Les déterminants technologiques de la persévérance des étudiants dans les cours à distance de niveau collégial : Les modalités de cours jouent-elles un rôle?
DOI:
https://doi.org/10.21432/cjlt27813Keywords:
modèle UTAUT, persévérance, cours à distance, enseignement supérieur, niveau collégial, modalités de coursAbstract
L’objectif de cette étude est d’identifier et d’analyser les déterminants technologiques de la persévérance dans les cours à distance de niveau collégial (n=61), issus du modèle Unified theory of acceptance and use of technology (UTAUT). Les résultats des analyses par équations structurelles (Partial Least Square) indiquent que parmi ces derniers déterminants, seulement les conditions facilitantes ont un impact significatif et positif sur l’intention comportementale d’utiliser les technologies des cours à distance (R2=54%) définie comme l’intention de l’étudiant de réaliser ce comportement, qui a son tour a un effet significatif et positif sur la persévérance, définie par l’intention de finir le cours à distance auquel l’étudiant est inscrit (R2=14,2%) et par l’intention de s’inscrire dans le futur dans d’autres cours à distance (R2=65%). Les analyses des ANOVA font ressortir des différences significatives entre les groupes d’étudiants assignés à des modalités différentes de cours à distance sur tous les facteurs technologiques laissant présager que des analyses différenciées, selon la modalité de cours, devraient être envisagées dans le futur.
This study aims to identify and analyze the technological determinants of persistence in college distance education courses (N=61), derived from the Unified theory of acceptance and use of technology (UTAUT) model. The results of the structural equation analyses (Partial Least Square) revealed that among these determinants, only facilitating conditions have a significant and positive impact on the behavioural intention to use distance learning technologies (R2 = 54%) defined as the student’s intention to display this behaviour. Moreover, behavioral intention to use distance learning technologies has a significant and positive effect on persistence, defined as the intention to finish the distance education course in which the student is enrolled (R2 = 14.2 %) and the intention to enroll in other distance education courses in the future (R2 = 65%). The ANOVA analyses revealed significant differences between the groups of students assigned to different courses delivery modes on all the technological factors, suggesting that differentiated analyses, depending on the course delivery mode, should be performed in the future.
References
Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50, 179-211. doi:10.1016/0749-5978(91)90020-T
Allen, I. E., Seaman, J., Poulin, R. et Straut, T. T. (2016). Online report card: Tracking online education in the United States. Repéré à http://onlinelearningsurvey.com/reports/onlinereportcard.pdf
Ariadurai, S. A. et Manohanthan, R. (2008). Instructional strategies in teaching engineering at a distance: Faculty perspective. International Review of Research in Open & Distance Learning, 9(2), 1-11. doi:10.19173/irrodl.v9i2.461
Bergeron, M. H. (2014). Innover pour favoriser l’accessibilité des études supérieures en région. Pédagogie Collégiale, 27(4), 15-20. Repéré à https://dspacecdc-test.inlibro.net/xmlui/bitstream/handle/11515/34330/Bergeron-27-4-2014.pdf?sequence=1
Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., (…) et Euan, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379-439. doi:10.3102/00346543074003379
Bocchi, J., Eastman, J. K. et Swift, C. O. (2004). Retaining the online learner: Profile of students in an online MBA program and implications for teaching them. Journal of Education for Business, 79(4), 245-253. doi:10.3200/JOEB.79.4.245-253
Bower, M., Dalgarno, B., Kennedy, G. E., Lee, M. J., & Kenney, J. (2015). Design and implementation factors in blended synchronous learning environments: Outcomes from a cross-case analysis. Computers & Education, 86, 1-17. doi: 10.1016/j.compedu.2015.03.006
Chenard, P., Doray, P., Dussault, E. L. et Ringuette, M. (2013). L'accessibilité aux études postsecondaires : un projet inachevé. Québec, QC : Presses de l'Université de Québec.
Cheng, G., et Chau, J. (2016). Exploring the relationships between learning styles, online participation, learning achievement and course satisfaction: An empirical study of a blended learning course. British Journal of Educational Technology, 47(2), 257-278. doi:10.1111/bjet.12243
Chin, W. W. (2001). PLS-Graph User’s Guide, Version 3.0. C.T. Bauer College of Business, University of Houston, Houston, TX. Repéré à http://carma.wayne.edu//Documents/OCT1405/PLSGRAPH3.0Manual.hubona.pdf
Choi, H. J. et Park, J. H. (2018). Testing a path-analytic model of adult dropout in online degree programs. Computers & Education, 116, 130-138. doi:10.1016/j.compedu.2017.09.005
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. doi: 10.2307/249008
Dinov, I. D., Sanchez, J. et Christou, N. (2008). Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses. Computers & Education, 50(1), 284-300. doi:10.1016/j.compedu.2006.06.003
Doherty, W. (2006). An analysis of multiple factors affecting retention in web-based community college courses. Internet & Higher Education, 9(4), 245-255. doi:10.1016/j.iheduc.2006.08.004
Fey, S., Emery, M. et Flora, C. (2008). Student issues in distance education programs: Do inter-institutional programs offer students more confusion or more opportunities? Journal of Asynchronous Learning Networks, 12(3-4), 71-83.
