A predictive model to explain student persistence in blended and online courses in higher education
DOI:
https://doi.org/10.24059/olj.v30i3.5059Keywords:
higher education, online courses, blended courses, student persistence factorsAbstract
This study sought to identify the significant predictors of students' persistence in blended and online courses in higher education. Drawing from Choi's model (2016), a structural model, comprising eight predictive and three moderating variables, was tested. The sample was composed of 348 students enrolled at two French-speaking Canadian universities who completed an online questionnaire. Data were analyzed using partial least squares structural equation modelling (PLS-SEM) and partial least squares multigroup analysis (PLS-MGA). The findings suggest that learner autonomy, student satisfaction, perception of a Community of Inquiry, and family responsibilities are the primary factors influencing student persistence in blended and online courses. Furthermore, the model explains between 9.3% and 46.4% of the variance in persistence, depending on whether we examine the moderating effect of age, gender or course modality. The paper presents recommendations for institutions and faculty seeking to enhance student persistence in blended or online courses.
References
Abuhassna, H. (2020). Examining students’ satisfaction and learning autonomy through web-based courses. International Journal of Advanced Trends in Computer Science and Engineering, 9(1), 356–370. https://doi.org/10.30534/ijatcse/2020/53912020
Ali, A., & Smith, D. (2015). Comparing social isolation effects on student attrition in online versus face-to-face courses in computer literacy. Issues in Informing Science and Information Technology, 12, 11–20.
Allen, I. E., Seaman, J., Poulin, R., & Straut, T. T. (2016). Tracking online education in the United States. Babson Survey Research Group. https://eric.ed.gov/?id=ED572777
Andrade, M. S., & Bunker, E. L. (2009). A model for self‐regulated distance language learning. Distance Education, 30(1), 47–61. https://doi.org/10.1080/01587910902845956
Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J. C., & Swan, K. P. (2008). Developing a community of inquiry instrument: Testing a measure of the Community of Inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3), 133–136. https://doi.org/10.1016/j.iheduc.2008.06.003
Armstrong, V. O., Tudor, T. R., & Hughes, G. D. (2021). Course retention in community colleges: Demographics, motivation, learning style, and locus of control. American Journal of Distance Education, 35(1), 32–47. https://doi.org/10.1080/08923647.2020.1825893
Bates, T., Donovan, T., Seaman, J., Mayer, D., Martel, É., Paul, R., Desbiens, B., Forssman, V., & Poulin, R. (2019). Évolution de la formation à distance et de l’apprentissage en ligne dans les universités et collèges du Canada: 2018 [Evolution of distance education and online learning in Canadian universities and colleges: 2018]. Association canadienne de recherche sur la formation en ligne. https://www.cdlra-acrfl.ca/wp-content/uploads/2020/07/2018_national_fr.pdf
Bawa, P. (2016). Retention in online courses: Exploring issues and solutions—A literature review. SAGE Open, 6(1), 1–11. https://doi.org/10.1177/2158244015621777
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.
Bolliger, D. U., & Wasilik, O. (2012). Student satisfaction in large undergraduate online courses. The Quarterly Review of Distance Education, 13(3), 153–165.
Burke, C. M. (2019). Retention of women in online education. An exploratory study of factors that aid in retention in an accelerated, online teacher education program. The Quarterly Review of Distance Education, 20(4), 27–44.
Cheah, J.-H., Thurasamy, R., Memon, M. A., Chuah, F., & Ting, H. (2020). Multigroup analysis using SmartPLS: Step-by-step guidelines for business research. Asian Journal of Business Research, 10(3), 1–19. https://doi.org/10.14707/ajbr.200087
Chen, K.-C., Jang, S.-J., & Branch, R. M. (2010). Autonomy, affiliation, and ability: Relative salience of factors that influence online learner motivation and learning outcomes. Knowledge Management & E-Learning: An International Journal, 2(1), 30–50. https://doi.org/10.34105/j.kmel.2010.02.004
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–342). SAGE.
Choi, H. J. (2016, November). Theoretical framework for adult dropout in a cyber university. Conference Session of Online Learning Consortium (OLC). Accelerate 2016, Orlando, FL.
