On Supporting Game-based Learning via Recommendations





Teacher education, Game Based Learning, Recommendation System, Interactive Learning


Over the last two decades, game-based learning has gained increasing popularity. In today's world, teachers are expected to utilize technological tools such as digital games as learning aids. Despite the multitude of studies examining the benefits of game-based learning, finding the most convenient game for a particular teaching purpose can be a challenging task given the vast number of similar games that are available on the market. With this study, we aim to provide teachers with a recommendation system that will assist them in selecting appropriate games from all the web-based game materials available. A key theoretical premise behind this work is to examine teaching from the perspective of teachers to develop their ability to teach. The purpose of this study is to develop a recommendation system that will assist teachers in selecting educational games based on the subjects they teach, that will be both personalized and use the experience of other researchers at the same time. We propose a system that utilizes the latest developments in signal processing and machine learning, specifically the tensor completion method. This is a machine learning technique from the family of collaborative filtering methods that fills in missing values in a dataset by analyzing its existing patterns.

Author Biographies

Aytuna Yamaç, Tampere University

Aytuna Yamaç is a Ph.D. candidate at Tampere university. She received her first masters degree from Bogazici University/Turkey and a second masters degree from Tampere University/Finland. She is currently working for an international project aiming to research the PATHWAYS of ACADEMICS in the CONTEXT of EDUCATIONAL INNOVATIONS and INSTITUTIONAL CHANGES together with Tallinn University and University of Malta. Her research interests are Digitalization, Internationalization, Teacher education, Teachers’ Professional Development.

Mehmet Yamaç, Tampere University

Mehmet Yamaç received B.S. and M.S. degrees in electrical and electronics engineering from Anadolu University and Bogaziçi University, respectively. He is currently pursuing a Ph.D. with the Department of Computing Sciences at Tampere University. Yamaç has authored over 30 papers and is a Senior Researcher with Huawei Technologies Oy, Tampere, focusing on computer vision, machine learning, and compressive sensing.

Kostas Stefanidis

Kostas Stefanidis is an Associate Professor at Tampere University and leads the Group on Recommender Systems. He has 10+ years of experience in academia, including roles at ICS-FORTH, NTNU, and CUHK. He received his PhD in personalized data management from the University of Ioannina, Greece. His research focuses on big data, personalization, and recommender systems, with recent work on socio-technical aspects. He has published 100+ papers in top-tier conferences and journals.