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Michigan Virtual

Effective Practices in Online Learning

Exploring Patterns of Time Investment in Courses Using Time Series Clustering Analysis

SEP 27, 2017
Effective Practices in Online Learning, Student Pacing

MVLRI® has launched a series of quantitative research reports exploring characteristics of students in state virtual school courses, specifically focused on those who took courses for credit recovery (CR). Among the two types of behavioral indicators, namely attempted scores and the number of minutes spent in the learning management system (LMS) on a weekly basis, the current report presented results from exploring the latter, the variable of academic time. The method of time series clustering partitioned data of weekly totals of minutes in the LMS into groups based on differences or similarities among data points, and in turn generated learning profiles. Interpretations of clustering results enhance our understanding of students’ academic learning time in virtual courses and any association between the time investment pattern and learning outcomes.

Growth Modeling with LMS Data: Data Preparation, Plotting, and Screening

SEP 27, 2017
Effective Practices in Online Learning, Student Pacing

MVLRI® has led various types of quantitative research over recent years. Those studies capitalized on data from the learning management system (LMS) and employed diverse analytic approaches in order to enhance our understanding of topics ranging from class size to students’ engagement patterns in courses. Those resources provide stakeholders opportunities to use the information and knowledge shared in these reports to extract, analyze, and interpret data to better track students’ learning activities, understand learners’ behavior in online courses, and identify their needs. In line with this idea, MVLRI launched a new project that focused on growth modeling. This report describes practical preliminary steps prior to fitting the LMS data into the growth model.

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