What Do Academic Users Want from an Adaptive Learning System ?

The academic user base of an Adaptive Learning System (ALS) can be partitioned in two: the learners and the teachers (encompassing both content authors and tutors). Learners come from a diverse set of backgrounds with varying abilities and motivation and hence, have very individual learning requirements [2, 6, 7]. The time and e ort they can devote to learning are nite. On the other hand, teachers can create and adapt learning material to individual learning requirements. However, the time and e ort they can devote to teaching are also nite. An ALS, through the virtues of adaptivity [4, 5, 3], can reconcile this mismatch by delivering individualized educational experiences to the learners while making the best use of the time and e ort invested by the teachers. The rst step in developing an ALS is requirements elicitation [9]. As part of this endeavor, it is instructive to interview the user base. Interviews help crystalize the expectations of learners and teachers with respect to an ALS. They may reveal requirements not previously envisaged as being key, requirements purported to be useful but are considered otherwise by the interviewees, and requirements from di erent partitions of the user base that are contradictory. Within the scope of the GRAPPLE Project, we have elicited requirements from learners and teachers across several European academic institutions through explorative, semi-structured interviews. In this report we describe the methodology we employed while preparing, conducting, and analyzing the interviews and we present our ndings along with some objective and subjective analysis. The GRAPPLE Project (Generic Responsive Adaptive Personalized Learning Environment) is an EU FP7 STREP project that aims to deliver a technology-enhanced learning (TEL) environment that guides learners through a learning experience, automatically adapting to personal preferences, prior knowledge, skills and competences, learning goals, and the personal or social context in which the learning takes place. This functionality will be provided as a set of adaptive learning services that will be integrated with existing Open Source and commercial Learning Management Systems (LMSs).