Challenges in educational game data modeling from the perspective of computing students: an empirical study

The use of Game Learning Analytics (GLA) is fundamental in assessing educational serious games, providing evidence of the player’s learning path through collecting and analyzing interaction data records. The GLBoard model facilitates the implementation of these techniques through a generic and flexible data template for any educational game. However, learning designers consider a previous step, data modeling, complex. In this regard, this work presents an empirical study to investigate the emerging difficulties of this process. Results of the interaction of computer science students indicate that the main challenges are process abstraction, game appropriation, time to carry out the process, and the need for prior knowledge of computer science disciplines programming, database, and systems modeling.