An official website of the United States government
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.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
Wang, N.; Rebolledo-Mendez; G., Dimitrova; V., Matsuda; Santos, O.C. (Ed.)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.more » « less
-
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.more » « less
-
N. Wang, G. Rebolledo-Mendez (Ed.)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 program- ming by delegating assessment and scaffolding of explanations to an intel- ligent 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 experi- mental 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.more » « less
-
Prompted self-explanation, in which learners are induced to explain how they have solved problems, is a powerful instructional technique. Self-explanation can be prompted within learning technology by asking learners to construct their own self-explanations or select explanations from a menu. The menu-based approach has led to the best learning outcomes in the relatively few cases it has been studied in the context of digital learning games, contrary to some self-explanation theory. In a classroom study of 214 5th and 6th graders, in which the students played a digital learning game, we compared three forms of prompted self-explanation: menu-based, scaffolded, and focused (i.e., open-ended text entry, but with a focused prompt). Students in the focused condition learned more than students in the menu-based condition at delayed posttest, with no other learning differences between the conditions. This suggests that focused self-explanations may be especially beneficial for retention and deeper knowledge.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3408877.3432423
