , H. L., et al. "Biomimetic Sonar Processing: Prom Dolphin Echoloc-Ation to Artificial Neural Networks." In Meyer and...34 In Meyer and Wilson (47). 65. Roitblat , H. L., et al. "Biomimetic Sonar Processing: From Dolphin Echolocation to Artificial Neural Networks." In

Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

NASA Astrophysics Data System (ADS)

Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

2017-05-01

The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

Creating a culture of patient-focused care through a learner-centered philosophy.

PubMed

Linscott, J; Spee, R; Flint, F; Fisher, A

1999-01-01

This paper will discuss the teaching-learning process used in the Patient-Focused Care Course at a major teaching hospital in Canada that is transforming nursing practice from a provider driven to a patient-focused approach. The experiential and reflective nature of the course offers opportunities for nurses to link theory with practice, to think critically and reflectively about their own values and beliefs and to translate that meaning into practice. The learning process reflects principles of adult learning based on Knowles andragogical model which differs from the traditional pedagogical model of teaching. The essence of andragogy is a constant unfolding process of discovery based on dialogue. Utilization of adult learning principles that support critical thinking and foster transformational change present an alternative to traditional ways of teaching and learning the art and science of nursing practice.

A Study of the Reasoning Abilities of Ninth Standard Students with Respect to Their Gender and Type of the Institution

ERIC Educational Resources Information Center

Sarsani, Mahender Reddy

2008-01-01

Reasoning and learning are closely related, both being the methods of solving problems, learning usually results from the process of reasoning. All inventions, discoveries, art, literature and advances in culture and civilization are based on thinking, reasoning and problem solving capacity of human being. A sound reasoning leads to better…

Active-learning strategies in computer-assisted drug discovery.

PubMed

Reker, Daniel; Schneider, Gisbert

2015-04-01

High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Efficient discovery of responses of proteins to compounds using active learning

PubMed Central

2014-01-01

Background Drug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive. Results This paper describes methods for making this global approach practical by constructing predictive models for many target responses to many compounds and using them to guide experimentation. We demonstrate for the first time that by jointly modeling targets and compounds using descriptive features and using active machine learning methods, accurate models can be built by doing only a small fraction of possible experiments. The methods were evaluated by computational experiments using a dataset of 177 assays and 20,000 compounds constructed from the PubChem database. Conclusions An average of nearly 60% of all hits in the dataset were found after exploring only 3% of the experimental space which suggests that active learning can be used to enable more complete characterization of compound effects than otherwise affordable. The methods described are also likely to find widespread application outside drug discovery, such as for characterizing the effects of a large number of compounds or inhibitory RNAs on a large number of cell or tissue phenotypes. PMID:24884564

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Child Predictors of Learning to Control Variables via Instruction or Self-Discovery

ERIC Educational Resources Information Center

Wagensveld, Barbara; Segers, Eliane; Kleemans, Tijs; Verhoeven, Ludo

2015-01-01

We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group. Prior to the intervention, cognitive, scientific,…

Teaching Slope of a Line Using the Graphing Calculator as a Tool for Discovery Learning

ERIC Educational Resources Information Center

Nichols, Fiona Costello

2012-01-01

Discovery learning is one of the instructional strategies sometimes used to teach Algebra I. However, little research is available that includes investigation of the effects of incorporating the graphing calculator technology with discovery learning. This study was initiated to investigate two instructional approaches for teaching slope of a line…

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

PubMed Central

Kell, Douglas B

2012-01-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. PMID:22252984

Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

PubMed

Kell, Douglas B

2012-03-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. Copyright © 2012 WILEY Periodicals, Inc.

A constructivist approach to studying the bullwhip effect by simulating the supply chain

NASA Astrophysics Data System (ADS)

González-Torre, Pilar L.; Adenso-Díaz, B.; Moreno, Plácido

2015-11-01

The Cider Game is a simulator for a supply chain-related learning environment. Its main feature is that it provides support to students in the constructivist discovery process when learning how to make logistics decisions, at the same time as noting the occurrence of the bullwhip phenomenon. This learning environment seeks a balance between direct instruction in the learning process on the part of the tutor, and a suitable and sufficient degree of freedom to regulate independent learning on the part of students. This article describes the basic learning mechanisms using the Cider Game and the graphical learning environments that it provides. We describe the functionality provided by this application, and analyse the effect over the rational understanding of the bullwhip phenomenon by the students and whether they are able to make decisions to minimise its impact, studying the differences when that decision-making learning is done individually or in groups.

The Development of Discovery-Inquiry Learning Model to Reduce the Science Misconceptions of Junior High School Students

ERIC Educational Resources Information Center

Tompo, Basman; Ahmad, Arifin; Muris, Muris

2016-01-01

The main objective of this research was to develop discovery inquiry (DI) learning model to reduce the misconceptions of Science student level of secondary school that is valid, practical, and effective. This research was an R&D (research and development). The trials of discovery inquiry (DI) learning model were carried out in two different…

The Discovery Process: What Mothers See and Do in Gaining Awareness of the Sexual Abuse of Their Children

ERIC Educational Resources Information Center

Plummer, Carol A.

2006-01-01

Objective: The aim of this study was to explore how mothers discovered that their children had been sexually abused. The exploration included learning from whom or in what ways mothers learned about the abuse, whether there were prior suspicions, if actions were taken to determine likelihood of abuse, and the barriers to recognizing abuse. Method:…

Automated Knowledge Discovery From Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael; DeCoste, Dennis; Mazzoni, Dominic; Scharenbroich, Lucas; Enke, Brian; Merline, William

2007-01-01

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Discover the pythagorean theorem using interactive multimedia learning

NASA Astrophysics Data System (ADS)

Adhitama, I.; Sujadi, I.; Pramudya, I.

2018-04-01

In learning process students are required to play an active role in learning. They do not just accept the concept directly from teachers, but also build their own knowledge so that the learning process becomes more meaningful. Based on the observation, when learning Pythagorean theorem, students got difficulty on determining hypotenuse. One of the solution to solve this problem is using an interactive multimedia learning. This article aims to discuss the interactive multimedia as learning media for students. This was a Research and Development (R&D) by using ADDIE model of development. The results obtained was multimedia which was developed proper for students as learning media. Besides, on Phytagorian theorem learning activity we also compare Discovery Learning (DL) model with interactive multimedia and DL without interactive multimedia, and obtained that DL with interactive gave positive effect better than DL without interactive multimedia. It was also obtainde that interactive multimedia can attract and increase the interest ot the students on learning math. Therefore, the use of interactive multimedia on DL procees can improve student learning achievement.

Automated discovery systems and the inductivist controversy

NASA Astrophysics Data System (ADS)

Giza, Piotr

2017-09-01

The paper explores possible influences that some developments in the field of branches of AI, called automated discovery and machine learning systems, might have upon some aspects of the old debate between Francis Bacon's inductivism and Karl Popper's falsificationism. Donald Gillies facetiously calls this controversy 'the duel of two English knights', and claims, after some analysis of historical cases of discovery, that Baconian induction had been used in science very rarely, or not at all, although he argues that the situation has changed with the advent of machine learning systems. (Some clarification of terms machine learning and automated discovery is required here. The key idea of machine learning is that, given data with associated outcomes, software can be trained to make those associations in future cases which typically amounts to inducing some rules from individual cases classified by the experts. Automated discovery (also called machine discovery) deals with uncovering new knowledge that is valuable for human beings, and its key idea is that discovery is like other intellectual tasks and that the general idea of heuristic search in problem spaces applies also to discovery tasks. However, since machine learning systems discover (very low-level) regularities in data, throughout this paper I use the generic term automated discovery for both kinds of systems. I will elaborate on this later on). Gillies's line of argument can be generalised: thanks to automated discovery systems, philosophers of science have at their disposal a new tool for empirically testing their philosophical hypotheses. Accordingly, in the paper, I will address the question, which of the two philosophical conceptions of scientific method is better vindicated in view of the successes and failures of systems developed within three major research programmes in the field: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group and the programme called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett and P. Thagard.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

PubMed

Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

2016-12-16

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

Learning to Love Your Discovery Tool: Strategies for Integrating a Discovery Tool in Face-to-Face, Synchronous, and Asynchronous Instruction

ERIC Educational Resources Information Center

Fawley, Nancy; Krysak, Nikki

2014-01-01

Some librarians embrace discovery tools while others refuse to use them. This lack of consensus can have consequences for student learning when there is inconsistent use, especially in large-scale instruction programs. The authors surveyed academic librarians whose institutions have a discovery tool and who teach information literacy classes in…

Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain

ERIC Educational Resources Information Center

Veermans, Koen; van Joolingen, Wouter; de Jong, Ton

2006-01-01

This article describes a study into the role of heuristic support in facilitating discovery learning through simulation-based learning. The study compares the use of two such learning environments in the physics domain of collisions. In one learning environment (implicit heuristics) heuristics are only used to provide the learner with guidance…

The Foreign-Language Teacher and Cognitive Psychology or Where Do We Go from Here?

ERIC Educational Resources Information Center

Rivers, Wilga M.

Research into the psychology of perception can uncover important discoveries for more efficient learning. There must be increased understanding of the processing of input and the pre-processing of output for improved language instruction. Educators must at the present time be extremely wary of basing what they do in the foreign-language classroom…

Reception Learning and Self-Discovery Learning in Histology: Students' Perceptions and Their Implications for Assessing the Effectiveness of Different Learning Modalities

ERIC Educational Resources Information Center

Campos-Sanchez, Antonio; Martin-Piedra, Miguel-Angel; Carriel, Victor; Gonzalez-Andrades, Miguel; Garzon, Ingrid; Sanchez-Quevedo, Maria-Carmen; Alaminos, Miguel

2012-01-01

Two questionnaires were used to investigate students' perceptions of their motivation to opt for reception learning (RL) or self-discovery learning (SDL) in histology and their choices of complementary learning strategies (CLS). The results demonstrated that the motivation to attend RL sessions was higher than the motivation to attend SDL to gain…

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Scenario Educational Software: Design and Development of Discovery Learning.

ERIC Educational Resources Information Center

Keegan, Mark

This book shows how and why the computer is so well suited to producing discovery learning environments. An examination of the literature outlines four basic modes of instruction: didactic, Socratic, inquiry, and discovery. Research from the fields of education, psychology, and physiology is presented to demonstrate the many strengths of…

Building Faculty Capacity through the Learning Sciences

ERIC Educational Resources Information Center

Moy, Elizabeth; O'Sullivan, Gerard; Terlecki, Melissa; Jernstedt, Christian

2014-01-01

Discoveries in the learning sciences (especially in neuroscience) have yielded a rich and growing body of knowledge about how students learn, yet this knowledge is only half of the story. The other half is "know how," i.e. the application of this knowledge. For faculty members, that means applying the discoveries of the learning sciences…

Triple Scheme of Learning Support Design for Scientific Discovery Learning Based on Computer Simulation: Experimental Research

ERIC Educational Resources Information Center

Zhang, Jianwei; Chen, Qi; Sun, Yanquing; Reid, David J.

2004-01-01

Learning support studies involving simulation-based scientific discovery learning have tended to adopt an ad hoc strategies-oriented approach in which the support strategies are typically pre-specified according to learners' difficulties in particular activities. This article proposes a more integrated approach, a triple scheme for learning…

Technology-Enhanced Discovery

ERIC Educational Resources Information Center

Harrow, Chris; Chin, Lillian

2014-01-01

Exploration, innovation, proof: For students, teachers, and others who are curious, keeping an open mind and being ready to investigate unusual or unexpected properties will always lead to learning something new. Technology can further this process, allowing various behaviors to be analyzed that were previously memorized or poorly understood. This…

A computational account of the development of the generalization of shape information.

PubMed

Doumas, Leonidas A A; Hummel, John E

2010-05-01

Abecassis, Sera, Yonas, and Schwade (2001) showed that young children represent shapes more metrically, and perhaps more holistically, than do older children and adults. How does a child transition from representing objects and events as undifferentiated wholes to representing them explicitly in terms of their attributes? According to RBC (Recognition-by-Components theory; Biederman, 1987), objects are represented as collections of categorical geometric parts ("geons") in particular categorical spatial relations. We propose that the transition from holistic to more categorical visual shape processing is a function of the development of geon-like representations via a process of progressive intersection discovery. We present an account of this transition in terms of DORA (Doumas, Hummel, & Sandhofer, 2008), a model of the discovery of relational concepts. We demonstrate that DORA can learn representations of single geons by comparing objects composed of multiple geons. In addition, as DORA is learning it follows the same performance trajectory as children, originally generalizing shape more metrically/holistically and eventually generalizing categorically. Copyright © 2010 Cognitive Science Society, Inc.

An Inquiry-Based Biochemistry Laboratory Structure Emphasizing Competency in the Scientific Process: A Guided Approach with an Electronic Notebook Format

ERIC Educational Resources Information Center

Hall, Mona L.; 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…

Interactive, Online, Adsorption Lab to Support Discovery of the Scientific Process

NASA Astrophysics Data System (ADS)

Carroll, K. C.; Ulery, A. L.; Chamberlin, B.; Dettmer, A.

2014-12-01

Science students require more than methods practice in lab activities; they must gain an understanding of the application of the scientific process through lab work. Large classes, time constraints, and funding may limit student access to science labs, denying students access to the types of experiential learning needed to motivate and develop new scientists. Interactive, discovery-based computer simulations and virtual labs provide an alternative, low-risk opportunity for learners to engage in lab processes and activities. Students can conduct experiments, collect data, draw conclusions, and even abort a session. We have developed an online virtual lab, through which students can interactively develop as scientists as they learn about scientific concepts, lab equipment, and proper lab techniques. Our first lab topic is adsorption of chemicals to soil, but the methodology is transferrable to other topics. In addition to learning the specific procedures involved in each lab, the online activities will prompt exploration and practice in key scientific and mathematical concepts, such as unit conversion, significant digits, assessing risks, evaluating bias, and assessing quantity and quality of data. These labs are not designed to replace traditional lab instruction, but to supplement instruction on challenging or particularly time-consuming concepts. To complement classroom instruction, students can engage in a lab experience outside the lab and over a shorter time period than often required with real-world adsorption studies. More importantly, students can reflect, discuss, review, and even fail at their lab experience as part of the process to see why natural processes and scientific approaches work the way they do. Our Media Productions team has completed a series of online digital labs available at virtuallabs.nmsu.edu and scienceofsoil.com, and these virtual labs are being integrated into coursework to evaluate changes in student learning.

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Low Data Drug Discovery with One-Shot Learning.

PubMed

Altae-Tran, Han; Ramsundar, Bharath; Pappu, Aneesh S; Pande, Vijay

2017-04-26

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

From Information Center to Discovery System: Next Step for Libraries?

ERIC Educational Resources Information Center

Marcum, James W.

2001-01-01

Proposes a discovery system model to guide technology integration in academic libraries that fuses organizational learning, systems learning, and knowledge creation techniques with constructivist learning practices to suggest possible future directions for digital libraries. Topics include accessing visual and continuous media; information…

Cheminformatics in Drug Discovery, an Industrial Perspective.

PubMed

Chen, Hongming; Kogej, Thierry; Engkvist, Ola

2018-05-18

Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Happy Birthday, Thomas Edison!

ERIC Educational Resources Information Center

Dalton, Edward A.

1997-01-01

Discusses the work and inventions of Thomas Edison and their use in making teachers and students aware of the importance of electrotechnology in their lives and in their futures. Enables students to learn about science, experimentation, research, the process of invention, and the thrill of discovery. Describes educational resources available from…

Learning about (Not by) Osmosis.

ERIC Educational Resources Information Center

Borovoy, Alexander

1991-01-01

Describes the process of osmosis from its discovery by Nollet in 1848 to modern applications. Uses experimental descriptions, illustrations, and photographs to explain osmosis. Discusses the technology of producing perfect filters and their applications in reverse osmosis to purify salt water and to filter blood in kidney machines. (PR)

Area Studies and Special Collections: Shared Challenges, Shared Strength

ERIC Educational Resources Information Center

Carter, Lisa R.; Whittaker, Beth M.

2015-01-01

Special collections and area studies librarians face similar challenges in the changing academic library environment, including the need to articulate the value of these specialized collections and to mainstream processes and practices into larger discovery, teaching, learning, and research efforts. For some institutions, these similarities have…

Heuristic of Self-Discovery: Group Encounter in the Prison College Classroom.

ERIC Educational Resources Information Center

Brasel, Kathleen D.

1982-01-01

Discusses the need for classes in interpersonal communication and values clarification in prison education. The socializing process of discussing personal values and beliefs, examining one's life, and learning of one's commonality with humanity are invaluable to the full education of the individual. (JOW)

ISO 19115 Experiences in NASA's Earth Observing System (EOS) ClearingHOuse (ECHO)

NASA Astrophysics Data System (ADS)

Cechini, M. F.; Mitchell, A.

2011-12-01

Metadata is an important entity in the process of cataloging, discovering, and describing earth science data. As science research and the gathered data increases in complexity, so does the complexity and importance of descriptive metadata. To meet these growing needs, the metadata models required utilize richer and more mature metadata attributes. Categorizing, standardizing, and promulgating these metadata models to a politically, geographically, and scientifically diverse community is a difficult process. An integral component of metadata management within NASA's Earth Observing System Data and Information System (EOSDIS) is the Earth Observing System (EOS) ClearingHOuse (ECHO). ECHO is the core metadata repository for the EOSDIS data centers providing a centralized mechanism for metadata and data discovery and retrieval. ECHO has undertaken an internal restructuring to meet the changing needs of scientists, the consistent advancement in technology, and the advent of new standards such as ISO 19115. These improvements were based on the following tenets for data discovery and retrieval: + There exists a set of 'core' metadata fields recommended for data discovery. + There exists a set of users who will require the entire metadata record for advanced analysis. + There exists a set of users who will require a 'core' set metadata fields for discovery only. + There will never be a cessation of new formats or a total retirement of all old formats. + Users should be presented metadata in a consistent format of their choosing. In order to address the previously listed items, ECHO's new metadata processing paradigm utilizes the following approach: + Identify a cross-format set of 'core' metadata fields necessary for discovery. + Implement format-specific indexers to extract the 'core' metadata fields into an optimized query capability. + Archive the original metadata in its entirety for presentation to users requiring the full record. + Provide on-demand translation of 'core' metadata to any supported result format. Lessons learned by the ECHO team while implementing its new metadata approach to support usage of the ISO 19115 standard will be presented. These lessons learned highlight some discovered strengths and weaknesses in the ISO 19115 standard as it is introduced to an existing metadata processing system.

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.

PubMed

Wójcikowski, Maciej; Zielenkiewicz, Piotr; Siedlecki, Pawel

2015-01-01

There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software. The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery. Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).

Using Rocks: A Discovery Approach to Multi-faceted Learning.

ERIC Educational Resources Information Center

Thomas, John I.

Pupils' natural questioning attitudes lead them to discovery in a learning center, in contrast to the lecture method, by which information is forced on students regardless of their interests. This paper describes learning experiences built around rocks. Materials placed in a rock center (rocks, stones, pebbles, magnifying glasses hammers, and…

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.

PubMed

Eyal-Altman, Noah; Last, Mark; Rubin, Eitan

2017-01-17

Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models. We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends - superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis. PCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.

The role of the basal ganglia in learning and memory: Insight from Parkinson's disease

PubMed Central

2013-01-01

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinson's disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning. PMID:21945835

Rote or Raft? Science and Adventure at a Summer Camp.

ERIC Educational Resources Information Center

Martin, Jenni

1997-01-01

Describes the group dynamics, science discovery processes, and activities involved in building a raft at camp. The project used recycled products and required group cooperation; critical thinking about density, buoyancy, and balance; use of familiar resources in creative ways; and application of previously learned facts. (SAS)

Advanced Quantitative Measurement Methodology in Physics Education Research

ERIC Educational Resources Information Center

Wang, Jing

2009-01-01

The ultimate goal of physics education research (PER) is to develop a theoretical framework to understand and improve the learning process. In this journey of discovery, assessment serves as our headlamp and alpenstock. It sometimes detects signals in student mental structures, and sometimes presents the difference between expert understanding and…

A Computerized Interactive Vocabulary Development System for Advanced Learners.

ERIC Educational Resources Information Center

Kukulska-Hulme, Agnes

1988-01-01

Argues that the process of recording newly encountered vocabulary items in a typical language learning situation can be improved through a computerized system of vocabulary storage based on database management software that improves the discovery and recording of meaning, subsequent retrieval of items for productive use, and memory retention.…

The Past, Present, and Future of Planetary Systems

NASA Astrophysics Data System (ADS)

Vanderburg, Andrew

2017-01-01

We are searching for planets using the Kepler spacecraft in its extended K2 mission. K2 data processing is more challenging than Kepler, but new techniques have permitted the discovery of hundreds of planet candidates. Our discoveries are yielding intriguing insights about the past, present, and future of planetary systems -- that is, the history of how planets might form and migrate, their present-day characteristics, and the ultimate fate of planetary systems. I will discuss what we have learned, in particular from the discovery of a hot Jupiter with close planetary companions, planets orbiting nearby bright stars, and a disintegrating minor planet transiting a white dwarf. This work was supported by the National Science Foundation Graduate Research Fellowship Program.

Using Innovative Tools to Teach Computer Application to Business Students--A Hawthorne Effect or Successful Implementation Here to Stay

ERIC Educational Resources Information Center

Khan, Zeenath Reza

2014-01-01

A year after the primary study that tested the impact of introducing blended learning and guided discovery to help teach computer application to business students, this paper looks into the continued success of using guided discovery and blended learning with learning management system in and out of classrooms to enhance student learning.…

Evaluation of National Institute for Learning Development and Discovery Educational Therapy Program

ERIC Educational Resources Information Center

Frimpong, Prince Christopher

2014-01-01

In Maryland, some Christian schools have enrolled students with learning disabilities (LDs) but do not have any interventional programs at the school to help them succeed academically. The purpose of this qualitative program evaluation was to evaluate the National Institute for Learning Development (NILD) and Discovery Therapy Educational Program…

A Guided Discovery Approach for Learning Metabolic Pathways

ERIC Educational Resources Information Center

Schultz, Emeric

2005-01-01

Learning the wealth of information in metabolic pathways is both challenging and overwhelming for students. A step-by-step guided discovery approach to the learning of the chemical steps in gluconeogenesis and the citric acid cycle is described. This approach starts from concepts the student already knows, develops these further in a logical…

Designing for Discovery Learning of Complexity Principles of Congestion by Driving Together in the TrafficJams Simulation

ERIC Educational Resources Information Center

Levy, Sharona T.; Peleg, Ran; Ofeck, Eyal; Tabor, Naamit; Dubovi, Ilana; Bluestein, Shiri; Ben-Zur, Hadar

2018-01-01

We propose and evaluate a framework supporting collaborative discovery learning of complex systems. The framework blends five design principles: (1) individual action: amidst (2) social interactions; challenged with (3) multiple tasks; set in (4) a constrained interactive learning environment that draws attention to (5) highlighted target…

Low Data Drug Discovery with One-Shot Learning

PubMed Central

2017-01-01

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. Model.2015, 55, 263–27425635324). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). PMID:28470045

Discovery stories in the science classroom

NASA Astrophysics Data System (ADS)

Arya, Diana Jaleh

School science has been criticized for its lack of emphasis on the tentative, dynamic nature of science as a process of learning more about our world. This criticism is the guiding force for this present body of work, which focuses on the question: what are the educational benefits for middle school students of reading texts that highlight the process of science in the form of a discovery narrative? This dissertation traces my journey through a review of theoretical perspectives of narrative, an analysis of first-hand accounts of scientific discovery, the complex process of developing age-appropriate, cohesive and engaging science texts for middle school students, and a comparison study (N=209) that seeks to determine the unique benefits of the scientific discovery narrative for the interest in and retained understanding of conceptual information presented in middle school science texts. A total of 209 middle school participants in nine different classrooms from two different schools participated in the experimental study. Each subject read two science texts that differed in topic (the qualities of and uses for radioactive elements and the use of telescopic technology to see planets in space) and genre (the discovery narrative and the "conceptually known exposition" comparison text). The differences between the SDN and CKE versions for each topic were equivalent in all possible ways (initial introduction, overall conceptual accuracy, elements of human interest, coherence and readability level), save for the unique components of the discovery narrative (i.e., love for their work, acknowledgement of the known, identification of the unknown and the explorative or experimental process to discovery). Participants generally chose the discovery narrative version as the more interesting of the two texts. Additional findings from the experimental study suggest that science texts in the form of SDNs elicit greater long-term retention of key conceptual information, especially when the readers have little prior knowledge of a given topic. Further, ethnic minority groups of lower socio-economic level (i.e., Latin and African-American origins) demonstrated an even greater benefit from the SDN texts, suggesting that a scientist's story of discovery can help to close the gap in academic performance in science.

Successes in drug discovery and design.

PubMed

2004-04-01

The Society for Medicines Research (SMR) held a one-day meeting on case histories in drug discovery on December 4, 2003, at the National Heart and Lung Institute in London. These meetings have been organized by the SMR biannually for many years, and this latest meeting proved extremely popular, attracting a capacity audience of more than 130 registrants. The purpose of these meetings is educational; they allow those interested in drug discovery to hear key learnings from recent successful drug discovery programs. There was no overall linking theme between the talks, other than each success story has led to the introduction of a new and improved product of therapeutic use. The drug discovery stories covered in the meeting were extremely varied and, put together, they emphasized that each successful story is unique and special. This meeting is also special for the SMR because it presents the "SMR Award for Drug Discovery" in recognition of outstanding achievement and contribution in the area. It should be remembered that drug discovery is an extremely risky business and an extremely costly and complicated process in which the success rate is, at best, low. (c) 2004 Prous Science. All rights reserved.

Hess Deep Interactive Lab: Exploring the Structure and Formation of the Oceanic Crust through Hands-On Models and Online Tools

NASA Astrophysics Data System (ADS)

Kurtz, N.; Marks, N.; Cooper, S. K.

2014-12-01

Scientific ocean drilling through the International Ocean Discovery Program (IODP) has contributed extensively to our knowledge of Earth systems science. However, many of its methods and discoveries can seem abstract and complicated for students. Collaborations between scientists and educators/artists to create accurate yet engaging demonstrations and activities have been crucial to increasing understanding and stimulating interest in fascinating geological topics. One such collaboration, which came out of Expedition 345 to the Hess Deep Rift, resulted in an interactive lab to explore sampling rocks from the usually inacessible lower oceanic crust, offering an insight into the geological processes that form the structure of the Earth's crust. This Hess Deep Interactive Lab aims to explain several significant discoveries made by oceanic drilling utilizing images of actual thin sections and core samples recovered from IODP expeditions. . Participants can interact with a physical model to learn about the coring and drilling processes, and gain an understanding of seafloor structures. The collaboration of this lab developed as a need to explain fundamental notions of the ocean crust formed at fast-spreading ridges. A complementary interactive online lab can be accessed at www.joidesresolution.org for students to engage further with these concepts. This project explores the relationship between physical and on-line models to further understanding, including what we can learn from the pros and cons of each.

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 Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

ERIC Educational Resources Information Center

Kunsting, Josef; Wirth, Joachim; Paas, Fred

2011-01-01

Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

Pre-Service Teachers' Cognitive Competencies to Use the Approaches in Mathematics Teaching: Discovery Learning

ERIC Educational Resources Information Center

Yilmaz, Rezan

2014-01-01

This study aims to present the cognitive competences of the pre-service teacher about discovery learning approach in mathematical education. The study was conducted with 37 mathematics pre-service teachers who study Special Teaching Methods lesson in a state university in Turkey. Throughout the lesson, the approaches used in learning were examined…

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

PubMed Central

Smith, J. David; Ell, Shawn W.

2015-01-01

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so. PMID:26332587

The Power of Music: The Use of Music Protocols to Enhance Neurological Function

ERIC Educational Resources Information Center

Summa-Chadwick, Martha

2009-01-01

Discoveries, reached through scientific and technological advances in the evidence-based empirical domain, about how the body physiologically responds to music have opened new possibilities for developing therapeutic archetypes to actively channel specific aspects of music to assist in the learning processes of children with special needs. The…

The Implementation of Team-Based Discovery Learning to Improve Students' Ability in Writing Research Proposal

ERIC Educational Resources Information Center

Arifani, Yudhi

2016-01-01

Writing research proposal in educational setting is a very complex process involving variety of elements. Consequently, analyzing the complex elements from introduction to data analysis sections in order to yield convinced research proposal writing through reviewing reputable journal articles is worth-contributing. The objectives of this research…

No Man (or Woman) Is an Island: Information Literacy, Affordances and Communities of Practice

ERIC Educational Resources Information Center

Lloyd, Anne

2005-01-01

Current understandings of information literacy are drawn from research within library and educational contexts, in which information literacy is identified as a suite of skills that facilitate the learning process. In these contexts, information literacy education focuses on information discovery through the development of a systematic set of…

Stars, Galaxies, Cosmos: The Past Decade, the Next Decade.

ERIC Educational Resources Information Center

Rubin, Vera C.

1980-01-01

This article focuses on discoveries in astronomy during the past 20 years using a wide range of observing techniques. The future is seen as a time when astronomers will learn more about the distribution of mass in the universe, the physics of energetic sources, and the intricate interconnections of astrophysical processes. (Author/SA)

A machine-learned computational functional genomics-based approach to drug classification.

