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7.391 Concept-Centered Teaching (MIT) 7.391 Concept-Centered Teaching (MIT)

Description

Do you like teaching, but find yourself frustrated by how little students seem to learn? Would you like to try teaching, but are nervous about whether you will be any good at it? Are you interested in new research on science education? Research in science education shows that the greatest obstacle to student learning is the failure to identify and confront the misconceptions with which the students enter the class or those that they acquire during their studies. This weekly seminar course focuses on developing the participants' ability to uncover and confront student misconceptions and to foster student understanding and retention of key concepts. Participants read primary literature on science education, uncover basic concepts often overlooked when teaching biology, and lead a small week Do you like teaching, but find yourself frustrated by how little students seem to learn? Would you like to try teaching, but are nervous about whether you will be any good at it? Are you interested in new research on science education? Research in science education shows that the greatest obstacle to student learning is the failure to identify and confront the misconceptions with which the students enter the class or those that they acquire during their studies. This weekly seminar course focuses on developing the participants' ability to uncover and confront student misconceptions and to foster student understanding and retention of key concepts. Participants read primary literature on science education, uncover basic concepts often overlooked when teaching biology, and lead a small week

Subjects

teaching | teaching | learning | learning | concept-centered | concept-centered | education | education | science education | science education | biology | biology | student learning | student learning | misconceptions | misconceptions | studies | studies | biology teaching | biology teaching | teaching environment | teaching environment | pre-conceived notions | pre-conceived notions | learning environment | learning environment | classroom | classroom | cooperative learning | cooperative learning | group learning | group learning | assessment | assessment | multiple intelligences | multiple intelligences

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.391 Concept-Centered Teaching (MIT) 7.391 Concept-Centered Teaching (MIT)

Description

Do you like teaching, but find yourself frustrated by how little students seem to learn? Would you like to try teaching, but are nervous about whether you will be any good at it? Are you interested in new research on science education? Research in science education shows that the greatest obstacle to student learning is the failure to identify and confront the misconceptions with which the students enter the class or those that they acquire during their studies. This weekly seminar course focuses on developing the participants' ability to uncover and confront student misconceptions and to foster student understanding and retention of key concepts. Participants read primary literature on science education, uncover basic concepts often overlooked when teaching biology, and lead a small week Do you like teaching, but find yourself frustrated by how little students seem to learn? Would you like to try teaching, but are nervous about whether you will be any good at it? Are you interested in new research on science education? Research in science education shows that the greatest obstacle to student learning is the failure to identify and confront the misconceptions with which the students enter the class or those that they acquire during their studies. This weekly seminar course focuses on developing the participants' ability to uncover and confront student misconceptions and to foster student understanding and retention of key concepts. Participants read primary literature on science education, uncover basic concepts often overlooked when teaching biology, and lead a small week

Subjects

teaching | teaching | learning | learning | concept-centered | concept-centered | education | education | science education | science education | biology | biology | student learning | student learning | misconceptions | misconceptions | studies | studies | biology teaching | biology teaching | teaching environment | teaching environment | pre-conceived notions | pre-conceived notions | learning environment | learning environment | classroom | classroom | cooperative learning | cooperative learning | group learning | group learning | assessment | assessment | multiple intelligences | multiple intelligences

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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MAS.714J Technologies for Creative Learning (MIT) MAS.714J Technologies for Creative Learning (MIT)

Description

This course explores how new technologies can help people learn new things in new ways. It analyzes principles and strategies underlying the design of innovative educational technologies and creative learning environments, drawing on specific case studies such as the LEGO Programmable Brick and Computer Clubhouse after-school learning centers. The course will include hands-on activities, analysis of learning experiences, and design of new tools and activities. This course explores how new technologies can help people learn new things in new ways. It analyzes principles and strategies underlying the design of innovative educational technologies and creative learning environments, drawing on specific case studies such as the LEGO Programmable Brick and Computer Clubhouse after-school learning centers. The course will include hands-on activities, analysis of learning experiences, and design of new tools and activities.

Subjects

learning | learning | e-learning | e-learning | distance learning | distance learning | educational technology | educational technology | learning environments | learning environments | school | school | pedagogy | pedagogy | instruction | instruction | method | method | education | education | teaching | teaching | teachers | teachers | constructionism | constructionism | toys | toys | innovation | innovation | communities | communities | mentorship | mentorship | play | play | MAS.714 | MAS.714 | STS.445 | STS.445

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Technology Enhanced Learning (2011) Technology Enhanced Learning (2011)

Description

In this course there is an introduction for educationalists to the current situation of education in the socio-technological environment. On it, we want to explore, analise and debate how technological changes in society are impacting or not in educational processes. In this course there is an introduction for educationalists to the current situation of education in the socio-technological environment. On it, we want to explore, analise and debate how technological changes in society are impacting or not in educational processes.

