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Investigating The Utility Of Self-Explanation Through Translation Activities With A Code-Tracing Tutor

Conference Paper/Proceedings
Investigating The Utility Of Self-Explanation Through Translation Activities With A Code-Tracing Tutor
Publication Year:
2023
Publication Source:
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics)
Volume:
13916 LNAI
Funding Type:
ECR:Core
Author(s):
Caughey, Maia; Muldner, Kasia
Supporting Project(s):

Code tracing is a foundational programming skill that involves simulating a program’s execution line by line, tracking how variables change at each step. To code trace, students need to understand what a given program line means, which can be accomplished by translating it into plain English. Translation can be characterized as a form of self-explanation, a general learning mechanism that involves making inferences beyond the instructional materials. Our work investigates if this form of self-explanation improves learning from a code-tracing tutor we created using the CTAT framework. We created two versions of the tutor. In the experimental version, students were asked to translate lines of code while solving code-tracing problems. In the control condition students were only asked to code trace without translating. The two tutor versions were compared using a between-subjects study (N = 44). The experimental group performed significantly better on translation and code-generation questions, but the control group performed significantly better on code-tracing questions. We discuss the implications of this finding for the design of tutors providing code-tracing support. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.