Learning While Working:

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

Authors

  • 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

DOI:

https://doi.org/10.18608/jla.2022.7625

Keywords:

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

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.

References

Abbott, A., & Forrest, J. (1986). Optimal matching methods for historical sequences. The Journal of Interdisciplinary History, 16(3), 471–494. https://doi.org/10.2307/204500

Abbott, A., & Hrycak, A. (1990). Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers. American Journal of Sociology, 96(1), 144–185. https://doi.org/10.1086/229495

Ackerman, P. L., & Beier, M. E. (2006). Determinants of domain knowledge and independent study learning in an adult sample. Journal of Educational Psychology, 98(2), 366–381. https://doi.org/10.1037/0022-0663.98.2.366

Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and bachelor’s degree attainment. http://www2.ed.gov/pubs/Toolbox/toolbox.html

Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. http://www2.ed.gov/rschstat/research/pubs/toolboxrevisit/toolbox.pdf

Attewell, P., Heil, S., & Reisel, L. (2012). What is academic momentum? And does it matter? Educational Evaluation and Policy Analysis, 34(1), 27–44. https://doi.org/10.3102%2F0162373711421958

Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In R. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 253–272). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.016

Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). Cambridge University Press. https://doi.org/10.1017/cbo9780511665684.003

Barban, N., & Billari, F. C. (2012). Classifying life course trajectories: A comparison of latent class and sequence analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(5), 765–784. https://doi.org/10.1111/j.1467-9876.2012.01047.x

Bills, D. B., & Wacker, M. E. (2003). Acquiring credentials when signals don’t matter: Employers’ support of employees who pursue postsecondary vocational degrees. Sociology of Education, 76(2), 170–187. https://doi.org/10.2307/3090275

Beier, M. E. (2021). Life-span learning and development and its implications for workplace training. Current Directions in Psychological Science, 31(1), 56–61. https://doi.org/10.1177/09637214211003891

Beier, M. E., & Ackerman, P. L. (2005). Age, ability, and the role of prior knowledge on the acquisition of new domain knowledge: Promising results in a real-world learning environment. Psychology and Aging, 20(2), 341–355. https://doi.org/10.1037/0882-7974.20.2.341

Beier, M. E., Torres, W. J., & Beal, D. J. (2020). Workplace aging and jobs in the twenty-first century. In S. J. Czaja, J. Sharit, & J. B. James (Eds.), Current and emerging trends in aging and work (pp. 13–32). Springer, Cham. https://doi.org/10.1007/978-3-030-24135-3_2

Bernacki, M. L. (2018). Examining the cyclical, loosely sequenced, and contingent features of self-regulated learning: Trace data and their analysis. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (p. 370–387). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315697048.ch24

Biemann, T., Mühlenbock, M., & Dlouhy, K. (2020). Going the distance in vocational behavior research: Introducing three extensions for optimal matching analysis based on distances between career sequences. Journal of Vocational Behaviour, 119, 103399. https://doi.org/10.1016/j.jvb.2020.103399

Blair-Loy, M. (1999). Career patterns of executive women in finance: An optimal matching analysis. American Journal of Sociology, 104(5), 1346–1397. https://doi.org/10.1086/210177

Boroujeni, M. S., & Dillenbourg, P. (2019). Discovery and temporal analysis of MOOC study patterns. Journal of Learning Analytics, 6(1), 16–33. https://doi.org/10.18608/jla.2019.61.2

Brock, G., Pihur, V., Datta, S., & Datta, S. (2011). clValid: An R package for cluster validation. Journal of Statistical Software, 25, 1–22. https://doi.org/10.18637/jss.v025.i04

Brown, M., DeMonbrun, R. M., & Teasley, S. (2018). Taken together: Conceptualizing students’ concurrent course enrollment across the post-secondary curriculum using temporal analytics. Journal of Learning Analytics, 5(3), 60–72. http://dx.doi.org/10.18608/jla.2018.53.5

Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521. https://dx.doi.org/10.1016/j.eswa.2015.02.052

Chan, T. W. (1995). Optimal matching analysis: A methodological note on studying career mobility. Work and Occupations, 22(4), 467–490. https://doi.org/10.1177/0730888495022004005

Clovis, M. A., & Chang, M. (2021). Effects of academic momentum on degree attainment for students beginning college at 2-year institutions. Journal of College Student Retention: Research, Theory & Practice, 23(2), 322–336. https://doi.org/10.1177%2F1521025119826245

Cornwell, B. (2015). Social sequence analysis: Methods and applications. Cambridge University Press.

