Design and Development of a Personalized Recommender System of Student Question-Generation Exercises for Programming Courses

Abstract

Computer programming courses often face the challenge of high dropout rates, prompting educators to seek effective solutions to improve student engagement and retention. Student Question-Generation (SQG) have been explored as potential remedy to this issue, enabling advancements in computer science education. However, the majority of existing SQG support systems lack personalized features to enhance student learning. In response to this gap, the study introduces a personal recommender system of SQG exercises tailored for programming courses. The system leverages individual student preferences and SQG exercise complexities to personalize learning experiences within SQG activities. This paper presents the design of the system and its user interface and delves into motivations and technical bases underlying its development.