New developments in the science of learning science of learning overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching - examples in history, mathematics and science teacher learning technology to support learning conclusions from new developments in the science of learning.

How people learn: Brain, mind, experience, and school.

1 Front Matter 2 Executive Summary 3 Part I: Introduction to Reading 4 1. Introduction 5 2. The Process of Learning to Read 6 Part II: Who Are We Talking About? 7 3. Who Has Reading Difficulties? 8 4. Predictors of Success and Failure in Reading 9 Part III: Prevention and Intervention 10 5. Preventing Reading Difficulties Before Kindergarten 11 6. Instructional Strategies for Kindergarten and the Primary Grades 12 7. Organizational Strategies for Kindergarten and the Primary Grades 13 8. Helping Children with Reading Difficulties in Grades 1 to 3 14 Part IV: Knowledge into Action 15 9. The Agents of Change 16 10. Recommendations for Practice and Research 17 References 18 Biographical Sketches 19 Index

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Preventing reading difficulties in young children

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

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Perceptual symbol systems.

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

(1992) Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings Journal of the Learning Sciences: Vol 2, No 2, pp 141-178

/pdf/design-experiments-theoretical-and-methodological-challenges-1aoqdo4gxg.pdf

Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings

Preface Similarity and analogical reasoning: a synthesis Stella Vosniadou and Andrew Ortony Part I. Similarity and the Structure of Concepts: 1. Similarity, typicality, and categorization Lance J. Rips 2. Similarity and decision making Edward E. Smith and Daniel N. Osherson 3. Intraconcept similarity and its implications for interconcept similarity Lawrence W. Barsalou 4. Two-tiered concept meaning, inferential matching, and conceptual cohesiveness Ryszard S. Michalski 5. From global similarities to kinds of similarities: the construction of dimensions in development Linda B. Smith 6. Comments on Part I. Psychological essentialism Douglas Medin and Andrew Ortony Part II. Analogical Reasoning: 7. The mechanisms of analogical learning Dedre Genter 8. A computational model of analogical problem solving Keith J. Holyoak and Paul R. Thagard 9. Use of analogy in production system architecture John R. Anderson and Ross Thompson 10. Toward a microstructural account of human reasoning David E. Rumelhart 11. Analogy and the exercise of creativity Philip N. Johnson-Laird 12. Comments on Part II. Levels of description in information-processing theories of analogy Stephen E. Palmer 13. Comments on Part II. The role of explanation in analogy or, the curse of an alluring name Gerald Dejong Part III. Similarity and Analogy in Development, Learning and Instruction: 14. Analogical learning and transfer: what develops? Ann L. Brown 15. Analogical reasoning as a mechanism in knowledge acquisition: a developmental perspective Stella Vosniadou 16. Remindings in learning and instruction Brian H. Ross 17. New approaches to instruction: because wisdom can't be told John D. Bransford, Jeffery J. Franks, Nancy J. Vye and Robert Sherwood 18. Multiple analogies for complex concepts: antidotes for analogy-induced misconception in advanced knowledge acquisition Rand J. Spiro, Paul J. Feltovich, Richard L. Coulson and Daniel K. Anderson 19. Comments on Part III. The activation and acquisition of knowledge William F. Brewer Afterword Allan Collins and Mark Burstein Name index Subject index.

http://old.nbu.bg/cogs/personal/kokinov/COG501/SimilarityandAnalogicalReasoning.pdf

Similarity and analogical reasoning

Mental Models of the Earth: A Study of Conceptual Change in Childhood

https://promathmedia.files.wordpress.com/2013/02/1-3-capturing-and-modeling-the-process-of-conceptual-change1.pdf

Capturing and modeling the process of conceptual change.

International handbook of research on conceptual change

https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog1801_4

Stella Vosniadou

Papers

Similarity and analogical reasoning

Mental Models of the Earth: A Study of Conceptual Change in Childhood

Capturing and modeling the process of conceptual change.

International handbook of research on conceptual change

Mental models of the day/night cycle☆