Dachner, A. M., Ellingson, J. E., Noe, R. A., & Saxton, B. M. (2021). The future of employee development. Human Resource Management Review, 31(2), Article 100732. https://doi.org/10.1016/j.hrmr.2019.100732

Deming, D., & Kahn, L. B. (2018). Skill requirements across firms and labor markets: Evidence from job postings for professionals. Journal of Labor Economics, 36(S1), S337–S369. https://doi.org/10.1086/694106

Dlouhy, K., & Biemann, T. (2018). Path dependence in occupational careers: Understanding occupational mobility development throughout individuals’ careers. Journal of Vocational Behaviour, 104, 86–97. https://doi.org/10.1016/j.jvb.2017.10.009

Doyle, W. R. (2011). Effect of increased academic momentum on transfer rates: An application of the generalized propensity score. Economics of Education Review, 30(1), 191–200. https://doi.org/10.1016/j.econedurev.2010.08.004

Duncan, A., Eicher, B., & Joyner, D. A. (2020). Enrollment motivations in an online graduate CS program: Trends & gender- and age-based differences. Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE ’20), 11–14 March 2020, Portland, OR, USA (pp. 1241–1247). ACM Press. https://doi.org/10.1145/3328778.3366848

Duncan, A., & Joyner, D. (2019). Peer advising at scale: Content and context of a learner-owned course evaluation system. Proceedings of the 6th ACM Conference on Learning @ Scale (L@S 2019), 24–25 June 2019, Chicago, IL, USA (Article No. 46). ACM Press. https://doi.org/10.1145/3330430.3333660

Durbin, R., Eddy, S. R., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press.

Elder, G. H., Jr., & Shanahan, M. J. (2006). The life course and human development. In R. M. Lerner & W. Damon (Eds.), Handbook of child psychology: Theoretical models of human development (pp. 665–715). John Wiley & Sons Inc. https://doi.org/10.1002/9780470147658.chpsy0112

Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M., Robinson, T. J., Summers, M. F., Werner-Washburne, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(3), 1–10. https://doi.org/10.1187/cbe.16-01-0038

Gabadinho, A., & Ritschard, G. (2013). Searching for typical life trajectories applied to childbirth histories. Gendered life courses — between individualization and standardization: A European approach applied to Switzerland (pp. 287–312). LIT Verlag https://www.researchgate.net/publication/287202533_Searching_for_typical_life_trajectories_applied_to_childbirth_histories

Gabadinho, A., Ritschard, G., Müeller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37. https://doi.org/10.18637/jss.v040.i04

Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2009). Mining sequence data in R with the TraMineR package: A user’s guide. University of Geneva, Department of Econometrics and Laboratory of Demography.

Gauvreau, S., Hurst, D., Cleveland-Innes, M., & Hawranik, P. (2016). Online professional skills workshops: Perspectives from distance education graduate students. International Review of Research in Open and Distributed Learning: IRRODL, 17(5), 91–108. https://doi.org/10.19173/irrodl.v17i5.2024

Ghislieri, C., Molino, M., & Cortese, C. G. (2018). Work and organizational psychology looks at the fourth industrial revolution: How to support workers and organizations? Frontiers in Psychology, 9, 2365. https://doi.org/10.3389/fpsyg.2018.02365

Goodman, J., Melkers, J., & Pallais, A. (2019). Can online delivery increase access to education? Journal of Labor Economics, 37(1), 1–34. https://doi.org/10.1086/698895

Groot, W., & De Brink, H. M. V. (2000). Education, training and employability. Applied Economics, 32(5), 573–581. https://doi.org/10.1080/000368400322471

Halpin, B., & Cban, T. W. (1998). Class careers as sequences: An optimal matching analysis of work-life histories. European Sociological Review, 14(2), 111–130. https://doi.org/10.1093/oxfordjournals.esr.a018230

Handel, M. J. (2012). Trends in job skill demands in OECD countries. OECD Social, Employment, and Migration Working Papers (No. 143). Organisation for Economic Co-operation and Development. https://doi.org/10.1787/5k8zk8pcq6td-en

Heckhausen, J., & Shane, J. (2015). How individuals navigate social mobility: Changing capacities and opportunities in careers across adulthood. In L. M. Finkelstein, D. M. Truxillo, F. Fraccaroli, & R. Kanfer (Eds.), Facing the challenges of a multi-age workforce: A use-inspired approach (pp. 313–320). Routledge/Taylor & Francis Group.

