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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. 

Computer Science (CS) education is critical in todays world, and introductory programming courses are considered extremely difficult and frustrating, often considered a major stumbling block for students willing to pursue computer programming related careers. In this paper, we describe the design of Socratic Tutor, an Intelligent Tutoring System that can help novice programmers to better understand programming concepts. The system was inspired by the Socratic method of teaching in which the main goal is to ask a set of guiding questions about key concepts and major steps or segments of complete code examples. To evaluate the Socratic Tutor, we conducted a pilot study with 34 computer science students and the results are promising in terms of learning gains. 

This paper reports the findings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong positive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, selfexplanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that participants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically evaluating such self-explanations may require novel techniques compared to self-explanations of narrative or scientific texts. 

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