Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts.
Despite its importance, research shows that not all learners are equally skilled at accurately …

The development of large language models offers new possibilities for enhancing adaptive
scaffolding of student learning in game-based learning environments. In this work, we …

Abstract Self-regulated learning (SRL), learners' monitoring and control of cognitive,
affective, metacognitive, and motivational processes, is essential for learning. However …

The process of setting goals and creating plans is crucial for self-regulated learning (SRL),
yet students often struggle to construct efficient plans and establish goals. Adaptive learning …

Game‐based learning environments hold significant promise for facilitating learning
experiences that are both effective and engaging. To support individualised learning and …

Learning analytics approaches to profiling students based on their study behaviour remain
limited in how they integrate temporality and change. To advance this area of work, the …

Using Machine Learning to Detect SMART Model Cognitive Operations in Mathematical
Problem-Solving Process Page 1 Using Machine Learning to Detect SMART Model Cognitive …

Oftentimes learners are unable to engage in effective self-regulated learning (SRL)
strategies while learning about complex topics. To combat this, intelligent tutoring systems …

Game-based learning environments (GBLEs) supplement classroom instruction so students
can demonstrate their scientific reasoning abilities and increase knowledge, providing a …

Monitoring the learning process during task solving through different channels will facilitate
a better understanding of the learning process. This understanding, in turn, will provide …