Studying the Interplay of Self-regulated Learning Cycles and Scaffolding Through Ordered Network Analysis Across Three Tutoring Systems

Self-regulated learning (SRL) is essential for learning across various contexts and domains. While technology-based learning environments can support SRL, comparisons of SRL processes across learning platforms and domains are scarce. As most prior research has investigated SRL patterns across learner performance levels, methods are lacking to investigate if adaptive support adequately supports learners’ temporal SRL during problem solving. This study leverages ordered network analysis (ONA) to investigate SRL processes in terms of processing information, making plans, enacting plans, and realizing errors across platform designs and domains. We analyzed think-aloud data from fifteen students working in three intelligent tutoring systems with high and low degrees of scaffolding spanning the domains of chemistry and formal logic. Students engaged in more SRL transitions in less scaffolded, open-ended platforms and when solving logic problems. Conversely, highly scaffolded environments allowed learners to enact problem-solving operations without prior planning more easily. Future research may investigate the degree to which such active learning without planning is desirable, as it might reduce learning differences predicated on SRL, but also fewer learning opportunities to plan. Our results suggest ONA is a useful methodology for studying the interplay of SRL and scaffolding during tutored problem solving.