Subjects

TEL | TEL | ctica y Organizacin Escolar | ctica y Organizacin Escolar | ulearning | ulearning | mlearning | mlearning | technology enhanced learning | technology enhanced learning | elearning | elearning | ICT | ICT

License

http://creativecommons.org/licenses/by-nc-sa/3.0/

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Supporting Distance Learners

Description

This is a set of course materials intended for tutors in blended learning or fully online programmes. It takes readers reflectively through what it means to support learners in e-learning environments of a variety of kinds –both at a distance, and in conventional contact tuition environments that are web supported. The materials have been designed for learning in developing contexts in which bandwidth is often a challenge.

Subjects

ukoer learner support tutors distance education e-learning web 2.0 constructivism distance learning facilitating asynchronous learning learner support lifelong distance learner mobile learning online learning open learning social presence tutorial using synchronous communication tools web conferencing web-based learning | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Quick Guides to Learning and Teaching: Action Learning

Description

An overview of the principles and process of using Action Learning as a teaching strategy. This is a particularly useful technique for supporting work-based learning as it supports the application of knowledge and personal effectiveness in a given context.The guide includes Top Tips and references to additioanl resources.

Subjects

reflective learning | work-based learning | action learning | quick guides to learning and teaching | action learning sets | small group learning | peer supported learning | ukpsf | jisc oer3 omac | teesside university | staff guide | top tips | practical guidance | pg cert | Education | X000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Continuity and learning Continuity and learning

Description

This free course, Continuity and learning, has a practical and professional development focus. You will explore interactive dimensions of workplace learning: how people and workplace cultures create formal, informal, planned and unplanned opportunities to learn. You will read about 'biographical learning' research, where adults develop narratives to better understand key points of their learning lives. You will plan and carry out a brief, informal interview with a colleague, and your colleague will interview you, on themes of learning and work. To complete this OpenLearn course you will write a short draft of the key points of the interview and reflect on your own learning biography. First published on Tue, 16 Feb 2016 as Continuity and learning. To find out more visit The Open Universit This free course, Continuity and learning, has a practical and professional development focus. You will explore interactive dimensions of workplace learning: how people and workplace cultures create formal, informal, planned and unplanned opportunities to learn. You will read about 'biographical learning' research, where adults develop narratives to better understand key points of their learning lives. You will plan and carry out a brief, informal interview with a colleague, and your colleague will interview you, on themes of learning and work. To complete this OpenLearn course you will write a short draft of the key points of the interview and reflect on your own learning biography. First published on Tue, 16 Feb 2016 as Continuity and learning. To find out more visit The Open Universit

Subjects

Education | Education | Educational Practice | Educational Practice | Professional Development in Education | Professional Development in Education | E101_1 | E101_1 | Primary school | Primary school | teaching assistant | teaching assistant | workplace learning | workplace learning | lifelong learning | lifelong learning | adult learning | adult learning | informal interview | informal interview

License

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open University

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An introduction to Open Educational Resources (OER) An introduction to Open Educational Resources (OER)

Description

In this free course, An introduction to Open Educational Resources (OER), you will look at some institutional OER initiatives. These will include MIT's open courseware, Carnegie Mellon's Open Learning Initiative and The Open University's OpenLearn. You will get a flavour of how different OER initiatives design and present the content for their audiences. This OpenLearn course will also give you the opportunity to find, discuss and redesign some OER material. You can choose your audience for the material and redesign it to fit their needs. First published on Wed, 09 Mar 2016 as An introduction to Open Educational Resources (OER). To find out more visit The Open University's Openlearn website. Creative-Commons 2016 In this free course, An introduction to Open Educational Resources (OER), you will look at some institutional OER initiatives. These will include MIT's open courseware, Carnegie Mellon's Open Learning Initiative and The Open University's OpenLearn. You will get a flavour of how different OER initiatives design and present the content for their audiences. This OpenLearn course will also give you the opportunity to find, discuss and redesign some OER material. You can choose your audience for the material and redesign it to fit their needs. First published on Wed, 09 Mar 2016 as An introduction to Open Educational Resources (OER). To find out more visit The Open University's Openlearn website. Creative-Commons 2016 This OpenLearn course will also give you the opportunity to find, discuss and redesign some OER material. You can choose your audience for the material and redesign it to fit their needs. First published on Wed, 09 Mar 2016 as An introduction to Open Educational Resources (OER). To find out more visit The Open University's Openlearn website. Creative-Commons 2016 This OpenLearn course will also give you the opportunity to find, discuss and redesign some OER material. You can choose your audience for the material and redesign it to fit their needs. First published on Wed, 09 Mar 2016 as An introduction to Open Educational Resources (OER). To find out more visit The Open University's Openlearn website. Creative-Commons 2016

Subjects

Education | Education | Educational Technology and Practice | Educational Technology and Practice | Educational Technology | Educational Technology | H800_1 | H800_1 | open educational resources | open educational resources | technology-enhanced learning | technology-enhanced learning | open learning pedagogies | open learning pedagogies | re-purposing | re-purposing | learning design | learning design

License

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open University

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9.675 The Development of Object and Face Recognition (MIT) 9.675 The Development of Object and Face Recognition (MIT)

Description

This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field. This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field.