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-12-01

The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing.

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

Substructure Discovery of Macro-Operators

DTIC Science & Technology

1988-05-01

Aspects of Scientific Discovery," in Machine Learning: An Artifcial Intelligence Approach, Vol. II. R. S. Michalski, J. G. Carbonell and T. M. Mitchell (ed... intelligent robot using this system could learn how to perform new tasks by watching tasks being performed by someone else. even if the robot does not possess...Substructure Discovery of Macro-Operators* Bradley L. Whitehall Artificial Intelligence Research Group Coordinated Science Laboratory ’University of Illinois at

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

PubMed Central

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Simulated drug discovery process to conduct a synoptic assessment of pharmacy students.

PubMed

Richardson, Alan; Curtis, Anthony D M; Moss, Gary P; Pearson, Russell J; White, Simon; Rutten, Frank J M; Perumal, Dhaya; Maddock, Katie

2014-03-12

OBJECTIVE. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. DESIGN. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. ASSESSMENT. Students' ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. CONCLUSION. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Simulated Drug Discovery Process to Conduct a Synoptic Assessment of Pharmacy Students

PubMed Central

Curtis, Anthony D.M.; Moss, Gary P.; Pearson, Russell J.; White, Simon; Rutten, Frank J.M.; Perumal, Dhaya; Maddock, Katie

2014-01-01

Objective. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. Design. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. Assessment. Students’ ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. Conclusion. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy. PMID:24672074

Neurophysiological mechanisms involved in language learning in adults

PubMed Central

Rodríguez-Fornells, Antoni; Cunillera, Toni; Mestres-Missé, Anna; de Diego-Balaguer, Ruth

2009-01-01

Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished. PMID:19933142

Learning from the past for TB drug discovery in the future

PubMed Central

Mikušová, Katarína; Ekins, Sean

2016-01-01

Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward. PMID:27717850

The role of the basal ganglia in learning and memory: insight from Parkinson's disease.

PubMed

Foerde, Karin; Shohamy, Daphna

2011-11-01

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinson's disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning. Copyright © 2011 Elsevier Inc. All rights reserved.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Strengthening ecological mindfulness through hybrid learning in vital coalitions

NASA Astrophysics Data System (ADS)

Sol, Jifke; Wals, Arjen E. J.

2015-03-01

In this contribution a key policy `tool' used in the Dutch Environmental Education and Learning for Sustainability Policy framework is introduced as a means to develop a sense of place and associated ecological mindfulness. The key elements of this tool, called the vital coalition, are described while an example of its use in practice, is analysed using a form of reflexive monitoring and evaluation. The example focuses on a multi-stakeholder learning process around the transformation of a somewhat sterile pre-school playground into an intergenerational green place suitable for play, discovery and engagement. Our analysis of the policy-framework and the case leads us to pointing out the importance of critical interventions at so-called tipping points within the transformation process and a discussion of the potential of hybrid learning in vital coalitions in strengthening ecological mindfulness. This paper does not focus on establishing an evidence base for the causality between this type of learning and a change in behavior or mindfulness among participants as a result contributing to a vital coalition but rather focusses on the conditions, processes and interventions that allow for such learning to take place in the first place.

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Museums, Adventures, Discovery Activities: Gifted Curriculum Intrinsically Differentiated.

ERIC Educational Resources Information Center

Haensly, Patricia A.

This paper discusses how museums, adventure programs, and discovery activities can become an intrinsically differentiated gifted curriculum for gifted learners. Museums and adventure programs are a forum for meaningful learning activities. The contextual characteristics of effectively designed settings for learning activities can, if the…

Formal and Informal Context Factors as Contributors to Student Engagement in a Guided Discovery-Based Program of Game Design Learning

ERIC Educational Resources Information Center

Reynolds, Rebecca; Chiu, Ming Ming

2013-01-01

This paper explored informal (after-school) and formal (elective course in-school) learning contexts as contributors to middle-school student attitudinal changes in a guided discovery-based and blended e-learning program in which students designed web games and used social media and information resources for a full school year. Formality of the…

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

The Laboratory. Guides for the Improvement of Instruction in Higher Education, No. 9.

ERIC Educational Resources Information Center

Alexander, Lawrence T.; And Others

This guide for the improvement of instruction in higher education is designed to aid the educator in planning and conducting laboratory instruction. The examples used refer primarily to science laboratories. Topics discussed include: deciding whether or not to use the laboratory method (with a discussion of discovery learning or the processes of…

10 CFR 708.2 - What are the definitions of terms used in this part?

Code of Federal Regulations, 2014 CFR

2014-01-01

... type of contract with DOE to perform work directly related to activities at DOE-owned or -leased..., but only with respect to work related to activities at DOE-owned or -leased facilities. Day means a calendar day. Discovery means a process used to enable the parties to learn about each other's evidence...

10 CFR 708.2 - What are the definitions of terms used in this part?

Code of Federal Regulations, 2012 CFR

2012-01-01

... type of contract with DOE to perform work directly related to activities at DOE-owned or -leased..., but only with respect to work related to activities at DOE-owned or -leased facilities. Day means a calendar day. Discovery means a process used to enable the parties to learn about each other's evidence...

10 CFR 708.2 - What are the definitions of terms used in this part?

Code of Federal Regulations, 2011 CFR

2011-01-01

... type of contract with DOE to perform work directly related to activities at DOE-owned or -leased..., but only with respect to work related to activities at DOE-owned or -leased facilities. Day means a calendar day. Discovery means a process used to enable the parties to learn about each other's evidence...

10 CFR 708.2 - What are the definitions of terms used in this part?

Code of Federal Regulations, 2013 CFR

2013-01-01

... type of contract with DOE to perform work directly related to activities at DOE-owned or -leased..., but only with respect to work related to activities at DOE-owned or -leased facilities. Day means a calendar day. Discovery means a process used to enable the parties to learn about each other's evidence...

Self-Reported Student Confidence in Troubleshooting Ability Increases after Completion of an Inquiry-Based PCR Practical

ERIC Educational Resources Information Center

Cook, Anthony L.; Snow, Elizabeth T.; Binns, Henrica; Cook, Peta S.

2015-01-01

Inquiry-based learning (IBL) activities are complementary to the processes of laboratory discovery, as both are focused on producing new findings through research and inquiry. Here, we describe the results of student surveys taken pre- and postpractical to an IBL undergraduate practical on PCR. Our analysis focuses primarily student perceptions of…

Astronomy Resources for Intercurricular Elementary Science (ARIES): Exploring Motion and Forces. What Works Clearinghouse Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2012

2012-01-01

"ARIES: Exploring Motion and Forces" is a physical science curriculum for students in grades 5-8 that employs 18 inquiry-centered, hands-on lessons called "explorations." The curriculum draws upon students' curiosity to explore phenomena, allowing for a discovery-based learning process. Group-centered lab work is designed to…

Detangling Spaghetti: Tracking Deep Ocean Currents in the Gulf of Mexico

ERIC Educational Resources Information Center

Curran, Mary Carla; Bower, Amy S.; Furey, Heather H.

2017-01-01

Creation of physical models can help students learn science by enabling them to be more involved in the scientific process of discovery and to use multiple senses during investigations. This activity achieves these goals by having students model ocean currents in the Gulf of Mexico. In general, oceans play a key role in influencing weather…

Immediate Dissemination of Student Discoveries to a Model Organism Database Enhances Classroom-Based Research Experiences

ERIC Educational Resources Information Center

Wiley, Emily A.; Stover, Nicholas A.

2014-01-01

Use of inquiry-based research modules in the classroom has soared over recent years, largely in response to national calls for teaching that provides experience with scientific processes and methodologies. To increase the visibility of in-class studies among interested researchers and to strengthen their impact on student learning, we have…

Using the Moon as a Tool for Discovery-Oriented Learning.

ERIC Educational Resources Information Center

Cummins, Robert Hays; Ritger, Scott David; Myers, Christopher Adam

1992-01-01

Students test the hypothesis that the moon revolves east to west around the earth, determine by observation approximately how many degrees the moon revolves per night, and develop a scale model of the earth-sun-moon system in this laboratory exercise. Students are actively involved in the scientific process and are introduced to the importance of…

Discovery Stories in the Science Classroom

ERIC Educational Resources Information Center

Arya, Diana Jaleh

2010-01-01

School science has been criticized for its lack of emphasis on the tentative, dynamic nature of science as a process of learning more about our world. This criticism is the guiding force for this present body of work, which focuses on the question: what are the educational benefits for middle school students of reading texts that highlight the…

Early patterns of commercial activity in graphene

NASA Astrophysics Data System (ADS)

Shapira, Philip; Youtie, Jan; Arora, Sanjay

2012-03-01

Graphene, a novel nanomaterial consisting of a single layer of carbon atoms, has attracted significant attention due to its distinctive properties, including great strength, electrical and thermal conductivity, lightness, and potential benefits for diverse applications. The commercialization of scientific discoveries such as graphene is inherently uncertain, with the lag time between the scientific development of a new technology and its adoption by corporate actors revealing the extent to which firms are able to absorb knowledge and engage in learning to implement applications based on the new technology. From this perspective, we test for the existence of three different corporate learning and activity patterns: (1) a linear process where patenting follows scientific discovery; (2) a double-boom phenomenon where corporate (patenting) activity is first concentrated in technological improvements and then followed by a period of technology productization; and (3) a concurrent model where scientific discovery in publications occurs in parallel with patenting. By analyzing corporate publication and patent activity across country and application lines, we find that, while graphene as a whole is experiencing concurrent scientific development and patenting growth, country- and application-specific trends offer some evidence of the linear and double-boom models.

Reinventing Discovery Learning: A Field-Wide Research Program

ERIC Educational Resources Information Center

Abrahamson, Dor; Kapur, Manu

2018-01-01

Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based…

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

PubMed Central

Wolverton, Christopher; Hattrick-Simpers, Jason; Mehta, Apurva

2018-01-01

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict. PMID:29662953

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

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

Ren, Fang; Ward, Logan; Williams, Travis

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, butmore » there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.« less

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

DOE PAGES

Ren, Fang; Ward, Logan; Williams, Travis; ...

2018-04-01

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, butmore » there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.« less

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.

Comparison of Caenorhabditis elegans NLP peptides with arthropod neuropeptides.

PubMed

Husson, Steven J; Lindemans, Marleen; Janssen, Tom; Schoofs, Liliane

2009-04-01

Neuropeptides are small messenger molecules that can be found in all metazoans, where they govern a diverse array of physiological processes. Because neuropeptides seem to be conserved among pest species, selected peptides can be considered as attractive targets for drug discovery. Much can be learned from the model system Caenorhabditis elegans because of the availability of a sequenced genome and state-of-the-art postgenomic technologies that enable characterization of endogenous peptides derived from neuropeptide-like protein (NLP) precursors. Here, we provide an overview of the NLP peptide family in C. elegans and discuss their resemblance with arthropod neuropeptides and their relevance for anthelmintic discovery.

A new approach to build VPLS with auto-discovery mechanism

NASA Astrophysics Data System (ADS)

Dong, Ximing; Yu, Shaohua

2005-11-01

VPLS is the key technology implemented to provide Layer 2 bridge-like services, connecting dispersed locations to work in a switched LAN over an MPLS backbone. However, implementing VPLS requires creating a complex matrix of services and locations that quickly becomes difficult to configure and maintain. To address this complexity, this paper proposes a new approach to automate the configuration and maintenance of VPLS networks, a node-discovery process in which each router advertises its VPLS-enabled status and capabilities to all other routers. Our approach can be summarized into four steps. (1) Discover other VPLS PE nodes with VPLS capabilities and create the VPLS capable PE routers list. We introduce a finite state machine which includes four states to illustrate the process how a VPLS peer can be discovered and the peer relations be kept alive. (2) Build MPLS LSP tunnels to all the PE routers in the list, according to the advertised VPLS protocol capabilities. (3) Use the lists to create targeted-LDP sessions for VPLS services discovery. (4) VC label assignment. The PE edge routers exchanges messages to define VC labels and bind them with each built PWE. The suggested auto-discovery mechanism is sensitive to any service provider's topology change and customer's service modification. The dynamic process for the FIB building, MAC address learning and withdrawal, is also covered as the result of VPLS auto-discovery. The suggested mechanism can be implemented as a software module and could be seamlessly integrated with currently deployed Metro Ethernet routing and switching platform.

Deep learning for neuroimaging: a validation study.

PubMed

Plis, Sergey M; Hjelm, Devon R; Salakhutdinov, Ruslan; Allen, Elena A; Bockholt, Henry J; Long, Jeffrey D; Johnson, Hans J; Paulsen, Jane S; Turner, Jessica A; Calhoun, Vince D

2014-01-01

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika; Avrekh, Ilya; Matthews, Bryan; Sharma, Manali; Oza, Nikunj

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Inference and Discovery in an Exploratory Laboratory. Technical Report No. 10.

ERIC Educational Resources Information Center

Shute, Valerie; And Others

This paper describes the results of a study done as part of a research program investigating the use of computer-based laboratories to support self-paced discovery learning in related to microeconomics, electricity, and light refraction. Program objectives include maximizing the laboratories' effectiveness in helping students learn content…

Scientific Discoveries the Year I Was Born

ERIC Educational Resources Information Center

Cherif, Abour

2012-01-01

The author has successfully used a learning activity titled "The Year I Was Born" to motivate students to conduct historical research and present key scientific discoveries from their birth year. The activity promotes writing, helps students enhance their scientific literacy, and also improves their attitude toward the learning of science. As one…

Effects of Generative Video on Students' Scientific Problem Posing. Draft.

ERIC Educational Resources Information Center

Hickey, Daniel T.; Petrosino, Anthony

A central premise of the discovery-learning and progressive education movements was that the child's own questions are the most appropriate starting point for instruction. Recent advances present new opportunities for discovery-oriented learning. This project has been attempting to create a classroom environment which affords students the…

Instructional and Learning Modes in Math. Module CMM:006:02.

ERIC Educational Resources Information Center

Rexroat, Melvin E.

This is the second module in a series on mathematics methods and materials for preservice elementary teachers. This module focuses on three instructional and learning modes: expository, guided discovery, and inquiry (pure discovery). Objectives for the module are listed, the prerequisites are stated, pre- and post-assessment standards are…

Do quality improvement collaboratives' educational components match the dominant learning style preferences of the participants?

PubMed

Weggelaar-Jansen, Anne Marie; van Wijngaarden, Jeroen; Slaghuis, Sarah-Sue

2015-06-20

Quality improvement collaboratives are used to improve healthcare by various organizations. Despite their popularity literature shows mixed results on their effectiveness. A quality improvement collaborative can be seen as a temporary learning organization in which knowledge about improvement themes and methods is exchanged. In this research we studied: Does the learning approach of a quality improvement collaborative match the learning styles preferences of the individual participants and how does that affect the learning process of participants? This research used a mixed methods design combining a validated learning style questionnaire with data collected in the tradition of action research methodology to study two Dutch quality improvement collaboratives. The questionnaire is based on the learning style model of Ruijters and Simons, distinguishing five learning style preferences: Acquisition of knowledge, Apperception from others, Discovery of new insights, Exercising in fictitious situations and Participation with others. The most preferred learning styles of the participants were Discovery and Participation. The learning style Acquisition was moderately preferred and Apperception and Exercising were least preferred. The educational components of the quality improvement collaboratives studied (national conferences, half-day learning sessions, faculty site visits and use of an online tool) were predominantly associated with the learning styles Acquisition and Apperception. We observed a decrease in attendance to the learning activities and non-conformance with the standardized set goals and approaches. We conclude that the participants' satisfaction with the offered learning approach changed over time. The lacking match between these learning style preferences and the learning approach in the educational components of the quality improvement collaboratives studied might be the reason why the participants felt they did not gain new insights and therefore ceased their participation in the collaborative. This study provides guidance for future organisers and participants of quality improvement collaboratives about which learning approaches will best suit the participants and enhance improvement work.

Attention without intention: explicit processing and implicit goal-setting in family medicine residents' written reflections.

PubMed

Shaughnessy, Allen F; Allen, Lucas; Duggan, Ashley

2017-05-01

Reflection, a process of self-analysis to promote learning through better understanding of one's experiences, is often used to assess learners' metacognitive ability. However, writing reflective exercises, not submitted for assessment, may allow learners to explore their experiences and indicate learning and professional growth without explicitly connecting to intentional sense-making. To identify core components of learning about medicine or medical education from family medicine residents' written reflections. Family medicine residents' wrote reflections about their experiences throughout an academic year. Qualitative thematic analysis to identify core components in 767 reflections written by 33 residents. We identified four themes of learning: 'Elaborated reporting' and 'metacognitive monitoring' represent explicit, purposeful self-analysis that typically would be characterised as reflective learning about medicine. 'Simple reporting' and 'goal setting' signal an analysis of experience that indicates learning and professional growth but that is overlooked as a component of learning. Identified themes elucidate the explicit and implicit forms of written reflection as sense-making and learning. An expanded theoretical understanding of reflection as inclusive of conscious sense-making as well as implicit discovery better enables the art of physician self-development.

A renaissance of neural networks in drug discovery.

PubMed

Baskin, Igor I; Winkler, David; Tetko, Igor V

2016-08-01

Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

40 CFR 22.19 - Prehearing information exchange; prehearing conference; other discovery.

Code of Federal Regulations, 2010 CFR

2010-07-01

... method of discovery sought, provide the proposed discovery instruments, and describe in detail the nature... finding that: (i) The information sought cannot reasonably be obtained by alternative methods of discovery... promptly supplement or correct the exchange when the party learns that the information exchanged or...

The Discovery Method in Training.

ERIC Educational Resources Information Center

Belbin, R. M.

In the form of a discussion between faceless people, this booklet concerns discovery learning and its advantages. Subjects covered in the discussions are: Introducing the Discovery Method; An Experiment with British Railways; The OECD Research Projects in U.S.A., Austria, and Sweden; How the Discovery Method Differs from Other Methods; Discovery…

NASA's Universe of Learning: Engaging Learners in Discovery

NASA Astrophysics Data System (ADS)

Cominsky, L.; Smith, D. A.; Lestition, K.; Greene, M.; Squires, G.

2016-12-01

NASA's Universe of Learning is one of 27 competitively awarded education programs selected by NASA's Science Mission Directorate (SMD) to enable scientists and engineers to more effectively engage with learners of all ages. The NASA's Universe of Learning program is created through a partnership between the Space Telescope Science Institute, Chandra X-ray Center, IPAC at Caltech, Jet Propulsion Laboratory Exoplanet Exploration Program, and Sonoma State University. The program will connect the scientists, engineers, science, technology and adventure of NASA Astrophysics with audience needs, proven infrastructure, and a network of over 500 partners to advance the objectives of SMD's newly restructured education program. The multi-institutional team will develop and deliver a unified, consolidated suite of education products, programs, and professional development offerings that spans the full spectrum of NASA Astrophysics, including the Exoplanet Exploration theme. Program elements include enabling educational use of Astrophysics mission data and offering participatory experiences; creating multimedia and immersive experiences; designing exhibits and community programs; providing professional development for pre-service educators, undergraduate instructors, and informal educators; and, producing resources for special needs and underserved/underrepresented audiences. This presentation will provide an overview of the program and process for mapping discoveries to products and programs for informal, lifelong, and self-directed learning environments.

The Thrill of Data Discovery: "Aha" Moment Helps Forge Bonds that Lead to Teamwork, a Literacy Initiative, and Higher Scores

ERIC Educational Resources Information Center

Repetti, Dawn M.

2004-01-01

When teachers at Madison Elementary School in Wauwatosa, Wisconsin attended a class to examine test data, they started a change process that led the whole school to learn differently--from teachers to students. This article discusses on how whole-faculty study teams have created stronger professional connections and collaboration between teachers…

Discovering ways to improve crop production and plant quality [Chapter 17

Treesearch

Kim M. Wilkinson

2009-01-01

Working with plants is a process of discovery. Being curious and aware, paying close attention, and staying open and adaptive are important practices. Books and people can help us learn about plants in the nursery, but the very best teachers are the plants themselves. "Research" is simply paying close attention, tracking what is happening and what is causing...

Machine learning properties of binary wurtzite superlattices

DOE PAGES

Pilania, G.; Liu, X. -Y.

2018-01-12

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

Machine learning properties of binary wurtzite superlattices

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

Pilania, G.; Liu, X. -Y.

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

Searching for Buried Treasure: Uncovering Discovery in Discovery-Based Learning

ERIC Educational Resources Information Center

Chase, Kiera; Abrahamson, Dor

2018-01-01

Forty 4th and 9th grade students participated individually in tutorial interviews centered on a problem-solving activity designed for learning basic algebra mechanics through diagrammatic modeling of an engaging narrative about a buccaneering giant burying and unearthing her treasure on a desert island. Participants were randomly assigned to…

Effects of Discovery Learning and Student Assessment on Academic Success

ERIC Educational Resources Information Center

Suphi, Nilgün; Yaratan, Hüseyin

2016-01-01

In this study the effect of Discovery Learning and course evaluation based on Bloom's Taxonomy on the academic success of undergraduate students in Northern Cyprus was investigated. One demographic questionnaire was distributed to 829 students and two questionnaires were distributed to these students' instructors in order to collect information on…

Re-Vitalizing the First Year Class through Student Engagement and Discovery Learning

ERIC Educational Resources Information Center

Steuter, Erin; Doyle, Judith

2010-01-01

The first year course in Sociology at Mount Allison University introduced students to social issues via dynamic class interactions and assignments that are designed to build conceptual and applied skills. Developments to the course organization have maximized the opportunities for discovery learning and have made the class an enjoyable teaching…

A systematic mapping study of process mining

NASA Astrophysics Data System (ADS)

Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo

2018-05-01

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

Discovery Curriculum: For Use with Middle Grade Students in or out of the Classroom.

ERIC Educational Resources Information Center

Wickless, Mimi

This teaching guide contains the Discovery Curriculum which was extensively field tested at The National Arbor Day Foundation's Discovery Camp. The Discovery Curriculum is designed to promote wise environmental stewardship through relevant, active learning opportunities. Goals for each participant include: (1) be aware of and able to cite examples…

A machine learning approach to computer-aided molecular design

NASA Astrophysics Data System (ADS)

Bolis, Giorgio; Di Pace, Luigi; Fabrocini, Filippo

1991-12-01

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called "first-person" knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

An Arrival and Departure Time Predictor for Scheduling Communication in Opportunistic IoT

PubMed Central

Pozza, Riccardo; Georgoulas, Stylianos; Moessner, Klaus; Nati, Michele; Gluhak, Alexander; Krco, Srdjan

2016-01-01

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process. PMID:27827909

An Arrival and Departure Time Predictor for Scheduling Communication in Opportunistic IoT.

PubMed

Pozza, Riccardo; Georgoulas, Stylianos; Moessner, Klaus; Nati, Michele; Gluhak, Alexander; Krco, Srdjan

2016-11-04

In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.

Data set for Journal article "The shale gas revolution: barriers, sustainability, and emerging opportunities"

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

Middleton, Richard Stephen

Shale gas and hydraulic refracturing has revolutionized the US energy sector in terms of prices, consumption, and CO 2 emissions. However, key questions remain including environmental concerns and extraction efficiencies that are leveling off. For the first time, we identify key discoveries, lessons learned, and recommendations from this shale gas revolution through extensive data mining and analysis of 23 years of production from 20,000 wells. Discoveries include identification of a learning-bydoing process where disruptive technology innovation led to a doubling in shale gas extraction, how refracturing with emerging technologies can transform existing wells, and how overall shale gas production ismore » actually dominated by long-term tail production rather than the high-profile initial exponentially-declining production in the first 12 months. We hypothesize that tail production can be manipulated, through better fracturing techniques and alternative working fluids such as CO 2, to increase shale gas recovery and minimize environmental impacts such as through carbon sequestration.« less

The shale gas revolution: Barriers, sustainability, and emerging opportunities

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

Middleton, Richard S.; Gupta, Rajan; Hyman, Jeffrey D.

Shale gas and hydraulic refracturing has revolutionized the US energy sector in terms of prices, consumption, and CO 2 emissions. However, key questions remain including environmental concerns and extraction efficiencies that are leveling off. For the first time, we identify key discoveries, lessons learned, and recommendations from this shale gas revolution through extensive data mining and analysis of 23 years of production from 20,000 wells. Discoveries include identification of a learning-by-doing process where disruptive technology innovation led to a doubling in shale gas extraction, how refracturing with emerging technologies can transform existing wells, and how overall shale gas production ismore » actually dominated by long-term tail production rather than the high-profile initial exponentially-declining production in the first 12 months. We hypothesize that tail production can be manipulated, through better fracturing techniques and alternative working fluids such as CO 2, to increase shale gas recovery and minimize environmental impacts such as through carbon sequestration.« less

Machine learning models for lipophilicity and their domain of applicability.

PubMed

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Laak, Antonius Ter; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-01-01

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

JR Live: Lessons Learned from Ship-to-Shore Interactions with the JOIDES Resolution

NASA Astrophysics Data System (ADS)

Cooper, S. K.

2016-02-01

Live ship-to-shore events have been conducted regularly from the International Ocean Discovery Program (IODP) research vessel JOIDES Resolution since 2009. These 45-minute events have reached thousands of students, educators and members of the general public with the JR's cutting edge science and technology and the excitement of discovery, science process and careers. Conducted by trained on-board Education/Outreach Officers on board the JR's two-month expeditions, the programs vary over time and have evolved with available technology. Each event incorporates collaboration between the Education Officer, scientists who are a part of the expedition science party, and requests from shore-side audiences. These collaborations have been successful in igniting interest among students and educators, providing scientists with outreach experiences and in meeting education standards and goals. Over the past six years, many lessons have been learned about procedures, technology, content, follow-up and impact. This session will share some of these lessons, identify opportunities for collaboration and engagement, and explore growth opportunities and directions.

The shale gas revolution: Barriers, sustainability, and emerging opportunities

DOE PAGES

Middleton, Richard S.; Gupta, Rajan; Hyman, Jeffrey D.; ...

2017-08-01

Shale gas and hydraulic refracturing has revolutionized the US energy sector in terms of prices, consumption, and CO 2 emissions. However, key questions remain including environmental concerns and extraction efficiencies that are leveling off. For the first time, we identify key discoveries, lessons learned, and recommendations from this shale gas revolution through extensive data mining and analysis of 23 years of production from 20,000 wells. Discoveries include identification of a learning-by-doing process where disruptive technology innovation led to a doubling in shale gas extraction, how refracturing with emerging technologies can transform existing wells, and how overall shale gas production ismore » actually dominated by long-term tail production rather than the high-profile initial exponentially-declining production in the first 12 months. We hypothesize that tail production can be manipulated, through better fracturing techniques and alternative working fluids such as CO 2, to increase shale gas recovery and minimize environmental impacts such as through carbon sequestration.« less

Exploiting multiple sources of information in learning an artificial language: human data and modeling.

PubMed

Perruchet, Pierre; Tillmann, Barbara

2010-03-01

This study investigates the joint influences of three factors on the discovery of new word-like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word-likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word-like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of different models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad-hoc modifications, while relying exclusively on general-purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. Copyright © 2009 Cognitive Science Society, Inc.

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

Cytokines as endogenous pyrogens.

PubMed

Dinarello, C A

1999-03-01

Cytokines are pleiotropic molecules mediating several pathologic processes. Long before the discovery of cytokines as immune system growth factors or as bone marrow stimulants, investigators learned a great deal about cytokines when they studied them as the endogenous mediators of fever. The terms "granulocytic" or "endogenous pyrogen" were used to describe substances with the biologic property of fever induction. Today, we recognize that pyrogenicity is a fundamental biologic property of several cytokines and hence the clinically recognizeable property of fever links host perturbations during disease with fundamental perturbations in cell biology. In this review, the discoveries made on endogenous pyrogens are revisited, with insights into the importance of the earlier work to the present-day understanding of cytokines in health and in disease.

A Knowledge Discovery framework for Planetary Defense

NASA Astrophysics Data System (ADS)

Jiang, Y.; Yang, C. P.; Li, Y.; Yu, M.; Bambacus, M.; Seery, B.; Barbee, B.

2016-12-01

Planetary Defense, a project funded by NASA Goddard and the NSF, is a multi-faceted effort focused on the mitigation of Near Earth Object (NEO) threats to our planet. Currently, there exists a dispersion of information concerning NEO's amongst different organizations and scientists, leading to a lack of a coherent system of information to be used for efficient NEO mitigation. In this paper, a planetary defense knowledge discovery engine is proposed to better assist the development and integration of a NEO responding system. Specifically, we have implemented an organized information framework by two means: 1) the development of a semantic knowledge base, which provides a structure for relevant information. It has been developed by the implementation of web crawling and natural language processing techniques, which allows us to collect and store the most relevant structured information on a regular basis. 2) the development of a knowledge discovery engine, which allows for the efficient retrieval of information from our knowledge base. The knowledge discovery engine has been built on the top of Elasticsearch, an open source full-text search engine, as well as cutting-edge machine learning ranking and recommendation algorithms. This proposed framework is expected to advance the knowledge discovery and innovation in planetary science domain.

