Mar Saneiro

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

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.

A Methodological Approach to Eliciting Affective Educational Recommendations

The emotional situation of the learner can influence the learning process. For this reason, we are researching how educational recommender systems can take advantage of affective computing to improve the recommendation support in educational scenarios. The paper reports works carried out involving 18 educators and 77 learners to elicit and design emotional feedback to be provided for learners in terms of personalized recommendations. To this end, user centered design methods and data mining techniques are used.

Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts

Affect detection is a challenging problem, even more in educational contexts, where emotions are spontaneous and usually subtle. In this paper, we propose a two-stage detection approach based on an initial binary discretization followed by a specific emotion prediction stage. The binary classification method uses several distinct sources of information to detect and filter relevant time slots from an affective point of view. An accuracy close to 75% at detecting whether the learner has felt an educationally relevant emotion on 20 second time slots has been obtained. These slots can then be further analyzed by a second classifier, to determine the specific user emotion.

Personalised Support for Students with Disabilities Based on Psychoeducational Guidelines

In this paper we present research works we are addressing in EU4ALL project (IST-2006-034778) to enable Higher Education (HE) institutions to support and attend the accessibility needs of their students. This approach is based on integrating learning and management of the learning in terms of workflows to support the different types of existing scenarios with a twofold objective. First, involving non-technical staff in their definition. Second, using standard-based learning management systems (LMS). A combination of design and runtime adaptations through IMS Learning Design (IMS-LD) specification is being used, following the aLFanet approach (IST-2001-33288).