Abstract

Games based learning (GBL) has a related field that it can draw upon: intelligent tutoring systems (ITS). Acommon concern in both fields is adaptivity, whereby the system can automatically adapt to the user. In order to supportthis adaptation, an ITS will generally include a user model and may also have a formal domain model. Work in this areastarted optimistically some years ago, but seems to have either lost some of that initial enthusiasm or been diverted intoother directions. We scan recent ITS literature to help consider why this might be, and suggest how GBL may be the fieldbest placed to take the work forward again. Learner models have long been seen as useful for adaptive learning systems.They include information about the learner which allows the system to adapt the course of learning materials andexercises to the learner's particular characteristics. In order to achieve a good quality of adaptation to the user, a detailedmodel of the required domain knowledge is typically added. The user and domain models then have to be broughttogether to lay out a course of exercises for the learner to do, and to track progress as the knowledge is learned. It's anattractive research programme, but recent work has moved to new issues, such as MOOCs. The reasons for that are partlyopportunistic and economical, but also suggest a deeper problem with the research programme. It is a costly task todevelop a domain model, and a suitable learner model that can take advantage of it. We suggest that GBL is in a goodposition to push through this cost barrier, because much of the effort is already implicitly involved in the game designprocess, which typically has to be more rigorously planned out in order to make the game a good one. One might thusexpect the next breakthroughs in adaptive learning systems to come from GBL. We further argue that the advantages toresearch, offered by the ITS framework, are also potentially beneficial to the way we teach the subject of GBL to ourstudents on game development courses.

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