Learning Content Recommender System for Instructors of Programming Courses

Hung Chau, Jordan Barria-Pineda, Peter Brusilovsky

Abstract: In this paper, we present a course-adaptive recommender system that assists instructors of programming courses in selecting the most relevant learning materials. The recommender system deduces the envisioned structure of a specific course using program examples prepared by the course instructor and recommends learning content items adapting to instructor’s intentions. We also present a study that assessed the quality of recommendations using datasets collected from different courses.