Training in Self-Explanation and Self-Regulation Strategies: Investigating the Effects of Knowledge Acquisition Activities on Problem Solving
Abstract
Previous research has found positive correlations between particular strategies students use while studying to explain instructional materials to themselves and student performance on associated problem-solving tasks (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Pirolli & Bielaczyc, 1989; Pirolli & Recker, 1994). In the study reported here, we investigate the causal nature of this relation. This was accomplished by identifying a set of self-explanation and self-regulation strategies used by high-performance students in our earlier studies. We used strategy training to manipulate students' application of these strategies and examined the impact of their use on student explanations and performance. Twenty-four university students with no prior programming experience worked through a sequence of programming lessons. Following introductory lessons, participants received interventions involving explicit training in the strategies (instructional group) or received a similar set of interventions but no explicit training (control group). The instructional group showed significantly greater gains than the control group in the use of self-explanation and self-regulation strategies from the pre- to postinterventions lessons. Increased strategy application was accompanied by significantly greater performance gains. The results indicate that the particular self-explanation and self-regulation strategies used in training contribute to learning and problem-solving performance.
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Self-reflection on learning experiences is a critical metacognitive skill of the educational process and offers substantial benefits for both knowledge retention and self-efficacy.
“Self-reflection on learning experiences is a critical metacognitive skill of the educational process [48], which offers substantial benefits for both knowledge retention and self-efficacy [5, 7, 14].”
From Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms (2024) · cites this paper as [7]
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- Authors
- Bielaczyc, K., Pirolli, P. L., & Brown, A. L.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Pirolli, Peter L.
- Venue
- Cognition and Instruction
- Publisher
- Informa UK Limited
- Published
- 1995-06
- Volume
- 13
- Issue
- 2
- Pages
- 221-252
- ISSN
- 0737-0008 (ISSN-L)
- 1532-690X
- OpenAlex
- W2027525087
- Cited by
- 422
- References
- 36
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 3 · 2022: 16 · 2021: 9 · 2020: 21 · 2019: 11 · 2018: 5 · 2017: 15 · 2016: 13 · 2015: 19 · 2014: 19 · 2013: 17 · 2012: 22
- FWCI
- 15.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognition and Instruction (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- self-explanation, self-regulation strategies, strategy training, problem solving, knowledge acquisition, programming education, novice programmers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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