%0 Conference Paper %1 10.1007/978-3-031-36272-9_6 %A Caughey, Maia %A Muldner, Kasia %B Artificial Intelligence in Education %C Cham %D 2023 %E Wang, Ning %E Rebolledo-Mendez, Genaro %E Matsuda, Noboru %E Santos, Olga C. %E Dimitrova, Vania %I Springer Nature Switzerland %K AIED2023 code-tracing intelligent-tutoring progtutor self-explanation %P 66--77 %T Investigating the Utility of Self-explanation Through Translation Activities with a Code-Tracing Tutor %X 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. %@ 978-3-031-36272-9

@inproceedings{10.1007/978-3-031-36272-9_6, abstract = {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.}, added-at = {2023-11-28T06:04:17.000+0100}, address = {Cham}, author = {Caughey, Maia and Muldner, Kasia}, biburl = {https://www.bibsonomy.org/bibtex/283a46c85a3174916d9eadbf045ab2fd6/brusilovsky}, booktitle = {Artificial Intelligence in Education}, description = {Investigating the Utility of Self-explanation Through Translation Activities with a Code-Tracing Tutor | SpringerLink}, editor = {Wang, Ning and Rebolledo-Mendez, Genaro and Matsuda, Noboru and Santos, Olga C. and Dimitrova, Vania}, interhash = {4dae19aa102d24376303011638b8d526}, intrahash = {83a46c85a3174916d9eadbf045ab2fd6}, isbn = {978-3-031-36272-9}, keywords = {AIED2023 code-tracing intelligent-tutoring progtutor self-explanation}, pages = {66--77}, publisher = {Springer Nature Switzerland}, timestamp = {2023-11-28T06:04:17.000+0100}, title = {Investigating the Utility of Self-explanation Through Translation Activities with a Code-Tracing Tutor}, year = 2023 }