Student Pacing
Out of Order, Still Out of Reach: Navigating Assignment Sequences for Michigan Virtual World Language Courses
In online asynchronous courses, students can submit assignments anytime during the enrollment window, often in any order they like. While previous research has focused on the timing of assignment submissions, Cuccolo & DeBruler highlighted how the order of assignment submissions is associated with lower course performance in STEM courses. This study expands that research to World Language courses, highlighting that students’ final course scores decreased as deviations from the pacing guide increased.
Out of Order, Out of Reach: Navigating Assignment Sequences for STEM Success
Pacing, or the timing of students’ assignment submissions, has been shown to have an important relationship to course performance. Less is known about how the submission order or sequencing of assignment submissions relates to course performance. This study found that the order in which students submitted assignments in their online STEM courses is related to their final grades, with students who submitted all assignments in line with pacing guide recommendations outperforming peers who did not. Indeed, students’ final grades decreased as deviations from the pacing guide increased.
The First Week in An Online Course: Differences Across Schools
This report investigates online course outcomes in high free or reduced-price lunch (FRL) schools vs. others. Students in high FRL schools had lower grades, delayed access, and fewer assignments submitted. Early engagement indicators significantly influenced final grades, highlighting the need for timely interventions to promote equity in online education.
Auto-grading versus Instructor Grading in Online English Courses
Automatic grading is commonly used as a pedagogical tool, and has become even more prevalent due to the growing popularity of Massive Open Online Courses. However, its effects on students’ learning outcomes in online high-school courses are not yet clear. This study therefore examined 738 enrollment records in high-school English Language Arts courses using hierarchical linear modeling, and found no effect of the quantity or proportion of auto-graded work on final grades. In addition, the results of decision-tree analyses suggested that, in the case of instructor-graded work, the ratio of points attempted to points earned emerged as a useful means of dividing student pass rates into three clusters.
Learning Trajectories in Online Mathematics Courses
Present research has devoted attention to a long-standing problem: how to better serve students who take K-12 online mathematics courses by investigating learner subgroups based on their semester-long learning trajectories. Mixture growth modeling was used to examine month-by-month scores students earned by completing assignments. The best-fitting model suggested four distinct subgroups representing (1) nearly linear growth, (2) exponential growth, (3) hardly any growth, (4) and early rapid growth. Follow-up analyses demonstrated that two different types of successful trajectories were more likely associated with advanced level courses, such as AP or Calculus courses, and foundation courses, such as Algebra and Geometry, were with the unpromising trajectory. Given those results, implications for practitioners and researchers were discussed from the perspective of self-regulated online learning and evidence-based mathematics instructional practices.
Course Engagement Patterns in Mathematics and Non-Mathematics Courses
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). The final report of this series was to extend the work exploring learning profiles to other subject areas most frequently taken by credit recovery (CR) students: Algebra 1, English Language & Literature 9, and U.S. History & Geography 1. We discussed clustering results as a way of providing data-driven benchmarks for the optimal course behavior patterns, which may be used by instructors and course mentors for guidance in monitoring students’ progress.
Exploring Patterns of Time Investment in Courses Using Time Series Clustering Analysis
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
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.
Examining Credit Recovery Learning Profile from Time-Series Clustering Analysis
The second report in the Credit Recovery series—Examining Credit Recovery Learning Profile from Time-Series Clustering Analysis—examines student learning behaviors in the first part of Algebra 2 courses. The ways that students engaged in coursework is targeted with two types of behavioral indicators, namely students’ attempted scores and the number of minutes spent in the learning management system (LMS) on a weekly basis.
Student Pathways Through Algebra 1 Courses
In 2016, the Institute for Learning Technologies (ILT) at Teachers College, Columbia University, received a fellowship from MVLRI to investigate learning pathways in Algebra 1A courses offered by Michigan Virtual School, with a focus on how students paced themselves throughout the semester, their online activity in different components of the course, and the difficulties encountered along the way.
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