Bibliographic Details
| Title: |
A Novice-Friendly Answer Interface with Code Behavior Visualization and AI Assistant for a Python Programming Learning Assistant System. |
| Authors: |
Fu, Zhida1 (AUTHOR), Funabiki, Nobuo1,2 (AUTHOR) funabiki@okayama-u.ac.jp, Zhu, Zihao1,3 (AUTHOR), Zhang, Yue1 (AUTHOR), Kao, Wen-Chung2 (AUTHOR), Lee, Yi-Fang3 (AUTHOR), Tseng, Pi-Kuang3 (AUTHOR) |
| Source: |
Information. May2026, Vol. 17 Issue 5, p509. 23p. |
| Subject Terms: |
*Python programming language, *Software visualization, *Tutors & tutoring, *High school students, *Computer programming education, *User interfaces, *Artificial intelligence, *Learning analytics |
| Abstract: |
Nowadays, Python is very popular as the first programming language for novices, including high school students, to learn due to its short code features with rich libraries. Thus, it is important to provide a learning environment supporting studies starting from the fundamentals, since students have no knowledge on how a program runs on a computer. Previously, we have developed a web-based programming learning assistant system (PLAS) to allow the self-study of major programming languages, including Python, by university students. It offers several types of exercise problems that have different learning goals and levels for step-by-step study. Any student answer is automatically marked at the answer interface for quick feedback. However, PLAS has not implemented functions to assist the learning needs of high school-level students. In this paper, we propose a novice-friendly answer interface for a Python programming learning assistant system (PyPLAS) that introduces a code behavior visualization and an AI assistant with learning logs. The visualization allows learners to observe the changes in variable states and the control flow. The assistant provides multi-level hints during learning and reflective feedback after it by analyzing the logs based on engagement, reasoning strategies, learning pace, and tool usage. For evaluation, we implemented the proposed interface using Python Flask for the web platform and Ollama as a locally deployed AI model. A pilot application was conducted with high school students solving introductory Python exercises in PyPLAS. The results showed high task completion, positive questionnaire responses toward embedded visualization and interface usability, and teacher-observed usefulness of the four-dimensional learning analytics for interpreting learner behaviors. These findings provide preliminary evidence for the feasibility and practical value of the proposed interface, while larger controlled studies are required to validate its instructional effectiveness. [ABSTRACT FROM AUTHOR] |
| Database: |
Academic Search Index |