Project Activities
The research team will conduct secondary data analyses using historical data received from the L@S platform team at ASU to explore differences in course taking and degree persistence between the online and on-campus programs at ASU. They will use machine learning and quasi-experimental methods to contrast the academic experiences of online students and on-campus students in terms of their course-taking patterns by major and to identify factors that facilitate or impede student progress towards earning a degree. They will also explore the influence of COVID-19 on course taking patterns and academic pathways.
Structured Abstract
Setting
The setting for this project is a large postsecondary institution in Arizona that includes both online and on-campus programs.
Sample
The sample includes two cohorts of full-time students who matriculated to the university between 2018-2021. The first cohort of students will include those who started college in fall 2021 after COVID-19 restrictions eased. The second cohort will include students who started their postsecondary education in 2018 and whose academic trajectories and program choices may have been impacted by COVID-19 restrictions. Total full-time enrollment during these years ranges from approximately 66,000 to 75,000 students.
The research team will explore specific combinations and sequence of courses that students are advised or required to take to continue in their major. These combinations and sequences can inform the development of an intervention to improve student outcomes including persistence and degree attainment.
Research design and methods
First, the research team will use data mining, machine learning, and descriptive methods to identify students' course-taking patterns and academic pathways at the postsecondary level. Using these analyses, the team will identify (a) course-taking patterns that are most typical for the online and on-campus programs by major and (b) influential courses that may facilitate or impede progression to earning a degree. They will also examine the extent to which these patterns differ by student characteristics (specifically race/ethnicity, sex, socioeconomic background, first generation status, cohort). Second, the team will employ predictive analytics and propensity score weighting to examine predictors of earning a degree for online and on-campus students and determine if there are significant differences in earning a degree between the online and on-campus programs. Third, the team will explore the influence of COVID-19 on course-taking patterns and academic pathways.
Control condition
Due to the design of this exploratory research, there is no control condition.
Key measures
Predictor variables include student demographic information, college transcript information (specifically enrollment, grades, major declaration) as well as detailed information on course participation (specifically participation in discussion forums). Outcome variables include course enrollment, declaration of and persistence in majors, and degree attainment.
Data analytic strategy
The research team will use data mining, machine learning, and descriptive methods to identify specific course-taking patterns for online and on-campus programs and influential courses within those sequences acting as gateways and roadblocks to persistence and degree attainment. The team will employ predictive analyses and propensity score weighting to examine the relationship between course-taking patterns and outcomes for online and on-campus students.
People and institutions involved
IES program contact(s)
Project contributors
Products and publications
This project will result in preliminary evidence of the features of online courses and the course-taking trajectories that support postsecondary student progress towards earning a degree. The project will also result in a final dataset to be shared, peer-reviewed publications and presentations, and additional dissemination products that reach education stakeholders such as practitioners and policymakers.
Publications:
ERIC Citations: Find available citations in ERIC for this award here.
Additional project information
This project is part of the Digital Learning Platforms to Enable Efficient Education Research Network (Digital Learning Platforms Network), which aims to leverage existing, widely used digital learning platforms for rigorous education research.
Supplemental information
Co-Principal Investigators: Circi, Ruhan; Witherspoon, Eben
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
