No abstract

We introduce a new type of online instructional design, online learning modules, that effectively allows instructors to conduct pre-and post-testing on the scale of every 20-30 minutes. This paper will focus on estimating students' test-taking effort on the pre-test by analyzing their response time using a multicomponent mixture model. In a study involving four online learning modules on mechanical energy, we found that only a small fraction of students display low test-taking effort on the pre-tests. We also show that data from frequent pre-and post-test can provide useful information regarding the instructional effectiveness of the learning materials in each OLM.

No abstract

This study investigates patterns in students' learning and problem-solving behavior as they proceed through a sequence of 10 mastery-based online learning modules and how these patterns correlate with overall course outcome. Students' interaction with each module, as measured by analyzing the platform log data, was categorized into nine different states. The student population was divided into top, middle and bottom cohorts based on their total course credit, and we visualized each cohort's distribution among the nine states over the 10 modules using a series of parallel coordinates graphs. We found that the patterns of interaction were mostly similar on the first six modules, but are significantly different on modules 7-10. For the later modules, the top cohort mostly concentrated on the state corresponding to high problem-solving effort after learning, while the majority of the bottom cohort did not access the learning materials after multiple failed attempts.