Early Prediction of Conceptual Understanding in Interactive Simulations [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Cock, Jade; Marras, Mirko; Giang, Christian; Käser, Tanja
Titel	Early Prediction of Conceptual Understanding in Interactive Simulations [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Prediction; Concept Formation; Scientific Concepts; Physics; Active Learning; Inquiry; Learning Analytics; Interaction; Computer Simulation; Classification; College Freshmen + Suchen Sie Ihr Suchwort? Vorhersage; Concept learning; Begriffsbildung; Physik; Aktives Lernen; Interaktion; Computergrafik; Computersimulation; Classification system; Klassifikation; Klassifikationssystem; Studienanfänger
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. To this end, we measure students' conceptual understanding through a task they need to solve through their exploration. Then, 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 show that the proposed approach is able to detect struggling students early on. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01