[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Factors affecting student dropout in MOOCs: a cause and effect decision‐making model

  • Published:
Journal of Computing in Higher Education Aims and scope Submit manuscript

Abstract

Massive open online courses (MOOCs) are among the latest e-learning initiative that have gained a wide popularity among many universities. Student dropout in MOOCs is a major concern in the higher education and policy-making communities. Most student dropout is caused by factors outside the institution’s control. In this study, a multiple-criteria decision-making method was used to identify the core factors and possible causal relationships responsible for the high dropout rate in MOOCs. Twelve factors, distributed across four dimensions, related to students’ dropout from online courses were identified from the literature. Then, a total of 17 experienced instructors in MOOCs from different higher education institutions were invited to assess the level of influence of these factors on each other. The results identified six core factors that directly influenced student dropout in MOOCs, these were: academic skills and abilities, prior experience, course design, feedback, social presence, and social support. Other factors such as interaction, course difficulty and time, commitment, motivation, and family/work circumstances were found to play a secondary role in relation to student dropout in MOOCs. The causal relationships between the primary and secondary factors were mapped and described. Outcomes from this study can offer the necessary insights for educators and decision makers to understand the cause–effect relationships between the factors influencing MOOC student dropout, thus providing relevant interventions in order to reduce the high dropout rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of online course student retention. Paper presented at the proceedings of the 34th international conference on information systems (ICIS’13).

  • Akyuz, E., & Celik, E. (2015). A fuzzy DEMATEL method to evaluate critical operational hazards during gas freeing process in crude oil tankers. Journal of Loss Prevention in the Process Industries,38, 243–253.

    Article  Google Scholar 

  • Aldowah, H., Al-Samarraie, H., & Ghazal, S. (2019). How course, contextual, and technological challenges are associated with instructors’ individual challenges to successfully implement e-learning: A developing country perspective. IEEE Access,7, 48792–48806.

    Article  Google Scholar 

  • Al-Samarraie, H. (2019). A scoping review of videoconferencing systems in higher education: Learning paradigms, opportunities, and challenges. International Review of Research in Open and Distributed Learning, 20(3), 122–140.

    Article  Google Scholar 

  • Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: A unified perspective from instructors and students. Studies in Higher Education,43(11), 2003–2019.

    Article  Google Scholar 

  • Amantha Kumar, J., & Al-Samarraie, H. (2019). An investigation of novice pre-university students’ views towards moocs: The case of malaysia. The Reference Librarian,60(2), 134–147.

    Article  Google Scholar 

  • Angelino, L. M., Williams, F. K., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators Online,4(2), n2.

    Article  Google Scholar 

  • Appiah-Kubi, K., & Rowland, D. (2016). PEER support in MOOCs: The role of social presence. Paper presented at the proceedings of the third (2016) ACM conference on learning @ scale.

  • Baragash, R. S., & Al-Samarraie, H. (2018a). An empirical study of the impact of multiple modes of delivery on student learning in a blended course. The Reference Librarian,59(3), 149–162.

    Article  Google Scholar 

  • Baragash, R. S., & Al-Samarraie, H. (2018b). Blended learning: Investigating the influence of engagement in multiple learning delivery modes on students’ performance. Telematics and Informatics,35(7), 2082–2098.

    Article  Google Scholar 

  • Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers & Education,94, 49–60.

    Article  Google Scholar 

  • Baran, E., & Correia, A.-P. (2009). Student-led facilitation strategies in online discussions. Distance Education,30(3), 339–361.

    Article  Google Scholar 

  • Belanger, Y., & Thornton, J. (2013). Bioelectricity: A quantitative approach Duke University’s first MOOC. Noida: EducationXpress.

    Google Scholar 

  • Bonk, C. J., Zhu, M., Kim, M., Xu, S., Sabir, N., & Sari, A. R. (2018). Pushing toward a more personalized MOOC: Exploring instructor selected activities, resources, and technologies for MOOC design and implementation. International Review of Research in Open and Distributed Learning, 19(4), 93–115.

    Article  Google Scholar 

  • Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment,8, 13–25.

    Google Scholar 

  • Brophy, J. (2013). Motivating students to learn. London: Routledge.

    Book  Google Scholar 

  • Chen, H., Phang, C. W., Zhang, C., & Cai, S. (2016). What kinds of forum activities are important for promoting learning continuance in MOOCs?. Paper presented at the PACIS.

