Course Design Approaches and Behavioral Patterns in Massive Open Online Courses for Professional Learning
DOI:
https://doi.org/10.24059/olj.v27i4.4054Abstract
Despite their growing importance, differential, process-oriented research on Massive Open Online Courses (MOOCs) for professional learning is scarce. This paper explores learner behavior in Enterprise MOOCs using lag sequential analysis. Data from 13 MOOCs on business and technology-related topics with a total of N = 72,668 active learners were examined. Starting from consistent high-level behavioral patterns, a deeper analysis reveals variations in interaction sequences according to the underlying course design approach. Lecture-oriented, system interaction-oriented, and discussion-oriented courses share a set of common patterns but also differ in various interaction sequences. Results point towards an isolated role of video playbacks across all course clusters, consumerist patterns in lecture-oriented courses, and a positive influence of metacognitively oriented interactions on learning outcomes. Accordingly, initial design recommendations include integrating interactive instructional elements in videos, promoting learner engagement in lecture-oriented courses, and fostering metacognition. Connecting interaction and achievement data may uncover promising behavior patterns that can be further supported by course design. Based on the initial findings, implications for future research and development are discussed.
References
Bakeman, R., & Gottman, J. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge University Press.
Blackmon, S. J., & Major, C. H. (2017). Wherefore art thou MOOC? Defining massive open online courses. Online Learning, 21(4), 195–221. https://doi.org/10.24059/olj.v21i4.1272
Bonk, C. J., Lee, M. M., Reeves, T. C., & Reynolds, T. H. (Eds.) (2015). MOOCs and open education around the world. Taylor and Francis. https://doi.org/10.4324/9781315751108
Boroujeni, M., & 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
Bozkurt, A. (2021). Surfing on three waves of MOOCs: An examination and snapshot of research in massive open online courses. Open Praxis, 13(3), 296–311. https://doi.org/10.5944/openpraxis.13.3.132
Chew, S. W., Cheng, I-L., & Chen, N.-S. (2017). Yet another perspective about designing and implementing a MOOC. In M. Jemni, Kinshuk, & M. Khribi (Eds.), Open education: From OERs to MOOCs. Lecture Notes in Educational Technology. Springer. https://doi.org/10.1007/978-3-662-52925-6_6
Condé, J., & Cisel, M. (2019). On the Use of MOOCs in companies: A panorama of current practices. In M. Calise, C. Delgado Kloos, J. Reich, J. Ruiperez-Valiente, & M. Wirsing (Eds.), Digital education: At the MOOC crossroads where the interests of academia and business converge. LNCS Vol. 11475 (pp. 37–46). Springer. https://doi.org/10.1007/978-3-030-19875-6_5
Egloffstein, M. (2018). Massive open online courses in digital workplace learning. In D. Ifenthaler (Ed.), Digital workplace learning: Bridging formal and informal learning with digital technologies (pp. 149–166). Springer. https://doi.org/10.1007/978-3-319-46215-8_9
Egloffstein, M., & Ifenthaler, D. (2017). Employee perspectives on MOOCs for workplace learning. TechTrends, 61(1), 65–70. https://doi.org/10.1007/s11528-016-0127-3
Egloffstein, M., Koegler, K., & Ifenthaler, D. (2019). Instructional quality of business MOOCs: Indicators and initial findings. Online Learning Journal, 23(4), 85–105. https://doi.org/10.24059/olj.v23i4.2091
Hamori, M. (2021). MOOCs at work: what induces employer support for them? The International Journal of Human Resource Management, 32(20), 4190–4214. https://doi.org/10.1080/09585192.2019.1616593
Hamori, M. (2023). Self-directed learning in massive open online courses and its application at the workplace: Does employer support matter? Journal of Business Research, 157, 113590. https://doi.org/10.1016/j.jbusres.2022.113590
Hou, H. T., Chang, K. E., & Sung, Y. T. (2010). Applying lag sequential analysis to detect visual behavioural patterns of online learning activities. British Journal of Educational Technology, 41(2), E25–E27. https://doi.org/10.1111/j.1467-8535.2009.00935.x
