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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

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


Filipe D. Pereira

Armando Toda

Elaine H.T. Oliveira

Seiji Isotani

Dion Laranjeira

Adriano Almeida

Jonas Mendonça


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.

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
Volume 12149
Pages 259-269
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743,1611-3349
ISBN 978-3-030-49662-3
Public URL


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