Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics

Problem-based learning (PBL) has been used in different domains, and there is overwhelming evidence of its value. As an emerging field with excellent prospects, learning analytics (LA)-especially multimodal learning analytics (MMLA)-has increasingly attracted the attention of researchers in PBL. However, current research on the integration of LA with PBL has not related LA results with specific PBL steps or paid enough attention to the interaction in peer learning, especially for text data generated from peer interaction. This study employed MMLA based on machine learning (ML) to quantify the process engagement of peer learning, identify log behaviors, self-regulation, and other factors, and then predict online PBL performance. Participants were 104 fourth-year students in an online course on social work and problem-solving. The MMLA model contained multimodal data from online discussions, log files, reports, and questionnaires. ML classification models were built to classify text data in online discussions. The results showed that self-regulation, messages post, message words, and peer learning engagement in representation, solution, and evaluation were predictive of online PBL performance. Hierarchical linear regression analyses indicated stronger predictive validity of the process indicators on online PBL performance than other indicators. This study addressed the scarcity of students' process data and the inefficiency of analyzing text data, as well as providing information on targeted learning strategies to scaffold students in online PBL.

Automated summary

This study used multimodal learning analytics (MMLA) based on machine learning (ML) to quantify the engagement of peer learning, identify log behaviors, self-regulation, and other factors, and predict online problem-based learning (PBL) performance. The results showed that self-regulation, messages post, message words, and peer learning engagement were predictive of online PBL performance. This study addressed the scarcity of students' process data and provided information on targeted learning strategies in online PBL.