Exploring The Effectiveness of Reading vs. Tutoring For Enhancing Code Comprehension For Novices
Abstract
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.
Publication Title
Proceedings of the ACM Symposium on Applied Computing
Recommended Citation
Oli, P., Banjade, R., Lekshmi Narayanan, A., Brusilovsky, P., & Rus, V. (2024). Exploring The Effectiveness of Reading vs. Tutoring For Enhancing Code Comprehension For Novices. Proceedings of the ACM Symposium on Applied Computing, 38-47. https://doi.org/10.1145/3605098.3636007
