Year: 2022
Type: article
Study design: Observational Study
Abstract: As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics applications typically oriented toward understanding student interaction with course content, instructors, and peers to the program level in order to gain insight into the ways in which adult learners manage their learning progress over multiple courses and multiple semesters. Using optimal matching analysis, we identify four distinct profiles of course enrollment behaviour among 1,801 successful graduates of an online master’s program that differ with respect to course load, semesters off, and graduation speed. We found that profiles differed significantly as a function of age and knowledge background, but not with respect to gender, ethnicity, or previous academic performance. Findings indicate the utility of expanding learning analytics focused on the micro-level of analysis to the macro-level of analysis and the utility of grounding learning analytics applications geared toward adult learners in a lifespan development perspective. Implications for program design and educational interventions are discussed.
Source: Journal of Learning Analytics
Institution: Georgia Institute of Technology
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
Funder: Alfred P. Sloan Foundation
OpenAlex ID: W4308963410