Choi, H. J., & Kim, B. U. (2018). Factors affecting adult student dropout rates in the Korean cyber-university degree programs. Journal of Continuing Higher Education, 66(1), 1–12. https://doi.org/10.1080/07377363.2017.1400357
Choi, H. J., & Park, J.-H. (2018). Testing a path-analytic model of adult dropout in online degree programs. Computers & Education, 116, 130–138. https://doi.org/10.1016/j.compedu.2017.09.005
Choi, H.-J. (2021). Factors affecting learners’ academic success in online liberal arts courses offered by a traditional Korean university. Sustainability, 13(16), 9175. https://doi.org/10.3390/su13169175
Delnoij, L. E. C., Dirkx, K. J. H., Janssen, J. P. W., & Martens, R. L. (2020). Predicting and resolving non-completion in higher (online) education – A literature review. Educational Research Review, 29, 1–17. https://doi.org/10.1016/j.edurev.2020.100313
Dempsey, P., & Zhang, J. (2019). Re-examining the construct validity and causal relationships of teaching, cognitive and social presence in Community of Inquiry framework. Online Learning, 23(1), 62–79. https://doi.org/10.24059/olj.v23i1.1419
Evans, T. N. (2009). An investigative study of factors that influence the retention rates in online programs at selected state, state-affiliated, and private universities (Publication No. 3388741) [Doctoral dissertation, Robert Morris University]. ProQuest Dissertations & Theses Global.
Fan, S., Trimble, A., Kember, D., Muir, T., Douglas, T., Wang, Y., Masters, J., & Mainsbridge, C. (2024). Supporting engagement and retention of online and blended-learning students: A qualitative study from an Australian University. The Australian Educational Researcher, 51(1), 403–421. https://doi.org/10.1007/s13384-022-00605-5
Fotiadou, A., Angelaki, C., & Mavroidis, I. (2017). Learner autonomy as a factor of the learning process in distance education. European Journal of Open, Distance and E-Learning, 20(1), 96–111. https://doi.org/10.1515/eurodl-2017-0006
Garratt-Reed, D., Roberts, L. D., & Heritage, B. (2016). Grades, student satisfaction and retention in online and face-to-face introductory psychology units: A test of equivalency theory. Frontiers in Psychology, 7, 1–10. https://doi.org/10.3389/fpsyg.2016.00673
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced issues in partial least squares structural equation modeling (2nd ed.). SAGE Publishing. https://us.sagepub.com/en-us/nam/advanced-issues-in-partial-least-squares-structural-equation-modeling/book243803
Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), 19–42.
Heilporn, G., & Lakhal, S. (2020). Investigating the reliability and validity of the community of inquiry framework: An analysis of categories within each presence. Computers & Education, 145. https://doi.org/10.1016/j.compedu.2019.103712
Heilporn, G., & Lakhal, S. (2022). Environmental facilitators and barriers to student persistence in online courses: Reliability and validity of new scales. The Journal of Continuing Higher Education, 70(1), 1–20. https://doi.org/10.1080/07377363.2020.1847972
James, S., Swan, K., & Daston, C. (2016). Retention, progression and the taking of online courses. Online Learning, 20(2), 1–22. https://doi.org/10.24059/olj.v20i2.780
Jin, G., Wodika, A., Darner, R., & Lai, J. (2023). Fostering scientific literacy with problem sets that generate cognitive presence and fulfill basic psychological needs. Journal of College Science Teaching, 52(7), 85–95. https://doi.org/10.1080/0047231X.2023.12315883
Johnson, N. (2024). Pan-Canadian report on digital learning. Canadian Digital Learning Research Association. https://cdlra-acrfl.ca/wp-content/uploads/2024/12/2024-Pan-Canadian-Report_EN.pdf
Joo, Y. J., Joung, S., & Kim, E. K. (2013). Structural relationships among e-learners’ sense of presence, usage, flow, satisfaction, and persistence. Educational Technology & Society, 16(2), 310–324.
Joo, Y. J., Lim, K. Y., & Kim, E. K. (2011). Online university students’ satisfaction and persistence: Examining perceived level of presence, usefulness and ease of use as predictors in a structural model. Computers & Education, 57(2), 1654–1664. https://doi.org/10.1016/j.compedu.2011.02.008
Ke, F., & Kwak, D. (2013). Constructs of student-centered online learning on learning satisfaction of a diverse online student body: A structural equation modeling approach. Journal of Educational Computing Research, 48(1), 97–122. https://doi.org/10.2190/EC.48.1.e
Kember, D. (1989). A longitudinal-process model of drop-out from distance education. The Journal of Higher Education, 60(3), 278–301. https://doi.org/10.2307/1982251
Kember, D. (1995). Open learning courses for adults: A model of student progress. Educational Technology Publications.
