TY - JOUR
T1 - Testing the instructional fit hypothesis
T2 - The case of self-explanation prompts
AU - Nokes, Timothy J.
AU - Hausmann, Robert G M
AU - VanLehn, Kurt
AU - Gershman, Sophia
N1 - Funding Information:
Acknowledgments This work was supported by the National Science Foundation, Grant Number SBE-0354420 to the Pittsburgh Science of Learning Center (http://www.learnlab.org). No endorsement should be inferred. Portions of the results were presented at the Cognitive Science Society’s 31st Annual Conference. We would like to thank the members of the Cognitive Science Learning Laboratory and several anonymous reviewers for their many helpful comments and suggestions.
PY - 2011/9
Y1 - 2011/9
N2 - Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work hypothesized that self-explanation facilitated mental-model revision (Chi, Handbook of research on conceptual change, 2000). To better understand the complex relationship between prior knowledge, cognitive processing, and changes to a learner's representation, two classes of self-explanation prompts (gap-filling and mental-model revision) were tested in the domain of physics problem solving. Prompts designed to focus the learner on gap-filling led to greater learning and a reduction in the amount of tutoring assistance required to solve physics problems. The results are interpreted as support for the instructional fit hypothesis-the idea that the efficacy of instruction is contingent on the match between the cognitive processing that the instruction elicits, how those processes modify the underlying knowledge representations for the task, and the utility of those representations for the task or problem.
AB - Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work hypothesized that self-explanation facilitated mental-model revision (Chi, Handbook of research on conceptual change, 2000). To better understand the complex relationship between prior knowledge, cognitive processing, and changes to a learner's representation, two classes of self-explanation prompts (gap-filling and mental-model revision) were tested in the domain of physics problem solving. Prompts designed to focus the learner on gap-filling led to greater learning and a reduction in the amount of tutoring assistance required to solve physics problems. The results are interpreted as support for the instructional fit hypothesis-the idea that the efficacy of instruction is contingent on the match between the cognitive processing that the instruction elicits, how those processes modify the underlying knowledge representations for the task, and the utility of those representations for the task or problem.
KW - Intelligent tutoring systems
KW - Prompting
KW - Self-explanation
KW - Worked examples
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U2 - 10.1007/s11251-010-9151-4
DO - 10.1007/s11251-010-9151-4
M3 - Article
AN - SCOPUS:79961173309
SN - 0020-4277
VL - 39
SP - 645
EP - 666
JO - Instructional Science
JF - Instructional Science
IS - 5
ER -