Heckhausen, J., Wrosch, C., & Schulz, R. (2010). A motivational theory of life-span development. Psychological Review, 117(1), 32–60. https://doi.org/10.1037/a0017668

Huffman, A., Culbertson, S. S., Henning, J. B., & Goh, A. (2013). Work–family conflict across the lifespan. Journal of Managerial Psychology, 28(7/8), 761–780. https://doi.org/10.1108/JMP-07-2013-0220

Jeffreys, M. R. (2007). Tracking students through program entry, progression, graduation, and licensure: Assessing undergraduate nursing student retention and success. Nurse Education Today, 27(5), 406–419. https://dx.doi.org/10.1016/j.nedt.2006.07.003

John, J. P., & Carnoy, M. (2019). The case of computer science education, employment, gender, and race/ethnicity in Silicon Valley, 1980–2015. Journal of Education and Work, 32(5), 421–435. https://doi.org/10.1080/13639080.2019.1679728

Jossberger, H., Brand‐Gruwel, S., Boshuizen, H., & Van de Wiel, M. (2010). The challenge of self‐directed and self‐regulated learning in vocational education: A theoretical analysis and synthesis of requirements. Journal of Vocational Education and Training, 62(4), 415–440. https://doi.org/10.1080/13636820.2010.523479

Joyner, D. (2018). Squeezing the limeade: Policies and workflows for scalable online degrees. Proceedings of the 5th ACM Conference on Learning @ Scale (L@S 2018), 26–28 June 2018, London, UK (Article No. 53). ACM Press. https://doi.org/10.1145/3231644.3231649

Joyner, D. A. (2020). Peripheral and semi-peripheral community: A new design challenge for learning at scale. Proceedings of the 7th ACM Conference on Learning @ Scale (L@S 2020), 12–14 August 2020, Virtual Event, USA (pp. 313–316). https://doi.org/10.1145/3386527.3406736

Kanfer, R., Beier, M. E., & Ackerman, P. L. (2012). Goals and motivation related to work in later adulthood: An organizing framework. European Journal of Work and Organizational Psychology, 22(3), 253–264. https://doi.org/10.1080/1359432X.2012.734298

Kanfer, R., & Blivin, J. (2019). Prospects and pitfalls in building the future workforce. In F. L. Oswald, T. S. Behrend, & L. L. Foster (Eds.), Workforce readiness and the future of work (pp. 251–260). Routledge.

King, D., & Cattlin, J. (2015). The impact of assumed knowledge entry standards on undergraduate mathematics teaching in Australia. International Journal of Mathematical Education in Science and Technology, 46(7), 1032–1045. https://doi.org/10.1080/0020739X.2015.1070440

Kruskal, J. B. (1983). An overview of sequence comparison. In D. Sankoff & J. B. Kruskal (Eds.), Time warps, string edits, and macromolecules: The theory and practice of sequence comparison (pp. 1–44). Addison-Wesley.

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40. https://er.educause.edu/-/media/files/article-downloads/erm1151.pdf

Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough: Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149–163. https://www.jstor.org/stable/jeductechsoci.15.3.149

Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9, 17–28.

Mardis, M. A., Ma, J., Jones, F. R., Ambavarapu, C. R., Kelleher, H. M., Spears, L. I., & McClure, C. R. (2018). Assessing alignment between information technology educational opportunities, professional requirements, and industry demands. Education and Information Technologies, 23(4), 1547–1584. https://doi.org/10.1007/s10639-017-9678-y

Martin, A. J., Wilson, R., Liem, G. A. D., & Ginns, P. (2013). Academic momentum at university/college: Exploring the roles of prior learning, life experience, and ongoing performance in academic achievement across time. The Journal of Higher Education, 84(5), 640–674. https://doi.org/10.1080/00221546.2013.11777304

Martin, P., Schoon, I., & Ross, A. (2008). Beyond transitions: Applying optimal matching analysis to life course research. International Journal of Social Research Methodology, 11(3), 179–199. https://doi.org/10.1080/13645570701622025

Méndez, G., Ochoa, X., Chiluiza, K., & De Wever, B. (2014). Curricular design analysis: A data-driven perspective. Journal of Learning Analytics, 1(3), 84–119. https://doi.org/10.18608/jla.2014.13.6

Munguia, P., & Brennan, A. (2020). Scaling the student journey from course-level information to program level progression and graduation: A model. Journal of Learning Analytics, 7(2), 84–94. https://doi.org/10.18608/jla.2020.72.5

Nelson, G. L., Strömbäck, F., Korhonen, A., Albluwi, I., Begum, M., Blamey, B., Jin, K. H., Lonati, V., MacKellar, B., & Monga, M. (2020). Assessing how pre-requisite skills affect learning of advanced concepts. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ’20), 15–19 June 2020, Virtual Event, Norway (pp. 506–507). https://doi.org/10.1145/3341525.3394990