  • Chen, J., Feng, J., Sun, X., Wu, N., Yang, Z., & Chen, S. (2019). MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Mathematical Problems in Engineering, 2019, 1–11.

    Google Scholar 

  • Chen, Y. (2014). Investigating MOOCs through blog mining. The International Review of Research in Open and Distributed Learning, 15(2), 85e106.

    Article  Google Scholar 

  • Clow, D. (2013). MOOCs and the funnel of participation. Paper presented at the proceedings of the third international conference on learning analytics and knowledge.

  • Dalalah, D., Hayajneh, M., & Batieha, F. (2011). A fuzzy multi-criteria decision making model for supplier selection. Expert Systems with Applications,38(7), 8384–8391.

    Article  Google Scholar 

  • Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 2012(3), 1–20.

    Article  Google Scholar 

  • El Said, G. R. (2017). Understanding how learners use massive open online courses and why they drop out: Thematic analysis of an interview study in a developing country. Journal of Educational Computing Research,55(5), 724–752.

    Article  Google Scholar 

  • Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: A qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education,29(1), 133–146.

    Article  Google Scholar 

  • Feng, W., Tang, J, & Liu, T. X. (2019). Understanding dropouts in MOOCs. PBC School of Finance of Tsinghua University and Association for the Advancement of Artificial Intelligence. Retrieved January 10, 2019, from http://keg.cs.tsinghua.edu.cn/jietang/publications/AAAI19-Feng-dropout-moocs.pdf.

  • Ferguson, R., & Clow, D. (2015). Examining engagement: Analysing learner subpopulations in massive open online courses (MOOCs). Paper presented at the proceedings of the 5th international conference on learning analytics and knowledge.

  • Fini, A. (2009). The technological dimension of a massive open online course: The case of the CCK08 course tools. The International Review of Research in Open and Distributed Learning, 10(5), 1–26.

    Article  Google Scholar 

  • Fontela, E., & Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 report. Geneva: Battelle Geneva Research Center.

    Google Scholar 

  • Gharakhani, D. (2012). The evaluation of supplier selection criteria by fuzzy DEMATEL method. Journal of Basic and Applied Scientific Research, 2(4), 3215–3224.

    Google Scholar 

  • Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students’ experience and satisfaction in a blended learning environment. IEEE Access,6, 77179–77201.

    Article  Google Scholar 

  • Gołąbeska, E. (2018). The Dematel method in the analysis of the residential real estate market in Bialystok. Real Estate Management and Valuation,26(1), 16–25.

    Article  Google Scholar 

  • Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal,52(5), 925–955.

    Article  Google Scholar 

  • Gütl, C., Rizzardini, R. H., Chang, V., & Morales, M. (2014). Attrition in MOOC: Lessons learned from drop-out students. Paper presented at the international workshop on learning technology for education in cloud.

  • Hadi, S. M., & Gagen, P. (2016). New model for measuring MOOCs completion rates. Paper presented at the European MOOCs Stakeholders Summit Conference, University of Graz (Austria). Retrieved from http://www.emoocs2016.eu.

  • Halawa, S., Greene, D., & Mitchell, J. (2014). Dropout prediction in MOOCs using learner activity features. Experiences and Best Practices in and Around MOOCs,7, 3–12.

    Google Scholar 

  • Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive open online courses based on the intention–behavior gap. Distance Education,38(3), 353–368.

    Article  Google Scholar 

  • Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review,12, 45–58.

    Article  Google Scholar 

  • Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education,98, 157–168.

    Article  Google Scholar 

  • Itani, A., Brisson, L., & Garlatti, S. (2018). Understanding learner’s drop-out in MOOCs. Paper presented at the international conference on intelligent data engineering and automated learning.

  • Jordan, K. (2013). Synthesising MOOC completion rates. MoocMoocher. Retrieved from https://moocmoocher.wordpress.com/2013/02/13/synthesising-mooc-completion-rates/.

  • Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning, 15(1), 134–160.

    Article  Google Scholar 

  • Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. The International Review of Research in Open and Distributed Learning, 16(3), 341–358.

    Article  Google Scholar 

  • Josek, M., Fernandez, J. A., Perez-Encinas, A., Zimonjic, B., De Vocht, L., & Falisse, M. (2016). The international-friendliness of universities: Research report of the ESNsurvey 2016. Brussels, Belgium: Erasmus Student Network AISBL.

  • Khalil, H., & Ebner, M. (2014). MOOCs completion rates and possible methods to improve retention-A literature review. Paper presented at the EdMedia: World conference on educational media and technology.

