Abstract
Despite the success of Learning Analytics (LA), there are two obstacles to its application in educational games, including transparency in assessing educational outcomes in real-time gameplay, and clarity in representing those results to players. Open learner model (OLM) is a valuable instrument with capability to improve learning that meets such challenges. However, OLMs usually suffer issues concerning interactivity and transparency, which mostly regard the assessment mechanism that is used to evaluate learners’ knowledge. Tackling down transparency issues would offer context for interpreting and comparing learner model information, as well as promoting interactivity. As there is lack of studies investigating the potential of OLMs in educational games, we argue that this work can provide a valuable starting point for applying OLMs or adaptive visualizations of players’ learner models within gameplay sessions, which, in turn, can help to address both issues of application of LA to game research and OLMs. As a case study, we introduce the proposed approach into our adaptive computational thinking game.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
