Social media have been extensively incorporated in higher education as an indispensable tool for learning. Nevertheless, research has conflicting findings about its effectiveness due to the highly reported digital distraction and poor peer learning engagement on social media. This study employed an innovative problem-based learning (PBL) pedagogy incorporating Learning Analytics to identify digital distraction, quantify the quality of peer learning engagement, and predict learning performance. Participants were 51 Taiwanese graduate students in blended Statistics courses under the PBL pedagogy. The multimodal Learning Analytics (LA) model contained data from learner discourse on Facebook groups and questionnaires, including learner characteristics, perceived digital distraction, subjective peer learning orientation, and objective peer learning engagement endorsed by machine learning (ML) models. Results showed that students reporting more digital distraction problems obtained lower final course grades, and those reporting stronger peer learning orientation received higher final course grades. Moreover, peer learning engagement objectively recognized by ML models has better predictive validity on academic performance than self-perceived peer learning orientation. The results of the multimodal LA models addressed the scarcity of studies involving learners’ process data in PBL and informed instructional practices and strategies to scaffold students’ statistics learning upon detecting those at risk of distracted attention problems and poor peer learning engagement.

Keywords: Learning analytics, Machine learning, Multimodal data, Digital distraction, Problem-based learning, Peer learning