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

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.