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.
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Notes
- 1.
In this work we use the term gamification feature interchangeably with gamification element, since these elements are used as input to ML algorithms.
- 2.
These features were chosen due to convenience, which means they were previously implemented within the system we used in this research.
- 3.
If a problem is solved above a threshold time, than the feedback is the message ‘time limit exceeded’, and the problem is not considered solved. For more information visit: www.urionlinejudge.com.br/judge/en/faqs/about/judge.
References
Bez, J.L., Tonin, N.A., Rodegheri, P.R.: Uri online judge academic: a tool for algorithms and programming classes. In: 2014 9th International Conference on Computer Science and Education (ICCSE), pp. 149–152. IEEE (2014)
Castro-Wunsch, K., Ahadi, A., Petersen, A.: Evaluating neural networks as a method for identifying students in need of assistance. In: Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education, pp. 111–116. ACM (2017)
Denden, M., et al.: iMoodle: an intelligent gamified moodle to predict “at-risk” students using learning analytics approaches. In: Tlili, A., Chang, M. (eds.) Data Analytics Approaches in Educational Games and Gamification Systems. Smart Computing and Intelligence. Springer, Singapore (2019). https://doi.org/10.1007/978-981-32-9335-9_6
Dichev, C., Dicheva, D.: Gamifying education: what is known, what is believed and what remains uncertain: a critical review. Int. J. Educ. Technol. Higher Educ. 14(1), 9 (2017)
Dwan, F., Elaine, H.T.O., David, F.: Predição de zona de aprendizagem de alunos de introdução à programação em ambientes de correção automática de código. In: Brazilian Symposium on Computers in Education, vol. 28, no. 1 (2017)
Estey, A., Coady, Y.: Can interaction patterns with supplemental study tools predict outcomes in CS1? In: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, pp. 236–241. ACM (2016)
Flores, E.G.R., Mena, J., Montoya, M.S.R., Velarde, R.R.: The use of gamification in xMOOCs about energy: effects and predictive models for participants’ learning. Australas. J. Educ. Technol. 43–59 (2020)
Meder, M., Plumbaum, T., Albayrak, S.: A primer on data-driven gamification design. In: Proceedings of the Data-Driven Gamification Design Workshop, pp. 12–17 (2017)
Munson, J.P., Zitovsky, J.P.: Models for early identification of struggling novice programmers. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 699–704. ACM (2018)
Ortiz-Rojas, M., Chiluiza, K., Valcke, M.: Gamification in computer programming: effects on learning, engagement, self-efficacy and intrinsic motivation. In: The 11th European Conference on Game-Based Learning ECGBL 2017, pp. 507–514, October 2017
Papadakis, S., Kalogiannakis, M.: Using gamification for supporting an introductory programming course. the case of classcraft in a secondary education classroom. In: Brooks, Anthony L., Brooks, E., Vidakis, N. (eds.) ArtsIT/DLI -2017. LNICST, vol. 229, pp. 366–375. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76908-0_35
Pereira, F., Oliveira, E., Fernandes, D., Junior, H., de Carvalho, L.S.G.: Otimização e automação da predição precoce do desempenho de alunos que utilizam juízes online: uma abordagem com algoritmo genético. In: Brazilian Symposium on Computers in Education, vol. 30, p. 1451 (2019)
Pereira, F.D., et al.: Early dropout prediction for programming courses supported by online judges. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11626, pp. 67–72. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23207-8_13
Toda, A.M., Valle, P.H.D., Isotani, S.: The dark side of gamification: an overview of negative effects of gamification in education. In: Cristea, A.I., Bittencourt, I.I., Lima, F. (eds.) HEFA 2017. CCIS, vol. 832, pp. 143–156. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97934-2_9
Acknowledgements
This research, in accordance with Article 48 of Decree nº 6.008/2006, was funded by Samsung Electronics da Amazônia Ltda, under the terms of Federal Law nº 8.387/1991, through agreement nº 003, signed with ICOMP/UFAM. We would also like to thank FAPESP (Project 2016/02765-2).
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Pereira, F.D. et al. (2020). Can We Use Gamification to Predict Students’ Performance? A Case Study Supported by an Online Judge. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_30
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