Learning Analytics for Assessing Hands-On Laboratory Skills in Science Classrooms Using Bayesian Network Analysis

Autor/inn/en	Jiang, Shiyan; Huang, Xudong; Sung, Shannon H.; Xie, Charles
Titel	Learning Analytics for Assessing Hands-On Laboratory Skills in Science Classrooms Using Bayesian Network Analysis
Quelle	In: Research in Science Education, 53 (2023) 2, S.425-444 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jiang, Shiyan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0157-244X
DOI	10.1007/s11165-022-10061-x
Schlagwörter	Learning Analytics; Hands on Science; Science Education; Laboratory Safety; Science Process Skills; Bayesian Statistics; Network Analysis; Correlation; Physics; Scientific Concepts; Concept Formation; Computer Simulation; Handheld Devices; Telecommunications; Middle School Students; Laboratory Experiments + Suchen Sie Ihr Suchwort? Naturwissenschaftliche Bildung; Netzplantechnik; Korrelation; Physik; Concept learning; Begriffsbildung; Computergrafik; Computersimulation; Telekommunikationstechnik; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Laboratory work; Laborarbeit
Abstract	Learning analytics, referring to the measurement, collection, analysis, and reporting of data about learners and their contexts in order to optimize learning and the environments in which it occurs, is proving to be a powerful approach for understanding and improving science learning. However, few studies focused on leveraging learning analytics to assess hands-on laboratory skills in K-12 science classrooms. This study demonstrated the feasibility of gauging laboratory skills based on students' process data logged by a mobile augmented reality (AR) application for conducting science experiments. Students can use the mobile AR technology to investigate a variety of science phenomena that involve concepts central to physics understanding. Seventy-two students from a suburban middle school in the Northeastern United States participated in this study. They conducted experiments in pairs. Mining process data using Bayesian networks showed that most students who participated in this study demonstrated some degree of proficiency in laboratory skills. Also, findings indicated a positive correlation between laboratory skills and conceptual learning. The results suggested that learning analytics provides a possible solution to measure hands-on laboratory learning in real-time and at scale. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01