Toward interactive context-aware affective educational recommendations in computer-assisted language learning

Abstract

This work explores the benefits of supporting learners affectively in a context-aware learning situation. This features a new challenge in related literature in terms of providing affective educational recommendations that take advantage of ambient intelligence and are delivered through actuators available in the environment, thus going beyond previous approaches which provided computer-based recommendation that present some text or tell aloud the learner what to do. To address this open issue, we have applied TORMES elicitation methodology, which has been used to investigate the potential of ambient intelligence for making more interactive recommendations in an emotionally challenging scenario (i.e. preparing for the oral examination of a second language learning course). Arduino open source electronics prototyping platform is used both to sense changes in the learners’ affective state and to deliver the recommendation in a more interactive way through different complementary sensory communication channels (sight, hearing, touch) to cope with a universal design. An Ambient Intelligence Context-aware Affective Recommender Platform (AICARP) has been built to support the whole experience, which represents a progress in the state of the art. In particular, we have come up with what is most likely the first interactive context-aware affective educational recommendation. The value of this contribution lies in discussing methodological and practical issues involved.