Effective Practices in Online Learning
Exploring the Impact of Student-, Instructor-, and Course-level Factors on Student Learning in Online English Language and Literature Courses
The number of K-12 students taking online courses has increased tremendously over the past few years. However, while most current research in online learning focuses either on comparing its overall effectiveness with traditional learning or examining perceptions or interactions using self-reported data, scant research has looked into online design elements and students’ learning outcome in K-12 settings. This report seeks to explore how the combination of three main online education components—student, instructor, and course design—contribute to students’ online learning success in high school English language and literature courses.
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.
Categories
- AI (5)
- Blended Teaching & Learning (14)
- Effective Practices in Online Learning (24)
- Effectiveness Reports (13)
- Mentoring (7)
- Michigan (44)
- Motivation & Social Emotional Learning (6)
- Online Teaching and Professional Development (21)
- Program Evaluation / Quality / Policy (27)
- Research in Review (11)
- Special Populations (13)
- Student Centered Learning (10)
- Student Pacing (11)