Learning While Working:

Course Enrollment Behaviour as a Macro-Level Indicator of Learning Management Among Adult Learners


  • Corey E. Tatel Georgia Institute of Technology
  • Sibley F. Lyndgaard Georgia Institute of Technology
  • Ruth Kanfer Georgia Institute of Technology
  • Julia E. Melkers Georgia Institute of Technology




lifespan development, course enrollment behavior, optimal matching, adult learning, curriculum analytics


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.


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How to Cite

Tatel, C. E., Lyndgaard, S. F., Kanfer, R. ., & Melkers, J. E. (2022). Learning While Working: : Course Enrollment Behaviour as a Macro-Level Indicator of Learning Management Among Adult Learners. Journal of Learning Analytics, 1-21. https://doi.org/10.18608/jla.2022.7625



Research Papers