BIR 2011 Workshops, LNBIP 106
Springer-Verlag Berlin Heidelberg
Intelligent tutoring systems rely on learner models in or- der to recommend useful learning resources. Learner models suffer from incomplete and inaccurate information about learners. In learning net- works, learners share their knowledge and experiences online and col- laboratively perform problem-solving tasks. In this paper, we present an approach that calculates learners´ competence scores based on their con- tributions and social interactions in a learning network. We aim at more comprehensive and accurate learner models, which allow more suitable recommendations of learning resources. Competence scores range from 0 to 100 points, each associated with a confidence level representing the calculation´s reliability. For evaluation, we conducted an experiment with 14 master students at university. The results show that our ap- proach tends to underestimate competences, while it calculates 54% of the scores accurately. Student feedback suggests to apply our approach for recommending future courses as well as forming student groups.
Information and Communication Technology