The Effect of Discovery Learning Method Application on Increasing Students' Listening Outcome and Social Attitude

ERIC Educational Resources Information Center

Hanafi

2016-01-01

Curriculum of 2013 has been started in schools appointed as the implementer. This curriculum, for English subject demands the students to improve their skills. To reach this one of the suggested methods is discovery learning since this method is considered appropriate to implement for increasing the students' ability especially to fulfill minimum…

Enhancing Learning Environments through Solution-based Knowledge Discovery Tools: Forecasting for Self-Perpetuating Systemic Reform.

ERIC Educational Resources Information Center

Tsantis, Linda; Castellani, John

2001-01-01

This article explores how knowledge-discovery applications can empower educators with the information they need to provide anticipatory guidance for teaching and learning, forecast school and district needs, and find critical markers for making the best program decisions for children and youth with disabilities. Data mining for schools is…

Discovery Learning: Zombie, Phoenix, or Elephant?

ERIC Educational Resources Information Center

Bakker, Arthur

2018-01-01

Discovery learning continues to be a topic of heated debate. It has been called a zombie, and this special issue raises the question whether it may be a phoenix arising from the ashes to which the topic was burnt. However, in this commentary I propose it is more like an elephant--a huge topic approached by many people who address different…

Augmented Reality-Based Simulators as Discovery Learning Tools: An Empirical Study

ERIC Educational Resources Information Center

Ibáñez, María-Blanca; Di-Serio, Ángela; Villarán-Molina, Diego; Delgado-Kloos, Carlos

2015-01-01

This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in…

The analysis of student’s critical thinking ability on discovery learning by using hand on activity based on the curiosity

NASA Astrophysics Data System (ADS)

Sulistiani, E.; Waluya, S. B.; Masrukan

2018-03-01

This study aims to determine (1) the effectiveness of Discovery Learning model by using Hand on Activity toward critical thinking abilities, and (2) to describe students’ critical thinking abilities in Discovery Learning by Hand on Activity based on curiosity. This study is mixed method research with concurrent embedded design. Sample of this study are students of VII A and VII B of SMP Daarul Qur’an Ungaran. While the subject in this study is based on the curiosity of the students groups are classified Epistemic Curiosity (EC) and Perceptual Curiosity (PC). The results showed that the learning of Discovery Learning by using Hand on Activity is effective toward mathematics critical thinking abilities. Students of the EC type are able to complete six indicators of mathematics critical thinking abilities, although there are still two indicators that the result is less than the maximum. While students of PC type have not fully been able to complete the indicator of mathematics critical thinking abilities. They are only strong on indicators formulating questions, while on the other five indicators they are still weak. The critical thinking abilities of EC’s students is better than the critical thinking abilities of the PC’s students.

Effectiveness of Discovery Learning-Based Transformation Geometry Module

NASA Astrophysics Data System (ADS)

Febriana, R.; Haryono, Y.; Yusri, R.

2017-09-01

Development of transformation geometry module is conducted because the students got difficulties to understand the existing book. The purpose of the research was to find out the effectiveness of discovery learning-based transformation geometry module toward student’s activity. Model of the development was Plomp model consisting preliminary research, prototyping phase and assessment phase. The research was focused on assessment phase where it was to observe the designed product effectiveness. The instrument was observation sheet. The observed activities were visual activities, oral activities, listening activities, mental activities, emotional activities and motor activities. Based on the result of the research, it is found that visual activities, learning activities, writing activities, the student’s activity is in the criteria very effective. It can be concluded that the use of discovery learning-based transformation geometry module use can increase the positive student’s activity and decrease the negative activity.

Do individual differences in children's curiosity relate to their inquiry-based learning?

NASA Astrophysics Data System (ADS)

van Schijndel, Tessa J. P.; Jansen, Brenda R. J.; Raijmakers, Maartje E. J.

2018-06-01

This study investigates how individual differences in 7- to 9-year-olds' curiosity relate to the inquiry-learning process and outcomes in environments differing in structure. The focus on curiosity as individual differences variable was motivated by the importance of curiosity in science education, and uncertainty being central to both the definition of curiosity and the inquiry-learning environment. Curiosity was assessed with the Underwater Exploration game (Jirout, J., & Klahr, D. (2012). Children's scientific curiosity: In search of an operational definition of an elusive concept. Developmental Review, 32, 125-160. doi:10.1016/j.dr.2012.04.002), and inquiry-based learning with the newly developed Scientific Discovery task, which focuses on the principle of designing informative experiments. Structure of the inquiry-learning environment was manipulated by explaining this principle or not. As intelligence relates to learning and possibly curiosity, it was taken into account. Results showed that children's curiosity was positively related to their knowledge acquisition, but not to their quality of exploration. For low intelligent children, environment structure positively affected their quality of exploration, but not their knowledge acquisition. There was no interaction between curiosity and environment structure. These results support the existence of two distinct inquiry-based learning processes - the designing of experiments, on the one hand, and the reflection on performed experiments, on the other - and link children's curiosity to the latter process.

Small molecule compound logistics outsourcing--going beyond the "thought experiment".

PubMed

Ramsay, Devon L; Kwasnoski, Joseph D; Caldwell, Gary W

2012-01-01

Increasing pressure on the pharmaceutical industry to reduce cost and focus internal resources on "high value" activities is driving a trend to outsource traditionally "in-house" drug discovery activities. Compound collections are typically viewed as drug discovery's "crown jewels"; however, in late 2007, Johnson & Johnson Pharmaceutical Research & Development (J PRD) took a bold step to move their entire North American compound inventory and processing capability to an external third party vendor. The authors discuss the combination model implemented, that of local compound logistics site support with an outsourced centralized processing center. Some of the lessons learned over the past five years were predictable while others were unexpected. The substantial cost savings, improved local service response and flexible platform to adjust to changing business needs resulted. Continued sustainable success relies heavily upon maintaining internal headcount dedicated to vendor management, an open collaboration approach and a solid information technology infrastructure with complete transparency and visibility.

Learning and Relevance in Information Retrieval: A Study in the Application of Exploration and User Knowledge to Enhance Performance

ERIC Educational Resources Information Center

Hyman, Harvey

2012-01-01

This dissertation examines the impact of exploration and learning upon eDiscovery information retrieval; it is written in three parts. Part I contains foundational concepts and background on the topics of information retrieval and eDiscovery. This part informs the reader about the research frameworks, methodologies, data collection, and…

Improving Junior High School Students' Mathematical Analogical Ability Using Discovery Learning Method

ERIC Educational Resources Information Center

Maarif, Samsul

2016-01-01

The aim of this study was to identify the influence of discovery learning method towards the mathematical analogical ability of junior high school's students. This is a research using factorial design 2x2 with ANOVA-Two ways. The population of this research included the entire students of SMPN 13 Jakarta (State Junior High School 13 of Jakarta)…

Molecular controls of arterial morphogenesis

PubMed Central

Simons, Michael; Eichmann, Anne

2015-01-01

Formation of arterial vasculature, here termed arteriogenesis, is a central process in embryonic vascular development as well as in adult tissues. While the process of capillary formation, angiogenesis, is relatively well understood, much remains to be learned about arteriogenesis. Recent discoveries point to the key role played by vascular endothelial growth factor receptor 2 (VEGFR2) in control of this process and to newly identified control circuits that dramatically influence its activity. The latter can present particularly attractive targets for a new class of therapeutic agents capable of activation of this signaling cascade in a ligand-independent manner, thereby promoting arteriogenesis in diseased tissues. PMID:25953926

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Feature Discovery by Competitive Learning.

ERIC Educational Resources Information Center

Rumelhart, David E.; Zipser, David

1985-01-01

Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)

Postgenomic strategies in antibacterial drug discovery.

PubMed

Brötz-Oesterhelt, Heike; Sass, Peter

2010-10-01

During the last decade the field of antibacterial drug discovery has changed in many aspects including bacterial organisms of primary interest, discovery strategies applied and pharmaceutical companies involved. Target-based high-throughput screening had been disappointingly unsuccessful for antibiotic research. Understanding of this lack of success has increased substantially and the lessons learned refer to characteristics of targets, screening libraries and screening strategies. The 'genomics' approach was replaced by a diverse array of discovery strategies, for example, searching for new natural product leads among previously abandoned compounds or new microbial sources, screening for synthetic inhibitors by targeted approaches including structure-based design and analyses of focused libraries and designing resistance-breaking properties into antibiotics of established classes. Furthermore, alternative treatment options are being pursued including anti-virulence strategies and immunotherapeutic approaches. This article summarizes the lessons learned from the genomics era and describes discovery strategies resulting from that knowledge.

Problems of Primary Education Today

ERIC Educational Resources Information Center

Dubova, M. V.

2014-01-01

Primary education in Russia has failed to adapt to the needs of post-Soviet society, and is still based on rote learning and memorization instead of learning through discovery and learning to use and apply what is learned.

Do Facilitate, Don’t Demonstrate: Meaningful Engagement for Science Outreach

NASA Astrophysics Data System (ADS)

Gelderman, Richard

2017-01-01

We are encouraged to hand over the learning experience to the students who must do the learning. After the 1957 launch of Sputnik it seemed that learning by discovery would replace lectures and other forms of learning by rote. The innovative Physical Science Study Committee (PSSC), Chemical Education Materials Study (ChEMS), and Biological Sciences Curriculum Study (BSCS) provided teachers with hands-on, activity-based curriculum materials emphasizing problem solving, process skills, and creativity. Our current reforms, based on the Next Generation Science Standards, stress that learner-centered strategies need to become commonplace throughout the classrooms of our formal education system. In this presentation, we share tips on how to double check your style of interactions for science outreach, to ensure the audience is working with a facilitator rather than simply enjoying an expert’s entertaining demonstration.

Learning to Predict Social Influence in Complex Networks

DTIC Science & Technology

2012-03-29

03/2010 – 17/03/2012 Abstract: First, we addressed the problem of analyzing information diffusion process in a social network using two kinds...algorithm which avoids the inner loop optimization during the search. We tested the performance using the structures of four real world networks, and...result of information diffusion that starts from the node. 2 We use “infected” and “activated” interchangeably. Efficient Discovery of Influential

Combating a Cook-Centric Past through Co-Curricular Learning: Year 9 Dig out Maps and Rulers to Challenge Generalisations about the Age of Discovery

ERIC Educational Resources Information Center

Worth, Paula

2014-01-01

Paula Worth presents in this article a means of challenging students' tendency to generalise even when they know that they should not. How can teachers encourage their students to say something meaningful about the past while avoiding making unwarranted generalisations? Worth takes teachers through the process of planning her own enquiry designed…

Innovative Programmable Bio-Nano-Chip Digitizes Biology Using Sensors That Learn Bridging Biomarker Discovery and Clinical Implementation

PubMed Central

Christodoulides, Nicolaos J.; McRae, Michael P.; Abram, Timothy J.; Simmons, Glennon W.; McDevitt, John T.

2017-01-01

The lack of standard tools and methodologies and the absence of a streamlined multimarker approval process have hindered the translation rate of new biomarkers into clinical practice for a variety of diseases afflicting humankind. Advanced novel technologies with superior analytical performance and reduced reagent costs, like the programmable bio-nano-chip system featured in this article, have potential to change the delivery of healthcare. This universal platform system has the capacity to digitize biology, resulting in a sensor modality with a capacity to learn. With well-planned device design, development, and distribution plans, there is an opportunity to translate benchtop discoveries in the genomics, proteomics, metabolomics, and glycomics fields by transforming the information content of key biomarkers into actionable signatures that can empower physicians and patients for a better management of healthcare. While the process is complicated and will take some time, showcased here are three application areas for this flexible platform that combines biomarker content with minimally invasive or non-invasive sampling, such as brush biopsy for oral cancer risk assessment; serum, plasma, and small volumes of blood for the assessment of cardiac risk and wellness; and oral fluid sampling for drugs of abuse testing at the point of need. PMID:28589118

Lessons learned from KSC processing on STS science, applications, and commercial payloads

NASA Technical Reports Server (NTRS)

Williams, W. E.; Ragusa, J. M.

1984-01-01

The present investigation is concerned with an evaluation of the lessons learned in connection with the flights of the Shuttle orbiters Columbia, Challenger, and Discovery. A description is provided of several general and specific lessons related to the processing of free-flying and attached payloads. John F. Kennedy Space Center (KSC), as the prime launch and landing site, is responsible for managing all payload-to-payload, payload-to-simulated orbiter, and payload-to-orbiter operations. For each payload, a KSC Launch Site Support Manager (LSSM) is named as the primary point of contact for the customer. Attention is given to aspects of planning interaction, payload types, and problems of ground processing. The discussed lessons are partly related to the value of early contact between customers and KSC representatives, the primary point of contact, the launch site support plan, and the importance of customer participation.

A Discovery Chemistry Experiment on Buffers

ERIC Educational Resources Information Center

Kulevich, Suzanne E.; Herrick, Richard S.; Mills, Kenneth V.

2014-01-01

The Holy Cross Chemistry Department has designed and implemented an experiment on buffers as part of our Discovery Chemistry curriculum. The pedagogical philosophy of Discovery Chemistry is to make the laboratory the focal point of learning for students in their first two years of undergraduate instruction. We first pose questions in prelaboratory…

Collected Notes on the Workshop for Pattern Discovery in Large Databases

NASA Technical Reports Server (NTRS)

Buntine, Wray (Editor); Delalto, Martha (Editor)

1991-01-01

These collected notes are a record of material presented at the Workshop. The core data analysis is addressed that have traditionally required statistical or pattern recognition techniques. Some of the core tasks include classification, discrimination, clustering, supervised and unsupervised learning, discovery and diagnosis, i.e., general pattern discovery.

Data Mining Citizen Science Results

NASA Astrophysics Data System (ADS)

Borne, K. D.

2012-12-01

Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

An analysis of K--5 teachers' beliefs regarding the uses of direct instruction, the discovery method, and the inquiry method in elementary science education

NASA Astrophysics Data System (ADS)

Kowalczyk, Donna Lee

The purpose of this study was to examine K--5 elementary teachers' reported beliefs about the use, function, and importance of Direct Instruction, the Discovery Method, and the Inquiry Method in the instruction of science in their classrooms. Eighty-two teachers completed questionnaires about their beliefs, opinions, uses, and ideas about each of the three instructional methods. Data were collected and analyzed using the Statistical Package of the Social Sciences (SPSS). Descriptive statistics and Chi-Square analyses indicated that the majority of teachers reported using all three methods to varying degrees in their classrooms. Guided Discovery was reported by the teachers as being the most frequently used method to teach science, while Pure Discovery was reportedly used the least frequently. The majority of teachers expressed the belief that a blend of all three instructional methods is the most effective strategy for teaching science at the elementary level. The teachers also reported a moderate level of confidence in teaching science. Students' ability levels, learning styles, and time/class schedule were identified as factors that most influence teachers' instructional choice. Student participation in hands-on activities, creative thinking ability, and developing an understanding of scientific concepts were reported as the learning behaviors most associated with student success in science. Data obtained from this study provide information about the nature and uses of Direct Instruction, the Discovery Method, and the Inquiry Method and teachers' perceptions and beliefs about each method's use in science education. Learning more about the science teaching and learning environment may help teachers, administrators, curriculum developers, and researchers gain greater insights about student learning, instructional effectiveness, and science curriculum development at the elementary level.

Classroom Journal Club: Collaborative Study of Contemporary Primary Literature in the Biomechanics Classroom.

PubMed

Kuxhaus, Laurel; Corbiere, Nicole C

2016-07-01

Current engineering pedagogy primarily focuses on developing technical proficiency and problem solving skills; the peer-review process for sharing new research results is often overlooked. The use of a collaborative classroom journal club can engage students with the excitement of scientific discovery and the process of dissemination of research results, which are also important lifelong learning skills. In this work, a classroom journal club was implemented and a survey of student perceptions spanning three student cohorts was collected. In this collaborative learning activity, students regularly chose and discussed a recent biomechanics journal article, and were assessed based on specific, individual preparation tasks. Most student-chosen journal articles were relevant to topics discussed in the regular class lecture. Surveys assessed student perceptions of the activity. The survey responses show that, across all cohorts, students both enjoyed the classroom journal club and recognized it as an important learning experience. Many reported discussing their journal articles with others outside of the classroom, indicating good engagement. The results demonstrate that student engagement with primary literature can foster both technical knowledge and lifelong learning skills.

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

ERIC Educational Resources Information Center

Hsu, I-Ching

2012-01-01

The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability, and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM lacks the…

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.

The Development of Learning Devices Based Guided Discovery Model to Improve Understanding Concept and Critical Thinking Mathematically Ability of Students at Islamic Junior High School of Medan

ERIC Educational Resources Information Center

Yuliani, Kiki; Saragih, Sahat

2015-01-01

The purpose of this research was to: 1) development of learning devices based guided discovery model in improving of understanding concept and critical thinking mathematically ability of students at Islamic Junior High School; 2) describe improvement understanding concept and critical thinking mathematically ability of students at MTs by using…

PERSONAL AND CIRCUMSTANTIAL FACTORS INFLUENCING THE ACT OF DISCOVERY.

ERIC Educational Resources Information Center

OSTRANDER, EDWARD R.

HOW STUDENTS SAY THEY LEARN WAS INVESTIGATED. INTERVIEWS WITH A RANDOM SAMPLE OF 74 WOMEN STUDENTS POSED QUESTIONS ABOUT THE NATURE, FREQUENCY, PATTERNS, AND CIRCUMSTANCES UNDER WHICH ACTS OF DISCOVERY TAKE PLACE IN THE ACADEMIC SETTING. STUDENTS WERE ASSIGNED DISCOVERY RATINGS BASED ON READINGS OF TYPESCRIPTS. EACH STUDENT WAS CLASSIFIED AND…

Towards self-learning based hypotheses generation in biomedical text domain.

PubMed

Gopalakrishnan, Vishrawas; Jha, Kishlay; Xun, Guangxu; Ngo, Hung Q; Zhang, Aidong

2018-06-15

The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top-K results. This level of efficiency enables the discovery algorithm to look for higher-order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to perform both open and closed discovery. We also experimentally validate that the core data-structures upon which the system bases its decision has a high concordance with the opinion of the experts.This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. The relevant JAVA codes are available at: https://github.com/vishrawas/Medline-Code_v2. Supplementary data are available at Bioinformatics online.

Learning from the Mars Rover Mission: Scientific Discovery, Learning and Memory

NASA Technical Reports Server (NTRS)

Linde, Charlotte

2005-01-01

Purpose: Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in knowledge production. This paper uses the Mars Exploration Rover mission as a site to explore this process. Approach: Observational study and analysis of the work of the MER science and engineering team during rover operations, to investigate how learning occurs, how it is recorded, and how these representations might be made available for subsequent missions. Findings: Learning occurred in many areas: planning science strategy, using instrumen?s within the constraints of the martian environment, the Deep Space Network, and the mission requirements; using software tools effectively; and running two teams on Mars time for three months. This learning is preserved in many ways. Primarily it resides in individual s memories. It is also encoded in stories, procedures, programming sequences, published reports, and lessons learned databases. Research implications: Shows the earliest stages of knowledge creation in a scientific mission, and demonstrates that knowledge management must begin with an understanding of knowledge creation. Practical implications: Shows that studying learning and knowledge creation suggests proactive ways to capture and use knowledge across multiple missions and generations. Value: This paper provides a unique analysis of the learning process of a scientific space mission, relevant for knowledge management researchers and designers, as well as demonstrating in detail how new learning occurs in a learning organization.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

The Harvard Clean Energy Project: High-throughput screening of organic photovoltaic materials using cheminformatics, machine learning, and pattern recognition

NASA Astrophysics Data System (ADS)

Olivares-Amaya, Roberto; Hachmann, Johannes; Amador-Bedolla, Carlos; Daly, Aidan; Jinich, Adrian; Atahan-Evrenk, Sule; Boixo, Sergio; Aspuru-Guzik, Alán

2012-02-01

Organic photovoltaic devices have emerged as competitors to silicon-based solar cells, currently reaching efficiencies of over 9% and offering desirable properties for manufacturing and installation. We study conjugated donor polymers for high-efficiency bulk-heterojunction photovoltaic devices with a molecular library motivated by experimental feasibility. We use quantum mechanics and a distributed computing approach to explore this vast molecular space. We will detail the screening approach starting from the generation of the molecular library, which can be easily extended to other kinds of molecular systems. We will describe the screening method for these materials which ranges from descriptor models, ubiquitous in the drug discovery community, to eventually reaching first principles quantum chemistry methods. We will present results on the statistical analysis, based principally on machine learning, specifically partial least squares and Gaussian processes. Alongside, clustering methods and the use of the hypergeometric distribution reveal moieties important for the donor materials and allow us to quantify structure-property relationships. These efforts enable us to accelerate materials discovery in organic photovoltaics through our collaboration with experimental groups.

Physics By Inquiry: Addressing Student Learning and Attitude

NASA Astrophysics Data System (ADS)

Sadaghiani, Homeyra R.

2008-10-01

In the last decade, the results of Physics Education Research and research-based instructional materials have been disseminated from traditional research universities to a wide variety of colleges and universities. Nevertheless, the ways in which different institutions implement these materials depend on their students and the institutional context. Even with the widespread use of these curriculums, the research documenting the effectiveness of these materials with different student populations is scarce. This paper describes the challenges associated with implementing Physics by Inquiry at California State Polytechnic University Pomona and confirms its effectiveness in promoting student conceptual knowledge of physics. However, despite the positive effect on student learning, the evidence suggests that the students did not appreciate the self-discovery aspect of the inquiry approach and characterized the learning process as difficult and unpleasant.

Molecular controls of arterial morphogenesis.

PubMed

Simons, Michael; Eichmann, Anne

2015-05-08

Formation of arterial vasculature, here termed arteriogenesis, is a central process in embryonic vascular development as well as in adult tissues. Although the process of capillary formation, angiogenesis, is relatively well understood, much remains to be learned about arteriogenesis. Recent discoveries point to the key role played by vascular endothelial growth factor receptor 2 in control of this process and to newly identified control circuits that dramatically influence its activity. The latter can present particularly attractive targets for a new class of therapeutic agents capable of activation of this signaling cascade in a ligand-independent manner, thereby promoting arteriogenesis in diseased tissues. © 2015 American Heart Association, Inc.

Teaching Technology Applied in the Main Stream: The Supermarket Discovery Center

ERIC Educational Resources Information Center

Filep, Robert T.; Gillette, Pearl

1969-01-01

Describes the approach and results of the Supermarket Discovery Center Demonstration Project, a program attempting to provide pre-school children with meaningful learning experiences while their parents are shopping. (LS)

Generalized lessons about sequence learning from the study of the serial reaction time task

PubMed Central

Schwarb, Hillary; Schumacher, Eric H.

2012-01-01

Over the last 20 years researchers have used the serial reaction time (SRT) task to investigate the nature of spatial sequence learning. They have used the task to identify the locus of spatial sequence learning, identify situations that enhance and those that impair learning, and identify the important cognitive processes that facilitate this type of learning. Although controversies remain, the SRT task has been integral in enhancing our understanding of implicit sequence learning. It is important, however, to ask what, if anything, the discoveries made using the SRT task tell us about implicit learning more generally. This review analyzes the state of the current spatial SRT sequence learning literature highlighting the stimulus-response rule hypothesis of sequence learning which we believe provides a unifying account of discrepant SRT data. It also challenges researchers to use the vast body of knowledge acquired with the SRT task to understand other implicit learning literatures too often ignored in the context of this particular task. This broad perspective will make it possible to identify congruences among data acquired using various different tasks that will allow us to generalize about the nature of implicit learning. PMID:22723815

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

Changes in Instructional System Design (ISD): Improving Training Product Delivery to United States Army Soldiers

DTIC Science & Technology

2005-03-18

simulation. This model is a basis of what is called discovery learning. Discovery learning is constructionist method of instruction, which is a concept in...2005 PAGES: 48 CLASSIFICATION: Unclassified The purpose of this study is to identify methods that could speed up the instructional system design...became obvious as the enemy attacked using asymmetric means and methods . For instance: during the war, a mine identification-training product was

Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove

ERIC Educational Resources Information Center

Kholod, Yana; Hoag, Erin; Muratore, Katlynn; Kosenkov, Dmytro

2018-01-01

The reported project-based laboratory unit introduces upper-division undergraduate students to the basics of computer-aided drug discovery as a part of a computational chemistry laboratory course. The students learn to perform model binding of organic molecules (ligands) to the DNA minor groove with computer-aided drug discovery (CADD) tools. The…

Metadata Effectiveness in Internet Discovery: An Analysis of Digital Collection Metadata Elements and Internet Search Engine Keywords

ERIC Educational Resources Information Center

Yang, Le

2016-01-01

This study analyzed digital item metadata and keywords from Internet search engines to learn what metadata elements actually facilitate discovery of digital collections through Internet keyword searching and how significantly each metadata element affects the discovery of items in a digital repository. The study found that keywords from Internet…

Discovery learning model with geogebra assisted for improvement mathematical visual thinking ability

NASA Astrophysics Data System (ADS)

Juandi, D.; Priatna, N.

2018-05-01

The main goal of this study is to improve the mathematical visual thinking ability of high school student through implementation the Discovery Learning Model with Geogebra Assisted. This objective can be achieved through study used quasi-experimental method, with non-random pretest-posttest control design. The sample subject of this research consist of 62 senior school student grade XI in one of school in Bandung district. The required data will be collected through documentation, observation, written tests, interviews, daily journals, and student worksheets. The results of this study are: 1) Improvement students Mathematical Visual Thinking Ability who obtain learning with applied the Discovery Learning Model with Geogebra assisted is significantly higher than students who obtain conventional learning; 2) There is a difference in the improvement of students’ Mathematical Visual Thinking ability between groups based on prior knowledge mathematical abilities (high, medium, and low) who obtained the treatment. 3) The Mathematical Visual Thinking Ability improvement of the high group is significantly higher than in the medium and low groups. 4) The quality of improvement ability of high and low prior knowledge is moderate category, in while the quality of improvement ability in the high category achieved by student with medium prior knowledge.

Developing computer-based training programs for basic mammalian histology: Didactic versus discovery-based design

NASA Astrophysics Data System (ADS)

Fabian, Henry Joel

Educators have long tried to understand what stimulates students to learn. The Swiss psychologist and zoologist, Jean Claude Piaget, suggested that students are stimulated to learn when they attempt to resolve confusion. He reasoned that students try to explain the world with the knowledge they have acquired in life. When they find their own explanations to be inadequate to explain phenomena, students find themselves in a temporary state of confusion. This prompts students to seek more plausible explanations. At this point, students are primed for learning (Piaget 1964). The Piagetian approach described above is called learning by discovery. To promote discovery learning, a teacher must first allow the student to recognize his misconception and then provide a plausible explanation to replace that misconception (Chinn and Brewer 1993). One application of this method is found in the various learning cycles, which have been demonstrated to be effective means for teaching science (Renner and Lawson 1973, Lawson 1986, Marek and Methven 1991, and Glasson & Lalik 1993). In contrast to the learning cycle, tutorial computer programs are generally not designed to correct student misconceptions, but rather follow a passive, didactic method of teaching. In the didactic or expositional method, the student is told about a phenomenon, but is neither encouraged to explore it, nor explain it in his own terms (Schneider and Renner 1980).

When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.

PubMed

Zhang, Wei; Ji, Lijuan; Chen, Yanan; Tang, Kailin; Wang, Haiping; Zhu, Ruixin; Jia, Wei; Cao, Zhiwei; Liu, Qi

2015-01-01

The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms. A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical AbstractThe analogy between web search and ligand-based drug discovery.

Towards personalized smart wheelchairs: Lessons learned from discovery interviews.

PubMed

Padir, Taşkin

2015-01-01

We posit that it is necessary to investigate the personalization of smart wheelchairs in three aspects interfaces for interaction, controllers for action (top-level, middle-level, and low-level), and feedback in interaction. Our team has been selected as an Innovation Corps (I-Corps) Team by the National Science Foundation to pursue customer discovery research to explore the commercial viability of smart wheelchairs. Through the process, our team has performed more than 110 interviews with powered wheelchair users, manufacturers, therapists, policy makers, and non-profit organization staff. Our findings revealed that the acceptability of fully autonomous systems by the users is still challenging and highly-dependent on the severity of the disability. Furthermore, the cost, ease-of-use and personalization are the most important factors in commercializing smart wheelchair technologies.

Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery

PubMed Central

Trifiletti, Daniel M.; Showalter, Timothy N.

2015-01-01

Several advances in large data set collection and processing have the potential to provide a wave of new insights and improvements in the use of radiation therapy for cancer treatment. The era of electronic health records, genomics, and improving information technology resources creates the opportunity to leverage these developments to create a learning healthcare system that can rapidly deliver informative clinical evidence. By merging concepts from comparative effectiveness research with the tools and analytic approaches of “big data,” it is hoped that this union will accelerate discovery, improve evidence for decision making, and increase the availability of highly relevant, personalized information. This combination offers the potential to provide data and analysis that can be leveraged for ultra-personalized medicine and high-quality, cutting-edge radiation therapy. PMID:26697409

Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery.

PubMed

Trifiletti, Daniel M; Showalter, Timothy N

2015-01-01

Several advances in large data set collection and processing have the potential to provide a wave of new insights and improvements in the use of radiation therapy for cancer treatment. The era of electronic health records, genomics, and improving information technology resources creates the opportunity to leverage these developments to create a learning healthcare system that can rapidly deliver informative clinical evidence. By merging concepts from comparative effectiveness research with the tools and analytic approaches of "big data," it is hoped that this union will accelerate discovery, improve evidence for decision making, and increase the availability of highly relevant, personalized information. This combination offers the potential to provide data and analysis that can be leveraged for ultra-personalized medicine and high-quality, cutting-edge radiation therapy.

