Journal ArticleDOI
A model of the self-explanation effect.
TLDR
Computational experiments indicate that Cascade's learning mechanisms are jointly sufficient to reproduce the self-explanation effect, and a computer model is described, Cascade, that accounts for these findings.Abstract:
Several investigators have taken protocols of students learning sophisticated skills, such as physics problem solving and LISP coding, by studying examples and solving problems. These investigations uncovered the self-explanation effect: Students who explain examples to themselves learn better, make more accurate self-assessments of their understanding, and use analogies more economically while solving problems. We describe a computer model, Cascade, that accounts for these findings. Explaining an example causes Cascade to acquire both domain knowledge and derivational knowledge. Derivational knowledge is used analogically to control search during problem solving. Domain knowledge is acquired when the current domain knowledge is incomplete and causes an impasse. If the impasse can be resolved by applying an overly general rule, then a specialization of the rule becomes a new domain rule. Computational experiments indicate that Cascade's learning mechanisms are jointly sufficient to reproduce the self-expl...read more
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What do you mean by collaborative learning
TL;DR: The Learning in Humans and Machines (LHM) workshop series as mentioned in this paper was a series of workshops on collaborative learning that gathered together 20 scholars from the disciplines of psychology, education and computer science.
Journal ArticleDOI
Eliciting Self‐Explanations Improves Understanding
TL;DR: This article showed that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text, and that prompted students who generated o large number of self-explaining (the high explainers) learned with greater understanding than low explainers.
Book
Cognitive Load Theory
TL;DR: Cognitive load theory uses evolutionary theory to consider human cognitive architecture and uses that architecture to devise novel, instructional procedures to generate instructional procedures, summarized in this chapter.
Journal ArticleDOI
The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes.
Michelene T. H. Chi,Ruth Wylie +1 more
TL;DR: The ICAP hypothesis as discussed by the authors predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase and suggest possible knowledge-change processes that support the hypothesis.
Journal ArticleDOI
An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor.
TL;DR: It is found that students who explained their steps during problem-solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps and were more successful on transfer problems.
References
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Book
Human Problem Solving
TL;DR: The aim of the book is to advance the understanding of how humans think by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory.
Book
The Architecture of Cognition
TL;DR: Adaptive Control of Thought (ACT*) as mentioned in this paper is a theory of the basic principles of operation built into the cognitive system and is the main focus of Anderson's theory of cognitive architecture.
Journal ArticleDOI
Reciprocal Teaching of Comprehension-Fostering and Comprehension-Monitoring Activities
TL;DR: In this article, two instructional studies directed at the comprehension-fostering and comprehension-monitoring activities of seventh grade poor comprehenders are reported, and the training method was that of reciprocal teaching, where the tutor and students took turns leading a dialogue centered on pertinent features of the text.
Journal ArticleDOI
Categorization and Representation of Physics Problems by Experts and Novices
TL;DR: Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, and completion of the representations depends on the knowledge associated with the categories.