Clustering children's learning behaviour to identify self-regulated learning support needs. (August 2023)

• Record Type: Journal Article
• Authors: Dijkstra, S.H.E.; Hinne, M.; Segers, E.; Molenaar, I.
• Is Part Of: Computers in human behavior. Volume 145(2023)
• Publication Date: 2023-08
• Subjects: Self-regulated learning -- Learning behaviour -- Bayesian nonparametric clustering; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2023.107754
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: When children are learning using adaptive learning technologies (ALTs), the technology builds a learner model, which creates temporal trajectories providing insight into how children's knowledge develops. Based on this learner model, ALTs adjust the difficulty of problems for each child, yet children still need to regulate their practice behaviour and uphold effort and accuracy. The temporal trajectories are consequently likely to, besides showing children's knowledge development, be indicative of children's regulation. Therefore, we explore clusters of these trajectories to further identify failure in children's self-regulated learning (SRL) and potential support needs. We propose a data-driven approach to cluster 354 trajectories of 134 5th graders learning three skills with different complexity. The resulting 9 clusters were interpreted using practice accuracy and effort as indicators of regulation of practice behaviour and prior and post-knowledge and learning gain as indicators of knowledge development. The differences between clusters regarding these indicators signal there are different levels of SRL failure and, consequently, different SRL support needs: high accuracy and knowledge development indicate minimal support needs, whereas clusters with low accuracy, showing no knowledge development, indicate extensive SRL support needs. In conclusion: clusters of temporal patterns in children's learning data can identify SRL support is needed. Highlights: Nine distinct clusters of learning behaviour were distilled with a data-driven approach. The clusters differed significantly on learning metrics. The clusters provide insight into cognitive knowledge and self-regulated learning.