Towards a New Platform Based on Learning Outcomes Analysis For Mobile Serious Games

Abstract

Nowadays, learning via smartphones has become one of the most popular teaching tools used by young people, thanks to the ease of use of such devices in the field of education. There are now a large number of both instructional applications and mobile serious games "MSGs" which are available in mobile applications stores. The diversity of such applications especially MSGs can guarantee a personalized learning experience for each learner. However, it is difficult to decide if a given MSG is efficient or not because this decision depends on several factors. One of those major factors is their ability to transmit knowledge effectively to the learners, in order to teach them new skills. This ability can be measured and then analyzed by using several techniques and algorithms like learning analytics, educational data mining, inference knowledge e.g. "Bayesian Knowledge Tracing", etc. Hence the need for the establishment of a user-friendly platform based on these algorithms, the proposed platform will be able to evaluate easily the learning outcomes of this kind of video games.