Towards a 21 century paradigm of chiropractic: stage 1, redesigning clinical learning.

PubMed

Ebrall, Phillip; Draper, Barry; Repka, Adrian

2008-01-01

To describe a formal process designed to determine the nature and extent of change that may enhance the depth of student learning in the pre-professional, clinical chiropractic environment. Project teams in the Royal Melbourne Institute of Technology (RMIT) School of Health Sciences and the Division of Chiropractic explored questions of clinical assessment in several health care disciplines of the School and the issue of implementing change in a manner that would be embraced by the clinicians who supervise student-learning in the clinical environment. The teams applied to RMIT for grant funding within the Learning and Teaching Investment Fund to support two proposed studies. Both research proposals were fully funded and are in process. The genesis of this work is the discovery that the predominant management plan in the chiropractic teaching clinics is based on diagnostic reductionism. It is felt this is counter-productive to the holistic dimensions of chiropractic practice taught in the classroom and non-supportive of chiropractic's paradigm shift towards wellness. A need is seen to improve processes around student assessment in the contemporary work-integrated learning that is a prime element of learning within the clinical disciplines of the School of Health Sciences, including chiropractic. Any improvements in the manner of clinical assessment within the chiropractic discipline will need to be accompanied by improvement in the training and development of the clinicians responsible for managing the provision of quality patient care by Registered Chiropractic Students.

A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity.

PubMed

Ultsch, Alfred; Kringel, Dario; Kalso, Eija; Mogil, Jeffrey S; Lötsch, Jörn

2016-12-01

The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 535 genes identified empirically as relevant to pain with the knowledge about the functions of thousands of genes. Starting from an accepted description of chronic pain as displaying systemic features described by the terms "learning" and "neuronal plasticity," a functional genomics analysis proposed that among the functions of the 535 "pain genes," the biological processes "learning or memory" (P = 8.6 × 10) and "nervous system development" (P = 2.4 × 10) are statistically significantly overrepresented as compared with the annotations to these processes expected by chance. After establishing that the hypothesized biological processes were among important functional genomics features of pain, a subset of n = 34 pain genes were found to be annotated with both Gene Ontology terms. Published empirical evidence supporting their involvement in chronic pain was identified for almost all these genes, including 1 gene identified in March 2016 as being involved in pain. By contrast, such evidence was virtually absent in a randomly selected set of 34 other human genes. Hence, the present computational functional genomics-based method can be used for candidate gene selection, providing an alternative to established methods.

Toxin-Induced Experimental Models of Learning and Memory Impairment

PubMed Central

More, Sandeep Vasant; Kumar, Hemant; Cho, Duk-Yeon; Yun, Yo-Sep; Choi, Dong-Kug

2016-01-01

Animal models for learning and memory have significantly contributed to novel strategies for drug development and hence are an imperative part in the assessment of therapeutics. Learning and memory involve different stages including acquisition, consolidation, and retrieval and each stage can be characterized using specific toxin. Recent studies have postulated the molecular basis of these processes and have also demonstrated many signaling molecules that are involved in several stages of memory. Most insights into learning and memory impairment and to develop a novel compound stems from the investigations performed in experimental models, especially those produced by neurotoxins models. Several toxins have been utilized based on their mechanism of action for learning and memory impairment such as scopolamine, streptozotocin, quinolinic acid, and domoic acid. Further, some toxins like 6-hydroxy dopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and amyloid-β are known to cause specific learning and memory impairment which imitate the disease pathology of Parkinson’s disease dementia and Alzheimer’s disease dementia. Apart from these toxins, several other toxins come under a miscellaneous category like an environmental pollutant, snake venoms, botulinum, and lipopolysaccharide. This review will focus on the various classes of neurotoxin models for learning and memory impairment with their specific mechanism of action that could assist the process of drug discovery and development for dementia and cognitive disorders. PMID:27598124

Toxin-Induced Experimental Models of Learning and Memory Impairment.

PubMed

More, Sandeep Vasant; Kumar, Hemant; Cho, Duk-Yeon; Yun, Yo-Sep; Choi, Dong-Kug

2016-09-01

Animal models for learning and memory have significantly contributed to novel strategies for drug development and hence are an imperative part in the assessment of therapeutics. Learning and memory involve different stages including acquisition, consolidation, and retrieval and each stage can be characterized using specific toxin. Recent studies have postulated the molecular basis of these processes and have also demonstrated many signaling molecules that are involved in several stages of memory. Most insights into learning and memory impairment and to develop a novel compound stems from the investigations performed in experimental models, especially those produced by neurotoxins models. Several toxins have been utilized based on their mechanism of action for learning and memory impairment such as scopolamine, streptozotocin, quinolinic acid, and domoic acid. Further, some toxins like 6-hydroxy dopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and amyloid-β are known to cause specific learning and memory impairment which imitate the disease pathology of Parkinson's disease dementia and Alzheimer's disease dementia. Apart from these toxins, several other toxins come under a miscellaneous category like an environmental pollutant, snake venoms, botulinum, and lipopolysaccharide. This review will focus on the various classes of neurotoxin models for learning and memory impairment with their specific mechanism of action that could assist the process of drug discovery and development for dementia and cognitive disorders.

Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal Phase III

DTIC Science & Technology

2015-04-30

It is a supervised learning method but best for Big Data with low dimensions. It is an approximate inference good for Big Data and Hadoop ...Each process produces large amounts of information ( Big Data ). There is a critical need for automation, validation, and discovery to help acquisition...can inform managers where areas might have higher program risk and how resource and big data management might affect the desired return on investment

Cross-organism learning method to discover new gene functionalities.

PubMed

Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

2016-04-01

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones, without influence of the evolutionary distance between the considered organisms. The generated ranked lists of reliably predicted annotations, which describe novel gene functionalities and have an associated likelihood value, are very valuable both to complement available annotations, for better coverage in biomedical knowledge discovery analyses, and to quicken the annotation curation process, by focusing it on the prioritized novel annotations predicted. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A "Simple Query Interface" Adapter for the Discovery and Exchange of Learning Resources

ERIC Educational Resources Information Center

Massart, David

2006-01-01

Developed as part of CEN/ISSS Workshop on Learning Technology efforts to improve interoperability between learning resource repositories, the Simple Query Interface (SQI) is an Application Program Interface (API) for querying heterogeneous repositories of learning resource metadata. In the context of the ProLearn Network of Excellence, SQI is used…

Immediate Dissemination of Student Discoveries to a Model Organism Database Enhances Classroom-Based Research Experiences

PubMed Central

Wiley, Emily A.; Stover, Nicholas A.

2014-01-01

Use of inquiry-based research modules in the classroom has soared over recent years, largely in response to national calls for teaching that provides experience with scientific processes and methodologies. To increase the visibility of in-class studies among interested researchers and to strengthen their impact on student learning, we have extended the typical model of inquiry-based labs to include a means for targeted dissemination of student-generated discoveries. This initiative required: 1) creating a set of research-based lab activities with the potential to yield results that a particular scientific community would find useful and 2) developing a means for immediate sharing of student-generated results. Working toward these goals, we designed guides for course-based research aimed to fulfill the need for functional annotation of the Tetrahymena thermophila genome, and developed an interactive Web database that links directly to the official Tetrahymena Genome Database for immediate, targeted dissemination of student discoveries. This combination of research via the course modules and the opportunity for students to immediately “publish” their novel results on a Web database actively used by outside scientists culminated in a motivational tool that enhanced students’ efforts to engage the scientific process and pursue additional research opportunities beyond the course. PMID:24591511

Immediate dissemination of student discoveries to a model organism database enhances classroom-based research experiences.

PubMed

Wiley, Emily A; Stover, Nicholas A

2014-01-01

Use of inquiry-based research modules in the classroom has soared over recent years, largely in response to national calls for teaching that provides experience with scientific processes and methodologies. To increase the visibility of in-class studies among interested researchers and to strengthen their impact on student learning, we have extended the typical model of inquiry-based labs to include a means for targeted dissemination of student-generated discoveries. This initiative required: 1) creating a set of research-based lab activities with the potential to yield results that a particular scientific community would find useful and 2) developing a means for immediate sharing of student-generated results. Working toward these goals, we designed guides for course-based research aimed to fulfill the need for functional annotation of the Tetrahymena thermophila genome, and developed an interactive Web database that links directly to the official Tetrahymena Genome Database for immediate, targeted dissemination of student discoveries. This combination of research via the course modules and the opportunity for students to immediately "publish" their novel results on a Web database actively used by outside scientists culminated in a motivational tool that enhanced students' efforts to engage the scientific process and pursue additional research opportunities beyond the course.

Data Pooling in a Chemical Kinetics Experiment: The Aquation of a Series of Cobalt(III) Complexes--A Discovery Chemistry Experiment

ERIC Educational Resources Information Center

Herrick, Richard S.; Mills, Kenneth V.; Nestor, Lisa P.

2008-01-01

An experiment in chemical kinetics as part of our Discovery Chemistry curriculum is described. Discovery Chemistry is a pedagogical philosophy that makes the laboratory the key center of learning for students in their first two years of undergraduate instruction. Questions are posed in the pre-laboratory discussion and assessed using pooled…

Telling Active Learning Pedagogies Apart: From Theory to Practice

ERIC Educational Resources Information Center

Cattaneo, Kelsey Hood

2017-01-01

Designing learning environments to incorporate active learning pedagogies is difficult as definitions are often contested and intertwined. This article seeks to determine whether classification of active learning pedagogies (i.e., project-based, problem-based, inquiry-based, case-based, and discovery-based), through theoretical and practical…

Workshops without Walls: Sharing Scientific Research through Educator Professional Development

NASA Astrophysics Data System (ADS)

Weir, H. M.; Edmonds, J. P.; Hallau, K.; Asplund, S. E.; Cobb, W. H.; Nittler, L. R.; Solomon, S. C.

2013-12-01

Scientific discoveries, large and small, are constantly being made. Whether it is the discovery of a new species or a new comet, it is a challenge to keep up. The media provide some assistance in getting the word out about the discoveries, but not the details or the challenges of the discovery. Professional development is essential for science educators to keep them abreast of the fascinating discoveries that are occurring. The problem is that not every educator has the opportunity to attend a workshop on the most recent findings. NASA's Discovery and New Frontiers Education and Public Outreach program has offered a series of multi-site professional development workshops that have taken place at four physical locations sites: The Johns Hopkins University Applied Physics Laboratory, the Jet Propulsion Laboratory, NASA Johnson Space Center, and the University of Arizona, as well as over the internet. All sites were linked via the Digital Learning Network, on which scientists and educator specialists shared information about their missions and activities. Participants interacted with speakers across the country to learn about Discovery and New Frontiers class missions. The third such annual workshop without walls, 'Challenge of Discovery,' was held on 9 April 2013. Educators from across the country delved into the stories behind some amazing NASA missions, from conception to science results. They learned how scientists, engineers, and mission operators collaborate to meet the challenges of complex missions to assure that science goals are met. As an example of science and engineering coming together, an Instrument Scientist and a Payload Operations Manager from the MESSENGER mission discussed the steps needed to observe Mercury's north polar region, gather data, and finally come to the conclusion that water ice is present in permanently shadowed areas inside polar impact craters. The participating educators were able to work with actual data and experience how the conclusion was reached. This example and others highlight the potential of such workshops to inform and engage educators.

E-Learning for Depth in the Semantic Web

ERIC Educational Resources Information Center

Shafrir, Uri; Etkind, Masha

2006-01-01

In this paper, we describe concept parsing algorithms, a novel semantic analysis methodology at the core of a new pedagogy that focuses learners attention on deep comprehension of the conceptual content of learned material. Two new e-learning tools are described in some detail: interactive concept discovery learning and meaning equivalence…

Discovery and Use of Online Learning Resources: Case Study Findings

ERIC Educational Resources Information Center

Recker, Mimi M.; Dorward, James; Nelson, Laurie Miller

2004-01-01

Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called "learning objects." Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article…

Assuring Integrity of Information Utility in Cyber-Learning Formats.

ERIC Educational Resources Information Center

Morrison, James L.; Stein, Linda L.

1999-01-01

Describes a cyber-learning project for the World Wide Web developed by faculty and librarians at the University of Delaware that combined discovery learning with problem-based learning to develop critical thinking and quality management for information. Undergraduates were to find, evaluate, and use information to generate an Internet marketing…

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

PubMed

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J

2018-03-01

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

[Discovery-based teaching and learning strategies in health: problematization and problem-based learning].

PubMed

Cyrino, Eliana Goldfarb; Toralles-Pereira, Maria Lúcia

2004-01-01

Considering the changes in teaching in the health field and the demand for new ways of dealing with knowledge in higher learning, the article discusses two innovative methodological approaches: problem-based learning (PBL) and problematization. Describing the two methods' theoretical roots, the article attempts to identify their main foundations. As distinct proposals, both contribute to a review of the teaching and learning process: problematization, focused on knowledge construction in the context of the formation of a critical awareness; PBL, focused on cognitive aspects in the construction of concepts and appropriation of basic mechanisms in science. Both problematization and PBL lead to breaks with the traditional way of teaching and learning, stimulating participatory management by actors in the experience and reorganization of the relationship between theory and practice. The critique of each proposal's possibilities and limits using the analysis of their theoretical and methodological foundations leads us to conclude that pedagogical experiences based on PBL and/or problematization can represent an innovative trend in the context of health education, fostering breaks and more sweeping changes.

Anti-resorptive osteonecrosis of the jaws: facts forgotten, questions answered, lessons learned.

PubMed

Carlson, Eric R; Schlott, Benjamin J

2014-05-01

Osteonecrosis of the jaws associated with bisphosphonate and other anti-resorptive medications (ARONJ) has historically been a poorly understood disease process in terms of its pathophysiology, prevention and treatment since it was originally described in 2003. In association with its original discovery 11 years ago, non-evidence based speculation of these issues have been published in the international literature and are currently being challenged. A critical analysis of cancer patients with ARONJ, for example, reveals that their osteonecrosis is nearly identical to that of cancer patients who are naive to anti-resorptive medications. In addition, osteonecrosis of the jaws is not unique to patients exposed to anti-resorptive medications, but is also seen in patients with osteomyelitis and other pathologic processes of the jaws. This article represents a review of facts forgotten, questions answered, and lessons learned in general regarding osteonecrosis of the jaws. Copyright © 2014 Elsevier Inc. All rights reserved.

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

PubMed Central

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

PubMed

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Commentary: building human capital: discovery, learning, and professional satisfaction.

PubMed

Cox, Malcolm; Kupersmith, Joel; Jesse, Robert L; Petzel, Robert A

2011-08-01

Physician satisfaction is an important contributor to a well-functioning health system. Mohr and Burgess report that physicians in the Veterans Health Administration (VA) who spend time in research have greater overall job satisfaction, that satisfaction tracks with aggregate facility research funding, and that satisfaction is higher among physicians working in VA facilities located on the same campus or within walking distance of an affiliated medical school. An environment conducive to research therefore not only advances science but also seems to be a key element of physician satisfaction. In addition to advancing scientific discovery and promoting greater physician satisfaction, these findings suggest that an environment of discovery and learning may yield benefits beyond specific academic endeavors and contribute more broadly to supporting health system performance.

Student Learning Centre (SLC) Embraces the New Melbourne Model of Teaching: Facilitating Collaborative Learning

ERIC Educational Resources Information Center

Ball, Sarah

2010-01-01

Learning is about discovery and change. As schools and universities look to the future, it is fundamental that they provide environments that facilitate collaborative learning and act as points for interaction and social activity. The redevelopment of the existing Engineering Library into a Student Learning Centre (SLC) embraces the new Melbourne…

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

PubMed Central

Sacchi, L.

2016-01-01

Summary Objectives We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data. PMID:27488403

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.

PubMed

Sacchi, L; Holmes, J H

2016-08-02

We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

The neuroscience of learning.

PubMed

Collins, John W

2007-10-01

Significant advances have been made in understanding the neurophysiological basis of learning, including the discovery of mirror neurons and the role of cyclic adenosine monophosphate (cAMP) responsive element binding (CREB) protein in learning. Mirror neurons help us visually compare an observed activity with a remembered action in our memory, an ability that helps us imitate and learn through watching. Long-term potentiation, the Hebb rule, and CREB protein are associated with the formation of long-term memories. Conversely, protein phosphatase 1 and glucocorticoids are neurophysiological phenomena that limit what can be learned and cause forgetfulness. Gardner's theory of multiple intelligences contends that different areas of the brain are responsible for different competencies that we all possess to varying degrees. These multiple intelligences can be used as strategies for improved learning. Repeating material, using mnemonics, and avoiding overwhelming stress are other strategies for improving learning. Imaging studies have shown that practice with resultant learning results in significantly less use of brain areas, indicating that the brain becomes more efficient. Experts have advantages over novices, including increased cognitive processing efficiency. Nurses are in a unique position to use their understanding of neurophysiological principles to implement better educational strategies to provide quality education to patients and others.

Hooks and Shifts: A Dialectical Study of Mediated Discovery

ERIC Educational Resources Information Center

Abrahamson, Dor; Trninic, Dragan; Gutierrez, Jose F.; Huth, Jacob; Lee, Rosa G.

2011-01-01

Radical constructivists advocate discovery-based pedagogical regimes that enable students to incrementally and continuously adapt their cognitive structures to the instrumented cultural environment. Some sociocultural theorists, however, maintain that learning implies discontinuity in conceptual development, because novices must appropriate expert…

Prognostics

NASA Technical Reports Server (NTRS)

Goebel, Kai; Vachtsevanos, George; Orchard, Marcos E.

2013-01-01

Knowledge discovery, statistical learning, and more specifically an understanding of the system evolution in time when it undergoes undesirable fault conditions, are critical for an adequate implementation of successful prognostic systems. Prognosis may be understood as the generation of long-term predictions describing the evolution in time of a particular signal of interest or fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem. Predictions are made using a thorough understanding of the underlying processes and factor in the anticipated future usage.

LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Stassun, K.; Brunner, R. J.; Djorgovski, S. G.; Graham, M.; Hakkila, J.; Mahabal, A.; Paegert, M.; Pesenson, M.; Ptak, A.; Scargle, J.; Informatics, LSST; Statistics Team

2011-01-01

The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Molecular Diagnostics of Ageing and Tackling Age-related Disease.

PubMed

Timmons, James A

2017-01-01

As average life expectancy increases there is a greater focus on health-span and, in particular, how to treat or prevent chronic age-associated diseases. Therapies which were able to control 'biological age' with the aim of postponing chronic and costly diseases of old age require an entirely new approach to drug development. Molecular technologies and machine-learning methods have already yielded diagnostics that help guide cancer treatment and cardiovascular procedures. Discovery of valid and clinically informative diagnostics of human biological age (combined with disease-specific biomarkers) has the potential to alter current drug-discovery strategies, aid clinical trial recruitment and maximize healthy ageing. I will review some basic principles that govern the development of 'ageing' diagnostics, how such assays could be used during the drug-discovery or development process. Important logistical and statistical considerations are illustrated by reviewing recent biomarker activity in the field of Alzheimer's disease, as dementia represents the most pressing of priorities for the pharmaceutical industry, as well as the chronic disease in humans most associated with age. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discovering Mendeleev's Model.

ERIC Educational Resources Information Center

Sterling, Donna

1996-01-01

Presents an activity that introduces the historical developments in science that led to the discovery of the periodic table and lets students experience scientific discovery firsthand. Enables students to learn about patterns among the elements and experience how scientists analyze data to discover patterns and build models. (JRH)

Improving Middle School Students’ Critical Thinking Skills Through Reading Infusion-Loaded Discovery Learning Model in the Science Instruction

NASA Astrophysics Data System (ADS)

Nuryakin; Riandi

2017-02-01

A study has been conducted to obtain a depiction of middle school students’ critical thinking skills improvement through the implementation of reading infusion-loaded discovery learning model in science instruction. A quasi-experimental study with the pretest-posttest control group design was used to engage 55 eighth-year middle school students in Tasikmalaya, which was divided into the experimental and control group respectively were 28 and 27 students. Critical thinking skills were measured using a critical thinking skills test in multiple-choice with reason format questions that administered before and after a given instruction. The test was 28 items encompassing three essential concepts, vibration, waves and auditory senses. The critical thinking skills improvement was determined by using the normalized gain score and statistically analyzed by using Mann-Whitney U test.. The findings showed that the average of students’ critical thinking skills normalized gain score of both groups were 59 and 43, respectively for experimental and control group in the medium category. There were significant differences between both group’s improvement. Thus, the implementation of reading infusion-loaded discovery learning model could further improve middle school students’ critical thinking skills than conventional learning.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

Investigating the Link between Self Directed Learning Readiness and Project-Based Learning Outcomes: The Case of International Masters Students in an Engineering Management Course

ERIC Educational Resources Information Center

Stewart, Rodney A.

2007-01-01

Modern learning approaches increasingly have fewer structured learning activities and more self-directed learning tasks guided through consultation with academics. Such tasks are predominately project-/problem-based where the student is required to follow a freely guided road map to self discovery while simultaneously achieving desired learning…

Personalized Learning Objects Recommendation Based on the Semantic-Aware Discovery and the Learner Preference Pattern

ERIC Educational Resources Information Center

Wang, Tzone I; Tsai, Kun Hua; Lee, Ming Che; Chiu, Ti Kai

2007-01-01

With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future,…

Transfer of New Earth Science Understandings to Classroom Teaching: Lessons Learned From Teachers on the Leading Edge

NASA Astrophysics Data System (ADS)

Butler, R.; Ault, C.; Bishop, E.; Southworth-Neumeyer, T.; Magura, B.; Hedeen, C.; Groom, R.; Shay, K.; Wagner, R.

2006-05-01

Teachers on the Leading Edge (TOTLE) provided a field-based teacher professional development program that explored the active continental margin geology of the Pacific Northwest during a two-week field workshop that traversed Oregon from the Pacific Coast to the Snake River. The seventeen teachers on this journey of geological discovery experienced regional examples of subduction-margin geology and examined the critical role of geophysics in connecting geologic features with plate tectonic processes. Two examples of successful transfer of science content learning to classroom teaching are: (1) Great Earthquakes and Tsunamis. This topic was addressed through instruction on earthquake seismology; field observations of tsunami geology; examination of tsunami preparedness of a coastal community; and interactive learning activities for children at an Oregon Museum of Science and Industry (OMSI) Science Camp. Teachers at Sunnyside Environmental School in Portland developed a story line for middle school students called "The Tsunami Hotline" in which inquiries from citizens serve as launch points for studies of tsunamis, earthquakes, and active continental margin geology. OMSI Science Camps is currently developing a new summer science camp program entitled "Tsunami Field Study" for students ages 12-14, based largely on TOTLE's Great Earthquakes and Tsunamis Day. (2) The Grand Cross Section. Connecting regional geologic features with plate tectonic processes was addressed many times during the field workshop. This culminated with teachers drawing cross sections from the Juan de Fuca Ridge across the active continental margin to the accreted terranes of northeast Oregon. Several TOTLE teachers have successfully transferred this activity to their classrooms by having student teams relate earthquakes and volcanoes to plate tectonics through artistic renderings of The Grand Cross Section. Analysis of program learning transfer to classroom teaching (or lack thereof) clearly indicates the importance of pedagogical content knowledge and having teachers share their wisdom in crafting new earth science content knowledge into learning activities. These lessons and adjustments to TOTLE program goals and strategies may be valuable to other Geoscience educators seeking to prepare K-12 teachers to convey the discoveries of EarthScope's USArray and Plate Boundary Observatory experiments to their students.

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

A Piagetian Learning Cycle for Introductory Chemical Kinetics.

ERIC Educational Resources Information Center

Batt, Russell H.

1980-01-01

Described is a Piagetian learning cycle based on Monte Carlo modeling of several simple reaction mechanisms. Included are descriptions of learning cycle phases (exploration, invention, and discovery) and four BASIC-PLUS computer programs to be used in the explanation of chemical reacting systems. (Author/DS)

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Cognitive Tutoring based on Intelligent Decision Support in the PENTHA Instructional Design Model

NASA Astrophysics Data System (ADS)

dall'Acqua, Luisa

2010-06-01

The research finality of this paper is how to support Authors to develop rule driven—subject oriented, adaptable course content, meta-rules—representing the disciplinary epistemology, model of teaching, Learning Path structure, and assessment parameters—for intelligent Tutoring actions in a personalized, adaptive e-Learning environment. The focus is to instruct the student to be a decision manager for himself, able to recognize the elements of a problem, select the necessary information with the perspective of factual choices. In particular, our research intends to provide some fundamental guidelines for the definition of didactical rules and logical relations, that Authors should provide to a cognitive Tutoring system through the use of an Instructional Design method (PENTHA Model) which proposes an educational environment, able to: increase productivity and operability, create conditions for a cooperative dialogue, developing participatory research activities of knowledge, observations and discoveries, customizing the learning design in a complex and holistic vision of the learning / teaching processes.

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Lessons learned from the development and manufacture of ceramic reusable surface insulation materials for the space shuttle orbiters

NASA Technical Reports Server (NTRS)

Banas, R. P.; Elgin, D. R.; Cordia, E. R.; Nickel, K. N.; Gzowski, E. R.; Aguiler, L.

1983-01-01

Three ceramic, reusable surface insulation materials and two borosilicate glass coatings were used in the fabrication of tiles for the Space Shuttle orbiters. Approximately 77,000 tiles were made from these materials for the first three orbiters, Columbia, Challenger, and Discovery. Lessons learned in the development, scale up to production and manufacturing phases of these materials will benefit future production of ceramic reusable surface insulation materials. Processing of raw materials into tile blanks and coating slurries; programming and machining of tiles using numerical controlled milling machines; preparing and spraying tiles with the two coatings; and controlling material shrinkage during the high temperature (2100-2275 F) coating glazing cycles are among the topics discussed.

The pervasive role of social learning in primate lifetime development.

PubMed

Whiten, Andrew; van de Waal, Erica

2018-01-01

In recent decades, an accelerating research effort has exploited a substantial diversity of methodologies to garner mounting evidence for social learning and culture in many species of primate. As in humans, the evidence suggests that the juvenile phases of non-human primates' lives represent a period of particular intensity in adaptive learning from others, yet the relevant research remains scattered in the literature. Accordingly, we here offer what we believe to be the first substantial collation and review of this body of work and its implications for the lifetime behavioral ecology of primates. We divide our analysis into three main phases: a first phase of learning focused on primary attachment figures, typically the mother; a second phase of selective learning from a widening array of group members, including some with expertise that the primary figures may lack; and a third phase following later dispersal, when a migrant individual encounters new ecological and social circumstances about which the existing residents possess expertise that can be learned from. Collating a diversity of discoveries about this lifetime process leads us to conclude that social learning pervades primate ontogenetic development, importantly shaping locally adaptive knowledge and skills that span multiple aspects of the behavioral repertoire.

Pedagogical Techniques Employed by the Television Show "MythBusters"

NASA Astrophysics Data System (ADS)

Zavrel, Erik

2016-11-01

"MythBusters," the long-running though recently discontinued Discovery Channel science entertainment television program, has proven itself to be far more than just a highly rated show. While its focus is on entertainment, the show employs an array of pedagogical techniques to communicate scientific concepts to its audience. These techniques include: achieving active learning, avoiding jargon, employing repetition to ensure comprehension, using captivating demonstrations, cultivating an enthusiastic disposition, and increasing intrinsic motivation to learn. In this content analysis, episodes from the show's 10-year history were examined for these techniques. "MythBusters" represents an untapped source of pedagogical techniques, which science educators may consider availing themselves of in their tireless effort to better reach their students. Physics educators in particular may look to "MythBusters" for inspiration and guidance in how to incorporate these techniques into their own teaching and help their students in the learning process.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

PubMed

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Video Game Learning Dynamics: Actionable Measures of Multidimensional Learning Trajectories

ERIC Educational Resources Information Center

Reese, Debbie Denise; Tabachnick, Barbara G.; Kosko, Robert E.

2015-01-01

Valid, accessible, reusable methods for instructional video game design and embedded assessment can provide actionable information enhancing individual and collective achievement. Cyberlearning through game-based, metaphor-enhanced learning objects (CyGaMEs) design and embedded assessment quantify player behavior to study knowledge discovery and…

How Effective Is Instructional Support for Learning with Computer Simulations?

ERIC Educational Resources Information Center

Eckhardt, Marc; Urhahne, Detlef; Conrad, Olaf; Harms, Ute

2013-01-01

The study examined the effects of two different instructional interventions as support for scientific discovery learning using computer simulations. In two well-known categories of difficulty, data interpretation and self-regulation, instructional interventions for learning with computer simulations on the topic "ecosystem water" were developed…

Machine learning for the New York City power grid.

PubMed

Rudin, Cynthia; Waltz, David; Anderson, Roger N; Boulanger, Albert; Salleb-Aouissi, Ansaf; Chow, Maggie; Dutta, Haimonti; Gross, Philip N; Huang, Bert; Ierome, Steve; Isaac, Delfina F; Kressner, Arthur; Passonneau, Rebecca J; Radeva, Axinia; Wu, Leon

2012-02-01

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce 1) feeder failure rankings, 2) cable, joint, terminator, and transformer rankings, 3) feeder Mean Time Between Failure (MTBF) estimates, and 4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or realtime, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City’s electrical grid.

