AP-coach: Formative feedback generation for learning introductory programming concepts

Publication Type

Conference Proceeding Article

Publication Date

12-2022

Abstract

In this work, we aim to improve code writing skill in Python-based introductory programming courses for first-year university students. In such courses, students as novice programmers would benefit from personalised and formative feedback to: 1) quickly identify issues in their computational thinking process or coding techniques, and 2) know how to proceed when facing a certain problem. Due to the large number of students, it is impractical for instructors to manually assess all the work of each student to provide tailored feedback. We design and implement Automatic Programming Coach (AP-Coach), a web-based tool for automatically generating formative feedback for exercises on basic programming concepts. AP-Coach combines software engineering techniques (code similarity measures based on abstract syntax trees, and unit testing), and AI techniques (machine translation) in a novel manner to provide relevant feedback. We report promising results for AP-Coach in the following aspects: 1) quantitative evaluation of code similarity computation and machine translation, 2) qualitative evaluation of the perceived quality and usability of auto-generated feedback, and 3) experience of a selected group of computing students using the system.

Keywords

learning programming, formative feedback, machine translation, code similarity.

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, Hong Kong, December 4-7: Proceedings

First Page

323

Last Page

330

ISBN

9781665491174

Identifier

10.1109/TALE54877.2022.00060

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/TALE54877.2022.00060

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