Hachey, A. C., Wladis, C. W. et Conway, K. M. (2014). Do prior online course outcomes provide more information than G.P.A. alone in predicting subsequent online course grades and retention? An observational study at an urban community college. Computers & Education, 72, 59-67. doi:10.1016/j.compedu.2013.10.012
Ibrahim, M., Rwegasira, K. S. P. et Taher, A. (2007). Institutional factors affecting students' intentions to withdraw from distance learning programs in the kingdom of Saudi Arabia the case of the Arab Open University (AOU). Online Journal of Distance Learning Administration, 10(1).
Johnson, J. et Galy, E. (2013). The use of E-learning tools for improving Hispanic students' academic performance. Journal of Online Learning & Teaching, 9(3), 328-340. Repéré à http://jolt.merlot.org/vol9no3/johnson_0913.pdf
Joo, Y. J., Joung, S. et Sim, W. J. (2011). Structural relationships among internal locus of control, institutional support, flow, and learner persistence in cyber universities. Computers in Human Behavior, 27(2), 714-722. doi:10.1016/j.chb.2010.09.007
Kember, D. (1989). A longitudinal-process model of drop-out from distance education. Journal of Higher Education , 60(3) , 278-301. doi:10.1080/00221546.1989.11775036
Khechine, H., Lakhal, S., Bytha, A. et Pascot, D. (2014). UTAUT model for blended learning. The role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning Objects, 10, 33-52. Repéré à http://www.ijello.org/Volume10/IJELLOv10p033-052Khechine0876.pdf
Khechine, H. et Lakhal, S. (2018). Technology as a double-edged sword: From behavior prediction with UTAUT to sStudents’ outcomes considering personal characteristics. Journal of Information Technology Education Research, 17, 63-102. Repéré à http://jite.informingscience.org/documents/Vol17/JITEv17ResearchP063-102Khechine4282.pdf
Kintu, M. J., Zhu, C. et Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(1), 7. doi:10.1186/s41239-017-0043-4
Kluwin, T. N. et Noretsky, M. (2005). A mixed-methods study of teachers of the deaf learning to integrate computers into their teaching. American Annals of the Deaf, 150(4), 350-357.
Kranzow, J. (2013). Faculty leadership in online education: Structuring courses to impact student satisfaction and persistence. Journal of Online Learning and Teaching, 9(1), 131-139. Repéré à http://jolt.merlot.org/vol9no1/kranzow_0313.pdf
Lakhal, S., Khechine, H. et 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. doi:10.1007/s12528-013-9069-3
Lakhal, S. et Bazinet, N. (2015, Juin). Technological factors explaining student dropout from online courses in higher education: A review. Dans EdMedia+ Innovate Learning (pp. 1806-1811). Association for the Advancement of Computing in Education (AACE).
Lakhal, S. et Khechine, H. (2016). Student intention to use desktop web-conferencing according to course delivery modes in higher education. International Journal of Management Education, 14(2), 146-160. doi:10.1016/j.ijme.2016.04.001
Lakhal, S. et Khechine, H. (2017). Relating personality (Big Five) to the core constructs of the Unified Theory of Acceptance and Use of Technology. Journal of Computers in Education, 4(3), 251-282. doi:10.1007/s40692-017-0086-5
Lee, K. (2017). Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education, 33, 15-23. doi:10.1016/j.iheduc.2017.01.001
Lee, Y. et 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. doi:10.1007/s11423-010-9177-y
McGee, P. et Reis, A. (2012). Blended Course Design: A Synthesis of Best Practices. Journal of Asynchronous Learning Networks, 16(4), 7-22.
Means, B., Toyama, Y., Murphy, R. et Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47.
