Effective Practices in Online Learning
Communicative Interactions with Teachers in K-12 Online Courses: From the Student Perspective
This study examined student-teacher communication practice in online courses from the student perspective. The present study provides the field with empirical evidence on the importance of student-teacher interactions through examining more varied outcome variables and relevant factors than what was often included in existing studies, and also exploring multiple sources of data.
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.
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.
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- Effective Practices in Online Learning (26)
- Effectiveness Reports (13)
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- Motivation & Social Emotional Learning (6)
- Online Teaching and Professional Development (21)
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