Inducing self-explanation: A meta-analysis and experiment

Thesis type
(Thesis) Ph.D.
Date created
Author: Bisra, Kiran
Self-explanation is a process by which learners generate inferences about causal connections or conceptual associations. This dissertation seeks to contribute to the literature on inducing self-explanations, by way of prompting, to facilitate learning. More specifically, this research seeks to understand the effects on learning gains when learners are prompted to self-explain in various contexts and with various prompts. As such, one goal of the dissertation is to provide a comprehensive review of prior research on self-explanation. A meta-analysis was conducted on research that investigated learning outcomes of participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = .55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts (SEPs) are a potentially powerful intervention across a range of instructional conditions. To further investigate the effect of various prompts on studying expository text, I conducted an online experiment employing a 2 x 2 x 3 factorial design, in which one factor was within subject. One hundred and twenty-six participants were randomly assigned to one of three self-explanation prompt conditions (content-free (generic), content-specific (specific), and no SEP). The results support the utilization of generic self-explanation prompts in comparison to specific self-explanation prompts and receiving no prompt. Specifically, the generic self-explanation group outperformed the other two groups on the reading comprehension outcome in the short-answer question format.
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Supervisor or Senior Supervisor
Thesis advisor: Nesbit, John
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