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Self-efficacy, or the belief in one's ability to accomplish a task or achieve a goal, can significantly influence the effectiveness of various instructional methods to induce learning gains. The importance of self-efficacy is particularly pronounced in complex subjects like Computer Science, where students with high self-efficacy are more likely to feel confident in their ability to learn and succeed. Conversely, those with low self-efficacy may become discouraged and consider abandoning the field. The work presented here examines the relationship between self-efficacy and students learning computer programming concepts. For this purpose, we conducted a randomized control trial experiment with university-level students who were randomly assigned into two groups: a control group where participants read Java programs accompanied by explanatory texts (a passive strategy) and an experimental group where participants self-explain while interacting through dialogue with an intelligent tutoring system (an interactive strategy). We report here the findings of this experiment with a focus on self-efficacy, its relation to student learning gains (to evaluate the effectiveness, we measure pre/post-test), and other important factors such as prior knowledge or experimental condition/instructional strategies as well as interaction effects
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This content will become publicly available on April 8, 2025
Exploring The Effectiveness of Reading vs. Tutoring For Enhancing Code Comprehension For Novices
This paper presents a comparison of two instructional strategies meant to help learners better comprehend code and learn programming concepts: reading code examples annotated with expert explanation (worked-out examples) versus scaffolded self-explanation of code examples using an automated system (Intelligent Tutoring System). A randomized controlled trial study was conducted with 90 university students who were assigned to either the control group (reading worked-out examples, a passive strategy) or the experimental group where participants were asked to self-explain and received help, if needed, in the form of questions from the tutoring system( scaffolded self-explanation, an interactive strategy). We found that students with low prior knowledge in the experimental condition had significantly higher learning gains than students with high prior knowledge. However, in the control condition, this distinction in learning outcomes based on prior knowledge was not observed. We also analyzed the effect of self-efficacy on learning gains and the nature of self-explanation. Low self-efficacy students learn almost twice as much in the interactive condition versus the passive condition although the difference was not significant probably because of low sample size. We also found that high self-efficacy students tend to provide more relational explanations whereas low self-efficacy students provide more multi-structural or line-by-line explanations.
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- PAR ID:
- 10518168
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Symposium on Applied Computing, SAC 2024
- ISBN:
- 9798400702433
- Page Range / eLocation ID:
- 38 to 47
- Subject(s) / Keyword(s):
- Intelligent Tutoring System Self-explanation Self-efficacy Code reading Code comprehension
- Format(s):
- Medium: X
- Location:
- Avila Spain
- Sponsoring Org:
- National Science Foundation
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