Kharchi, Z. (2021). Comprendre la persévérance aux études postsecondaires selon deux perspectives: Celle de l’institution et celle de l’étudiant [Understanding persistence in post-secondary education from two perspectives: that of the institution and that of the student]. Canadian Journal for New Scholars in Education, 12(2), 169–174.
Kim, S.-W., & Park, S.-S. (2015). Analysis of factors causing adult female learners to drop out of e-learning courses in Korea. International Journal on E-Learning, 14(2), 223–233.
Lakhal, S. (2019). Les déterminants technologiques de la persévérance des étudiants dans les cours à distance de niveau collégial: Les modalités de cours jouent-elles un rôle? [Technological Determinants of Student Persistence in College Distance Education Courses: Do Course Modalities Play a Role?] La Revue Canadienne de l’apprentissage et de la technologie, 45(2), 1–26. https://doi.org/10.21432/cjlt27813
Lakhal, S., & Bélisle, M. (2020). A continuum of blended and online learning. The Canadian Journal for the Scholarship of Teaching and Learning, 11(3), 1–8. https://doi.org/10.5206/cjsotl-rcacea.2020.3.13420
Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education, 25(2), 93–121. https://doi.org/10.1007/s12528-013-9069-3
Lakhal, S., Khechine, H., & Mukamurera, J. (2021). Explaining persistence in online courses in higher education: A difference-in-differences analysis. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00251-4
Landrum, B. (2020). Examining students’ confidence to learn online, self-regulation skills and perceptions of satisfaction and usefulness of online classes. Online Learning, 24(3), 128–146. https://doi.org/10.24059/olj.v24i3.2066
Lee, K. (2017). Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education, 33, 15–23. https://doi.org/10.1016/j.iheduc.2017.01.001
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. https://doi.org/10.1007/s11423-010-9177-y
Lewis, W. A. (2019). First-time online students’ perspectives towards social presence and satisfaction (Publication No. 13805724) [Doctoral dissertation, Northcentral University]. ProQuest Dissertations & Theses Global.
Ludwig, C., & Tassinari, M. G. (2023). Foreign language learner autonomy in online learning environments: The teachers’ perspectives. Innovation in Language Learning and Teaching, 17(2), 217–234. https://doi.org/10.1080/17501229.2021.2012476
Luz, M. R. M. P. D., Rolando, L. G. R., Salvador, D. F., & Sousa, A. (2018). Characterization of the reasons why Brazilian science teachers drop out of online professional development courses. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3642
Markle, G. (2015). Factors influencing persistence among nontraditional university students. Adult Education Quarterly, 65(3), 267–285. https://doi.org/10.1177/0741713615583085
Mohammadian, P., Boroon, P. R., Tang, S., Pakzad, M., & Gojgini, S. (2021). Success and retention of community college students in hybrid versus face-to-face anatomy courses. Journal of STEM Education: Innovations and Research, 22(1).
Moore, M. G. (2018a). Preface. In M. G. Moore & W. C. Diehl, Handbook of distance education (4th ed., pp. xi–xv). Routledge.
Moore, M. G. (2018b). The theory of transactional distance. In M. G. Moore & W. C. Diehl (Eds.), Handbook of distance education (4th ed., pp. 31–46). Routledge.
National Center for Education Statistics. (2023). Digest of education statistics (Number and percentage of students enrolled in degree-granting postsecondary institutions, by distance education participation, location of student, level of enrollment, and control and level of institution: Fall 2021 and fall 2022). U.S. Department of Education. https://nces.ed.gov/programs/digest/d23/tables/dt23_311.15.asp
Özüdoğru, M. (2021). Pre-service teachers perceptions related to the distance teacher education learning environment and community of inquiry. Journal of Pedagogical Research, 5(4), 43–61. https://doi.org/10.33902/JPR.2021472945
Papi, C., Sauvé, L., Desjardins, G., & Gérin-Lajoie, S. (2022). De la multiplicité des facteurs à prendre en compte pour mieux comprendre l’abandon en formation à distance [The multiplicity of factors to be taken into account for a better understanding of distance learning dropouts]. Distances et médiations des savoirs, 37, 1–19. https://doi.org/10.4000/dms.6904
Paris, J. (2024). Les présences du modèle Community of Inquiry en tant que facteurs explicatifs de la persévérance dans les cours en ligne en enseignement supérieur/The presences of the Community of Inquiry model as explanatory factors for persistence in online courses in higher education. [Doctoral dissertation, Université de Sherbrooke]. Savoirs UdeS. https://savoirs.usherbrooke.ca/handle/11143/22123
Paris, J., Lakhal, S., & Mukamurera, J. (2025). Relationships between presence, satisfaction, and persistence in higher education online courses: Assessing the moderating effects of gender, age, and online experience using PLS-MGA. Educational Technology & Society, 28(1), 178–193.
