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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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Prompting for Free Self-Explanations Promotes Better Code Comprehension
We present in this paper a summary analysis of log files collected during an experiment designed to test the hypothesis that prompting for free self-explanations leads to better comprehension of computer code examples. Indeed, the results indicate that students who were prompted to self-explain while trying to understand code examples performed significantly better at predicting the correct output of the examples than students who were just prompted to read the code examples and predict their output.
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- Award ID(s):
- 1822816
- PAR ID:
- 10290873
- Date Published:
- Journal Name:
- Proceedings of The 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Worked examples are an educational tool widely used in introductory computer science classes, primarily for programming and code-tracing concepts. Prior research supports the use of worked examples as a scaffolding mechanism to help students build a solid foundation before tackling problems on their own. Whether breaking down the intricacies of code or explaining abstract theoretical concepts, worked examples offer a structured approach that nurtures a deeper understanding during self-study. This study explores how peer-created worked examples, shown through detailed step-by-step videos, aid student learning in an intermediate-level computer science course, namely computer systems. Our results suggest that worked-example videos are a useful study aid for intermediate computer science courses, such as computer systems. Students who watched the worked-example videos found them to be very helpful, and ranked them as the top study aid for succeeding on quizzes. Additionally, students with access to worked-example videos performed moderately better on quizzes compared to students without worked-example videos. Our results and experiences also suggest that worked-example videos are beneficial to the students who created them as well as their peers who use them.more » « less
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Culbertson, J.; Perfors, A.; Rabagliati, H.; Ramenzoni, V. (Ed.)One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g.,Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial.more » « less
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