Self-explanations could increase student’s comprehension in
complex domains; however, it works most efficiently with a human tutor
who could provide corrections and scaffolding. In this paper, we present
our attempt to scale up the use of self-explanations in learning programming
by delegating assessment and scaffolding of explanations to an intelligent
tutor. To assess our approach, we performed a randomized control
trial experiment that measured the impact of automatic assessment and
scaffolding of self-explanations on code comprehension and learning. The
study results indicate that low-prior knowledge students in the experimental
condition learn more compared to high-prior knowledge in the
same condition but such difference is not observed in a similar grouping
of students based on prior knowledge in the control condition.
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A Comparative Study of Free Self-Explanations and Socratic Tutoring Explanations for Source Code Comprehension
We present in this paper the results of a randomized control trial experiment that compared the effectiveness of two instructional strategies that scaffold learners' code comprehension processes: eliciting Free Self-Explanation and a Socratic Method. Code comprehension, i.e., understanding source code, is a critical skill for both learners and professionals. Improving learners' code comprehension skills should result in improved learning which in turn should help with retention in intro-to-programming courses which are notorious for suffering from very high attrition rates due to the complexity of programming topics. To this end, the reported experiment is meant to explore the effectiveness of various strategies to elicit self-explanation as a way to improve comprehension and learning during complex code comprehension and learning activities in intro-to-programming courses. The experiment showed pre-/post-test learning gains of 30% (M = 0.30, SD = 0.47) for the Free Self-Explanation condition and learning gains of 59% (M = 0.59,SD = 0.39) for the Socratic method. Furthermore, we investigated the behavior of the two strategies as a function of students' prior knowledge which was measured using learners' pretest score. For the Free Self-Explanation condition, there was no significant difference in mean learning gains for low vs. high knowledge students. The magnitude of the difference in performance (mean difference= 0.02,95% CI: -0.34 to 0.39) was very small (eta squared = 0.006). Likewise, the Socratic method showed no significant difference in mean learning gains between low vs. high performing students. The magnitude of the performance difference (mean difference =-0.24,95% CI: -0.534 to 0.03) was large (eta squared = 0.10). These findings suggest that eliciting self-explanations can be used as an effective strategy and that guided self-explanations as in the Socratic method condition is more effective at inducing learning gains.
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- NSF-PAR ID:
- 10301173
- Date Published:
- Journal Name:
- Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
- Page Range / eLocation ID:
- 219 to 225
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3408877.3432423
