Can We Use Gamification to Predict Students’ Performance? A Case Study Supported by an Online Judge

Pereira, Filipe D.; Toda, Armando; Oliveira, Elaine H.T.; Cristea, Alexandra I.; Isotani, Seiji; Laranjeira, Dion; Almeida, Adriano; Mendonça, Jonas

Abstract

The impact of gamification has been typically evaluated via self-report assessments (questionnaires, surveys, etc.). In this work, we analise the use of gamification elements as parameters, to predict whether students are going to fail or not in a programming course. Additionally, unlike prior research, we verify how usage of gamification features can predict student performance not only as a discrete, but as a continuous measure as well, via classification and regression, respectively. Moreover, we apply our approach onto two programming courses from two different universities and involve three gamification features, i.e., ranking, score, and attempts. Our results for both predictions are notable: by using data from only the first quarter of the course, we obtain 89% accuracy for the binary classification task, and explain 78% of the students’ final grade variance, with a mean absolute error of 1.05, for regression. Additionally and interestingly, initial observations point also to gamification elements used in the online judge encouraging competition and collaboration. For the former, students that solved more problems, with fewer attempts, achieved higher scores and ranking. For the latter, students formed groups to generate ideas, then implemented their own solution.

Citation

Pereira, F. D., Toda, A., Oliveira, E. H., Cristea, A. I., Isotani, S., Laranjeira, D., Almeida, A., & Mendonça, J. (2021, June). Can We Use Gamification to Predict Students’ Performance? A Case Study Supported by an Online Judge. Presented at ITS 2020, Athens / Virtual

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ITS 2020
• Start Date: Jun 8, 2021
• End Date: Jun 12, 2021
• Online Publication Date: Jun 3, 2020
• Publication Date: 2020
• Deposit Date: Nov 5, 2021
• Publicly Available Date: Nov 5, 2021
• Print ISSN: 0302-9743
• Volume: 12149
• Pages: 259-269
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743,1611-3349
• ISBN: 978-3-030-49662-3
• DOI: https://doi.org/10.1007/978-3-030-49663-0_30
• Public URL: https://durham-repository.worktribe.com/output/1139023