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

• In Event: AERA Roundtable Session 1
  In Roundtable Session: Mixed-Methods Research Applications and Innovations

Abstract

This study explored a Bayesian assessment model for physics students in motion learning. The data was collected by using 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 findings reported that there were three different patterns for a physics student's motion learning: lower performance, middle performance, and higher performance. In each pattern, the students may have different performance combinations of the twelve bottom components as shown in Figure 3, and used to collect students’ performance data.

Author

• Zhidong Zhang, The University of Texas-Rio Grande Valley