Zhou, Q; Suraworachet, W; Cukurova, M; (2021) Different modality, different design, different results: Exploring self-regulated learner clusters' engagement behaviours at individual, group and cohort activities. In: Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education (MAIED 2021) At the 22nd International Conference on Artificial Intelligence in Education (AIED 2021). (pp. pp. 28-40). CEUR

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Abstract

Self-Regulated Learning (SRL) competence is an important aspect of online learning. SRL is an internal process, but analytics can offer an externalisation trigger to allow for observable effects on learner behaviours. The purpose of this paper is to explore the relationship between students' SRL competence and their learning engagement behaviours observed in multimodal data. In a postgraduate course with 42 students, eighteen features from three types of data in seven learning activities were extracted to investigate multi-level SRL competence students' engagement behaviours. The results revealed that students with different SRL competence clusters might exhibit different behaviours in individual, group, and cohort level learning activities. Also, students with similar SRL competence might exhibit significantly different engagement behaviours in different learning activities, depending on the learning design. Therefore, while using engagement data in AIED systems; the modality of the data, specific analysis techniques used to process it, and the contextual particularities of the learning design should all be explicitly presented. So that, they can be considered in the interpretations of automated decisions about student achievement.

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