Subjects

computational theories of human cognition | computational theories of human cognition | principles of inductive learning and inference | principles of inductive learning and inference | representation of knowledge | representation of knowledge | computational frameworks | computational frameworks | Bayesian models | Bayesian models | hierarchical Bayesian models | hierarchical Bayesian models | probabilistic graphical models | probabilistic graphical models | nonparametric statistical models | nonparametric statistical models | Bayesian Occam's razor | Bayesian Occam's razor | sampling algorithms for approximate learning and inference | sampling algorithms for approximate learning and inference | probabilistic models defined over structured representations such as first-order logic | probabilistic models defined over structured representations such as first-order logic | grammars | grammars | relational schemas | relational schemas | core aspects of cognition | core aspects of cognition | concept learning | concept learning | concept categorization | concept categorization | causal reasoning | causal reasoning | theory formation | theory formation | language acquisition | language acquisition | social inference | social inference

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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5.95J Teaching College-Level Science (MIT) 5.95J Teaching College-Level Science (MIT)

Description

This seminar focuses on the knowledge and skills necessary for teaching science and engineering in higher education. Topics include: using current research in student learning to improve teaching; developing courses; lecturing; promoting students' ability to think critically and solve problems; communicating with a diverse student body; using educational technology; creating effective assignments and tests; and utilizing feedback to improve instruction. Students research and teach a topic of particular interest. This subject is appropriate for both novices and those with teaching experience. This seminar focuses on the knowledge and skills necessary for teaching science and engineering in higher education. Topics include: using current research in student learning to improve teaching; developing courses; lecturing; promoting students' ability to think critically and solve problems; communicating with a diverse student body; using educational technology; creating effective assignments and tests; and utilizing feedback to improve instruction. Students research and teach a topic of particular interest. This subject is appropriate for both novices and those with teaching experience.

Subjects

teaching skills | teaching skills | learning objectives | learning objectives | lecturing | lecturing | active learning | active learning | feedback | feedback | interactive lessons | interactive lessons | pedagogy | pedagogy | student learning | student learning | educational technology | educational technology | STEM (science | STEM (science | technology | technology | engineering | engineering | and mathematics) | and mathematics) | teaching philosophy statement | teaching philosophy statement | 5.95 | 5.95 | 7.59 | 7.59 | 8.395 | 8.395 | 18.094 | 18.094

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.867 Machine Learning (MIT) 6.867 Machine Learning (MIT)

Description

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. 6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Subjects

machine learning algorithms | machine learning algorithms | statistical inference | statistical inference | representation | representation | generalization | generalization | model selection | model selection | linear/additive models | linear/additive models | active learning | active learning | boosting | boosting | support vector machines | support vector machines | hidden Markov models | hidden Markov models | Bayesian networks | Bayesian networks | classification | classification | linear regression | linear regression | modern machine learning methods | modern machine learning methods

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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MAS.622J Pattern Recognition and Analysis (MIT) MAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class. This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Subjects

MAS.622 | MAS.622 | 1.126 | 1.126 | pattern recognition | pattern recognition | feature detection | feature detection | classification | classification | probability theory | probability theory | pattern analysis | pattern analysis | conditional probability | conditional probability | bayes rule | bayes rule | random vectors | decision theory | random vectors | decision theory | ROC curves | ROC curves | likelihood ratio test | likelihood ratio test | fisher discriminant | fisher discriminant | template-based recognition | template-based recognition | feature extraction | feature extraction | eigenvector and multilinear analysis | eigenvector and multilinear analysis | linear discriminant | linear discriminant | perceptron learning | perceptron learning | optimization by gradient descent | optimization by gradient descent | support vecotr machines | support vecotr machines | K-nearest-neighbor classification | K-nearest-neighbor classification | parzen estimation | parzen estimation | unsupervised learning | unsupervised learning | clustering | clustering | vector quantization | vector quantization | K-means | K-means | Expectation-Maximization | Expectation-Maximization | Hidden markov models | Hidden markov models | viterbi algorithm | viterbi algorithm | Baum-Welch algorithm | Baum-Welch algorithm | linear dynamical systems | linear dynamical systems | Kalman filtering | Kalman filtering | Bayesian networks | Bayesian networks | decision trees | decision trees | reinforcement learning | reinforcement learning | genetic algorithms | genetic algorithms

