Formative Fugues: Helping Learners Understand Complex Systems through Causal Inference and Lag Sequential Analysis
Malavarapu, A., Lyons, L. and Uzzo, s. Formative Fugues: Helping Learners Understand Complex Systems through Causal Inference and Lag Sequential Analysis. With In Argyrakis Panos. CCS2020 - Conference on Complex System 2020 - Book of Abstracts. Presented at the Conference on Complex Systems 2020 (CCS2020), online: Zenodo. http://doi.org/10.5281/zenodo.4427919. (2020).
Next Generation Science Standards and the National Research Council recognize systems thinking as an essential skill to address the global challenges of the 21st century. But the habits of mind needed to understand complex systems are not readily learned through traditional approaches. Interactive digital simulations can help learners understand and tackle complex, open-ended problems, providing them with opportunities to explore rich, real-world problem spaces collaboratively, while introducing them to complex systems epistemologies. Through our work, we tackle these challenges by reconceptualizing formative feedback to allow learners to make informed decisions about their own exploration paths. We developed a novel data-driven approach, formative fugues, that employ causal inference and lag sequential analysis to characterize the exploration paths of prior learners and generate situationally relevant formative feedback.