Park, J.-H. (2007). Factors related to learner dropout in online learning. In F. M. Nafukho, T. H. Chermack, & C. M. Graham (Eds.), Proceedings of the 2007 Academy of Human Resource Development Annual Conference (p. 25(1)-25(8)). Academy of Human Resource Development. https://pdfs.semanticscholar.org/8c7f/8377a3f4825607d65893ab1fc916ac69aa90.pdf
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.
Rawal, A. P., & Razak, R. A. (2022). Student retention models in online higher education- A narrative review of models from past two decades. Journal of Positive School Psychology, 6(4), 6734–6743.
Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4 (Version 4.1.0.9) [Computer software]. SmartPLS GmbH. http://www.smartpls.com
Roland, N., Clercq, M. D., Dupont, S., Parmentier, P., & Frenay, M. (2015). Vers une meilleure compréhension de la persévérance et de la réussite académique: Analyse critique de ces concepts adaptée au contexte belge francophone [Towards a better understanding of perseverance and academic success: Critical analysis of these concepts adapted to the French-speaking Belgian context]. Revue internationale de pédagogie de l’enseignement supérieur, 31(3), 1–19.
Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. https://doi.org/10.1016/S1096-7516(02)00158-6
Saadatmand, M., Uhlin, L., Hedberg, M., Åbjörnsson, L., & Kvarnström, M. (2017). Examining learners’ interaction in an open online course through the Community of Inquiry framework. European Journal of Open, Distance and E-Learning, 20(1), 61–79.
Salloum, S. R. (2011). Student perceptions of computer-mediated communication tools in online learning: Helpfulness and effects on teaching, social and cognitive presence (Publication No. 3491416) [Doctoral dissertation, University of North Carolina]. ProQuest Dissertations & Theses Global.
Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202. https://doi.org/10.1057/s41270-019-00058-3
Seaman, J., Allen, I. E., & Seaman, J. (2018). Grade increase: tracking distance education in the United States. Babson Survey Research Group.
Shah, M., & Cheng, M. (2019). Exploring factors impacting student engagement in open access courses. Open Learning: The Journal of Open, Distance and e-Learning, 34(2), 187–202. https://doi.org/10.1080/02680513.2018.1508337
Shaikh, U. U., & Asif, Z. (2022). Persistence and dropout in higher online education: Review and categorization of factors. Frontiers in Psychology, 13, 902070. https://doi.org/10.3389/fpsyg.2022.902070
Sultan, S., & Hagger, M. (2013). Psychological and socio-demographic correlates of academic performance distinguishing completers and non-completers in distance education. Journal on Educational Psychology, 7(2), 23–29.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press.
Turk, M., Heddy, B. C., & Danielson, R. W. (2022). Teaching and social presences supporting basic needs satisfaction in online learning environments: How can presences and basic needs happily meet online? Computers & Education, 180. https://doi.org/10.1016/j.compedu.2022.104432
Wang, C.-H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779
Wei, H.-C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48–69. https://doi.org/10.1080/01587919.2020.1724768
Wladis, C., Conway, K., & Hachey, A. C. (2017). Using course-level factors as predictors of online course outcomes: A multi-level analysis at a US urban community college. Studies in Higher Education, 42(1), 184–200. https://doi.org/10.1080/03075079.2015.1045478
Xavier, M., & Meneses, J. (2020). Dropout in online higher education. A scoping review from 2014 to 2018. eLearn Center, Universitat Oberta de Catalunya. https://doi.org/10.7238/uoc.dropout.factors.2020
Xu, D., & Jaggars, S. S. (2011). The effectiveness of distance education across Virginia’s community colleges: Evidence from introductory college-level math and english courses. Educational Evaluation and Policy Analysis, 33(3), 360–377. JSTOR.
York, J. A. (2014). Student attrition in higher education: Development of an instrument to assess attrition factors in distance learning only educational environments (Publication No. 3580438) [Doctoral dissertation, Southern Connecticut State University]. ProQuest Dissertations & Theses Global.
Zhu, C. (2017). University student satisfaction and perceived effectiveness of a blended learning course. International Journal of Learning Technology, 12(1), 66. https://doi.org/10.1504/IJLT.2017.083996
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jonathan Paris, Sawsen Lakhal
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
