Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning

Abstract

Student engagement and motivation during learning activities is tied to better learning behaviors and outcomes and has prompted the development of learner-guided environments. These systems attempt to personalize learning by allowing students to select their own tasks and activities. However, recent evidence suggests that not all students are equally capable of guiding their own learning. Some students are highly self-regulated learners and are able to select learning goals, identify appropriate tasks and activities to achieve these goals and monitor their progress resulting in improved learning and motivational benefits over traditional learning tasks. Students who lack these skills are markedly less successful in self-guided learning environments and require additional scaffolding to be able to navigate them successfully. Prior work has examined these phenomena within the learner-guided environment, Crystal Island, and identified the need for early prediction of students’ self-regulated learning abilities. This work builds upon these findings and presents a dynamic Bayesian approach that significantly improves the classification accuracy of student self-regulated learning skills.