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Designing Case-Based E-Learning Designing Case-Based E-Learning

Description

Content Type:  Resource Tutorial on design principles for developing case-based digital learning materials. This was presented to OBGYN residents and faculty at St. Paul Hospital Millennium Medical College in Ethiopia.  Author: Dr. Cary Engleberg Institution: University of Michigan Content Type:  Resource Tutorial on design principles for developing case-based digital learning materials. This was presented to OBGYN residents and faculty at St. Paul Hospital Millennium Medical College in Ethiopia.  Author: Dr. Cary Engleberg Institution: University of Michigan

Subjects

cases | cases | digital learning | digital learning | elearning | elearning | healthoernetwork | healthoernetwork | instructional design | instructional design | media-enabling learning | media-enabling learning | multimedia design | multimedia design | pedagogy | pedagogy | sphmmc | sphmmc | tutorials | tutorials

License

http://creativecommons.org/licenses/by/4.0/

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7.343 Biological Bases of Learning and Memory (MIT) 7.343 Biological Bases of Learning and Memory (MIT)

Description

How does the brain come to learn whether a stimulus is annoying, rewarding or neutral? How does remembering how to ride a bicycle differ from remembering scenes from a movie? In this course, students will explore the concept that learning and memory have a physical basis that can be observed as biochemical, physiological and/or morphological changes to neural tissue. Our goal will be to understand the strategies and techniques biologists use to search for the memory trace: the "holy grail" of modern neuroscience. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interact How does the brain come to learn whether a stimulus is annoying, rewarding or neutral? How does remembering how to ride a bicycle differ from remembering scenes from a movie? In this course, students will explore the concept that learning and memory have a physical basis that can be observed as biochemical, physiological and/or morphological changes to neural tissue. Our goal will be to understand the strategies and techniques biologists use to search for the memory trace: the "holy grail" of modern neuroscience. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interact

Subjects

learning | learning | memory | memory | neural tissue | neural tissue | neuronal connections | neuronal connections | synapse formation | synapse formation | synapse stabilization | synapse stabilization | synaptic transmission | synaptic transmission | synaptic plasticity | synaptic plasticity | neuromodulation | neuromodulation | experience-dependent circuit remodeling | experience-dependent circuit remodeling | neuroscience | neuroscience | pre- and post-synaptic mechanisms | pre- and post-synaptic mechanisms | neurotransmitter release | neurotransmitter release | activity-regulated genes | activity-regulated genes | hippocampus | hippocampus | long-term potentiation | long-term potentiation | long-term depression | long-term depression | cerebellar plasticity | cerebellar plasticity | Non-Associative | Non-Associative | Associative | Associative | cpg15 | cpg15 | experience-dependent synaptic plasticity | experience-dependent synaptic plasticity | perceptual learning | perceptual learning | observational learning | observational learning

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Copyright Training for College Lecturers - A short practical online course

Description

Jisc Legal has produced a ‘need to know’ online training course in copyright law – designed to bring college staff and those supporting college staff up to speed on legally using other people’s materials in teaching and learning. It is a standalone learning module which takes about an hour to complete and consists of some video, some audio segments, some animations and some text pages. It is available to Further Education institutions in the UK to train lecture staff on the proper way to use other people’s work in their own work.

Subjects

copyright lecturer further education institutions learning online course staff development training preparing | compiling and delivering learning content to learners | ownership in the context of learning materials | making copies of and digitising images | short practical online course | music and text materials particularly for uploading to virtual learning environments | video | copyright licences in the context of learning materials | altering and adapting materials lawfully | EDUCATION / TRAINING / TEACHING | G

License

Attribution-NonCommercial-NoDerivatives 4.0 International Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/

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Disciplinary Thinking - Feedback: annotated resource list

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: assessment tasks

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | assessment | senlef | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: handout 1 to accompany workshop - 'Considering feedback through a disciplinary lens'

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | assessment | senlef | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: presentation

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: theme overview

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: workshop guidance

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Feedback: workshop plan

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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Disciplinary Thinking - Learning Spaces: activities

Description

http://disciplinarythinking.wordpress.com

Subjects

ukoer | education | discthink | disciplinary thinking | hedera | university of bath | omac | learning spaces | classroom design | mobile learning | e-learning | learning environment | student experience | learning technologies | Education | X000

License

Attribution-Share Alike 2.0 UK: England & Wales Attribution-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-sa/2.0/uk/ http://creativecommons.org/licenses/by-sa/2.0/uk/

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