Huang, H., Jew, L., & Qi, D. (2023). Take a MOOC and then drop: A systematic review of MOOC engagement pattern and dropout factor. Heliyon 9(4), e15220. https://doi.org/10.1016/j.heliyon.2023.e15220
Ifenthaler, D. (2017). Learning analytics design. In L. Lin, & J. M. Spector (Eds.), The sciences of learning and instructional design: Constructive articulation between communities (pp. 202–211). Routledge. https://doi.org/10.4324/9781315684444-13
Ifenthaler, D., Gibson, D. C., & Dobozy, E. (2018). Informing learning design through analytics: Applying network graph analysis. Australasian Journal of Educational Technology, 34(2), 117–132. https://doi.org/10.14742/ajet.3767
Ifenthaler, D., Gibson, D. C., Prasse, D., Shimada, A., & Yamada, M. (2021). Putting learning back into learning analytics: Actions for policy makers, researchers, and practitioners. Educational Technology Research and Development, 69(4), 2131–2150. https://doi.org/10.1007/s11423-020-09909-8
Kimmons, R., Graham, C. R., & West, R. E. (2020). The PICRAT model for technology integration in teacher preparation. Contemporary Issues in Technology and Teacher Education, 20(1), 176–198. https://citejournal.org/volume-20/issue-1-20/general/the-picrat-model-for-technology-integration-in-teacher-preparation/
Knight, S., Wise, A., & Chen, B. (2017). Time for change: Why learning analytics needs temporal analysis. Journal of Learning Analytics, 4(3), 7–17. https://doi.org/10.18608/jla.2017.43.2
Li, N., Kidziński, Ł., Jermann, P., & Dillenbourg, P. (2015). MOOC video interaction patterns: What do they tell us? In G. Conole, T. Klobučar, C. Rensing, J. Konert, & É. Lavoué (Eds.), Design for teaching and learning in a networked world. LNCS Vol. 9307 (pp. 197–210). Springer. https://doi.org/10.1007/978-3-319-24258-3_15
Li, S., Du, J., & Sun, J. (2022). Unfolding the learning behaviour patterns of MOOC learners with different levels of achievement. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-022-00328-8
Li, S., Wang, S., Du, J., Pei, Y., & Shen, X. (2021). MOOC learners' time-investment patterns and temporal-learning characteristics. Journal of Computer Assisted Learning, 38(1), 152–166. https://doi.org/10.1111/jcal.12597
Littenberg-Tobias, J., & Reich, J. (2020). Evaluating access, quality, and equity in online learning: A case study of a MOOC-based blended professional degree. The Internet and Higher Education, 47. https://doi.org/10.1016/j.iheduc.2020.100759
Liu, B., Wu, Y., Xing, W., Cheng, G., & Guo, S. (2021). Exploring behavioural differences between certificate achievers and explorers in MOOCs. Asia Pacific Journal of Education, 42(4), 802–814. https://doi.org/10.1080/02188791.2020.1868974
Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers and Education, 80, 77–83. https://doi.org/10.1016/j.compedu.2014.08.005
Martin, F., & Borup, J. (2022). Online learner engagement: Conceptual definitions, research themes, and supportive practices. Educational Psychologist, 57(3), 162–177. https://doi.org/10.1080/00461520.2022.2089147
Moore, R. L. (2022). Introducing mesocredentials: Connecting MOOC achievement with academic credit. Distance Education, 43(2), 271–289. https://doi.org/10.1080/01587919.2022.2064823
Moore, R. L., & Blackmon, S. J. (2022). From the learner's perspective: A systematic review of MOOC learner experiences (2008–2021). Computers & Education 190, 104596. https://doi.org/10.1016/j.compedu.2022.104596
Ogunyemi, A. A., Quaicoe, J. S., & Bauters, M. (2022). Indicators for enhancing learners’ engagement in massive open online courses: A systematic review. Computers and Education Open, 100088. https://doi.org/10.1016/j.caeo.2022.100088
Ou, C., Joyner, D. A., & Goel, A. K. (2019). Designing and developing video lessons for online learning: A seven-principle model. Online Learning Journal, 23(2), 82–104. https://doi.org/10.24059/olj.v23i2.1449
Reich, J., & Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science 363(6423), 130–131. https://doi.org/10.1126/science.aav7958