Describing Online Learning Content to Facilitate Resource Discovery and Sharing: The Development of the RU LOM Core

ERIC Educational Resources Information Center

Krull, G. E.; Mallinson, B. J.; Sewry, D. A.

2006-01-01

The development of Internet technologies has the ability to provide a new era of easily accessible and personalised learning, facilitated through the flexible deployment of small, reusable pieces of digital learning content over networks. Higher education institutions can share and reuse digital learning resources in order to improve their…

Shades of Pink: Preschoolers Make Meaning in a Reggio-Inspired Classroom

ERIC Educational Resources Information Center

Kim, Bo Sun

2012-01-01

Shades of Pink study describes how six preschoolers and their teacher engaged in a collaborative learning project through which they learned about the shades of a color--in this case, pink. As the children learned through experimenting and discussing their theories, they represented ideas using art as a tool for discovery and learning. The study…

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

PubMed

Kireeva, Natalia V; Ovchinnikova, Svetlana I; Kuznetsov, Sergey L; Kazennov, Andrey M; Tsivadze, Aslan Yu

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes

NASA Astrophysics Data System (ADS)

Kireeva, Natalia V.; Ovchinnikova, Svetlana I.; Kuznetsov, Sergey L.; Kazennov, Andrey M.; Tsivadze, Aslan Yu.

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

Social learning, culture and the 'socio-cultural brain' of human and non-human primates.

PubMed

Whiten, Andrew; van de Waal, Erica

2017-11-01

Noting important recent discoveries, we review primate social learning, traditions and culture, together with associated findings about primate brains. We survey our current knowledge of primate cultures in the wild, and complementary experimental diffusion studies testing species' capacity to sustain traditions. We relate this work to theories that seek to explain the enlarged brain size of primates as specializations for social intelligence, that have most recently extended to learning from others and the cultural transmission this permits. We discuss alternative theories and review a variety of recent findings that support cultural intelligence hypotheses for primate encephalization. At a more fine-grained neuroscientific level we focus on the underlying processes of social learning, especially emulation and imitation. Here, our own and others' recent research has established capacities for bodily imitation in both monkeys and apes, results that are consistent with a role for the mirror neuron system in social learning. We review important convergences between behavioural findings and recent non-invasive neuroscientific studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Scientific Discoveries: What Is Required for Lasting Impact.

PubMed

Lømo, Terje

2016-01-01

I have been involved in two scientific discoveries of some impact. One is the discovery of long-term potentiation (LTP), the phenomenon that brief, high-frequency impulse activity at synapses in the brain can lead to long-lasting increases in their efficiency of transmission. This finding demonstrated that synapses are plastic, a property thought to be necessary for learning and memory. The other discovery is that nerve-evoked muscle impulse activity, rather than putative trophic factors, controls the properties of muscle fibers. Here I describe how these two discoveries were made, the unexpected difficulties of reproducing the first discovery, and the controversies that followed the second discovery. I discuss why the first discovery took many years to become generally recognized, whereas the second caused an immediate sensation and entered textbooks and major reviews but is now largely forgotten. In the long run, discovering a new phenomenon has greater impact than falsifying a popular hypothesis.

The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory

NASA Astrophysics Data System (ADS)

Masci, Frank J.; Laher, Russ R.; Rebbapragada, Umaa D.; Doran, Gary B.; Miller, Adam A.; Bellm, Eric; Kasliwal, Mansi; Ofek, Eran O.; Surace, Jason; Shupe, David L.; Grillmair, Carl J.; Jackson, Ed; Barlow, Tom; Yan, Lin; Cao, Yi; Cenko, S. Bradley; Storrie-Lombardi, Lisa J.; Helou, George; Prince, Thomas A.; Kulkarni, Shrinivas R.

2017-01-01

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by ≃10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ≃97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.

The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory

NASA Technical Reports Server (NTRS)

Masci, Frank J.; Laher, Russ R.; Rebbapragada, Umaa D.; Doran, Gary B.; Miller, Adam A.; Bellm, Eric; Kasliwal, Mansi; Ofek, Eran O.; Surace, Jason; Shupe, David L.;

Cognitive Neuroscience Discoveries and Educational Practices

ERIC Educational Resources Information Center

Sylwester, Robert

2006-01-01

In this article, the author describes seven movement-related areas of cognitive neuroscience research that will play key roles in shifting the current behavioral orientation of teaching and learning to an orientation that also incorporates cognitive neuroscience discoveries. These areas of brain research include: (1) mirroring system; (2) plastic…

The Parallelism between Scientists' and Students' Resistance to New Scientific Ideas.

ERIC Educational Resources Information Center

Campanario, Juan Miguel

2002-01-01

Compares resistance by scientists to new ideas in scientific discovery with students' resistance to conceptual change in scientific learning. Studies the resistance by students to abandoning their misconceptions concerning scientific topics and the resistance by scientists to scientific discovery. (Contains 64 references.) (Author/YDS)

Historical milestones and discoveries that shaped the toxicology sciences.

PubMed

Hayes, Antoinette N; Gilbert, Steven G

2009-01-01

Knowledge of the toxic and healing properties of plants, animals, and minerals has shaped civilization for millennia. The foundations of modern toxicology are built upon the significant milestones and discoveries of serendipity and crude experimentation. Throughout the ages, toxicological science has provided information that has shaped and guided society. This chapter examines the development of the discipline of toxicology and its influence on civilization by highlighting significant milestones and discoveries related to toxicology. The examples shed light on the beginnings of toxicology, as well as examine lessons learned and re-learned. This chapter also examines how toxicology and the toxicologist have interacted with other scientific and cultural disciplines, including religion, politics, and the government. Toxicology has evolved to a true scientific discipline with its own dedicated scientists, educational institutes, sub-disciplines, professional societies, and journals. It now stands as its own entity while traversing such fields as chemistry, physiology, pharmacology, and molecular biology. We invite you to join us on a path of discovery and to offer our suggestions as to what are the most significant milestones and discoveries in toxicology. Additional information is available on the history section of Toxipedia (www.toxipedia.org).

Mitochondrial transcription in mammalian cells

PubMed Central

Shokolenko, Inna N.; Alexeyev, Mikhail F.

2017-01-01

As a consequence of recent discoveries of intimate involvement of mitochondria with key cellular processes, there has been a resurgence of interest in all aspects of mitochondrial biology, including the intricate mechanisms of mitochondrial DNA maintenance and expression. Despite four decades of research, there remains a lot to be learned about the processes that enable transcription of genetic information from mitochondrial DNA to RNA, as well as their regulation. These processes are vitally important, as evidenced by the lethality of inactivating the central components of mitochondrial transcription machinery. Here, we review the current understanding of mitochondrial transcription and its regulation in mammalian cells. We also discuss key theories in the field and highlight controversial subjects and future directions as we see them. PMID:27814650

A survey of automated methods for sensemaking support

NASA Astrophysics Data System (ADS)

Llinas, James

2014-05-01

Complex, dynamic problems in general present a challenge for the design of analysis support systems and tools largely because there is limited reliable a priori procedural knowledge descriptive of the dynamic processes in the environment. Problem domains that are non-cooperative or adversarial impute added difficulties involving suboptimal observational data and/or data containing the effects of deception or covertness. The fundamental nature of analysis in these environments is based on composite approaches involving mining or foraging over the evidence, discovery and learning processes, and the synthesis of fragmented hypotheses; together, these can be labeled as sensemaking procedures. This paper reviews and analyzes the features, benefits, and limitations of a variety of automated techniques that offer possible support to sensemaking processes in these problem domains.

The Psychophysics of Algebra Expertise: Mathematics Perceptual Learning Interventions Produce Durable Encoding Changes

ERIC Educational Resources Information Center

Bufford, Carolyn A.; Mettler, Everett; Geller, Emma H.; Kellman, Philip J.

2014-01-01

Mathematics requires thinking but also pattern recognition. Recent research indicates that perceptual learning (PL) interventions facilitate discovery of structure and recognition of patterns in mathematical domains, as assessed by tests of mathematical competence. Here we sought direct evidence that a brief perceptual learning module (PLM)…

Globalization of Knowledge Discovery and Information Retrieval in Teaching and Learning

ERIC Educational Resources Information Center

Zaidel, Mark; Guerrero, Osiris

2008-01-01

Developments in communication and information technologies in the last decade have had a significant impact on instructional and learning activities. For many students and educators, the Internet became the significant medium for sharing instruction, learning and communication. Access to knowledge beyond boundaries and cultures has an impact on…

Teacher-Student Communication Games: Some Experiments on Instruction.

ERIC Educational Resources Information Center

Olson, David R.; And Others

This inquiry began with the observation that learning from instruction is radically more efficient for obtaining information than learning by discovery. A series of seven experiments was conducted to determine some of the factors involved in learning from verbal instruction. The perspective adopted was that of communication theory, in which the…

The Biological Basis of Learning and Individuality.

ERIC Educational Resources Information Center

Kandel, Eric R.; Hawkins, Robert D.

1992-01-01

Describes the biological basis of learning and individuality. Presents an overview of recent discoveries that suggest learning engages a simple set of rules that modify the strength of connection between neurons in the brain. The changes are cited as playing an important role in making each individual unique. (MCO)

Writing-to-Learn Activities to Provoke Deeper Learning in Calculus

ERIC Educational Resources Information Center

Jaafar, Reem

2016-01-01

For students with little experience in mathematical thinking and conceptualization, writing-to-learn activities (WTL) can be particularly effective in promoting discovery and understanding. For community college students embarking on a first calculus course in particular, writing activities can help facilitate the transition from an "apply…

A Guided Discovery Approach for Learning Glycolysis.

ERIC Educational Resources Information Center

Schultz, Emeric

1997-01-01

Argues that more attention should be given to teaching students how to learn the rudiments of specific metabolic pathways. This approach describes a unique way of learning the glycolytic pathway in stepwise fashion. The pedagogy involves clear rote components that are connected to a set of generalizations that develop and enhance important…

School Garden Wizard: Home

Science.gov Websites

! Gardening and plant-based learning open a door to discovery of the living world. It stimulates even as it achieve learning goals in ways that are recommended by the National Science Standards and most state and Learning Inspiring Stories A Teacher's Perspective Gardening Tools Seasonal Considerations Special Needs

Learned Helplessness: A Theory for the Age of Personal Control.

ERIC Educational Resources Information Center

Peterson, Christopher; And Others

Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

De-novo discovery of differentially abundant transcription factor binding sites including their positional preference.

PubMed

Keilwagen, Jens; Grau, Jan; Paponov, Ivan A; Posch, Stefan; Strickert, Marc; Grosse, Ivo

2011-02-10

Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open-source Java framework Jstacs and as a stand-alone application at http://www.jstacs.de/index.php/Dispom.

How music training enhances working memory: a cerebrocerebellar blending mechanism that can lead equally to scientific discovery and therapeutic efficacy in neurological disorders.

PubMed

Vandervert, Larry

2015-01-01

Following in the vein of studies that concluded that music training resulted in plastic changes in Einstein's cerebral cortex, controlled research has shown that music training (1) enhances central executive attentional processes in working memory, and (2) has also been shown to be of significant therapeutic value in neurological disorders. Within this framework of music training-induced enhancement of central executive attentional processes, the purpose of this article is to argue that: (1) The foundational basis of the central executive begins in infancy as attentional control during the establishment of working memory, (2) In accordance with Akshoomoff, Courchesne and Townsend's and Leggio and Molinari's cerebellar sequence detection and prediction models, the rigors of volitional control demands of music training can enhance voluntary manipulation of information in thought and movement, (3) The music training-enhanced blending of cerebellar internal models in working memory as can be experienced as intuition in scientific discovery (as Einstein often indicated) or, equally, as moments of therapeutic advancement toward goals in the development of voluntary control in neurological disorders, and (4) The blending of internal models as in (3) thus provides a mechanism by which music training enhances central executive processes in working memory that can lead to scientific discovery and improved therapeutic outcomes in neurological disorders. Within the framework of Leggio and Molinari's cerebellar sequence detection model, it is determined that intuitive steps forward that occur in both scientific discovery and during therapy in those with neurological disorders operate according to the same mechanism of adaptive error-driven blending of cerebellar internal models. It is concluded that the entire framework of the central executive structure of working memory is a product of the cerebrocerebellar system which can, through the learning of internal models, incorporate the multi-dimensional rigor and volitional-control demands of music training and, thereby, enhance voluntary control. It is further concluded that this cerebrocerebellar view of the music training-induced enhancement of central executive control in working memory provides a needed mechanism to explain both the highest level of scientific discovery and the efficacy of music training in the remediation of neurological impairments.

Surveillance, Phagocytosis, and Inflammation: How Never-Resting Microglia Influence Adult Hippocampal Neurogenesis

PubMed Central

Sierra, Amanda; Beccari, Sol; Diaz-Aparicio, Irune; Encinas, Juan M.; Comeau, Samuel; Tremblay, Marie-Ève

2014-01-01

Microglia cells are the major orchestrator of the brain inflammatory response. As such, they are traditionally studied in various contexts of trauma, injury, and disease, where they are well-known for regulating a wide range of physiological processes by their release of proinflammatory cytokines, reactive oxygen species, and trophic factors, among other crucial mediators. In the last few years, however, this classical view of microglia was challenged by a series of discoveries showing their active and positive contribution to normal brain functions. In light of these discoveries, surveillant microglia are now emerging as an important effector of cellular plasticity in the healthy brain, alongside astrocytes and other types of inflammatory cells. Here, we will review the roles of microglia in adult hippocampal neurogenesis and their regulation by inflammation during chronic stress, aging, and neurodegenerative diseases, with a particular emphasis on their underlying molecular mechanisms and their functional consequences for learning and memory. PMID:24772353

Symmetry as Bias: Rediscovering Special Relativity

NASA Technical Reports Server (NTRS)

Lowry, Michael R.

1992-01-01

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

Leadership Decision Making and the Use of Data

ERIC Educational Resources Information Center

Guerra-Lopez, Ingrid; Blake, Anne M.

2011-01-01

Intelligence gathering, or data collection, is a preliminary and critical stage of decision making. Two key approaches to intelligence gathering are "discovery" and "idea imposition." The discovery approach allows us to learn about possibilities by gathering intelligence in order to identify and weigh options. The idea imposition approach limits…

The Discovery Approach to Mathematics.

ERIC Educational Resources Information Center

Wilson, Lois Fair

Summarized are presentations made at a one-day teachers' workshop organized by the Bicultural Socialization Project to discuss the materials to be used in mathematics learning centers in the project classrooms. The first chapter discusses the basic philosophy, whereby pupils are to be encouraged to enjoy the discovery of mathematical relationships…

GeoSearch: A lightweight broking middleware for geospatial resources discovery

NASA Astrophysics Data System (ADS)

Gui, Z.; Yang, C.; Liu, K.; Xia, J.

2012-12-01

With petabytes of geodata, thousands of geospatial web services available over the Internet, it is critical to support geoscience research and applications by finding the best-fit geospatial resources from the massive and heterogeneous resources. Past decades' developments witnessed the operation of many service components to facilitate geospatial resource management and discovery. However, efficient and accurate geospatial resource discovery is still a big challenge due to the following reasons: 1)The entry barriers (also called "learning curves") hinder the usability of discovery services to end users. Different portals and catalogues always adopt various access protocols, metadata formats and GUI styles to organize, present and publish metadata. It is hard for end users to learn all these technical details and differences. 2)The cost for federating heterogeneous services is high. To provide sufficient resources and facilitate data discovery, many registries adopt periodic harvesting mechanism to retrieve metadata from other federated catalogues. These time-consuming processes lead to network and storage burdens, data redundancy, and also the overhead of maintaining data consistency. 3)The heterogeneous semantics issues in data discovery. Since the keyword matching is still the primary search method in many operational discovery services, the search accuracy (precision and recall) is hard to guarantee. Semantic technologies (such as semantic reasoning and similarity evaluation) offer a solution to solve these issues. However, integrating semantic technologies with existing service is challenging due to the expandability limitations on the service frameworks and metadata templates. 4)The capabilities to help users make final selection are inadequate. Most of the existing search portals lack intuitive and diverse information visualization methods and functions (sort, filter) to present, explore and analyze search results. Furthermore, the presentation of the value-added additional information (such as, service quality and user feedback), which conveys important decision supporting information, is missing. To address these issues, we prototyped a distributed search engine, GeoSearch, based on brokering middleware framework to search, integrate and visualize heterogeneous geospatial resources. Specifically, 1) A lightweight discover broker is developed to conduct distributed search. The broker retrieves metadata records for geospatial resources and additional information from dispersed services (portals and catalogues) and other systems on the fly. 2) A quality monitoring and evaluation broker (i.e., QoS Checker) is developed and integrated to provide quality information for geospatial web services. 3) The semantic assisted search and relevance evaluation functions are implemented by loosely interoperating with ESIP Testbed component. 4) Sophisticated information and data visualization functionalities and tools are assembled to improve user experience and assist resource selection.

The Heuristic Method, Precursor of Guided Inquiry: Henry Armstrong and British Girls' Schools, 1890-1920

ERIC Educational Resources Information Center

Rayner-Canham, Geoff; Rayner-Canham, Marelene

2015-01-01

Though guided-inquiry learning, discovery learning, student-centered learning, and problem-based learning are commonly believed to be recent new approaches to the teaching of chemistry, in fact, the concept dates back to the late 19th century. Here, we will show that it was the British chemist, Henry Armstrong, who pioneered this technique,…

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

The Human Mind As General Problem Solver

NASA Astrophysics Data System (ADS)

Gurr, Henry

2011-10-01

Since leaving U Cal Irvine Neutrino Research, I have been a University Physics Teacher, and an Informal Researcher Of Human Functionality. My talk will share what I discovered about the best ways to learn, many of which are regularities that are to be expected from the Neuronal Network Properties announced in the publications of physicist John Joseph Hopfield. Hopfield's Model of mammalian brain-body, provides solid instructive understanding of how best Learn, Solve Problems, Live! With it we understand many otherwise puzzling features of our intellect! Examples Why 1) Analogies and metaphors powerful in class instruction, ditto poems. 2) Best learning done in physical (Hands-On) situations with tight immediate dynamical feedback such as seen in learning to ride bike, drive car, speak language, etc. 3) Some of the best learning happens in seeming random exploration, bump around, trial and error. 4) Scientific discoveries happen, with no apparent effort, at odd moments. 5) Important discoveries DEPEND on considerable frustrating effort, then Flash of Insight AHA EURIKA.

Evaluation of two CD-ROMs from a series on cell biology.

PubMed

Sander, Uwe; Kerlen, Gertraude; Steinke, Mattias; Huk, Thomas; Floto, Christian

2003-01-01

Two CD-ROMs from a series dealing with various major aspects of cell biology are evaluated in this paper using quantitative and qualitative approaches. The findings delimit similarities and differences of the two CD-ROMs and shed light on how the programs could be used in the learning process and how they should not be. The overall impression, as well as the graphical and technical features, received a predominantly good rating. The defined target groups were reached (e.g., students in secondary schools), different learning approaches were supported (e.g., discovery and autonomous learning), the CD-ROMs' usability was assessed as being easy and intuitive, and the majority of the evaluators were satisfied with the level of interactivity. Navigational problems encountered in CD-ROM 1 were overcome by a successful implementation of new navigational functions in CD-ROM 2. Most students found the CD-ROM to be a suitable complement to, or an extension of, their lessons. We conclude that many, but not all of the requirements for the various stages of the learning process could be satisfied with the existing CD-ROMs. The requirements not met are discussed to obtain insights that could help to improve the production of multimedia learning material. The use of quantitative and qualitative approaches in the evaluation of learning modules is discussed, as the study began by collecting and analyzing anecdotal reviews and was then extended to include a qualitative evaluation.

Evaluation of Two CD-ROMs from a Series on Cell Biology

PubMed Central

Sander, Uwe; Kerlen, Gertraude; Steinke, Mattias; Huk, Thomas; Floto, Christian

2003-01-01

Two CD-ROMs from a series dealing with various major aspects of cell biology are evaluated in this paper using quantitative and qualitative approaches. The findings delimit similarities and differences of the two CD-ROMs and shed light on how the programs could be used in the learning process and how they should not be. The overall impression, as well as the graphical and technical features, received a predominantly good rating. The defined target groups were reached (e.g., students in secondary schools), different learning approaches were supported (e.g., discovery and autonomous learning), the CD-ROMs' usability was assessed as being easy and intuitive, and the majority of the evaluators were satisfied with the level of interactivity. Navigational problems encountered in CD-ROM 1 were overcome by a successful implementation of new navigational functions in CD-ROM 2. Most students found the CD-ROM to be a suitable complement to, or an extension of, their lessons. We conclude that many, but not all of the requirements for the various stages of the learning process could be satisfied with the existing CD-ROMs. The requirements not met are discussed to obtain insights that could help to improve the production of multimedia learning material. The use of quantitative and qualitative approaches in the evaluation of learning modules is discussed, as the study began by collecting and analyzing anecdotal reviews and was then extended to include a qualitative evaluation. PMID:14673491

Validity and Practitality of Acid-Base Module Based on Guided Discovery Learning for Senior High School

NASA Astrophysics Data System (ADS)

Yerimadesi; Bayharti; Jannah, S. M.; Lufri; Festiyed; Kiram, Y.

2018-04-01

This Research and Development(R&D) aims to produce guided discovery learning based module on topic of acid-base and determine its validity and practicality in learning. Module development used Four D (4-D) model (define, design, develop and disseminate).This research was performed until development stage. Research’s instruments were validity and practicality questionnaires. Module was validated by five experts (three chemistry lecturers of Universitas Negeri Padang and two chemistry teachers of SMAN 9 Padang). Practicality test was done by two chemistry teachers and 30 students of SMAN 9 Padang. Kappa Cohen’s was used to analyze validity and practicality. The average moment kappa was 0.86 for validity and those for practicality were 0.85 by teachers and 0.76 by students revealing high category. It can be concluded that validity and practicality was proven for high school chemistry learning.

DNA: The Strand that Connects Us All

ScienceCinema

Kaplan, Matt [Univ. of Arizona, Tucson, AZ (United States). Genetics Core Facility

2018-04-26

Learn how the methods and discoveries of human population genetics are applied for personal genealogical reconstruction and anthropological testing. Dr. Kaplan starts with a short general review of human genetics and the biology behind this form of DNA testing. He looks at how DNA testing is performed and how samples are processed in the University of Arizona laboratory. He also examines examples of personal genealogical results from Family Tree DNA and personal anthropological results from the Genographic Project. Finally, he describes the newest project in the UA laboratory, the DNA Shoah Project.

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

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

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

DeepLensing: The Use of Deep Machine Learning to Find Strong Gravitational Lenses in Astronomical Surveys

NASA Astrophysics Data System (ADS)

Nord, Brian

2017-01-01

Strong gravitational lenses have potential as very powerful probes of dark energy and cosmic structure. However, efficiently finding lenses poses a significant challenge—especially in the era of large-scale cosmological surveys. I will present a new application of deep machine learning algorithms to find strong lenses, as well as the strong lens discovery program of the Dark Energy Survey (DES).Strong lenses provide unique information about the evolution of distant galaxies, the nature of dark energy, and the shapes of dark matter haloes. Current and future surveys, like DES and the Large Synoptic Survey Telescope, present an opportunity to find many thousands of strong lenses, far more than have ever been discovered. By and large, searches have heretofore relied on the time-consuming effort of human scanners. Deep machine learning frameworks, like convolutional neural nets, have revolutionized the task of image recognition, and have a natural place in the processing of astronomical images, including the search for strong lenses.Over five observing seasons, which started in August 2013, DES will carry out a wide-field survey of 5000 square degrees of the Southern Galactic Cap. DES has identified nearly 200 strong lensing candidates in the first two seasons of data. We have performed spectroscopic follow-up on a subsample of these candidates at Gemini South, confirming over a dozen new strong lenses. I will present this DES discovery program, including searches and spectroscopic follow-up of galaxy-scale, cluster-scale and time-delay lensing systems.I will focus, however, on a discussion of the successful search for strong lenses using deep learning methods. In particular, we show that convolutional neural nets present a new set of tools for efficiently finding lenses, and accelerating advancements in strong lensing science.

Learning Outdoors: Leader Guide, Grade 3. 4-H Discovery.

ERIC Educational Resources Information Center

Abell, John R.; Newman, Jerry A.

The United States has a rich natural resource heritage. It is important to educate students in the principles of conservation so that these natural resources may endure for generations. This guide is designed to help leaders to learn to organize groups of children and conduct successful meetings; provide fun, safe, outdoor learning experiences for…

Open the Door, Let's Explore More! Field Trips of Discovery for Young Children.

ERIC Educational Resources Information Center

Redleaf, Rhoda

Designed as a resource for teachers and parents, this guide contains activities to help children in primary grades learn from walks and field trips. Chapter 1, "Experience and Learning," discusses general information about how young children learn and the contribution of field trips to children's perception, language, memory, and logical…

Supporting Creativity and Imagination in the Early Years. Supporting Early Learning

ERIC Educational Resources Information Center

Duffy, Bernadette

2006-01-01

Learning through the arts has the potential to stimulate open ended activity that encourages discovery, exploration, experimentation and invention, thus contributing to children's development in all areas of learning and helping to make the curriculum meaningful to them. In this book, the author draws on her extensive experience of promoting young…

Model of Distributed Learning Objects Repository for a Heterogenic Internet Environment

ERIC Educational Resources Information Center

Kaczmarek, Jerzy; Landowska, Agnieszka

2006-01-01

In this article, an extension of the existing structure of learning objects is described. The solution addresses the problem of the access and discovery of educational resources in the distributed Internet environment. An overview of e-learning standards, reference models, and problems with educational resources delivery is presented. The paper…

Issues in Researching Self-Regulated Learning as Patterns of Events

ERIC Educational Resources Information Center

Winne, Philip H.

2014-01-01

New methods for gathering and analyzing data about events that comprise self-regulated learning (SRL) support discoveries about patterns among events and tests of hypotheses about roles patterns play in learning. Five such methodologies are discussed in the context of four key questions that shape investigations into patterns in SRL. A framework…

Analysis of student’s scientific attitude behaviour change effects blended learning supported by I-spring Suite 8 application

NASA Astrophysics Data System (ADS)

Budiharti, Rini; Waras, N. S.

2018-05-01

This article aims to describe the student’s scientific attitude behaviour change as treatment effect of Blended Learning supported by I-Spring Suite 8 application on the material balance and the rotational dynamics. Blended Learning models is learning strategy that integrate between face-to-face learning and online learning by combination of various media. Blended Learning model supported I-Spring Suite 8 media setting can direct learning becomes interactive. Students are guided to actively interact with the media as well as with other students to discuss getting the concept by the phenomena or facts presented. The scientific attitude is a natural attitude of students in the learning process. In interactive learning, scientific attitude is so needed. The research was conducted using a model Lesson Study which consists of the stages Plan-Do-Check-Act (PDCA) and applied to the subject of learning is students at class XI MIPA 2 of Senior High School 6 Surakarta. The validity of the data used triangulation techniques of observation, interviews and document review. Based on the discussion, it can be concluded that the use of Blended Learning supported media I-Spring Suite 8 is able to give the effect of changes in student behaviour on all dimensions of scientific attitude that is inquisitive, respect the data or fact, critical thinking, discovery and creativity, open minded and cooperation, and perseverance. Display e-learning media supported student worksheet makes the students enthusiastically started earlier, the core until the end of learning

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.

How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology.

PubMed

Sanders, Jason C; Showalter, Timothy N

2018-01-01

Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health-care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in genomics with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning.

Analysis and synthesis of abstract data types through generalization from examples

NASA Technical Reports Server (NTRS)

Wild, Christian

1987-01-01

The discovery of general patterns of behavior from a set of input/output examples can be a useful technique in the automated analysis and synthesis of software systems. These generalized descriptions of the behavior form a set of assertions which can be used for validation, program synthesis, program testing and run-time monitoring. Describing the behavior is characterized as a learning process in which general patterns can be easily characterized. The learning algorithm must choose a transform function and define a subset of the transform space which is related to equivalence classes of behavior in the original domain. An algorithm for analyzing the behavior of abstract data types is presented and several examples are given. The use of the analysis for purposes of program synthesis is also discussed.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

PubMed

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D; Duvenaud, David; Maclaurin, Dougal; Blood-Forsythe, Martin A; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

NASA Astrophysics Data System (ADS)

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Duvenaud, David; MacLaurin, Dougal; Blood-Forsythe, Martin A.; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P.; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

PubMed

Chung, Michael Jae-Yoon; Friesen, Abram L; Fox, Dieter; Meltzoff, Andrew N; Rao, Rajesh P N

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

PubMed Central

Chung, Michael Jae-Yoon; Friesen, Abram L.; Fox, Dieter; Meltzoff, Andrew N.; Rao, Rajesh P. N.

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration. PMID:26536366

A Cybernetic Design Methodology for 'Intelligent' Online Learning Support

NASA Astrophysics Data System (ADS)

Quinton, Stephen R.

