학술논문

When Is Reading More Effective than Tutoring? An Analysis through the Lens of Students' Self-Efficacy among Novices in Computer Science
Document Type
Speeches/Meeting Papers
Reports - Research
Source
Grantee Submission. 2023.
Subject
Computer Science Education
College Students
Self Efficacy
Programming
Interaction
Dialogs (Language)
Intelligent Tutoring Systems
Written Language
Achievement Gains
Prior Learning
Educational Strategies
Independent Reading
Scaffolding (Teaching Technique)
Novices
Instructional Effectiveness
Language
English
Abstract
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 students' 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. [This paper was published in: "Proceedings of the 7th Educational Data Mining in Computer Science Education (CSEDM) Workshop, In conjunction with The 13th International Conference on Learning Analytics Knowledge (LAK23)," 2023.]

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