In online social learning environments, tagging has demonstrated its potential to facilitate search, to improve recommendations and to foster reflection and learning.Studies have shown that shared understanding needs to be established in the group as a prerequisite for learning. We hypothesise that this can be fostered through tag recommendation strategies that contribute to semantic stabilization. In this study, we investigate the application of two tag recommenders that are inspired by models of human memory: (i) the base-level learning equation BLL and (ii) Minerva. BLL models the frequency and recency of tag use while Minerva is based on frequency of tag use and semantic context. We test the impact of both tag recommenders on semantic stabilization in an online study with 56 students completing a group-based inquiry learning project in school. We find that displaying tags from other group members contributes significantly to semantic stabilization in the group, as compared to a strategy where tags from the students' individual vocabularies are used. Testing for the accuracy of the different recommenders revealed that algorithms using frequency counts such as BLL performed better when individual tags were recommended. When group tags were recommended, the Minerva algorithm performed better. We conclude that tag recommenders, exposing learners to each other's tag choices by simulating search processes on learners' semantic memory structures, show potential to support semantic stabilization and thus, inquiry-based learning in groups.
|Titel||Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK 2017)|
|Herausgeber (Verlag)||Association of Computing Machinery|
|Publikationsstatus||Veröffentlicht - 2017|