The World Wide Web (WWW) provides learners and knowledge workers convenient access to vast stores of information, so much that present methods for refinement of a query or search result are inadequate - there is far too much potentially useful material. The problem often encountered is that users usually do not recognise what may be useful until they have progressed some way through the discovery, learning, and knowledge acquisition process. Additional support is needed to structure and identify potentially relevant information, and to provide constructive feedback. In short, support for learning is needed. The learning envisioned here is not simply the capacity to recall facts or to recognise objects. The focus is on learning that results in the construction of knowledge. Although most online learning platforms are efficient at delivering information, most do not provide tools that support learning as envisaged in this chapter. It is conceivable that Web-based learning environments can incorporate software systems that assist learners to form new associations between concepts and synthesise information to create new knowledge. This chapter details the rationale and theory behind a research study that aims to evolve Web-based learning environments into 'intelligent thinking' systems that respond to natural language human input. Rather than functioning simply as a means of delivering information, it is argued that online learning solutions will 1 day interact directly with students to support their conceptual thinking and cognitive development.

Geomorphic and Aqueous Chemistry of a Portion of the Upper Rio Tinto System, Spain

NASA Technical Reports Server (NTRS)

Osburn, M. R.; Fernandez-Remolar, D. C.; Arvidson, R. E.; Morris, R. V.; Ming, D.; Prieto-Ballesteros, O.; Amils, R.; Stein, T. C.; Heil-Chapdelaine, V.; Friedlander, L. R.;

Modalities, Relations, and Learning

NASA Astrophysics Data System (ADS)

Müller, Martin Eric

While the popularity of statistical, probabilistic and exhaustive machine learning techniques still increases, relational and logic approaches are still a niche market in research. While the former approaches focus on predictive accuracy, the latter ones prove to be indispensable in knowledge discovery.

FindIt@Flinders: User Experiences of the Primo Discovery Search Solution

ERIC Educational Resources Information Center

Jarrett, Kylie

2012-01-01

In September 2011, Flinders University Library launched FindIt@Flinders, the Primo discovery layer search to provide simultaneous results from the Library's collections and subscription databases. This research project was an exploratory case study which aimed to show whether students were finding relevant information for their course learning and…

Paraphrasing and Prediction with Self-Explanation as Generative Strategies for Learning Science Principles in a Simulation

ERIC Educational Resources Information Center

Morrison, Jennifer R.; Bol, Linda; Ross, Steven M.; Watson, Ginger S.

2015-01-01

This study examined the incorporation of generative strategies for the guided discovery of physics principles in a simulation. Participants who either paraphrased or predicted and self-explained guided discovery assignments exhibited improved performance on an achievement test as compared to a control group. Calibration accuracy (the…

ATOMIC PHYSICS, AN AUTOINSTRUCTIONAL PROGRAM, VOLUME 2, SUPPLEMENT.

ERIC Educational Resources Information Center

DETERLINE, WILLIAM A.; KLAUS, DAVID J.

THE AUTOINSTRUCTIONAL MATERIALS IN THIS TEXT WERE PREPARED FOR USE IN AN EXPERIMENTAL STUDY, OFFERING SELF-TUTORING MATERIAL FOR LEARNING ATOMIC PHYSICS. THE TOPICS COVERED ARE (1) ISOTOPES AND MASS NUMBERS, (2) MEASURING ATOMIC MASS, (3) DISCOVERY OF THE NUCLEUS, (4) STRUCTURE OF THE NUCLEUS, (5) DISCOVERY OF THE NEUTRON, (6) NUCLEAR REACTIONS,…

Interacting with… What? Exploring Children's Social and Sensory Practices in a Science Discovery Centre

ERIC Educational Resources Information Center

Dicks, Bella

2013-01-01

This paper presents findings from a qualitative UK study exploring the social practices of schoolchildren visiting an interactive science discovery centre. It is promoted as a place for "learning through doing", but the multi-modal, ethnographic methods adopted suggest that children were primarily engaged in (1) sensory pleasure-taking…

Teaching Tip: Using Rapid Game Prototyping for Exploring Requirements Discovery and Modeling

ERIC Educational Resources Information Center

Dalal, Nikunj

2012-01-01

We describe the use of rapid game prototyping as a pedagogic technique to experientially explore and learn requirements discovery, modeling, and specification in systems analysis and design courses. Students have a natural interest in gaming that transcends age, gender, and background. Rapid digital game creation is used to build computer games…

How does non-formal marine education affect student attitude and knowledge? A case study using SCDNR's Discovery program

NASA Astrophysics Data System (ADS)

McGovern, Mary Francis

Non-formal environmental education provides students the opportunity to learn in ways that would not be possible in a traditional classroom setting. Outdoor learning allows students to make connections to their environment and helps to foster an appreciation for nature. This type of education can be interdisciplinary---students not only develop skills in science, but also in mathematics, social studies, technology, and critical thinking. This case study focuses on a non-formal marine education program, the South Carolina Department of Natural Resources' (SCDNR) Discovery vessel based program. The Discovery curriculum was evaluated to determine impact on student knowledge about and attitude toward the estuary. Students from two South Carolina coastal counties who attended the boat program during fall 2014 were asked to complete a brief survey before, immediately after, and two weeks following the program. The results of this study indicate that both student knowledge about and attitude significantly improved after completion of the Discovery vessel based program. Knowledge and attitude scores demonstrated a positive correlation.

Understanding How Young Children Learn: Bringing the Science of Child Development to the Classroom

ERIC Educational Resources Information Center

Ostroff, Wendy

2012-01-01

Because little kids can't tell you how their minds work and what makes them learn, you need this book about new scientific discoveries that explain how young children learn and what teachers can do to use those findings to enhance classroom teaching. Discover where the desire to learn comes from and what occurs during children's development to…

A Conceptual Paper on the Application of the Picture Word Inductive Model Using Bruner's Constructivist View of Learning and the Cognitive Load Theory

ERIC Educational Resources Information Center

Jiang, Xuan; Perkins, Kyle

2013-01-01

Bruner's constructs of learning, specifically the structure of learning, spiral curriculum, and discovery learning, in conjunction with the Cognitive Load Theory, are used to evaluate the Picture Word Inductive Model (PWIM), an inquiry-oriented inductive language arts strategy designed to teach K-6 children phonics and spelling. The PWIM reflects…

ALCF Data Science Program: Productive Data-centric Supercomputing

NASA Astrophysics Data System (ADS)

Romero, Nichols; Vishwanath, Venkatram

The ALCF Data Science Program (ADSP) is targeted at big data science problems that require leadership computing resources. The goal of the program is to explore and improve a variety of computational methods that will enable data-driven discoveries across all scientific disciplines. The projects will focus on data science techniques covering a wide area of discovery including but not limited to uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows. Project teams will be among the first to access Theta, ALCFs forthcoming 8.5 petaflops Intel/Cray system. The program will transition to the 200 petaflop/s Aurora supercomputing system when it becomes available. In 2016, four projects have been selected to kick off the ADSP. The selected projects span experimental and computational sciences and range from modeling the brain to discovering new materials for solar-powered windows to simulating collision events at the Large Hadron Collider (LHC). The program will have a regular call for proposals with the next call expected in Spring 2017.http://www.alcf.anl.gov/alcf-data-science-program This research used resources of the ALCF, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Democratizing data science through data science training.

PubMed

Van Horn, John Darrell; Fierro, Lily; Kamdar, Jeana; Gordon, Jonathan; Stewart, Crystal; Bhattrai, Avnish; Abe, Sumiko; Lei, Xiaoxiao; O'Driscoll, Caroline; Sinha, Aakanchha; Jain, Priyambada; Burns, Gully; Lerman, Kristina; Ambite, José Luis

2018-01-01

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

Democratizing data science through data science training

PubMed Central

Van Horn, John Darrell; Fierro, Lily; Kamdar, Jeana; Gordon, Jonathan; Stewart, Crystal; Bhattrai, Avnish; Abe, Sumiko; Lei, Xiaoxiao; O’Driscoll, Caroline; Sinha, Aakanchha; Jain, Priyambada; Burns, Gully; Lerman, Kristina; Ambite, José Luis

2017-01-01

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training. PMID:29218890

Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models.

PubMed

Seiter, J; Derungs, A; Schuster-Amft, C; Amft, O; Tröster, G

2015-01-01

Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

Computer-aided drug discovery research at a global contract research organization

NASA Astrophysics Data System (ADS)

Kitchen, Douglas B.

2017-03-01

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

Computer-aided drug discovery research at a global contract research organization.

PubMed

Kitchen, Douglas B

2017-03-01

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

3 CFR 8435 - Proclamation 8435 of October 7, 2009. Leif Erikson Day, 2009

Code of Federal Regulations, 2010 CFR

2010-01-01

... determined not to turn back, in order to learn what lay beyond the setting sun. This same spirit lived within... their pioneering spirit continues to embody our Nation’s unbounded enthusiasm for discovery and learning...

A fortran program for Monte Carlo simulation of oil-field discovery sequences

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.

1993-01-01

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

"Re"storying the Present by "Re"visiting the Past: Unexpected Moments of Discovery and Illumination through Museum Learning

ERIC Educational Resources Information Center

Kawalilak, Colleen; Groen, Janet

2016-01-01

Two adult educators, guided by autoethnography as methodology, share the restorying of their own lifelong learning narratives and unexpected insights gained from having experienced the powerful potential of museum learning and culture. Having previously regarded museum visits as an experience that primarily tapped the intellectual, cognitive…

Active Learning for Discovery and Innovation in Criminology with Chinese Learners

ERIC Educational Resources Information Center

Li, Jessica C. M.; Wu, Joseph

2015-01-01

Whereas a great deal of literature based upon the context of Western societies has concluded criminology is an ideal discipline for active learning approach, it remains uncertain if this learning approach is applicable to Chinese learners in the discipline of criminology. This article describes and provides evidence of the benefits of using active…

Reconceptualizing Teacher Education Programs: Applying Dewey's Theories to Service-Learning with Early Childhood Preservice Teachers

ERIC Educational Resources Information Center

Lake, Vickie E.; Winterbottom, Christian; Ethridge, Elizabeth A.; Kelly, Loreen

2015-01-01

Dewey's concept of enabling children to explore based on their own interests has evolved into investigations and projects using methods of exploration, experimentation, and discovery--three tenets of service-learning. Using mixed methodology, the authors examined the implementation of service-learning in a teacher education program. A total of 155…

The Future of Music Education in Kenya: Implementation of Curriculum and Instructional Teaching Strategies

ERIC Educational Resources Information Center

Mochere, Joyce M.

2017-01-01

This paper is an evaluation of the parameters of the concept of music curriculum that examines principles underlying the teaching and learning of music. The paper also discusses the practical nature of music education and the need for experiential learning. Music educators worldwide advocate for methods that allow for discovery learning and hence…

Writing for Mathematics Discovery-Learning: A Model for Composition Courses.

ERIC Educational Resources Information Center

Weaver, Laura H.

Focusing on how expert writers in various disciplines convey complex ideas, this paper shows how the techniques used by the mathematician, Clark Kimberling, in various writings can (1) be transferred to other disciplines, (2) show learning taking place, and (3) provide models for students to re-enact learning in all subject areas. The paper…

Learning theories application in nursing education

PubMed Central

Aliakbari, Fatemeh; Parvin, Neda; Heidari, Mohammad; Haghani, Fariba

2015-01-01

Learning theories are the main guide for educational systems planning in the classroom and clinical training included in nursing. The teachers by knowing the general principles of these theories can use their knowledge more effectively according to various learning situations. In this study, Eric, Medline, and Cochrane databases were used for articles in English and for the Persian literature, Magiran, Iran doc, Iran medex, and Sid databases were used with the help of keywords including social cognitive learning, learning theory, behavioral theory, cognitive theory, constructive theory, and nursing education. The search period was considered from 1990 to 2012. Some related books were also studied about each method, its original vision, the founders, practical application of the training theory, especially training of nursing and its strengths and weaknesses. Behaviorists believe that learning is a change in an observable behavior and it happens when the communication occurs between the two events, a stimulus and a response. Among the applications of this approach is the influence on the learner's emotional reactions. Among the theories of this approach, Thorndike and Skinner works are subject to review and critique. Cognitive psychologists unlike the behaviorists believe that learning is an internal process objective and they focus on thinking, understanding, organizing, and consciousness. Fundamentalists believe that learners should be equipped with the skills of inquiry and problem solving in order to learn by the discovery and process of information. Among this group, we will pay attention to analyze Wertheimer, Brunner, Ausubel theories, Ganyeh information processing model, in addition to its applications in nursing education. Humanists in learning pay attention to the feelings and experiences. Carl Rogers support the retention of learning-centered approach and he is believed to a semantic continuum. At the other end of the continuum, experiential learning is located with the meaning and meaningful. It applies the minds and feelings of the person. From this group, the main focus will be on the works of Rogers and Novels. Finally, it could be concluded that the usage of any of these theoriesin its place would be desired and useful. PMID:25767813

Learning theories application in nursing education.

PubMed

Aliakbari, Fatemeh; Parvin, Neda; Heidari, Mohammad; Haghani, Fariba

2015-01-01

Learning theories are the main guide for educational systems planning in the classroom and clinical training included in nursing. The teachers by knowing the general principles of these theories can use their knowledge more effectively according to various learning situations. In this study, Eric, Medline, and Cochrane databases were used for articles in English and for the Persian literature, Magiran, Iran doc, Iran medex, and Sid databases were used with the help of keywords including social cognitive learning, learning theory, behavioral theory, cognitive theory, constructive theory, and nursing education. The search period was considered from 1990 to 2012. Some related books were also studied about each method, its original vision, the founders, practical application of the training theory, especially training of nursing and its strengths and weaknesses. Behaviorists believe that learning is a change in an observable behavior and it happens when the communication occurs between the two events, a stimulus and a response. Among the applications of this approach is the influence on the learner's emotional reactions. Among the theories of this approach, Thorndike and Skinner works are subject to review and critique. Cognitive psychologists unlike the behaviorists believe that learning is an internal process objective and they focus on thinking, understanding, organizing, and consciousness. Fundamentalists believe that learners should be equipped with the skills of inquiry and problem solving in order to learn by the discovery and process of information. Among this group, we will pay attention to analyze Wertheimer, Brunner, Ausubel theories, Ganyeh information processing model, in addition to its applications in nursing education. Humanists in learning pay attention to the feelings and experiences. Carl Rogers support the retention of learning-centered approach and he is believed to a semantic continuum. At the other end of the continuum, experiential learning is located with the meaning and meaningful. It applies the minds and feelings of the person. From this group, the main focus will be on the works of Rogers and Novels. Finally, it could be concluded that the usage of any of these theoriesin its place would be desired and useful.

Building Knowledge Graphs for NASA's Earth Science Enterprise

NASA Astrophysics Data System (ADS)

Zhang, J.; Lee, T. J.; Ramachandran, R.; Shi, R.; Bao, Q.; Gatlin, P. N.; Weigel, A. M.; Maskey, M.; Miller, J. J.

2016-12-01

Inspired by Google Knowledge Graph, we have been building a prototype Knowledge Graph for Earth scientists, connecting information and data in NASA's Earth science enterprise. Our primary goal is to advance the state-of-the-art NASA knowledge extraction capability by going beyond traditional catalog search and linking different distributed information (such as data, publications, services, tools and people). This will enable a more efficient pathway to knowledge discovery. While Google Knowledge Graph provides impressive semantic-search and aggregation capabilities, it is limited to search topics for general public. We use the similar knowledge graph approach to semantically link information gathered from a wide variety of sources within the NASA Earth Science enterprise. Our prototype serves as a proof of concept on the viability of building an operational "knowledge base" system for NASA Earth science. Information is pulled from structured sources (such as NASA CMR catalog, GCMD, and Climate and Forecast Conventions) and unstructured sources (such as research papers). Leveraging modern techniques of machine learning, information retrieval, and deep learning, we provide an integrated data mining and information discovery environment to help Earth scientists to use the best data, tools, methodologies, and models available to answer a hypothesis. Our knowledge graph would be able to answer questions like: Which articles discuss topics investigating similar hypotheses? How have these methods been tested for accuracy? Which approaches have been highly cited within the scientific community? What variables were used for this method and what datasets were used to represent them? What processing was necessary to use this data? These questions then lead researchers and citizen scientists to investigate the sources where data can be found, available user guides, information on how the data was acquired, and available tools and models to use with this data. As a proof of concept, we focus on a well-defined domain - Hurricane Science linking research articles and their findings, data, people and tools/services. Modern information retrieval, natural language processing machine learning and deep learning techniques are applied to build the knowledge network.

Theory and practice: How do we teach our students about light?

NASA Astrophysics Data System (ADS)

Creath, Katherine

2007-08-01

As optical scientists and engineers we have an educational paradigm that stresses passing knowledge from teacher to student. We are also taught to use inductive reasoning to solve problems. Yet many of the fundamental questions in optics such as the topic of this conference "What are photons?" require that we use retroductive reasoning to deduce the possible and probable cause of the observations and measurements we make. We can agree that we don't have all the answers for many fundamental questions in optics. The retroductive reasoning process requires a different way of thinking from our traditional classroom setting. Most of us learned to do this through working in a research lab or industry. With the amount of information and new discoveries to consider, it makes it difficult to cover everything in the classroom. This paper looks at transformational learning techniques and how they have been applied in science and engineering. These techniques show promise to prepare our students to learn how to learn and develop skills they can directly apply to research and industry.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

2012-12-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

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

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

Building a Trustworthy Environmental Science Data Repository: Lessons Learned from the ORNL DAAC

NASA Astrophysics Data System (ADS)

Wei, Y.; Santhana Vannan, S. K.; Boyer, A.; Beaty, T.; Deb, D.; Hook, L.

2017-12-01

The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, https://daac.ornl.gov) for biogeochemical dynamics is one of NASA's Earth Observing System Data and Information System (EOSDIS) data centers. The mission of the ORNL DAAC is to assemble, distribute, and provide data services for a comprehensive archive of terrestrial biogeochemistry and ecological dynamics observations and models to facilitate research, education, and decision-making in support of NASA's Earth Science. Since its establishment in 1994, ORNL DAAC has been continuously building itself into a trustworthy environmental science data repository by not only ensuring the quality and usability of its data holdings, but also optimizing its data publication and management process. This paper describes the lessons learned from ORNL DAAC's effort toward this goal. ORNL DAAC has been proactively implementing international community standards throughout its data management life cycle, including data publication, preservation, discovery, visualization, and distribution. Data files in standard formats, detailed documentation, and metadata following standard models are prepared to improve the usability and longevity of data products. Assignment of a Digital Object Identifier (DOI) ensures the identifiability and accessibility of every data product, including the different versions and revisions of its life cycle. ORNL DAAC's data citation policy assures data producers receive appropriate recognition of use of their products. Web service standards, such as OpenSearch and Open Geospatial Consortium (OGC), promotes the discovery, visualization, distribution, and integration of ORNL DAAC's data holdings. Recently, ORNL DAAC began efforts to optimize and standardize its data archival and data publication workflows, to improve the efficiency and transparency of its data archival and management processes.

Solution NMR Spectroscopy in Target-Based Drug Discovery.

PubMed

Li, Yan; Kang, Congbao

2017-08-23

Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

PubMed

Points, Laurie J; Taylor, James Ward; Grizou, Jonathan; Donkers, Kevin; Cronin, Leroy

2018-01-30

Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

What and how do students learn in an interprofessional student-run clinic? An educational framework for team-based care

PubMed Central

Lie, Désirée A.; Forest, Christopher P.; Walsh, Anne; Banzali, Yvonne; Lohenry, Kevin

2016-01-01

Background The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students. Purpose To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients. Methods The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups. Sessions were audiotaped, and transcripts were independently coded and adjudicated. Major themes about learning content and processes were extracted. Grounded theory was followed after data synthesis and interpretation to establish a framework for interprofessional learning. Results Thirty-six students from four professions (medicine, physician assistant, occupational therapy, and pharmacy) participated in eight uniprofessional groups; 14 students participated in three multiprofessional groups (N = 50). Theme saturation was achieved. Six common themes about learning content from uniprofessional groups were role recognition, team-based care appreciation, patient experience, advocacy-/systems-based models, personal skills, and career choices. Occupational therapy students expressed self-advocacy, and medical students expressed humility and self-discovery. Synthesis of themes from all groups suggests a learning continuum that begins with the team huddle and continues with shared patient care and social interactions. Opportunity to observe and interact with other professions in action is key to the learning process. Discussion Interprofessional SRC participation promotes learning ‘with, from, and about’ each other. Participation challenges misconceptions and sensitizes students to patient experiences, health systems, advocacy, and social responsibility. Learning involves interprofessional interactions in the patient encounter, reinforced by formal and informal communications. Participation is associated with interest in serving the underserved and in primary care careers. The authors proposed a framework for interprofessional learning with implications for optimal learning environments to promote team-based care. Future research is suggested to identify core faculty functions and best settings to advance and enhance student preparation for future collaborative team practice. PMID:27499364

What and how do students learn in an interprofessional student-run clinic? An educational framework for team-based care.

PubMed

Lie, Désirée A; Forest, Christopher P; Walsh, Anne; Banzali, Yvonne; Lohenry, Kevin

2016-01-01

Background The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students. Purpose To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients. Methods The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups. Sessions were audiotaped, and transcripts were independently coded and adjudicated. Major themes about learning content and processes were extracted. Grounded theory was followed after data synthesis and interpretation to establish a framework for interprofessional learning. Results Thirty-six students from four professions (medicine, physician assistant, occupational therapy, and pharmacy) participated in eight uniprofessional groups; 14 students participated in three multiprofessional groups (N = 50). Theme saturation was achieved. Six common themes about learning content from uniprofessional groups were role recognition, team-based care appreciation, patient experience, advocacy-/systems-based models, personal skills, and career choices. Occupational therapy students expressed self-advocacy, and medical students expressed humility and self-discovery. Synthesis of themes from all groups suggests a learning continuum that begins with the team huddle and continues with shared patient care and social interactions. Opportunity to observe and interact with other professions in action is key to the learning process. Discussion Interprofessional SRC participation promotes learning 'with, from, and about' each other. Participation challenges misconceptions and sensitizes students to patient experiences, health systems, advocacy, and social responsibility. Learning involves interprofessional interactions in the patient encounter, reinforced by formal and informal communications. Participation is associated with interest in serving the underserved and in primary care careers. The authors proposed a framework for interprofessional learning with implications for optimal learning environments to promote team-based care. Future research is suggested to identify core faculty functions and best settings to advance and enhance student preparation for future collaborative team practice.

What and how do students learn in an interprofessional student-run clinic? An educational framework for team-based care.

PubMed

Lie, Désirée A; Forest, Christopher P; Walsh, Anne; Banzali, Yvonne; Lohenry, Kevin

2016-01-01

The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students. To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients. The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups. Sessions were audiotaped, and transcripts were independently coded and adjudicated. Major themes about learning content and processes were extracted. Grounded theory was followed after data synthesis and interpretation to establish a framework for interprofessional learning. Thirty-six students from four professions (medicine, physician assistant, occupational therapy, and pharmacy) participated in eight uniprofessional groups; 14 students participated in three multiprofessional groups (N = 50). Theme saturation was achieved. Six common themes about learning content from uniprofessional groups were role recognition, team-based care appreciation, patient experience, advocacy-/systems-based models, personal skills, and career choices. Occupational therapy students expressed self-advocacy, and medical students expressed humility and self-discovery. Synthesis of themes from all groups suggests a learning continuum that begins with the team huddle and continues with shared patient care and social interactions. Opportunity to observe and interact with other professions in action is key to the learning process. Interprofessional SRC participation promotes learning 'with, from, and about' each other. Participation challenges misconceptions and sensitizes students to patient experiences, health systems, advocacy, and social responsibility. Learning involves interprofessional interactions in the patient encounter, reinforced by formal and informal communications. Participation is associated with interest in serving the underserved and in primary care careers. The authors proposed a framework for interprofessional learning with implications for optimal learning environments to promote team-based care. Future research is suggested to identify core faculty functions and best settings to advance and enhance student preparation for future collaborative team practice.

Drawing Connections Between Local and Global Observations: An Essential Element of Geoscience Education

NASA Astrophysics Data System (ADS)

Manduca, C. A.; Mogk, D. W.

2002-12-01

One of the hallmarks of geoscience research is the process of moving between observations and interpretations on local and global scales to develop an integrated understanding of Earth processes. Understanding this interplay is an important aspect of student geoscience learning which leads to an understanding of the fundamental principles of science and geoscience and of the connections between local natural phenomena or human activity and global processes. Several techniques that engage students in inquiry and discovery (as recommended in the National Science Education Standards, NRC 1996, Shaping the Future of Undergraduate Earth Science Education, AGU, 1997) hold promise for helping students make these connections. These include the development of global data sets from local observations (e.g. GLOBE); studying small scale or local phenomenon in the context of global models (e.g. carbon storage in local vegetation and its role in the carbon cycle); or an analysis of local environmental issues in a global context (e.g. a comparison of local flooding to flooding in other countries and analysis in the context of weather, geology and development patterns). Research on learning suggests that data-rich activities linking the local and global have excellent potential for enhancing student learning because 1) students have already developed observations and interpretations of their local environment which can serve as a starting point for constructing new knowledge and 2) this context may motivate learning and develop understanding that can be transferred to other situations. (How People Learn, NRC, 2001). Faculty and teachers at two recent workshops confirm that projects that involve local or global data can engage students in learning by providing real world context, creating student ownership of the learning process, and developing scientific skills applicable to the complex problems that characterize modern science and society. Workshop participants called for increased dissemination of examples of effective practice, evaluation of the impact of data-rich activities on learning, and further development of data access infrastructure and services. (for additional workshop results and discussion see http://serc.carleton.edu/research_education/usingdata)

Exoplanet Science in the Classroom: Learning Activities for an Introductory Physics Course

ERIC Educational Resources Information Center

Della-Rose, Devin; Carlson, Randall; de La Harpe, Kimberly; Novotny, Steven; Polsgrove, Daniel

2018-01-01

Discovery of planets outside our solar system, known as extra-solar planets or exoplanets for short, has been at the forefront of astronomical research for over 25 years. Reports of new discoveries have almost become routine; however, the excitement surrounding them has not. Amazingly, as groundbreaking as exoplanet science is, the basic physics…

Invention versus Direct Instruction: For Some Content, It's a Tie

ERIC Educational Resources Information Center

Chase, Catherine C.; Klahr, David

2017-01-01

An important, but as yet unresolved pedagogical question is whether discovery-oriented or direct instruction methods lead to greater learning and transfer. We address this issue in a study with 101 fourth and fifth grade students that contrasts two distinct instructional methods. One is a blend of discovery and direct instruction called…

The Discovery Method; An International Experiment in Retraining. Employment of Older Workers, 6.

ERIC Educational Resources Information Center

Belbin, R.M.

Several demonstration programs were used in training older workers in four member countries of the Organisation for Economic Co-operation and Development. The Austrian program was a stonemasonry course for persons aged 18 to 55, one group using traditional methods and the other, the discovery (discrimination learning) method. In the United…

Evaluation Tool for the Application of Discovery Teaching Method in the Greek Environmental School Projects

ERIC Educational Resources Information Center

Kalathaki, Maria

2015-01-01

Greek school community emphasizes on the discovery direction of teaching methodology in the school Environmental Education (EE) in order to promote Education for the Sustainable Development (ESD). In ESD school projects the used methodology is experiential teamwork for inquiry based learning. The proposed tool checks whether and how a school…

Using Comparative Planetology in Exhibit Development

NASA Astrophysics Data System (ADS)

Dusenbery, P. B.; Harold, J. B.; Morrow, C. A.

2004-12-01

It is critically important for the public to better understand the scientific process. Museum exhibitions are an important part of informal science education that can effectively reach public audiences as well as school groups. They provide an important gateway for the public to learn about compelling scientific endeavors. The Space Science Institute (SSI) is a national leader in producing traveling science exhibitions and their associated educational programming (i.e. interactive websites, educator workshops, public talks, instructional materials). The focus of this presentation will be on three of its exhibit projects: MarsQuest (currently on tour), Alien Earths (in fabrication), and Giant Planets (in development). MarsQuest is enabling millions of Americans to share in the excitement of the scientific exploration of Mars and to learn more about their own planet in the process. Alien Earths will bring origins-related research and discoveries to students and the American public. It has four interrelated exhibit areas: Our Place in Space, Star Birth, PlanetQuest, and Search for Life. Exhibit visitors will explore the awesome events surrounding the birth of stars and planets; they will join scientists in the hunt for planets outside our solar system including those that may be in "habitable zones" around other stars; and finally they will be able to learn about how scientists are looking for signs of life beyond Earth. Giant Planets: Exploring the Outer Solar System will take advantage of the excitement generated by the Cassini mission and bring planetary and origins research and discoveries to students and the public. It will be organized around four thematic areas: Our Solar System; Colossal Worlds; Moons, Rings, and Fields; and Make Space for Kids. Giant Planets will open in 2007. This talk will focus on the importance of making Earth comparisons in the conceptual design of each exhibit and will show several examples of how these comparisons were manifested in the MarsQuest & Alien Earths exhibitions.

Goal-directed imitation: the means to an end.