Renz, J., Meinel, C., & Link, C. (2019). openSAP: Why are enterprise MOOCs working? International Journal of Advanced Corporate Learning, 12(3), 59–69. https://doi.org/10.3991/ijac.v12i3.11262
Rohloff, T., Schwerer, F., Schenk, N., & Meinel, C. (2020). openSAP: Learner behavior and activity in self-paced enterprise MOOCs. International Journal of Advanced Corporate Learning, 13(2), 30–40. https://doi.org/10.3991/ijac.v13i2.16531
Şahin, M., Egloffstein, M., Bothe, M., Rohloff, T., Schenk, N., Schwerer, F., & Ifenthaler, D. (2021). Behavioral patterns in enterprise MOOCs at openSAP. In C. Meinel, T. Staubitz, S. Schweiger, C. Friedl, J. Kiers, M. Ebner, A. Lorenz, G. Ubachs, C. Mongenet, J. A. Ruipérez-Valiente, & M. Cortes Mendez (Eds.), EMOOCs 2021 (pp. 281–288). Universitätsverlag Potsdam. https://doi.org/10.25932/publishup-51030
Şahin, M., Keskin, S., & Yurdugül H. (2020). Sequential analysis of online learning behaviors according to e-learning readiness. In P. Isaias, D. G. Sampson, & D. Ifenthaler (Eds.), Online Teaching and Learning in Higher Education (pp. 117–131). Springer. https://doi.org/10.1007/978-3-030-48190-2_7
Saint, J., Gaševic, D., Matcha, W., Uzir, N. A., & Pardo, A. (2020). Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning. In V. Kovanović, M. Scheffel, N. Pinkwart, & K. Verbert (Eds.), LAK 2020 Conference Proceedings (pp. 402–411). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375487
Schwerer, F., & Egloffstein, M. (2016). Participation and achievement in enterprise MOOCs for professional learning. In D. G. Sampson (Ed.), Proceedings of the 13th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2016) (pp. 269–276). https://files.eric.ed.gov/fulltext/ED571404.pdf
Serth, S., Staubitz, T., van Elten, M., & Meinel C. (2022) Measuring the effects of course modularizations in online courses for life-long learners. Frontiers in Education, 7, 1008545. https://doi.org/10.3389/feduc.2022.100854
Shah, V., Murthy, S., Warriem, J., Sahasrabudhe, S., Banerjee, G., & Iyer, S. (2022). Learner-centric MOOC model: A pedagogical design model towards active learner participation and higher completion rates. Educational Technology Research and Development 70, 263–288. https://doi.org/10.1007/s11423-022-10081-4
Shang, J., Xiao, R., & Zhang, Y. (2020). A sequential analysis on the online learning behaviors of Chinese adult learners: Take the KGC learning platform as an example. In S. Cheung, R. Li, K. Phusavat, N. Paoprasert, & L. Kwok (Eds.), Blended Learning. Education in a Smart Learning Environment. ICBL 2020. Lecture Notes in Computer Science, vol 12218 (pp. 61–76). Springer. https://doi.org/10.1007/978-3-030-51968-1_6
Terzi Müftüoglu, C., Sahin, M., & Yurdugül, H. (2023). Cellwise residual testing in two-way contingency tables: Post-hoc tests for chi-square analysis. Educational Technology Theory and Practice, 13(1), 304–328. https://doi.org/10.17943/etku.1075830
Wald, A. (1973). Sequential analysis. Dover Publications.
Yoon, M., Lee, J., & Jo, I-H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The Internet and Higher Education 50, 100806. https://doi.org/10.1016/j.iheduc.2021.100806
Zhu, M., & Bonk, C. J. (2019). Designing MOOCs to facilitate participant self-monitoring for self-directed learning. Online Learning Journal, 23(4), 106–134. https://doi.org/10.24059/olj.v23i4.2037
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Marc Egloffstein, Muhttin Şahin, Dirk Ifenthaler
This work is licensed under a Creative Commons Attribution 4.0 International License.
As a condition of publication, the author agrees to apply the Creative Commons – Attribution International 4.0 (CC-BY) License to OLJ articles. See: https://creativecommons.org/licenses/by/4.0/.
This licence allows anyone to reproduce OLJ articles at no cost and without further permission as long as they attribute the author and the journal. This permission includes printing, sharing and other forms of distribution.
Author(s) hold copyright in their work, and retain publishing rights without restrictions
