تحلیل یادگیری برای ارزیابی بازی های جدی
Abstract
Graphical abstract
Subject classification codes
1. Introduction
2. Methodology
3. Case study: Conectado
4. Case study: DownTown
5. Case study: First Aid Game
6. Discussion
7. Conclusions
Acknowledgments
References
Abstract
Serious Games have already proved their advantages in different educational environments. Combining them with Game Learning Analytics can further improve the life-cycle of serious games, by informing decisions that shorten development time and reduce development iterations while improving their impact, therefore fostering their adoption. Game Learning Analytics is an evidence-based methodology based on in-game user interaction data, and can provide insight about the game-based educational experience promoting aspects such as a better assessment of the learning process. In this article, we review our experiences and results applying Game Learning Analytics for serious games in three different scenarios: (1) validating and deploying a game to raise awareness about cyberbullying, (2) validating the design of a game to improve independent living of users with intellectual disabilities and (3) improving the evaluation of a game on first aid techniques. These experiences show different uses of game learning analytics in the context of serious games to improve their design, evaluation and deployment processes. Building up from these experiences, we discuss the results obtained and provide lessons learnt from these different applications, to provide an approach that can be generalized to improve the design and application of a wide range of serious games in different educational settings.
Introduction
The gaming industry has experienced a vast growth worldwide in recent years (Entertainment Software Association, 2017). The application of games with a non-entertainment primary purpose, so-called Serious Games (SGs) (Abt, 1970), can provide multiple benefits (Boyle et al., 2016) in environments where games were not traditionally present. Such is the case of education, where non-interactive contents still constitute the majority of learning materials, and there is little consensus about how to best include technology in the classrooms (Adams Becker et al., 2017). However, this slow adoption of learning games in a broad sense, contrasts with the use of games in specific educational domains (e.g. business, military (Kato & Klerk, 2017)) and with the major presence of games in the spare time of students (Pew Research Center, 2018). New techniques such as Learning Analytics (LA), are trying to provide insight about the educational processes and improve the common educational scenarios benefiting from data-driven approaches. LA, as defined in (Long, Siemens, Gráinne, & Gašević, ۲۰۱۱), aims to measure, collect, analyze and report data from learning tools, such as LMSs (Learning Management Systems) or MOOCs (Massive Open Online Courses), to extract useful information about how students learn with the purpose of understanding and optimizing their learning processes and contexts (Sclater, 2017).