Incollection,
Discovering Behavior Patterns of Self-Regulated Learners in an Inquiry-Based Learning Environment
Artificial Intelligence in Education, volume 7926 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
DOI: 10.1007/978-3-642-39112-5_22
Abstract
Inquiry-based learning has been proposed as a natural and authentic way for students to engage with science. Inquiry-based learning environments typically require students to guide their own learning and inquiry processes as they gather data, make and test hypotheses and draw conclusions. Some students are highly self-regulated learners and are able to guide and monitor their own learning activities effectively. Unfortunately, many students lack these skills and are consequently less successful in open-ended, inquiry-based environments. This work examines differences in inquiry behavior patterns in an open-ended, game-based learning environment, Crystal Island. Differential sequence mining is used to identify meaningful behavior patterns utilized by Low, Medium, and High self-regulated learners. Results indicate that self-regulated learners engage in more effective problem solving behaviors and demonstrate different patterns of use of the provided cognitive tools. The identified patterns help provide further insight into the role of SRL in inquiry-based learning and inform future approaches for scaffolding.
