Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions

TL;DR

This study demonstrates that personalized Parsons puzzles as scaffolding significantly increase practice engagement among students in Python programming, compared to simply providing complete solutions, thereby enhancing learning experiences.

Contribution

The paper introduces the use of personalized Parsons puzzles as scaffolding in programming education, showing their effectiveness in boosting student engagement over traditional solution displays.

Findings

Students with Parsons puzzles practiced longer when struggling.

Personalized scaffolding improves engagement in coding practice.

Traditional complete solutions do not promote as much practice.

Abstract

As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in a Python learning classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete solution (CC condition), simulating the most traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer than those who received complete solutions when struggling.