Müller, T. (2008). Persistence of women in online degree-completion programs. International Review of Research in Open & Distance Learning, 9(2), 1-18. Repéré à http://www.irrodl.org/index.php/irrodl/article/view/455
Myers, M. P. et Schiltz, P. M. (2012). Use of Elluminate in online teaching of statistics in the health sciences. Journal of Research in Innovative Teaching, 5(1), 53-62. Repéré à http://www.my-ecoach.com/online/resources/274/journal-of-research-in-innovative-teaching-volume-5.pdf#page=62
Ojokheta, K. (2010). A path-analytic study of some correlates predicting persistence and student's success in distance education in Nigeria. Turkish Online Journal of Distance Education (TOJDE), 11(1), 181-192. Repéré à http://tojde.anadolu.edu.tr/yonetim/icerik/makaleler/562-published.pdf
Ouédraogo, B. (2011). Les déterminants de l’intégration pédagogique des Technologies de l’Information et de la Communication (TIC) par les enseignants à l’Université de Ouagadougou (Burkina Faso) (thèse de doctorat non publiée). Université de Montréal, Montréal, Canada. Repéré à https://papyrus.bib.umontreal.ca/xmlui/bitstream/handle/1866/5114/OUEDRAOGO_Boukary_2011_these.pdf?sequence=2&isAllowed=y
Park, J. (2007). Factors related to learner dropout in online learning. Dans Nafukho, F. M., Chermack, T. H. et Graham, C. M. (dir.). Proceedings of the 2007 Academy of Human Resource Development Annual Conference, 25-1–25-8. Indianapolis, IN: AHRD.
Peraya, D. (2007). Pédagogie universitaire et TIC: regards sur l’hybridation et ses impacts. 24e Congrès de l'Association internationale de pédagogie universitaire, 16-18 mai 2007 (p. 1-11). Repéré à https://tecfa.unige.ch/tecfa/teaching/bachelor_74111/Cours%2009-10/Les_effets_mediatisation_suite/conf_aipuV4_dper-1.pdf
Peraya, D. et Viens, J. (2005). Relier les projets «TIC et innovation pédagogique»: Y a-t-il un pilote à bord, après Dieu bien sûr…? Dans T. Karsenti et F. Larose (dir.), Intégration pédagogique des TIC dans le travail enseignant (p. 15-60). Sainte-Foy, QC : Presses de l'Université du Québec.
Price, F. et Kadi-Hanifi, K. (2011). E-motivation! The role of popular technology in student motivation and retention. Research in Post-Compulsory Education, 16(2), 173-187. doi:10.1080/13596748.2011.575278
Ringle, C. M., Wende, S. et Will, A. (2010). Finite mixture partial least squares analysis: Methodology and numerical examples. Dans Handbook of Partial Least Squares (pp. 195-218). Springer Berlin Heidelberg.
Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1-16. doi:10.1016/S1096-7516(02)00158-6
Roy, V. (2011). Représentations sociales d’enseignantes et d’enseignants du collégial au regard de la médiation pédagogique et du processus de médiatisation lors du recours aux TIC en formation mixte et distante (thèse de doctorat non publiée), Université de Sherbrooke, Sherbrooke, Canada.
Schubert-Irastorza, C. et Fabry, D. (2011). Improving student satisfaction with online faculty performance. Journal of Research in Innovative Teaching, 4(1), 168-179.
Sutton, S. C. et Nora, A. (2008). An exploration of college persistence for students enrolled in web-enhanced courses: A multivariate analytic approach. Journal of College Student Retention: Research, Theory & Practice, 10(1), 21-37. doi:10.2190/CS.10.1.c
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125. doi:10.3102/00346543045001089
Venkatesh, V., Morris, M. G., Davis, G. B. et Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540
Wang, S.-K. et Hsu, H.-Y. (2008). Use of the webinar tool to support training: the effects of webinar-learning implementation from trainers’ perspective. Journal of Online Interactive Learning, 7(3), 175-194. Repéré à https://pdfs.semanticscholar.org/66b9/67863348deb093a70bc3abc83d96f6cd479e.pdf
Xenos, M., Pierrakeas, C. et Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education, 39(4), 361-377. doi:10.1016/S0360-1315(02)00072-6
Yoo, S. J. et Huang, W. D. (2013). Engaging online adult learners in higher education: motivational factors impacted by gender, age, and prior experiences. The Journal of Continuing Higher Education, 61(3), 151-164. doi:10.1080/07377363.2013.836823
Downloads
Published
Issue
Section
License
Copyright (c) 2019 Sawsen Lakhal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright Notice
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an International Creative Commons Attribution-NonCommercial License (CC-BY-NC 4.0) that allows others to share the work for non-commercial purposes, with an acknowledgement of the work's authorship and initial publication in this journal.
