EdTech professor Andy Hung and five colleagues from other universities have had a paper accepted by the academic journal IEEE Transactions on Learning Technologies. The article is called A hybrid trust-based recommender system for informal personal learning environments.
The needs for life-long learning and the rapid development of information technologies promote the development of
various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major
issues, especially when learners face information overload and there is no knowledge authority within the learning environment.
This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study
was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other
social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group
of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based
algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.