#48: Enhance Code Understanding through Prompted Self-Explanation with Mixed-Up Code Puzzles: Novice Preferences and Outcomes

As AI enters computer science (CS) education, novices need early opportunities to strengthen code understanding. This work responds to this need by investigating pairing mixed-up code puzzles (Parsons puzzles) with self-explanation (SE) prompts. Drawing on learning sciences work, we designed two SE prompts: Select, where students engage in SE by choosing correct options, and Fill, where they engage in SE by typing answers. In an in-depth within-subjects study (N=10), all novices valued SE prompts for understanding code after solving puzzles. Select was strongly preferred to Fill for its efficiency and scaffolding. Fill was removed due to frustration from likely non-learning mental effort. A between-subjects study (N=110) in an intro-level CS classroom compared practice with puzzles-only to puzzles plus Select SEs. The group solving puzzles with Select showed significantly greater pre-to-posttest learning gains with similar perceived in-practice mental effort.