A Novel Rule-Based Online Judge Recommender System to Promote Computer Programming Education

Reducing students’ high dropout rates in the computer programming courses is a challenging problem of great concern in computer science education. Online Judge (OJ) systems were recently being investigated to address this problem and promote computer programming education. Most of the existing OJ systems have been confined only for evaluation purposes, and do not provide any personalized recommendations to enhance the productivity of a student. With this motivation, this paper proposes a novel rule-based OJ recommender system to promote computer programming education. The proposed system involves the following five steps: (i) scoring the programs submitted by a student automatically, (ii) generation of a transactional database, (iii) clustering the database with respect to their scores and other evaluation parameters, (iv) discovering interesting association rules that exist in each of the cluster’s data, and (v) providing appropriate recommendations to the users. Experimental results on the data generated by a real-world OJ system demonstrate that the proposed system is efficient.