PubMed

Hayes, Spencer J; Ashford, Derek; Bennett, Simon J

2008-02-01

The effects of goal-directed imitation and observational learning were examined whilst learning a goal-directed motor skill (three-ball cascade juggling). An observational learning (OL) group observed a model and a control (CON) group received minimal verbal instructions regarding how to hold and release the juggling balls. The OL group performed more juggling cycles across practice and retention than the CON group. In addition, the OL group's upper limb coordination and ball flight trajectory pattern were more similar to the model's movements than the CON group. These data show that when the to-be-learnt movement pattern and end-goal are not specified by the task's mechanical constraints, or can be achieved by modifying a pre-existing motor skill, individuals have difficulty learning on the basis of discovery processes alone. Under these circumstances, observational learning is effective because it conveys to the individual the specific means by which the end-goal can be achieved. These findings lead us to suggest that when the end-goal and the means to achieve the end-goal are directly linked, the means are given sufficient weight in the goal hierarchy such that the model's movement is imitated.

Hidden physics models: Machine learning of nonlinear partial differential equations

NASA Astrophysics Data System (ADS)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning.

PubMed

Zhe, Shandian; Xu, Zenglin; Qi, Yuan; Yu, Peng

2014-01-01

A key step for Alzheimer's disease (AD) study is to identify associations between genetic variations and intermediate phenotypes (e.g., brain structures). At the same time, it is crucial to develop a noninvasive means for AD diagnosis. Although these two tasks-association discovery and disease diagnosis-have been treated separately by a variety of approaches, they are tightly coupled due to their common biological basis. We hypothesize that the two tasks can potentially benefit each other by a joint analysis, because (i) the association study discovers correlated biomarkers from different data sources, which may help improve diagnosis accuracy, and (ii) the disease status may help identify disease-sensitive associations between genetic variations and MRI features. Based on this hypothesis, we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels based on Gaussian process ordinal regression; in return, the disease status is used to guide the discovery of relationships between the data sources. The sparse projection matrices not only reveal the associations but also select groups of biomarkers related to AD. To learn the model from data, we develop an efficient variational expectation maximization algorithm. Simulation results demonstrate that our approach achieves higher accuracy in both predicting ordinal labels and discovering associations between data sources than alternative methods. We apply our approach to an imaging genetics dataset of AD. Our joint analysis approach not only identifies meaningful and interesting associations between genetic variations, brain structures, and AD status, but also achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

Climate Discovery Online Courses for Educators from NCAR

NASA Astrophysics Data System (ADS)

Henderson, S.; Ward, D. L.; Meymaris, K. K.; Johnson, R. M.; Gardiner, L.; Russell, R.

2008-12-01

The National Center for Atmospheric Research (NCAR) has responded to the pressing need for professional development in climate and global change sciences by creating the Climate Discovery online course series. This series was designed with the secondary geoscience educator in mind. The online courses are based on current and credible climate change science. Interactive learning techniques are built into the online course designs with assignments that encourage active participation. A key element of the online courses is the creation of a virtual community of geoscience educators who exchange ideas related to classroom implementation, student assessment, and lessons plans. Geoscience educators from around the country have participated in the online courses. The ongoing interest from geoscience educators strongly suggests that the NCAR Climate Discovery online courses are a timely and needed professional development opportunity. The intent of NCAR Climate Discovery is to positively impact teachers' professional development scientifically authentic information, (2) experiencing guided practice in conducting activities and using ancillary resources in workshop venues, (3) gaining access to standards-aligned lesson plans, kits that promote hands-on learning, and scientific content that are easily implemented in their classrooms, and (4) becoming a part of a community of educators with whom they may continue to discuss the challenges of pedagogy and content comprehension in teaching climate change in the Earth system context. Three courses make up the Climate Discovery series: Introduction to Climate Change; Earth System Science - A Climate Change Perspective; and Understanding Climate Change Today. Each course, instructed by science education specialists, combines geoscience content, information about current climate research, hands-on activities, and group discussion. The online courses use the web-based Moodle courseware system (open- source software similar to Blackboard and webCT), utilizing its features to promote dialogue as well as provide rich online content and media. A key element of the online courses is the development and support of an online learning community, an essential component in successful online courses. Interactive learning techniques are built into the course designs with assignments that encourage active participation. Educators (both formal and informal) use the courses as a venue to exchange ideas and teaching resources. A unique feature of the courses is the emphasis on hands-on activities, a hallmark of our professional development efforts. This presentation will focus on the lessons learned in the development of the three online courses and our successful recruitment and retention efforts.

Efficient Modeling and Active Learning Discovery of Biological Responses

PubMed Central

Naik, Armaghan W.; Kangas, Joshua D.; Langmead, Christopher J.; Murphy, Robert F.

2013-01-01

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random. PMID:24358322

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

PubMed

Drier, Yotam; Domany, Eytan

2011-03-14

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

PubMed

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Ontogeny of Manipulative Behavior and Nut-Cracking in Young Tufted Capuchin Monkeys ("Cebus Apella"): A Perception-Action Perspective

ERIC Educational Resources Information Center

de Resende, Briseida Dogo; Ottoni, Eduardo B.; Fragaszy, Dorothy M.

2008-01-01

How do capuchin monkeys learn to use stones to crack open nuts? Perception-action theory posits that individuals explore producing varying spatial and force relations among objects and surfaces, thereby learning about affordances of such relations and how to produce them. Such learning supports the discovery of tool use. We present longitudinal…

Semantic Features of Math Problems: Relationships to Student Learning and Engagement

ERIC Educational Resources Information Center

Slater, Stefan; Baker, Ryan; Ocumpaugh, Jaclyn; Inventado, Paul; Scupelli, Peter; Heffernan, Neil

2016-01-01

The creation of crowd-sourced content in learning systems is a powerful method for adapting learning systems to the needs of a range of teachers in a range of domains, but the quality of this content can vary. This study explores linguistic differences in teacher-created problem content in ASSISTments using a combination of discovery with models…

Undergraduate Research in Agriculture: Constructivism and the Scholarship of Discovery

ERIC Educational Resources Information Center

Splan, Rebecca K.; Porr, C. A. Shea; Broyles, Thomas W.

2011-01-01

Experiential learning is a hallmark of undergraduate education programs in the agricultural sciences, and is aligned with constructivist learning theory. This interpretivist qualitative study used historical research methodology to analyze the epistemological underpinnings of constructivism and explore the construct's relationship to undergraduate…

Locate, Plan, Develop, Use An Outdoor Classroom.

ERIC Educational Resources Information Center

Soil Conservation Service (USDA), Upper Darby, PA.

Designed to aid educational institutions and community organizations in selecting, planning, developing and using outdoor learning areas as outdoor classrooms, this guide includes: (1) Learning by Discovery (scientific, cultural, and recreational goals); (2) The Initial Planning Effort (use of: a planning committee including teachers,…

Birding with Children: A Nest of Activities.

ERIC Educational Resources Information Center

Ard, Linda; Wilkerson, Kristen

1996-01-01

Describes hummingbirds and how they can serve as sources of learning and enjoyment for young children. Gives information on feeding, breeding, and behavior of hummingbirds, and on their natural predators. Outlines activities for "discovery," making feeders, watching and charting hummingbirds, and other creative learning activities. (BGC)

When Does Provision of Instruction Promote Learning?

ERIC Educational Resources Information Center

Lee, Hee Seung; Anderson, Abraham; Betts, Shawn; Anderson, John R.

2011-01-01

Contradictory evidence has been reported on the effects of discovery learning approach and the role of instructional explanations. By manipulating the presence of instruction (verbal explanation) and transparency of problem structures, we investigated how effects of instructional explanations differed depending on the transparency of problem…

Racism in nursing education: a reflective journey.

PubMed

Markey, Kathleen; Tilki, Mary

This article discusses the personal and professional journey of discovery experienced by a nurse lecturer as a result of engagement in a project exploring the impact of racism in the nursing classroom. The findings of the study demonstrated the existence and complexity of racism, the impact of racism on student learning, the limitations of lecturers in recognizing and addressing racism and organizational factors which perpetuate institutional racism. The authors describe the insight gained from the research process and how this has influenced the practice of the first author and how reflection and mentorship by the second author have challenged personal ethnocentricity, encouraged new ways of thinking, enhanced confidence and encouraged experiential teaching strategies. The article highlights the ways in which nurse lecturers might become culturally competent and in particular addressing issues of racism in the classroom and enabling learning which is applicable in practice.

Unsupervised learning of natural languages

PubMed Central

Solan, Zach; Horn, David; Ruppin, Eytan; Edelman, Shimon

2005-01-01

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics. PMID:16087885

Unsupervised learning of natural languages.

PubMed

Solan, Zach; Horn, David; Ruppin, Eytan; Edelman, Shimon

2005-08-16

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.

Big data analytics for early detection of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Ivanova, Desislava

2017-12-01

This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big dataand further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.

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.

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

What Will Classroom Teachers Do With Shared Research Results?

NASA Astrophysics Data System (ADS)

Passow, M. J.; Weissel, J. K.; Cormier, M.; Newman, K. R.

2005-12-01

Scientists are passionate about the research problems they investigate, and wish to share their discoveries as widely as possible. Similarly, classroom teachers who are passionate about their subject can better foster student learning. One way to enhance such passions involves bringing teachers and scientists together to discuss cutting-edge discoveries and develop curricular materials based on the respective strengths of educators and investigators. Our presentation describes one example of this approach based on research about gas blowout structures offshore Virginia and North Carolina. Methane venting processes along continental margins may have important climatic, geotechnical, hazard, and resource implications. In 2000, shipboard surveys documented that large structures offshore VA-NC resulted from massive gas expulsion. Gas appears to be trapped in shelf edge deltas and stresses resulting from downslope creep is favoring its release. Scientists undertook a new expedition in 2004 to determine if there is present-day discharge of methane-rich fluids through the floors or walls of the blowouts or whether these seepage sites are relict features, and to gain insight into the origin of the vented methane. In July 2005, 12 teachers from New York and New Jersey met with the co-PIs (Weissel and Cormier), graduate student (Newman), and educational specialist (Passow) over a 2-day workshop to learn about how scientific problems are identified, how a research cruise is organized, what was learned through the measurements and analysis, and what might be possible significant impacts from such understandings. Based on what they learned, participants began development of classroom activities, Internet-based investigations, and constructed-response assessment items utilizing data and concepts from the project and other sources. The resulting curriculum units are designed for use in middle and high school chemistry, physics, earth science, and technology courses. Curricular units include "Using Real-Life Problems to Learn Scientific Principles," "Mapping the Unseen Floors," "Landslide or Not," and a board game based on conducting a scientific research cruise. Materials are available through www.earth2class.org. Over the following academic year, participants will continue to develop instructional materials, field-test them, and provide peer training through in-district and regional professional development opportunities. The scientists and educational specialist will provide support to ensure scientific accuracy and pedagogical soundness. The project will utilize DLESE as an additional effective dissemination and evaluation mechanism. In these ways, the scientists and core of educators may be able to share these discoveries with hundreds of teachers and thousands of students.

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

PubMed

Benson, Neil

2015-08-01

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

Physics Guided Data Science in the Earth Sciences

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2017-12-01

Even as the geosciences are becoming relatively data-rich owing to remote sensing and archived model simulations, established physical understanding and process knowledge cannot be ignored. The ability to leverage both physics and data-intensive sciences may lead to new discoveries and predictive insights. A principled approach to physics guided data science, where physics informs feature selection, output constraints, and even the architecture of the learning models, is motivated. The possibility of hybrid physics and data science models at the level of component processes is discussed. The challenges and opportunities, as well as the relations to other approaches such as data assimilation - which also bring physics and data together - are discussed. Case studies are presented in climate, hydrology and meteorology.

Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0.

PubMed

Hernández-Solana, Ángela; Perez-Diaz-de-Cerio, David; Valdovinos, Antonio; Valenzuela, Jose Luis

2017-08-30

The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios.

Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0

PubMed Central

2017-01-01

The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios. PMID:28867786

7 CFR 1.642 - When must a party supplement or amend information it has previously provided?

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 1 2010-01-01 2010-01-01 false When must a party supplement or amend information it... When must a party supplement or amend information it has previously provided? (a) Discovery. A party must promptly supplement or amend any prior response to a discovery request if it learns that the...

Discovery Lab in the Chemistry Lecture Room: Design and Evaluation of Audio-Visual Constructivist Methodology of Teaching Descriptive Inorganic Chemistry.

ERIC Educational Resources Information Center

Young, Barbara N.; Hoffman, Lyubov

Demonstration of chemical reactions is a tool used in the teaching of inorganic descriptive chemistry to enable students to understand the fundamental concepts of chemistry through the use of concrete examples. For maximum benefit, students need to learn through discovery to observe, interpret, hypothesize, and draw conclusions; however, chemical…

On Line Instruction: An Opportunity to Re-Examine and Re-Invent Pedagogy

ERIC Educational Resources Information Center

Rosenthal, Irene

2010-01-01

Author recounts ten discoveries she made about on-line instruction that were beyond her field of vision when she was still viewing it though the lens of traditional classroom instruction. The discoveries include what she learned by reviewing the research in effective course design and a discourse analysis she conducted of the number and types of…

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Interdisciplinary Aspects of Learning: Physics and Psychology

ERIC Educational Resources Information Center

Oleg, Yavoruk

2015-01-01

The article deals with interdisciplinary aspects of learning in the case of physics and psychology. It describes the lab-based academic course focused on: observation and experimentation; discovery of new scientific facts; measurement; identification of errors; the study of psychological characteristics of people (time perception, the reaction…

ZAPs: Using Interactive Programs for Learning Psychology

ERIC Educational Resources Information Center

Hulshof, Casper D.; Eysink, Tessa H. S.; Loyens, Sofie; de Jong, Ton

2005-01-01

ZAPs are short, self-contained computer programs that encourage students to experience psychological phenomena in a vivid, self-explanatory way, and that are meant to evoke enthusiasm about psychological topics. ZAPs were designed according to principles that originate from experiential and discovery learning theories. The interactive approach…

Search Pathways: Modeling GeoData Search Behavior to Support Usable Application Development

NASA Astrophysics Data System (ADS)

Yarmey, L.; Rosati, A.; Tressel, S.

2014-12-01

Recent technical advances have enabled development of new scientific data discovery systems. Metadata brokering, linked data, and other mechanisms allow users to discover scientific data of interes across growing volumes of heterogeneous content. Matching this complex content with existing discovery technologies, people looking for scientific data are presented with an ever-growing array of features to sort, filter, subset, and scan through search returns to help them find what they are looking for. This paper examines the applicability of available technologies in connecting searchers with the data of interest. What metrics can be used to track success given shifting baselines of content and technology? How well do existing technologies map to steps in user search patterns? Taking a user-driven development approach, the team behind the Arctic Data Explorer interdisciplinary data discovery application invested heavily in usability testing and user search behavior analysis. Building on earlier library community search behavior work, models were developed to better define the diverse set of thought processes and steps users took to find data of interest, here called 'search pathways'. This research builds a deeper understanding of the user community that seeks to reuse scientific data. This approach ensures that development decisions are driven by clearly articulated user needs instead of ad hoc technology trends. Initial results from this research will be presented along with lessons learned for other discovery platform development and future directions for informatics research into search pathways.

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

A Challenging Pie to Splice: Drugging the Spliceosome.

PubMed

León, Brian; Kashyap, Manoj K; Chan, Warren C; Krug, Kelsey A; Castro, Januario E; La Clair, James J; Burkart, Michael D

2017-09-25

Since its discovery in 1977, the study of alternative RNA splicing has revealed a plethora of mechanisms that had never before been documented in nature. Understanding these transitions and their outcome at the level of the cell and organism has become one of the great frontiers of modern chemical biology. Until 2007, this field remained in the hands of RNA biologists. However, the recent identification of natural product and synthetic modulators of RNA splicing has opened new access to this field, allowing for the first time a chemical-based interrogation of RNA splicing processes. Simultaneously, we have begun to understand the vital importance of splicing in disease, which offers a new platform for molecular discovery and therapy. As with many natural systems, gaining clear mechanistic detail at the molecular level is key towards understanding the operation of any biological machine. This minireview presents recent lessons learned in this emerging field of RNA splicing chemistry and chemical biology. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Communicating the Science from NASA's Astrophysics Missions

NASA Astrophysics Data System (ADS)

Hasan, Hashima; Smith, Denise A.

2015-01-01

Communicating science from NASA's Astrophysics missions has multiple objectives, which leads to a multi-faceted approach. While a timely dissemination of knowledge to the scientific community follows the time-honored process of publication in peer reviewed journals, NASA delivers newsworthy research result to the public through news releases, its websites and social media. Knowledge in greater depth is infused into the educational system by the creation of educational material and teacher workshops that engage students and educators in cutting-edge NASA Astrophysics discoveries. Yet another avenue for the general public to learn about the science and technology through NASA missions is through exhibits at museums, science centers, libraries and other public venues. Examples of the variety of ways NASA conveys the excitement of its scientific discoveries to students, educators and the general public will be discussed in this talk. A brief overview of NASA's participation in the International Year of Light will also be given, as well as of the celebration of the twenty-fifth year of the launch of the Hubble Space Telescope.

Discovering latent commercial networks from online financial news articles

NASA Astrophysics Data System (ADS)

Xia, Yunqing; Su, Weifeng; Lau, Raymond Y. K.; Liu, Yi

2013-08-01

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.

Conceptualising and creating a global learning health system.

PubMed

Friedman, Charles; Rigby, Michael

2013-04-01

In any country the health sector is important in terms of human wellbeing and large in terms of economics. The health sector might therefore be expected to be a finely tuned enterprise, utilising corporate knowledge in a constant process of critically reviewing and improving its activities and processes. However, this is seldom the case. Health systems and practice are highly variable and lag behind research discovery. This contrasts strongly with commercial bodies, and particularly service industries, where the concept of the learning organisation is strongly seen as the key to optimisation. A learning organisation accesses for analytic purposes operational data, which though captured and recorded for day-to-day transactions at the customer level, become also the basis of understanding changes in both demand and delivery process. In health care, the concept of the learning organisation is well grounded ethically. Anything which can improve health, including understanding of optimal care delivery processes and how to improve longer term outcomes, should be seized upon to drive service improvement - but currently this occurs haphazardly. The limitations of paper-based systems, priority given to digitalization of financial transactions, concerns about electronic data insecurity, and other factors have inhibited progress towards organisational learning at a national scale. But in recent years, new means of capturing, managing, and exchanging data have created new opportunities, while ever increasing pressures on health systems have produced strengthened incentive. In the United States, the current policy and investment impetus to electronic health records and concomitantly their 'meaningful use' create opportunities to build the foundations for data re-use for corporate learning - and thus for societal gain. In Europe and other settings there are islands of innovation, but not yet a coherent culture or impetus to build foundations for a learning health system. This paper considers how to move forward, in the light of the urgent need for smarter health systems where experience becomes the fuel for rapid improvement, and best practices are routinely identified and applied. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Immersive Theater - a Proven Way to Enhance Learning Retention

NASA Astrophysics Data System (ADS)

Reiff, P. H.; Zimmerman, L.; Spillane, S.; Sumners, C.

2014-12-01

The portable immersive theater has gone from our first demonstration at fall AGU 2003 to a product offered by multiple companies in various versions to literally millions of users per year. As part of our NASA funded outreach program, we conducted a test of learning in a portable Discovery Dome as contrasted with learning the same materials (visuals and sound track) on a computer screen. We tested 200 middle school students (primarily underserved minorities). Paired t-tests and an independent t-test were used to compare the amount of learning that students achieved. Interest questionnaires were administered to participants in formal (public school) settings and focus groups were conducted in informal (museum camp and educational festival) settings. Overall results from the informal and formal educational setting indicated that there was a statistically significant increase in test scores after viewing We Choose Space. There was a statistically significant increase in test scores for students who viewed We Choose Space in the portable Discovery Dome (9.75) as well as with the computer (8.88). However, long-term retention of the material tested on the questionnaire indicated that for students who watched We Choose Space in the portable Discovery Dome, there was a statistically significant long-term increase in test scores (10.47), whereas, six weeks after learning on the computer, the improvements over the initial baseline (3.49) were far less and were not statistically significant. The test score improvement six weeks after learning in the dome was essentially the same as the post test immediately after watching the show, demonstrating virtually no loss of gained information in the six week interval. In the formal educational setting, approximately 34% of the respondents indicated that they wanted to learn more about becoming a scientist, while 35% expressed an interest in a career in space science. In the informal setting, 26% indicated that they were interested in pursuing a career in space science.

16 CFR 3.31A - Expert discovery.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 16 Commercial Practices 1 2013-01-01 2013-01-01 false Expert discovery. 3.31A Section 3.31A... PRACTICE FOR ADJUDICATIVE PROCEEDINGS Discovery; Compulsory Process § 3.31A Expert discovery. (a) The... later than 1 day after the close of fact discovery, meaning the close of discovery except for...

16 CFR 3.31A - Expert discovery.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Expert discovery. 3.31A Section 3.31A... PRACTICE FOR ADJUDICATIVE PROCEEDINGS Discovery; Compulsory Process § 3.31A Expert discovery. (a) The... later than 1 day after the close of fact discovery, meaning the close of discovery except for...

16 CFR 3.31A - Expert discovery.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Expert discovery. 3.31A Section 3.31A... PRACTICE FOR ADJUDICATIVE PROCEEDINGS Discovery; Compulsory Process § 3.31A Expert discovery. (a) The... later than 1 day after the close of fact discovery, meaning the close of discovery except for...

The role of collaborative ontology development in the knowledge negotiation process

NASA Astrophysics Data System (ADS)

Rivera, Norma

Interdisciplinary research (IDR) collaboration can be defined as the process of integrating experts' knowledge, perspectives, and resources to advance scientific discovery. The flourishing of more complex research problems, together with the growth of scientific and technical knowledge has resulted in the need for researchers from diverse fields to provide different expertise and points of view to tackle these problems. These collaborations, however, introduce a new set of "culture" barriers as participating experts are trained to communicate in discipline-specific languages, theories, and research practices. We propose that building a common knowledge base for research using ontology development techniques can provide a starting point for interdisciplinary knowledge exchange, negotiation, and integration. The goal of this work is to extend ontology development techniques to support the knowledge negotiation process in IDR groups. Towards this goal, this work presents a methodology that extends previous work in collaborative ontology development and integrates learning strategies and tools to enhance interdisciplinary research practices. We evaluate the effectiveness of applying such methodology in three different scenarios that cover educational and research settings. The results of this evaluation confirm that integrating learning strategies can, in fact, be advantageous to overall collaborative practices in IDR groups.

The Power of Inquiry as a Way of Learning in Undergraduate Education at a Large Research University

ERIC Educational Resources Information Center

Fowler, Debra A.; Matthews, Pamela R.; Schielack, Jane F.; Webb, Robert C.; Wu, X. Ben

2012-01-01

Inquiry-guided learning (IGL) is not new to Texas A&M University, a large research-extensive institution. The ideas of asking questions and seeking answers have always been associated at this university with both learning and discovery. In this article the authors present how, as a natural extension, Texas A&M University infuses IGL more…

Learning Management Systems: Practical Considerations for the Selection and Implementation of an E-learning Platform for the Navy

DTIC Science & Technology

2007-01-28

is interested in B2B and B2C e-commerce, enterprise resource planning, e-procurement, supply-chain management, data mining, and knowledge discovery... social networking tools, collaborative spaces, knowledge management, “connecting-enabling” protocols like RSS, and other tools. The intent of the ILE...delivered to them, what learning pedagogy is appropriate for them, the optimal level of social interaction for learning, and available resources

Science is Cool with NASA's "Space School Musical"

NASA Astrophysics Data System (ADS)

Asplund, S.

2011-10-01

To help young learners understand basic solar system science concepts and retain what they learn, NASA's Discovery and New Frontiers Programs have collaborated with KidTribe to create "Space School Musical," an innovative approach for teaching about the solar system. It's an educational "hip-hopera" that raps, rhymes, moves and grooves its way into the minds and memories of students and educators alike. The solar system comes alive, combining science content with music, fun lyrics, and choreography. Kids can watch the videos, learn the songs, do the cross-curricular activities, and perform the show themselves. The videos, songs, lyrics, and guides are available to all with free downloads at http://discovery.nasa.gov/

Movement Education-Past-Present-Future.

ERIC Educational Resources Information Center

Fowler, John S.

Physical education in England at the secondary school level was dominated in the 1950's by a formal, disciplinary method of teaching known as the "Swedish Drill," developed from the remedial gymnastics of P. H. Ling. However, at the elementary education level, a change towards informality, discovery learning, learning environments, and…

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data

ERIC Educational Resources Information Center

Yang, Shuo

2017-01-01

With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and…

Scaffolding for Discovery in the Third Plane

ERIC Educational Resources Information Center

Ewert-Krocker, Laurie

2015-01-01

Laurie Ewert-Krocker emphasizes the teacher's role in nature's prepared environment. Without directing or controlling the child's work, learning spaces can be maximized for concentration by connecting the adolescent's intrinsic learning to the beauty and order of the natural world. The most artful balance is the global understanding of the…

Individual Differences in Learning from an Intelligent Discovery World: Smithtown.

ERIC Educational Resources Information Center

Shute, Valerie J.

"Smithtown" is an intelligent computer program designed to enhance an individual's scientific inquiry skills as well as to provide an environment for learning principles of basic microeconomics. It was hypothesized that intelligent computer instruction on applying effective interrogative skills (e.g., changing one variable at a time…

Making the Science Literacy Connection: After-School Science Clubs

ERIC Educational Resources Information Center

Moore-Hart, Margaret A.; Liggit, Peggy; Daisey, Peggy

2004-01-01

Children make discoveries spontaneously while participating in hands-on science learning experiences. The students in this study were attending an after-school science program that was organized around authentic literacy activities and hands-on science learning experiences related to the theme of wetlands. Literacy connections formed natural…

A Role for Neuroscience in Shaping Contemporary Education Policy

ERIC Educational Resources Information Center

Shore, Rebecca; Bryant, Joel

2011-01-01

Advanced technologies have made it possible for neuroscientists to make remarkable discoveries regarding how our brains learn. This research should provide new insights into the designs of learning environments. This essay is an attempt to suggest how the possibilities of neuroscience might be employed to meet contemporary educational demands,…

A Dynamic Community of Discovery: Planning, Learning, and Change

ERIC Educational Resources Information Center

Gordon, Michelle; Ireland, Martha; Wong, Mina

2011-01-01

Ryerson University's Prior Learning and Competency Evaluation and Documentation (PLACED) program is funded by the Government of Ontario to engage internationally educated professionals (IEPs), employers, and regulatory/occupational bodies in the use of competency-based practices. In 2008, the authors created a self-assessment tool for IEPs that…

Expeditionary Learning in Information Systems: Definition, Implementation, and Assessment

ERIC Educational Resources Information Center

Abrahams, Alan S.; Singh, Tirna

2013-01-01

In the natural sciences, collecting, cataloguing, and comparing living specimens have long been a popular, collaborative mode of discovery and learning. New species are discovered, and the relationships between species are theorized. From Aristotle's "History of Animals" to Darwin's "On the Origin of Species", and beyond, this…

Animals without Backbones: The Invertebrate Story. Grade Level 5-9.

ERIC Educational Resources Information Center

Jerome, Brian; Fuqua, Paul

This guide, when used in tandem with the videotape "Animals Without Backbones," helps students learn about invertebrates. These materials promote hands-on discovery and learning. The guide is composed of six curriculum-based teaching units: (1) "Getting Started"; (2) "Porifera"; (3) "Cnidarians"; (4) "Worms"; (5) "Mollusks"; (6) "Arthropods"; and…

A Practice-Oriented Review of Learning Objects

ERIC Educational Resources Information Center

Sinclair, J.; Joy, M.; Yau, J. Y.-K.; Hagan, S.

2013-01-01

Reusable learning objects support packaging of educational materials allowing their discovery and reuse. Open educational resources emphasize the need for open licensing and promote sharing and community involvement. For both teachers and learners, finding appropriate tried and tested resources on a topic of interest and being able to incorporate…

A Comparison of the Effects of Two Instructional Sequences Involving Science Laboratory Activities.

ERIC Educational Resources Information Center

Ivins, Jerry Edward

This study attempted to determine if students learn science concepts better when laboratories are used to verify concepts already intorduced through lectures and textbooks (verification laboratories or whether achievement and retention are improved when laboratories are used to introduce new concepts (directed discovery learning laboratories). The…

Building Cognition: The Construction of Computational Representations for Scientific Discovery.

PubMed

Chandrasekharan, Sanjay; Nersessian, Nancy J

2015-11-01

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. Copyright © 2014 Cognitive Science Society, Inc.

Learning chronobiology by improving Wikipedia.

