Designing Learning Technologies to Support Students’ Self-Regulated Learning

This webinar took place on March 24th, 2023

Self-regulated learning is an essential predictor of students’ learning, problem-solving, and reasoning across tasks, domains, and contexts. Cognitive, affective, metacognitive, and motivational processes play a crucial role in students’ ability to monitor and regulate their learning when using advanced learning technologies (ALTs; serious games, intelligent tutoring systems, simulations, immersive virtual learning environments) accurately, dynamically, and effectively in STEM fields. Unfortunately, not all learners successfully monitor and regulate their learning.

In this session, Professor Azevedo begins by discussing the cognitive, affective, motivational, and emotional self-regulatory processes that challenge (e.g., lack of cognitive strategies, poor emotional regulation skills, lack of interest and self-efficacy, and poor comprehension skills) students’ learning with ALTs. He next discusses how to translate current theories, models, and frameworks of self-regulated learning for designing instruction that detects, fosters, and supports students’ self-regulated learning. He does this in the context of instructional environments that embed students with ALTs and other agents (e.g., teachers, peers, and virtual agents). For example, he discusses how the features and affordances of ALTs can be designed to develop learners’ self-regulatory processes more systematically, such as using non-player characters as scaffolding agents, multiple representations to enhance comprehension of complex STEM materials, and providing agency in serious games to enhance motivation and affective engagement.

Lastly, he discusses using the same design features as well a trace data (e.g., eye movements, facial expressions of emotions) as assessment tools to measure students’ use and transfer of self-regulatory processes.