  • Kim, T., Yang, M., Bae, J., Min, B., Lee, I., & Kim, J. (2017). Escape from infinite freedom: Effects of constraining user freedom on the prevention of dropout in an online learning context. Computers in Human Behavior,66, 217–231.

    Article  Google Scholar 

  • Kizilcec, R. F., & Halawa, S. (2015). Attrition and achievement gaps in online learning. Paper presented at the proceedings of the second (2015) ACM conference on learning @ scale.

  • Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale. ACM Transactions on Computer-Human Interaction (TOCHI), 22(2), 6.

    Article  Google Scholar 

  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development,59(5), 593–618.

    Article  Google Scholar 

  • Lee, Y., Choi, J., & Kim, T. (2013). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology,44(2), 328–337.

    Article  Google Scholar 

  • Li, K., & Moore, D. R. (2018). Motivating students in massive open online courses (MOOCs) using the attention, relevance, confidence, satisfaction (arcs) model. Journal of Formative Design in Learning,2(2), 102–113.

    Article  Google Scholar 

  • Li, R.-J. (1999). Fuzzy method in group decision making. Computers & Mathematics with Applications,38(1), 91–101.

    Article  Google Scholar 

  • Lin, R.-J. (2013). Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production, 40, 32–39.

    Article  Google Scholar 

  • Liu, M., Kang, J., Cao, M., Lim, M., Ko, Y., Myers, R., et al. (2014). Understanding MOOCs as an emerging online learning tool: Perspectives from the students. American Journal of Distance Education,28(3), 147–159.

    Article  Google Scholar 

  • Lu, X., Wang, S., Huang, J., Chen, W., & Yan, Z. (2017). What decides the dropout in MOOCs? Paper presented at the International Conference on Database Systems for Advanced Applications.

  • Ma, L., & Lee, C. S. (2019). Investigating the adoption of mooc s: A technology–user–environment perspective. Journal of Computer Assisted learning,35(1), 89–98.

    Article  Google Scholar 

  • McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice. University of Prince Edward Island. Social Sciences and Humantities Research Council’s Knowledge Synthesis Grants on the Digital Economy.

  • McMahon, M. (2013). A study of the causes of attrition among adult on a fully online training course. Irish Journal of Academic Practice,2(1), 10.

    Google Scholar 

  • Moore, J. C., & Fetzner, M. J. (2009). The road to retention: A closer look at institutions that achieve high course completion rates. Journal of Asynchronous Learning Networks,13(3), 3–22.

    Google Scholar 

  • Moore, M. G. (1989). Three types of interaction. London: Taylor & Francis.

    Google Scholar 

  • Muhammad, M. N., & Cavus, N. (2017). Fuzzy DEMATEL method for identifying LMS evaluation criteria. Procedia Computer Science,120, 742–749.

    Article  Google Scholar 

  • Muñoz-Merino, P. J., Ruipérez-Valiente, J. A., Alario-Hoyos, C., Pérez-Sanagustín, M., & Kloos, C. D. (2015). Precise Effectiveness Strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs. Computers in Human Behavior,47, 108–118.

    Article  Google Scholar 

  • Nagrecha, S., Dillon, J. Z., & Chawla, N. V. (2017). MOOC dropout prediction: lessons learned from making pipelines interpretable. Paper presented at the proceedings of the 26th international conference on world wide web companion.

  • Nistor, N., & Neubauer, K. (2010). From participation to dropout: Quantitative participation patterns in online university courses. Computers & Education,55(2), 663–672.

    Article  Google Scholar 

  • Onah, D. F. O, Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: Behavioural patterns. EDULEARN14 proceedings (pp. 5825–5834).

  • Park, J.-H. (2007). Factors related to learner dropout in online learning. In Proceedings of the 2007 Academy of Human Resource Development Annual Conference, Indianapolis, IN (pp. 251–258).

  • Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Journal of Educational Technology & Society, 12(4), 207–217.

    Google Scholar 

  • Rosé, C. P., Carlson, R., Yang, D., Wen, M., Resnick, L., Goldman, P., & Sherer, J. (2014). Social factors that contribute to attrition in MOOCs. Paper presented at the proceedings of the first ACM conference on learning@ scale conference.

  • Rostaminezhad, M. A., Mozayani, N., Norozi, D., & Iziy, M. (2013). Factors related to e-learner dropout: Case study of IUST elearning center. Procedia-Social and Behavioral Sciences,83, 522–527.