PubMed

Chiang, C D; Lewis, C L; Wright, M D E; Agapova, S; Akers, B; Azad, T D; Banerjee, K; Carrera, P; Chen, A; Chen, J; Chi, X; Chiou, J; Cooper, J; Czurylo, M; Downs, C; Ebstein, S Y; Fahey, P G; Goldman, J W; Grieff, A; Hsiung, S; Hu, R; Huang, Y; Kapuria, A; Li, K; Marcu, I; Moore, S H; Moseley, A C; Nauman, N; Ness, K M; Ngai, D M; Panzer, A; Peters, P; Qin, E Y; Sadhu, S; Sariol, A; Schellhase, A; Schoer, M B; Steinberg, M; Surick, G; Tsai, C A; Underwood, K; Wang, A; Wang, M H; Wang, V M; Westrich, D; Yockey, L J; Zhang, L; Herzog, E D

2012-08-01

Although chronobiology is of growing interest to scientists, physicians, and the general public, access to recent discoveries and historical perspectives is limited. Wikipedia is an online, user-written encyclopedia that could enhance public access to current understanding in chronobiology. However, Wikipedia is lacking important information and is not universally trusted. Here, 46 students in a university course edited Wikipedia to enhance public access to important discoveries in chronobiology. Students worked for an average of 9 h each to evaluate the primary literature and available Wikipedia information, nominated sites for editing, and, after voting, edited the 15 Wikipedia pages they determined to be highest priorities. This assignment (http://www.nslc.wustl.edu/courses/Bio4030/wikipedia_project.html) was easy to implement, required relatively short time commitments from the professor and students, and had measurable impacts on Wikipedia and the students. Students created 3 new Wikipedia sites, edited 12 additional sites, and cited 347 peer-reviewed articles. The targeted sites all became top hits in online search engines. Because their writing was and will be read by a worldwide audience, students found the experience rewarding. Students reported significantly increased comfort with reading, critiquing, and summarizing primary literature and benefited from seeing their work edited by other scientists and editors of Wikipedia. We conclude that, in a short project, students can assist in making chronobiology widely accessible and learn from the editorial process.

19 CFR 210.61 - Discovery and compulsory process.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery and compulsory process. 210.61 Section 210.61 Customs Duties UNITED STATES INTERNATIONAL TRADE COMMISSION INVESTIGATIONS OF UNFAIR PRACTICES IN IMPORT TRADE ADJUDICATION AND ENFORCEMENT Temporary Relief § 210.61 Discovery and compulsory...

Marketed Marine Natural Products in the Pharmaceutical and Cosmeceutical Industries: Tips for Success

PubMed Central

Martins, Ana; Vieira, Helena; Gaspar, Helena; Santos, Susana

2014-01-01

The marine environment harbors a number of macro and micro organisms that have developed unique metabolic abilities to ensure their survival in diverse and hostile habitats, resulting in the biosynthesis of an array of secondary metabolites with specific activities. Several of these metabolites are high-value commercial products for the pharmaceutical and cosmeceutical industries. The aim of this review is to outline the paths of marine natural products discovery and development, with a special focus on the compounds that successfully reached the market and particularly looking at the approaches tackled by the pharmaceutical and cosmetic companies that succeeded in marketing those products. The main challenges faced during marine bioactives discovery and development programs were analyzed and grouped in three categories: biodiversity (accessibility to marine resources and efficient screening), supply and technical (sustainable production of the bioactives and knowledge of the mechanism of action) and market (processes, costs, partnerships and marketing). Tips to surpass these challenges are given in order to improve the market entry success rates of highly promising marine bioactives in the current pipelines, highlighting what can be learned from the successful and unsuccessful stories that can be applied to novel and/or ongoing marine natural products discovery and development programs. PMID:24549205

Lung tumor diagnosis and subtype discovery by gene expression profiling.

PubMed

Wang, Lu-yong; Tu, Zhuowen

2006-01-01

The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.

The Effects of the Compasslearning Odyssey Spiral-Up Program on Discovery Education Scores of Sixth-Grade Gifted and High-Performing Language Arts Students

ERIC Educational Resources Information Center

Kelsey, Carmen Freeman

2012-01-01

The purpose of this study was to examine the relationship between the implementation of the Response to Intervention (RTI) model CompassLearning Odyssey and the performance of middle school language arts students on the Discovery Education Test B and Tennessee Comprehensive Assessment Program (TCAP) along with examining teacher perceptions of high…

Serotonergic Regulation of Prefrontal Cortical Circuitries Involved in Cognitive Processing: A Review of Individual 5-HT Receptor Mechanisms and Concerted Effects of 5-HT Receptors Exemplified by the Multimodal Antidepressant Vortioxetine.

PubMed

Leiser, Steven C; Li, Yan; Pehrson, Alan L; Dale, Elena; Smagin, Gennady; Sanchez, Connie

2015-07-15

It has been known for several decades that serotonergic neurotransmission is a key regulator of cognitive function, mood, and sleep. Yet with the relatively recent discoveries of novel serotonin (5-HT) receptor subtypes, as well as an expanding knowledge of their expression level in certain brain regions and localization on certain cell types, their involvement in cognitive processes is still emerging. Of particular interest are cognitive processes impacted in neuropsychiatric and neurodegenerative disorders. The prefrontal cortex (PFC) is critical to normal cognitive processes, including attention, impulsivity, planning, decision-making, working memory, and learning or recall of learned memories. Furthermore, serotonergic dysregulation within the PFC is implicated in many neuropsychiatric disorders associated with prominent symptoms of cognitive dysfunction. Thus, it is important to better understand the overall makeup of serotonergic receptors in the PFC and on which cell types these receptors mediate their actions. In this Review, we focus on 5-HT receptor expression patterns within the PFC and how they influence cognitive behavior and neurotransmission. We further discuss the net effects of vortioxetine, an antidepressant acting through multiple serotonergic targets given the recent findings that vortioxetine improves cognition by modulating multiple neurotransmitter systems.

Analysis of latency performance of bluetooth low energy (BLE) networks.

PubMed

Cho, Keuchul; Park, Woojin; Hong, Moonki; Park, Gisu; Cho, Wooseong; Seo, Jihoon; Han, Kijun

2014-12-23

Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes.

Analysis of Latency Performance of Bluetooth Low Energy (BLE) Networks

PubMed Central

Cho, Keuchul; Park, Woojin; Hong, Moonki; Park, Gisu; Cho, Wooseong; Seo, Jihoon; Han, Kijun

2015-01-01

Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes. PMID:25545266

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

Information flow through threespine stickleback networks without social transmission

PubMed Central

Atton, N.; Hoppitt, W.; Webster, M. M.; Galef, B. G.; Laland, K. N.

2012-01-01

Social networks can result in directed social transmission of learned information, thus influencing how innovations spread through populations. Here we presented shoals of threespine sticklebacks (Gasterosteous aculeatus) with two identical foraging tasks and applied network-based diffusion analysis (NBDA) to determine whether the order in which individuals in a social group contacted and solved the tasks was affected by the group's network structure. We found strong evidence for a social effect on discovery of the foraging tasks with individuals tending to discover a task sooner when others in their group had previously done so, and with the spread of discovery of the foraging tasks influenced by groups' social networks. However, the same patterns of association did not reliably predict spread of solution to the tasks, suggesting that social interactions affected the time at which the tasks were discovered, but not the latency to its solution following discovery. The present analysis, one of the first applications of NBDA to a natural animal system, illustrates how NBDA can lead to insight into the mechanisms supporting behaviour acquisition that more conventional statistical approaches might miss. Importantly, we provide the first compelling evidence that the spread of novel behaviours can result from social learning in the absence of social transmission, a phenomenon that we refer to as an untransmitted social effect on learning. PMID:22896644

Use Hierarchical Storage and Analysis to Exploit Intrinsic Parallelism

NASA Astrophysics Data System (ADS)

Zender, C. S.; Wang, W.; Vicente, P.

2013-12-01

Big Data is an ugly name for the scientific opportunities and challenges created by the growing wealth of geoscience data. How to weave large, disparate datasets together to best reveal their underlying properties, to exploit their strengths and minimize their weaknesses, to continually aggregate more information than the world knew yesterday and less than we will learn tomorrow? Data analytics techniques (statistics, data mining, machine learning, etc.) can accelerate pattern recognition and discovery. However, often researchers must, prior to analysis, organize multiple related datasets into a coherent framework. Hierarchical organization permits entire dataset to be stored in nested groups that reflect their intrinsic relationships and similarities. Hierarchical data can be simpler and faster to analyze by coding operators to automatically parallelize processes over isomorphic storage units, i.e., groups. The newest generation of netCDF Operators (NCO) embody this hierarchical approach, while still supporting traditional analysis approaches. We will use NCO to demonstrate the trade-offs involved in processing a prototypical Big Data application (analysis of CMIP5 datasets) using hierarchical and traditional analysis approaches.

Spitzer observatory operations: increasing efficiency in mission operations

NASA Astrophysics Data System (ADS)

Scott, Charles P.; Kahr, Bolinda E.; Sarrel, Marc A.

2006-06-01

This paper explores the how's and why's of the Spitzer Mission Operations System's (MOS) success, efficiency, and affordability in comparison to other observatory-class missions. MOS exploits today's flight, ground, and operations capabilities, embraces automation, and balances both risk and cost. With operational efficiency as the primary goal, MOS maintains a strong control process by translating lessons learned into efficiency improvements, thereby enabling the MOS processes, teams, and procedures to rapidly evolve from concept (through thorough validation) into in-flight implementation. Operational teaming, planning, and execution are designed to enable re-use. Mission changes, unforeseen events, and continuous improvement have often times forced us to learn to fly anew. Collaborative spacecraft operations and remote science and instrument teams have become well integrated, and worked together to improve and optimize each human, machine, and software-system element. Adaptation to tighter spacecraft margins has facilitated continuous operational improvements via automated and autonomous software coupled with improved human analysis. Based upon what we now know and what we need to improve, adapt, or fix, the projected mission lifetime continues to grow - as does the opportunity for numerous scientific discoveries.

A case study of one school system's adoption and implementation of an elementary science program

NASA Astrophysics Data System (ADS)

Kelly, Michael Patrick

2000-10-01

The researcher's purpose in this study was to examine the process used by the Minot Public Schools to adopt and implement a new elementary science program from Silver Burdett Ginn called Discovery Works. Using case study methods within a naturalistic design, the researcher investigated teachers' concerns as they adopted and implemented Discovery Works in their classrooms. Data were gathered using the Concerns Based Adoption Model (CBAM) instrument, interviews with adoption committee members, classroom teachers, grade level meetings, and document analysis of field notes related to each phase of the study. Content analysis methods were used to analyze the data. Emergent themes were presented and substantiated in the data, in terms of six research questions that guided this research. The data were analyzed both quantitatively and qualitatively to provide a rich, thick description that and enabled the researcher to confirm and triangulate the concerns of teachers in this study. The quantitative data revealed a general nonuser profile by teachers as they implemented Discovery Works. Three major themes of concerns emerged from a qualitative analysis of the data. The first theme was implementation, including issues related to teacher attitudes and inservice needs. The second theme, management issues, had five concerns subsumed within it. These included concerns related to time, materials, storage, reorder, and cooperative groups. The third theme, effects on students, included issues concerning hands-on methods of teaching science, vocabulary, especially at the upper elementary, and assessment issues. Possible solutions to resolve each of the concerns were presented. Major conclusions are that teacher concerns about Discovery Works were normal for any group experiencing a new innovation. Teachers and students enjoyed using the hands-on materials, and that Minot Public Schools has taken a small, but important step forward on the road to science education reform. Although there is still a strong emphasis on the language arts of science, as opposed to scientific inquiry, there has been increased learning of science content and the process skills necessary to do scientific inquiry. Recommendations were made for professional development activities that would assist teachers in the next phase of the implementation process.

General view of the Orbiter Discovery on runway 33 at ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

General view of the Orbiter Discovery on runway 33 at Kennedy Space Center shortly after landing. The orbiter is processed and prepared for being towed to the Orbiter Processing Facility for continued post flight processing and pre flight preparations for its next mission. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

Learning to push and learning to move: the adaptive control of contact forces

PubMed Central

Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

2015-01-01

To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions. PMID:26594163

What songbirds teach us about learning

NASA Astrophysics Data System (ADS)

Brainard, Michael S.; Doupe, Allison J.

2002-05-01

Bird fanciers have known for centuries that songbirds learn their songs. This learning has striking parallels to speech acquisition: like humans, birds must hear the sounds of adults during a sensitive period, and must hear their own voice while learning to vocalize. With the discovery and investigation of discrete brain structures required for singing, songbirds are now providing insights into neural mechanisms of learning. Aided by a wealth of behavioural observations and species diversity, studies in songbirds are addressing such basic issues in neuroscience as perceptual and sensorimotor learning, developmental regulation of plasticity, and the control and function of adult neurogenesis.

Analysis and synthesis of abstract data types through generalization from examples

NASA Technical Reports Server (NTRS)

Wild, Christian

1987-01-01

The discovery of general patterns of behavior from a set of input/output examples can be a useful technique in the automated analysis and synthesis of software systems. These generalized descriptions of the behavior form a set of assertions which can be used for validation, program synthesis, program testing, and run-time monitoring. Describing the behavior is characterized as a learning process in which the set of inputs is mapped into an appropriate transform space such that general patterns can be easily characterized. The learning algorithm must chose a transform function and define a subset of the transform space which is related to equivalence classes of behavior in the original domain. An algorithm for analyzing the behavior of abstract data types is presented and several examples are given. The use of the analysis for purposes of program synthesis is also discussed.

Knowledge discovery based on experiential learning corporate culture management

NASA Astrophysics Data System (ADS)

Tu, Kai-Jan

2014-10-01

A good corporate culture based on humanistic theory can make the enterprise's management very effective, all enterprise's members have strong cohesion and centripetal force. With experiential learning model, the enterprise can establish an enthusiastic learning spirit corporate culture, have innovation ability to gain the positive knowledge growth effect, and to meet the fierce global marketing competition. A case study on Trend's corporate culture can offer the proof of industry knowledge growth rate equation as the contribution to experiential learning corporate culture management.

Trained Inquiry Skills on Heat and Temperature Concepts

NASA Astrophysics Data System (ADS)

Hasanah, U.; Hamidah, I.; Utari, S.

2017-09-01

Inquiry skills are skills that aperson needs in developing concepts, but the results of the study suggest that these skills haven’t yet been trained along with the development of concepts in science feeding, found the difficulties of students in building the concept scientifically. Therefore, this study aims to find ways that are effective in training inquiry skills trough Levels of Inquiry (LoI) learning. Experimental research with one group pretest-postest design, using non-random sampling samples in one of vocational high school in Cimahi obtained purposively 33 students of X class. The research using the inquiry skills test instrument in the form of 15questions multiple choice with reliability in very high category. The result of data processing by using the normalized gain value obtained an illustration that the ways developed in the LoI are considered effective trained inquiry skills in the middle category. Some of the ways LoI learning are considered effective in communicating aspects through discovery learning, predicting trough interactive demonstration, hypotheses through inquiry lesson, and interpreting data through inquiry lab, but the implementation of LoI learning in this study hasn’t found a way that is seen as effective for trespassing aspects of designing an experiment.

Using Observational Journals in an Introductory Astronomy Course

NASA Astrophysics Data System (ADS)

Sadler, P.

2000-05-01

The might of science is in its power to predict. Yet, students rarely are exposed to anything but others' stories concerning how nature behaves. Students do not experience the frustration and elation that discovery brings. For ten years, our introductory astronomy course has used observational journals as a key component in the learning process. Every night, as the planets, stars, and moon dance by, astronomy students use the opportunity to collect and analyze their own data describing heavenly motions. For most, finding the patterns in original data provides an opportunity to fashion and test their own predictive models for the first time in their lives. Such efforts provide an alternative to lecture and laboratory for mastering key scientific concepts and modifying student misconceptions. Students have learned how to represent visual information through a variety of graphs, built and improved their own measurement instruments, and drawn on artistic and writing skills. We will examine the steps required to make observational journals a productive learning activity: careful recordkeeping, classroom discussion, instructor feedback, and reflective writing. I will show examples of how students' work progress through increasing levels of cognitive sophistication that match well with learning theories.

Using concept building in optics to improve student research skills

NASA Astrophysics Data System (ADS)

Masters, Mark F.; Grove, Timothy T.

2014-07-01

Optics is a core component of an undergraduate physics degree. Not only is optics a fascinating topic on its own, but a good understanding of optics helps students gain valuable insight into more complex topics. A working knowledge of optics is vital for the experimental investigation of astronomy, quantum mechanics, and a host of other research endeavors involving optical measurements. Research is also a critical part of a student's education. Participation in research brings tremendous benefits to a student. So what do the students gain by participation in research? They learn independence. They learn how to plan a project. They learn the process of discovery. They learn that all answers are not always found on the internet, from professors, in books and publications (in that order). Research makes the "book" learning real. But what skills do the students need to be able to do research? Most of our experimental research opportunities involve optics. We have students working on investigations that range from atomic spectroscopy of rubidium to Rayleigh scattering to optical tweezers to quantum optics. When a student starts research, we want them to be ready to go. We don't want them to have to relearn material (or for us to reteach material) that they should already have mastered in earlier classes.

Synaptic Ensemble Underlying the Selection and Consolidation of Neuronal Circuits during Learning.

PubMed

Hoshiba, Yoshio; Wada, Takeyoshi; Hayashi-Takagi, Akiko

2017-01-01

Memories are crucial to the cognitive essence of who we are as human beings. Accumulating evidence has suggested that memories are stored as a subset of neurons that probably fire together in the same ensemble. Such formation of cell ensembles must meet contradictory requirements of being plastic and responsive during learning, but also stable in order to maintain the memory. Although synaptic potentiation is presumed to be the cellular substrate for this process, the link between the two remains correlational. With the application of the latest optogenetic tools, it has been possible to collect direct evidence of the contributions of synaptic potentiation in the formation and consolidation of cell ensemble in a learning task specific manner. In this review, we summarize the current view of the causative role of synaptic plasticity as the cellular mechanism underlying the encoding of memory and recalling of learned memories. In particular, we will be focusing on the latest optoprobe developed for the visualization of such "synaptic ensembles." We further discuss how a new synaptic ensemble could contribute to the formation of cell ensembles during learning and memory. With the development and application of novel research tools in the future, studies on synaptic ensembles will pioneer new discoveries, eventually leading to a comprehensive understanding of how the brain works.

Rapid-Learning System for Cancer Care

PubMed Central

Abernethy, Amy P.; Etheredge, Lynn M.; Ganz, Patricia A.; Wallace, Paul; German, Robert R.; Neti, Chalapathy; Bach, Peter B.; Murphy, Sharon B.

2010-01-01

Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is “rapid-learning health care.” In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled “A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care” in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action. PMID:20585094

Individual and social learning processes involved in the acquisition and generalization of tool use in macaques

PubMed Central

Macellini, S.; Maranesi, M.; Bonini, L.; Simone, L.; Rozzi, S.; Ferrari, P. F.; Fogassi, L.

2012-01-01

Macaques can efficiently use several tools, but their capacity to discriminate the relevant physical features of a tool and the social factors contributing to their acquisition are still poorly explored. In a series of studies, we investigated macaques' ability to generalize the use of a stick as a tool to new objects having different physical features (study 1), or to new contexts, requiring them to adapt the previously learned motor strategy (study 2). We then assessed whether the observation of a skilled model might facilitate tool-use learning by naive observer monkeys (study 3). Results of study 1 and study 2 showed that monkeys trained to use a tool generalize this ability to tools of different shape and length, and learn to adapt their motor strategy to a new task. Study 3 demonstrated that observing a skilled model increases the observers' manipulations of a stick, thus facilitating the individual discovery of the relevant properties of this object as a tool. These findings support the view that in macaques, the motor system can be modified through tool use and that it has a limited capacity to adjust the learnt motor skills to a new context. Social factors, although important to facilitate the interaction with tools, are not crucial for tool-use learning. PMID:22106424

Adaptive cultural transmission biases in children and nonhuman primates.

PubMed

Price, Elizabeth E; Wood, Lara A; Whiten, Andrew

2017-08-01

Comparative and evolutionary developmental analyses seek to discover the similarities and differences between humans and non-human species that might illuminate both the evolutionary foundations of our nature that we share with other animals, and the distinctive characteristics that make human development unique. As our closest animal relatives, with whom we last shared common ancestry, non-human primates have been particularly important in this endeavour. Such studies have focused on social learning, traditions, and culture, and have discovered much about the 'how' of social learning, concerned with key underlying processes such as imitation and emulation. One of the core discoveries is that the adaptive adjustment of social learning options to different contexts is not unique to human, therefore multiple new strands of research have begun to focus on more subtle questions about when, from whom, and why such learning occurs. Here we review illustrative studies on both human infants and young children and on non-human primates to identify the similarities shared more broadly across the primate order, and the apparent specialisms that distinguish human development. Adaptive biases in social learning discussed include those modulated by task comprehension, experience, conformity to majorities, and the age, skill, proficiency and familiarity of potential alternative cultural models. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Doing that thing that scientists do: A discovery-driven module on protein purification and characterization for the undergraduate biochemistry laboratory classroom.

PubMed

Garrett, Teresa A; Osmundson, Joseph; Isaacson, Marisa; Herrera, Jennifer

2015-01-01

In traditional introductory biochemistry laboratory classes students learn techniques for protein purification and analysis by following provided, established, step-by-step procedures. Students are exposed to a variety of biochemical techniques but are often not developing procedures or collecting new, original data. In this laboratory module, students develop research skills through work on an original research project and gain confidence in their ability to design and execute an experiment while faculty can enhance their scholarly pursuits through the acquisition of original data in the classroom laboratory. Students are prepared for a 6-8 week discovery-driven project on the purification of the Escherichia coli cytidylate kinase (CMP kinase) through in class problems and other laboratory exercises on bioinformatics and protein structure analysis. After a minimal amount of guidance on how to perform the CMP kinase in vitro enzyme assay, SDS-PAGE, and the basics of protein purification, students, working in groups of three to four, develop a protein purification protocol based on the scientific literature and investigate some aspect of CMP kinase that interests them. Through this process, students learn how to implement a new but perhaps previously worked out procedure to answer their research question. In addition, they learn the importance of keeping a clear and thorough laboratory notebook and how to interpret their data and use that data to inform the next set of experiments. Following this module, students had increased confidence in their ability to do basic biochemistry techniques and reported that the "self-directed" nature of this lab increased their engagement in the project. © 2015 The International Union of Biochemistry and Molecular Biology.

Repeated mild closed head injury impairs short-term visuospatial memory and complex learning.

PubMed

Hylin, Michael J; Orsi, Sara A; Rozas, Natalia S; Hill, Julia L; Zhao, Jing; Redell, John B; Moore, Anthony N; Dash, Pramod K

2013-05-01

Concussive force can cause neurocognitive and neurobehavioral dysfunction by inducing functional, electrophysiological, and/or ultrastructural changes within the brain. Although concussion-triggered symptoms typically subside within days to weeks in most people, in 15%-20% of the cases, symptomology can continue beyond this time point. Problems with memory, attention, processing speed, and cognitive flexibility (e.g., problem solving, conflict resolution) are some of the prominent post-concussive cognitive symptoms. Repeated concussions (with loss or altered consciousness), which are common to many contact sports, can exacerbate these symptoms. The pathophysiology of repeated concussions is not well understood, nor is an effective treatment available. In order to facilitate drug discovery to treat post-concussive symptoms (PCSs), there is a need to determine if animal models of repeated mild closed head injury (mCHI) can mimic the neurocognitive and histopathological consequences of repeated concussions. To this end, we employed a controlled cortical impact (CCI) device to deliver a mCHI directly to the skull of mice daily for 4 days, and examined the ensuing neurological and neurocognitive functions using beam balance, foot-fault, an abbreviated Morris water maze test, context discrimination, and active place avoidance tasks. Repeated mCHI exacerbated vestibulomotor, motor, short-term memory and conflict learning impairments as compared to a single mCHI. Learning and memory impairments were still observed in repeated mCHI mice when tested 3 months post-injury. Repeated mCHI also reduced cerebral perfusion, prolonged the inflammatory response, and in some animals, caused hippocampal neuronal loss. Our results show that repeated mCHI can reproduce some of the deficits seen after repeated concussions in humans and may be suitable for drug discovery studies and translational research.

Understanding price discovery in interconnected markets: Generalized Langevin process approach and simulation

NASA Astrophysics Data System (ADS)

Schenck, Natalya A.; Horvath, Philip A.; Sinha, Amit K.

2018-02-01

While the literature on price discovery process and information flow between dominant and satellite market is exhaustive, most studies have applied an approach that can be traced back to Hasbrouck (1995) or Gonzalo and Granger (1995). In this paper, however, we propose a Generalized Langevin process with asymmetric double-well potential function, with co-integrated time series and interconnected diffusion processes to model the information flow and price discovery process in two, a dominant and a satellite, interconnected markets. A simulated illustration of the model is also provided.

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Transforming Education Research through Open Video Data Sharing

ERIC Educational Resources Information Center

Gilmore, Rick O.; Adolph, Karen E.; Millman, David S.; Gordon, Andrew

2016-01-01

Open data sharing promises to accelerate the pace of discovery in the developmental and learning sciences, but significant technical, policy, and cultural barriers have limited its adoption. As a result, most research on learning and development remains shrouded in a culture of isolation. Data sharing is the rare exception (Gilmore, 2016). Many…

Pennies and Eggs: Initiation into Inquiry Learning for Preservice Elementary Education Teachers

ERIC Educational Resources Information Center

Wink, Donald J.; Hwang-Choe, Jeong Hye

2008-01-01

Two labs incorporating the Science Writing Heuristic are described that introduce scientific inquiry in a course for preservice students majoring in elementary education. One lab adapts a previously described discovery learning opportunity involving the change in composition and mass of pennies in 1982. The other involves the use of flotation…

The Impact of Guidance during Problem-Solving Prior to Instruction on Students' Inventions and Learning Outcomes

ERIC Educational Resources Information Center

Loibl, Katharina; Rummel, Nikol

2014-01-01

Multiple studies have shown benefits of problem-solving prior to instruction (cf. Productive Failure, Invention) in comparison to direct instruction. However, students' solutions prior to instruction are usually erroneous or incomplete. In analogy to "guided" discovery learning, it might therefore be fruitful to lead students…

Renewing Liberal Education as Vocational Discernment

ERIC Educational Resources Information Center

Sullivan, William M.

2014-01-01

A major discovery, or rediscovery, of this time is that an education that matters--an education that enhances capacities and expands outlooks--is one that engages the whole student. Research in learning has shown that making sense of the world and learning to use knowledge and skills in responsible and engaged ways--long the developmental goals of…

The Embodied Narrative Nature of Learning: Nurture in School

ERIC Educational Resources Information Center

Delafield-Butt, Jonathan T.; Adie, Jillian

2016-01-01

Learning is participatory and embodied. It requires active participation from both teacher and learner to come together to co-create shared projects of discovery that allow meaning to unfold and develop between them. This article advances theory on the intersubjective and embodied nature of cognition and meaning-making as constituted by co-created…

Defence of Foreign Language Teaching in Secondary Schools

ERIC Educational Resources Information Center

Van Passel, F. J. A.

1974-01-01

Shows the necessity of foreign language education for cognitive and attitudinal purposes as well as for utilitarian reasons. Foreign language learning/teaching can be of great educational value when it follows the thread of the logical and psychological steps in the creative/discovery procedure. A learning algorithm is mapped on page 61. See FL…

Has the Construct "Intelligence" Determined Our Perception of Cognitive Hierarchy?

ERIC Educational Resources Information Center

Fuller, Renee

The discovery that retarded children can learn to read with comprehension suggests a critique of current educational testing and teaching practices. IQ tests, consisting of segmental, out-of-context tasks, originally were based on turn-of-the-century educational techniques that emphasized rote and segmental learning. Currently, most IQ tests still…

Students' Individual Schematization Pathways--Empirical Reconstructions for the Case of Part-of-Part Determination for Fractions

ERIC Educational Resources Information Center

Glade, Matthias; Prediger, Susanne

2017-01-01

According to the design principle of progressive schematization, learning trajectories towards procedural rules can be organized as independent discoveries when the learning arrangement invites the students first to develop models for mathematical concepts and model-based informal strategies; then to explore the strategies and to discover pattern…

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…

Technologically and Artistically Enhanced Multi-Sensory Computer-Programming Education

ERIC Educational Resources Information Center

Katai, Zoltan; Toth, Laszlo

2010-01-01

Over the last decades more and more research has analysed relatively new or rediscovered teaching-learning concepts like blended, hybrid, multi-sensory or technologically enhanced learning. This increased interest in these educational forms can be explained by new exciting discoveries in brain research and cognitive psychology, as well as by the…

Designing a WebQuest

ERIC Educational Resources Information Center

Salsovic, Annette R.

2009-01-01

A WebQuest is an inquiry-based lesson plan that uses the Internet. This article explains what a WebQuest is, shows how to create one, and provides an example. When engaged in a WebQuest, students use technology to experience cooperative learning and discovery learning while honing their research, writing, and presentation skills. It has been found…

Dissociable roles of medial and lateral PFC in rule learning.

PubMed

Cao, Bihua; Li, Wei; Li, Fuhong; Li, Hong

2016-11-01

Although the neural basis of rule learning is of great interest to cognitive neuroscientists, the pattern of transient brain activation during rule discovery remains to be investigated. In this study, we measured event-related functional magnetic resonance imaging (fMRI) during distinct phases of rule learning. Twenty-one healthy human volunteers were presented with a series of cards, each containing a clock-like display of 12 circles numbered sequentially. Participants were instructed that a fictitious animal would move from one circle to another either in a regular pattern (according to a rule hidden in consecutive trials) or randomly. Participants were then asked to judge whether a given step followed a rule. While the rule-search phase evoked more activation in the posterior lateral prefrontal cortex (LPFC), the rule-following phase caused stronger activation in the anterior medial prefrontal cortex (MPFC). Importantly, the intermediate phase, the rule-discovery phase evoked more activations in MPFC and dorsal anterior cingulate cortex (dACC) than rule search, and more activations in LPFC than rule following. Therefore, we can conclude that the medial and lateral PFC have dissociable contributions in rule learning.