Early Prediction of Conceptual Understanding in Interactive Simulations
Jade Cock, Mirko Marras, Christian Giang, Tanja Käser
Jun 30, 2021 16:00 UTC+2
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Session B2
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Zoom link
Keywords: skip-grams, early classification, interactive simulations, conceptual understanding
Abstract:
Interactive simulations allow students to independently explore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and therefore adaptive guidance has the potential to improve student learning. Providing effective support is, however, also a challenge because it is not clear how effective inquiry in such environments looks like. Previous research in this area has mostly focused on grouping students with similar strategies or identifying learning strategies through sequence mining.In this paper, we investigate features and models for an early prediction of conceptual understanding based on clickstream data of students using an interactive Physics simulation. We measure students' conceptual understanding through a task they need to solve through their exploration. We propose a novel pipeline to transform clickstream data into predictive features, using latent feature representations and interaction frequency vectors for different components of the environment. Our results on interaction data from 192 undergraduate students demonstrate that the proposed approach is able to detect struggling students early on.