Search

Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction. Therefore, evaluations that measure user satisfaction should also be performed before deploying a recommender system in a real target environment. Such evaluations are laborious and complicated compared to the traditional, data-centric evaluations, though. In this study, we carried out a user-centric evaluation of state-of-the-art recommender systems as well as a graph-based approach in the ecologically valid setting of an authentic social learning platform. We also conducted a data-centric evaluation on the same data to investigate the added value of user-centric evaluations and how user satisfaction of a recommender system is related to its performance in terms of accuracy metrics. Our findings suggest that user-centric evaluation results are not necessarily in line with data-centric evaluation results. We conclude that the traditional evaluation of recommender systems in terms of prediction accuracy only does not suffice to judge performance of recommender systems on the user side. Moreover, the user-centric evaluation provides valuable insights in how candidate algorithms perform on each of the five quality metrics for recommendations: usefulness, accuracy, novelty, diversity, and serendipity.

This paper represents our newly developed software for emotion recognition from facial expressions. Besides allowing emotion recognition from image files and recorded video files, it uses webcam data to provide real-time, continuous, and unobtrusive facial emotional expressions. It uses FURIA algorithm for unordered fuzzy rule induction to offer timely and appropriate feedback based on learners’ facial expressions. The main objective of this study was first to validate the use of webcam data for a real-time and accurate analysis of facial expressions in e-learning environments. Second, transform these facial expressions to detected emotional states using the FURIA algorithm. We measured the performance of the software with ten participants, provided them with the same computer-based tasks, requested them a hundred times to mimic specific facial expressions, and recorded all sessions on video. We used the recorded video files to feed our newly developed software. We then used two experts’ opinions to annotate and rate participants’ recorded behaviours and to validate the software’s results. The software provides accurate and reliable results with the overall accuracy of 83.2%, which is comparable to the recognition by humans. This study will help to increase the quality of e-learning.

A large-scale experiment is presented which examines the feasibility of using a new method of peer-support called Ad Hoc Transient Groups (AHTGs) to foster Social Capital of Learning Network participants. In AHTGs participants that have a request are helped by other participants in a dedicated private space (‘ad-hoc’) which exists for a limited amount of time only (‘transience’). To test the hypotheses that AHTGs foster Social Capital, AHTGs were introduced to a subset of the eTwinning Learning Network (+130.000 teachers). To validate the results, a No-Intervention group and a comparison group that used a Forum to ask questions instead of AHTGs were also examined. Results show that AHTGs seem to foster Social Capital on the level of Relationship Characteristics and Mutual Support. Results on Sense of Connectedness were inconclusive. It is concluded that AHTGs have a decentralizing effect, making the network less dependent on a few key participants. Furthermore, AHTGs have clearly been shown to have a low threshold to ask a question. Within the Forum group only a few core participants asked questions, yet many participants replied. It is concluded that AHTGs foster Social Capital in a different way when compared to a forum.

Fostering social capital in online learning networks is essential for networked learning. Peer-support through so-called ad hoc transient groups (AHTGs) can help to socialise the network. A user-centric qualitative experiment was carried out to gauge the effect of AHTGs on social capital. Participants, who had access to AHTGs and a forum, were asked to fill in questionnaires and to participate in email interviews. The results confirm the hypothesis that participants believe the use of AHTGs will positively affect all aspects of social capital, except strengthening existing relationships. The participants viewed the AHTGs and the forum that was provided as sufficiently distinct from each other that these interaction environments could coexist and be mutually beneficial.