Bayesian Mixed Effects Model and Data Visualization for Understanding Item Response Time and Response Order in Open Online Assessment

• Main Authors: Yan Liu, Audrey Béliveau, Henrike Besche, Amery D. Wu, Xingyu Zhang, Melanie Stefan, Johanna Gutlerner, Chanmin Kim
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-01-01
• Series: Frontiers in Education
• Subjects: open-book online assessment; open online assessment; classroom assessment; response time; response order; Bayesian generalized linear mixed effects model (B-GLMM)
• Online Access: https://www.frontiersin.org/articles/10.3389/feduc.2020.607260/full

Open (open-book) online assessment has become a great tool in higher education, which is frequently used for monitoring learning progress and teaching effectiveness. It has been gaining popularity because it is flexible to use and makes response behavior data available for researchers to study response processes. However, some challenges are encountered in analyzing these data, such as how to handle outlying response time, how to make use of the information from item response order, how item response time, response order and item scores are related, and how to help classroom teachers quickly check whether student responses are aligned with the design of the assessment. The purposes of this study are 3-fold: (1) to provide a solution for handling outlying response times due to the design of open online formative assessments (i.e., ample or unrestricted testing time), (2) to propose a new measure for investigating the item response order, and (3) to discuss two analytical approaches that are useful for studying response behaviors–data visualization and the Bayesian generalized linear mixed effects model (B-GLMM). An application of these two approaches is illustrated using open online quiz data. Our findings obtained from B-GLMM showed that item response order was related to item response time, but not to item scores; and item response time was related to item scores, but its effects were moderated by the cognitive level. Additionally, the findings from both B-GLMM and data visualization were consistent, which assisted instructors to see the alignment of student responses with the assessment design.