Promoting Self-Directed Learning in Simulation-Based Discovery Learning Environments Through Intelligent Support

Abstract

Providing learners with computer-generated feedback on their learning process in simulation-based discovery environments cannot be based on a detailed model of the learning process due to the “open” character of discovery learning. This paper describes a method for generating adaptive feedback for discovery learning based on an “opportunistic” learning model that takes the current hypothesis of the learner and the experiments performed to test this hypothesis as input. The method was applied in a simulation–based learning environment in the physics domain of collisions. Additionally, the method was compared to an environment in which subjects received predefined feedback on their hypotheses, not taking the experimentation behavior into account. Results showed that overall both groups did not differ on knowledge acquired. A further analysis indicated that, in their learning processes, the learners in the experimental condition built upon their intuitive knowledge base, whereas the learners in the control condition built upon their conceptual knowledge base. In addition, measures of the learning process showed that the subjects in the experimental condition adopted a more inquiry-based learning strategy compared to the subjects in the control condition. We concluded, therefore, that providing learners with adaptive feedback had a different and beneficial effect on the learning process compared to more traditional predefined feedback.