Neubert, J. C., Mainert, J., Kretzschmar, A., & Greiff, S. (2015). The assessment of 21st century skills in industrial and organizational psychology: Complex and collaborative problem solving. Industrial and Organizational Psychology, 8(2), 238–268. https://doi.org/10.1017/iop.2015.14

Oi, M., Okubo, F., Shimada, A., Yin, C., & Ogata, H. (2015). Analysis of preview and review patterns in undergraduates e- book logs. In H. Ogata et al. (Eds.), Proceedings of the 23rd International Conference on Computers in Education (ICCE 2015), 30 November–4 December, Hangzhou, China (pp. 166–171). Asia-Pacific Society for Computers in Education.

Ostrowsky, L. (1999). College dropouts and standardized tests. Academic Questions, 12(2), 74–81. https://www.nas.org/academic-questions/12/2/college_dropouts_and_standardized_tests

Pailhé, A., Robette, N., & Solaz, A. (2013). Work and family over the life-course: A typology of French long-lasting couples using optimal matching. Longitudinal and Life Course Studies, 4(3), 196–217. http://dx.doi.org/10.14301/llcs.v4i3.250

Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60(6), 723–735. https://doi.org/10.1080/00313831.2015.1066436

Pardos, Z. A., Fan, Z., & Jiang, W. (2019). Connectionist recommendation in the wild: On the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction, 29, 487–525. https://doi.org/10.1007/s11257-019-09218-7

Pardos, Z. A., & Jiang, W. (2020, March). Designing for serendipity in a university course recommendation system. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 23–27 March 2020, Frankfurt, Germany (pp. 350–359). ACM Press. https://doi.org/10.1145/3375462.3375524

Pardos, Z. A., & Nam, A. J. H. (2020). A university map of course knowledge. PloS One, 15(9), e0233207. https://doi.org/10.1371/journal.pone.0233207.

Park, D. S., Schmidt, R. W., Akiri, C., Kwak, S., & Joyner, D. A. (2020, August). Affordable degrees at scale: New phenomenon or new hype? Proceedings of the 7th ACM Conference on Learning @ Scale (L@S 2020), 12–14 August 2020, Virtual Event, USA (pp. 25–35). https://doi.org/10.1145/3386527.3405923

Pascale, A. B. (2018). “Co-existing lives”: Understanding and facilitating graduate student sense of belonging. Journal of Student Affairs Research and Practice, 55(4), 399–411. https://doi.org/10.1080/19496591.2018.1474758

Perry, B., Boman, J., Care, W. D., Edwards, M., & Park, C. (2008). Why do students withdraw from online graduate nursing and health studies education? Journal of Educators Online, 5(1). https://www.learntechlib.org/p/54627/

Raji, M., Duggan, J., DeCotes, B., Huang, J., & Vander Zanden, B. (2017). Modelling and visualizing student flow. IEEE Transactions on Big Data, 7(3), 510–523. https://dx.doi.org/10.1109/TBDATA.2018.2840986

Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7–12. https://doi.org/10.18608/jla.2015.21.2

Roschelle, J., Bakia, M., Toyama, Y., & Patton, C. (2011). Eight issues for learning scientists about education and the economy. The Journal of the Learning Sciences, 20(1), 3–49. https://doi.org/10.1080/10508406.2011.528318

Rospigliosi, A. P., Greener, S., Bourner, T., & Sheehan, M. (2014). Human capital or signalling, unpacking the graduate premium. International Journal of Social Economics, 41(5), 420–432. https://doi.org/10.1108/IJSE-03-2013-0056

Rotatori, D., Lee, E. J., & Sleeva, S. (2021). The evolution of the workforce during the fourth industrial revolution. Human Resource Development International, 24(1), 92–103. https://doi.org/10.1080/13678868.2020.1767453

Sarıyalçınkaya, A. D., Karal, H., Altinay, F., & Altinay, Z. (2021). Reflections on adaptive learning analytics: Adaptive learning analytics. In A. Azevedo, J. Azevedo, J. Onohuome Uhomoibhi, & E. Ossiannilsson (Eds.), Advancing the power of learning analytics and big data in education (pp. 61–84). IGI Global. https://doi.org/10.4018/978-1-7998-7103-3.ch003

Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3), 3–26. https://www.jstor.org/stable/10.2307/jeductechsoci.15.3.3

