Abstract
Machine learning (ML) is the field of science that combines knowledge from artificial intelligence, statistics and mathematics intending to give computers the ability to learn from data without being explicitly programmed to do so. It falls under the umbrella of Data Science and is usually developed by Computer Engineers becoming what is known as Data Scientists. Developing the necessary competences in this field is not a trivial task, and applying innovative methodologies such as gamification can smooth the initial learning curve. In this context, communities offering platforms for open competitions such as Kaggle can be used as a motivating element. The main objective of this work is to gamify the classroom with the idea of providing students with valuable hands-on experience by means of addressing a real problem, as well as the possibility to cooperate and compete simultaneously to acquire ML competences. The innovative teaching experience carried out during two years meant a great motivation, an improvement of the learning capacity and a continuous recycling of knowledge to which Computer Engineers are faced to.
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Notes
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Please, check the agenda of each tutorial in the GitHub repositories https://github.com/ayrna/tutorial-scikit-learn-asl and https://github.com/ayrna/taller-sklearn-asl-2019, for the first and the second TIP, respectively.
References
Aalst, W.: Data science in action. In: Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1
Alpaydin, E.: Machine Learning. MIT Press, Cambridge (2021)
Brunner, R.J., Kim, E.J.: Teaching data science. Procedia Comput. Sci. 80, 1947–1956 (2016)
Brynjolfsson, E., Mitchell, T., Rock, D.: What can machines learn, and what does it mean for occupations and the economy? In: AEA Papers and Proceedings, vol. 108, pp. 43–47 (2018)
Bureau, E.T.: 11.5 mn job openings by 2026, sky-high salaries: why data science is booming. The Economic Times (2020). https://bit.ly/3tsDVwZ
Caponetto, I., Earp, J., Ott, M.: Gamification and education: a literature review. In: European Conference on Games Based Learning, vol. 1, p. 50. Academic Conferences International Limited (2014)
Chesani, F., Galassi, A., Mello, P., Trisolini, G.: A game-based competition as instrument for teaching artificial intelligence. In: Conference of the Italian Association for Artificial Intelligence, pp. 72–84. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-70169-1_6
Chow, W.: A pedagogy that uses a kaggle competition for teaching machine learning: an experience sharing. In: 2019 IEEE International Conference on Engineering, Technology and Education (TALE), pp. 1–5. IEEE (2019)
Columbus, L.: Linkedin’s fastest growing jobs today are in data science and machine learning. Retrieved April 10, 2018 (2017)
Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From game design elements to gamefulness: defining “gamification”. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek 2011, pp. 9–15. Association for Computing Machinery, New York (2011)
Durán-Rosal, A., Fernández, J., Gutiérrez, P., Hervás-Martínez, C.: Detection and prediction of segments containing extreme significant wave heights. Ocean Eng. 142, 268–279 (2017)
Garcia-Algarra, J.: Introductory machine learning for non stem students. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 7–10. PMLR (2021)
Guijo-Rubio, D., et al.: Ordinal regression algorithms for the analysis of convective situations over madrid-barajas airport. Atmos. Res. 236, 104798 (2020)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Hicks, S.C., Irizarry, R.A.: A guide to teaching data science. Am. Stat. 72(4), 382–391 (2018)
Jackson, A.E.: The 50 best jobs in America for 2018. Glassdoor (2018). https://bit.ly/3vN5EKL
Kross, S., Guo, P.J.: Practitioners teaching data science in industry and academia: expectations, workflows, and challenges. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2019)
Lee, J.J., Hammer, J.: Gamification in education: what, how, why bother? Acad. Exchange Q. 15(2), 146 (2011)
Legaki, Z., Hamari, J.: Gamification in statistics education: a literature review. In: GamiFIN Conference 2020, CEUR workshop proceedings, pp. 41–51 (2020)
Miller, S., Hughes, D.: The quant crunch: how the demand for data science skills is disrupting the job market. Burning Glass Technologies (2017)
Milosz, M., Milosz, E.: Gamification in engineering education–a preliminary literature review. In: 2020 IEEE Global Engineering Education Conference (EDUCON), pp. 1975–1979. IEEE (2020)
National Academies of Sciences, Engineering, and Medicine and others: data science for undergraduates: opportunities and options. National Academies Press (2018)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Polak, J., Cook, D.: A study on student performance, engagement, and experience with kaggle inclass data challenges. J. Stat. Data Sci. Educ. 29(1), 63–70 (2021)
Ramamurthy, B.: A practical and sustainable model for learning and teaching data science. In: Proceedings of the 47th ACM Technical Symposium on Computing Science Education, pp. 169–174 (2016)
Sailer, M., Homner, L.: The gamification of learning: a meta-analysis. Educ. Psychol. Rev. 32(1), 77–112 (2020)
Stack-Overflow, I.: Stack overflow developer survey 2021 (2021). https://bit.ly/3y0KhbB
Acknowledgements
The two Teaching Innovation Projects have been funded by the University of Córdoba with references 2017-1-5008 and 2018-1-5015. This work has also been partially subsidised by the “Agencia Española de Investigación (España)” (grant reference: PID2020-115454GB-C22/AEI/10.13039/501100011033); the “Consejería de Salud y Familia (Junta de Andalucía)” (grant reference: PS-2020–780); and the “Consejería de Transformación Económica, Industria, Conocimiento y Universidades (Junta de Andalucía) y Programa Operativo FEDER 2014–2020” (grant references: UCO-1261651 and PY20_00074). David Guijo-Rubio’s teaching was funded by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system from the Ministry of Universities funded by the European Union - NextGenerationEU (Ref. UCOR01MS). The teaching of Víctor M. Vargas was funded by the “Programa Predoctoral de Formación al Profesorado Universitario (FPU)” of the Ministry of Science, Innovation and Universities (Ref. FPU18/00358). The teaching of Antonio M. Gómez-Orellana was funded by “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” (Ref. PREDOC-00489).
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Durán-Rosal, A.M., Guijo-Rubio, D., Vargas, V.M., Gómez-Orellana, A.M., Gutiérrez, P.A., Fernández, J.C. (2023). Gamifying the Classroom for the Acquisition of Skills Associated with Machine Learning: A Two-Year Case Study. In: García Bringas, P., et al. International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022). CISIS ICEUTE 2022 2022. Lecture Notes in Networks and Systems, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-18409-3_22
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