Title Gamifying the Classroom for the Acquisition of Skills Associated with Machine Learning: A Two-Year Case Study
Authors DURAN ROSAL, ANTONIO MANUEL, Guijo-Rubio D. , Vargas V.M. , Gómez-Orellana A.M. , Gutiérrez P.A. , Fernández J.C.
External publication No
Means Lecture Notes in Networks and Systems
Scope Conference Paper
Nature Científica
SJR Quartile 4
SJR Impact 0.151
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142769846&doi=10.1007%2f978-3-031-18409-3_22&partnerID=40&md5=d2b284ca691fdaf1825a9dad8bcc0b34
Publication date 05/11/2022
ISI 000937141000022
Scopus Id 2-s2.0-85142769846
DOI 10.1007/978-3-031-18409-3_22
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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