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Rule formation in simulation-based discovery learning: optimized clustering based on Levenshtein edit distance

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Thesis type
(Thesis) Ph.D.
Date created
Author: Obaid, Teeba
This study investigates how tools for formulating rules affect learning in a simulation of series electric circuits. Computer simulations can enhance exploratory learning but pose challenges to testing hypotheses, designing experiments, and interpreting data. I analyzed students' engineering tactics and search strategies in a simulation supplemented with tools guiding how to formulate rules. Participants were randomly assigned to a control or one of two experimental groups. Detectable strategy differences between groups were observed. Sequence analysis, leveraging Levenshtein edit distance, K-means clusters, silhouette coefficient, and generalized median method, revealed unique learning paths labeled Reinforced Confirmers, Dual-mode Strategy Diversifiers, Multi-strategy Jugglers, Self-regulated Revisers, and Methodical Integrators. This research contributes insights about effective instructional strategies for discovery learning in simulations, particularly how to improve knowledge integration and self- regulated learning in complex scientific domains.
133 pages.
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Copyright is held by the author(s).
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: H., Winne, Philip
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