A Mixed Methods Design for Assessing Physics Learning in the Online Learning Environment

Zhidong Zhang
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Zhidong Zhang: The University of Texas-Rio Grande Valley

Journal of Education and Development, 2022, vol. 6, issue 2, 1-8

Abstract: This study explored a Bayesian assessment model for physics students in motion learning. The simulated data was applied in examination of the Bayesian assessment model, The study used a mixed-methods design. The exploratory sequential model was developed based on a motion learning student model, which was a structured data collection template. The combination of the student model and the Bayesian network model provided an assessment tool for assessing physics students¡¯ learning in a dynamic process. The study reported that there were three different patterns for a physics student motion learning: lower performance, middle performance, and higher performance. In each pattern, the students may have different performance combinations of the twelve bottom components. These are shown in Figure 4 and used to collect students¡¯ performance data.

Keywords: physics learning; motion learning; mixed-methods design; exploratory sequential model; student model (search for similar items in EconPapers)
JEL-codes: I21 I24 J13 Z13 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:cxp:jededu:v:6:y:2022:i:2:p:1-8

DOI: 10.20849/jed.v6i2.1142

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