    Article  Google Scholar 

  • Seker, S., & Zavadskas, E. (2017). Application of fuzzy DEMATEL method for analyzing occupational risks on construction sites. Sustainability,9(11), 2083.

    Article  Google Scholar 

  • Shah, D. (2018). By the Numbers: MOOCS in 2018 Class Central (2018). Retrieved December 16, 2018, from https://www.classcentral.com/report/mooc-stats-2018/.

  • Shapiro, H. B., Lee, C. H., Wyman Roth, N. E., Li, K., Çetinkaya-Rundel, M., & Dorian, A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers & Education,110, 35–50.

    Article  Google Scholar 

  • Shawky, D., & Badawi, A. (2019). Towards a personalized learning experience using reinforcement learning. In A. Hassanien (Ed.), Machine learning paradigms: Theory and application (pp. 169–187). Cham: Springer.

    Chapter  Google Scholar 

  • Shieh, J.-I., Wu, H.-H., & Huang, K.-K. (2010). A DEMATEL method in identifying key success factors of hospital service quality. Knowledge-Based Systems,23(3), 277–282.

    Article  Google Scholar 

  • Tsai, S.-B., Chien, M.-F., Xue, Y., Li, L., Jiang, X., Chen, Q., et al. (2015). Using the fuzzy DEMATEL to determine environmental performance: A case of printed circuit board industry in Taiwan. PLoS One, 10(6), e0129153.

    Article  Google Scholar 

  • Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs? Journal of Online Learning and Teaching,11(1), 17.

    Google Scholar 

  • Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., & Tingley, D. (2017). Delving deeper into MOOC student dropout prediction. arXiv preprint arXiv:1702.06404.

  • 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.

    Google Scholar 

  • Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior,58, 119–129.

    Article  Google Scholar 

  • Yamba-Yugsi, M., & Lujan-Mora, S. (2017). MOOCs: Factors that decrease desertion in students. ENFOQUE UTE,8, 1–15.

    Article  Google Scholar 

  • Yang, D., Sinha, T., Adamson, D., & Rosé, C. P. (2013). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. Paper presented at the proceedings of the 2013 NIPS data-driven education workshop.

  • Yang, D., Wen, M., & Rose, C. (2014). Peer influence on attrition in massive open online courses. Proceedings of Educational Data Mining,5, 2.

    Google Scholar 

  • York, C. S., & Richardson, J. C. (2012). Interpersonal interaction in online learning: Experienced online instructors’ perceptions of influencing factors. Journal of Asynchronous Learning Networks,16(4), 83–98.

    Google Scholar 

  • Yousef, A. M. F., Chatti, M. A., Schroeder, U., & Wosnitza, M. (2014). What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. Paper presented at 2014 IEEE 14th international conference on the advanced learning technologies (ICALT).

  • Yuan, L., & Powell, S. (2013). MOOCs and open education: Implications for Higher Education: A white paper. Retrieved March 10, 2019, from http://publications.cetis.org.uk/wp-content/uploads/2013/03/MOOCs-and-Open-Education.pdf.

  • Zhang, Q., Peck, K. L., Hristova, A., Jablokow, K. W., Hoffman, V., Park, E., et al. (2016). Exploring the communication preferences of MOOC learners and the value of preference-based groups: Is grouping enough? Educational Technology Research and Development,64(4), 809–837.

    Article  Google Scholar 

  • Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors and perceptions in MOOCs. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 1882–1895). ACM.

  • Zhenghao, C., Alcorn, B., Christensen, G., Eriksson, N., Koller, D., & Emanuel, E. (2015, September 22). Who’s benefiting from MOOCs, and Why. Harvard Business Review. Retrieved from https://hbr.org/2015/09/whos-benefiting-from-moocs-and-why.

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research Group No. (RG-1438-062).

Author information

Authors and Affiliations

  1. Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Penang, Malaysia

    Hanan Aldowah

  2. School of Media and Performing Arts, Coventry University, Coventry, UK

    Hosam Al-Samarraie

  3. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

    Ahmed Ibrahim Alzahrani & Nasser Alalwan

Authors
  1. Hanan Aldowah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hosam Al-Samarraie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Ibrahim Alzahrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nasser Alalwan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hosam Al-Samarraie.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aldowah, H., Al-Samarraie, H., Alzahrani, A.I. et al. Factors affecting student dropout in MOOCs: a cause and effect decision‐making model. J Comput High Educ 32, 429–454 (2020). https://doi.org/10.1007/s12528-019-09241-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12528-019-09241-y

Keywords

Search

Navigation