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851

Sterns, H. L., & Harrington, A. K. (2019). Lifespan perspectives on learning and training. In B. B. Baltes, C. W. Rudolph, & H. Zacher (Eds.), Work across the lifespan (pp. 323–341). Academic Press. https://doi.org/10.1016/B978-0-12-812756-8.00013-X

Sterns, H. L., & Spokus, D. M. (2020). Training the older workers: Pathways and pitfalls. In S. J. Czaja, J. Sharit, & J. B. James (Eds.), Current and emerging trends in aging and work (pp. 259–278). Springer, Cham. https://doi.org/10.1007/978-3-030-24135-3_13

Szafran, R. F. (2001). The effect of academic load on success for new college students: Is lighter better? Research in Higher Education, 42(1), 27–50. https://doi.org/10.1023/A:1018712527023

Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. https://doi.org/10.1016/j.compedu.2015.08.004

Tannenbaum, S. I., Beard, R. L., McNall, L. A., & Salas, E. (2010). Informal learning and development in organizations. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, training, and development in organizations (pp. 303–332). Routledge.

Taylor, S., & Munguia, P. (2018, March). Towards a data archiving solution for learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 260–264). ACM Press. https://doi.org/10.1145/3170358.3170415

Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2017). Towards actionable learning analytics using dispositions. IEEE Transactions on Learning Technologies, 10(1), 6–16. https://doi.org/10.1109/TLT.2017.2662679

Torres, W. J., & Beier, M. E. (2018). Adult development in the wild: The determinants of autonomous learning in a massive open online course. Learning and Individual Differences, 65, 207–217. https://doi.org/10.1016/j.lin dif.2018.06.003

Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.

Wang, X., Chan, H. Y., Phelps, L. A., & Washbon, J. I. (2015). Fuel for success: Academic momentum as a mediator between dual enrollment and educational outcomes of two-year technical college students. Community College Review, 43(2), 165–190. https://doi.org/10.1177%2F0091552115569846

Wikle, J. S., & West, R. E. (2019). An analysis of discussion forum participation and student learning outcomes. International Journal on E-Learning, 18(2), 205–228. https://www.learntechlib.org/p/181356/

Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127. https://www.learntechlib.org/p/104037/

Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276. https://doi.org/10.1080/00461520.2010.517150

Winne, P. H. (2017). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The handbook of learning analytics, 1st ed. (pp. 241–249). Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.021

Winne, P. H., Jamieson-Noel, D., & Muis, K. (2002). Methodological issues and advances in researching tactics, strategies, and self-regulated learning. In P. R. Pintrich & M. L. Maehr (Eds.), Advances in motivation and achievement: New directions in measures and methods (pp. 121–155). JAI Press Inc.

Winne, P. H., & Perry, N. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Academic Press. https://doi.org/10.1016/B978-012109890-2/50045-7

Wise, A. F. (2014, March). Designing pedagogical interventions to support student use of learning analytics. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 203–211). ACM Press. https://doi.org/10.1145/2567574.2567588

Wladis, C., Wladis, K., & Hachey, A. C. (2014). The role of enrollment choice in online education: Course selection rationale and course difficulty as factors affecting retention. Online Learning, 18(3). http://dx.doi.org/10.24059/olj.v18i3.391

World Economic Forum. (2020). The future of jobs report 2020. Geneva, Switzerland. http://hdl.voced.edu.au/10707/555914

Xu, L., & Song, R. (2016). Influence of work–family–school role conflicts and social support on psychological wellbeing among registered nurses pursuing advanced degree. Applied Nursing Research, 31, 6–12. https://doi.org/10.1016/j.apnr.2015.12.005

Yamada, M., Shimada, A., Okubo, F., Oi, M., Kojima, K., & Ogata, H. (2017). Learning analytics of the relationships among self-regulated learning, learning behaviors, and learning performance. Research and Practice in Technology Enhanced Learning, 12(1), 1–17. https://doi.org/10.1186/s41039-017-0053-9

Yoon, S. A., & Hmelo-Silver, C. E. (2017). What do learning scientists do? A survey of the ISLS membership. Journal of the Learning Sciences, 26(2), 167–183. https://doi.org/10.1080/10508406.2017.1279546

York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research, and Evaluation, 20(1), 5. https://doi.org/10.7275/hz5x-tx03

Zhang, Y. L. (2022). Early academic momentum: Factors contributing to community college transfer students’ STEM degree attainment. Journal of College Student Retention: Research, Theory & Practice, 23(4), 873–902. https://doi.org/10.1177%2F1521025119881130

Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. https://doi.org/10.1016/j.learninstruc.2012.03.004

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Published

2022-12-16

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, 9(3), 104-124. https://doi.org/10.18608/jla.2022.7625