Résumés
Abstract
Online academic courses provide students with flexible learning opportunities by allowing them to make choices regarding diverse aspects of their learning process; hence, such courses support personalized learning. This study aimed to analyze the ways students make use of flexibility in online academic courses based on learning time, place, and access to learning resources, as well as to investigate how this relates to differences in course achievement. The study examined 587 students in four online courses. Educational data mining (EDM) methodology was used to trace students’ behavior in the courses and to compute 34 variables, which describe their use of flexibility. The results show that students developed different patterns of learning time, place, and access to content, which indicates that flexibility was used substantially. Students’ achievements were significantly related to patterns of learning time and access to learning resources. Understanding the different patterns of flexibility usage may support the design of personalized learning and increase collaboration among students with similar characteristics.
Keywords:
- higher education,
- online learning,
- LMS,
- flexible learning,
- personalized learning,
- learning behavior,
- course achievement,
- educational data mining,
- online academic courses
Veuillez télécharger l’article en PDF pour le lire.
Télécharger
Parties annexes
Bibliography
- Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253-274). New York, NY: Cambridge University Press.
- Bates, T. (2001). National strategies for e-learning in post-secondary education and training, In Fundamentals of educational planning, 70 (pp. 1-134). Paris: UNESCO.
- Bergamin, P. B., Ziska, S., Werlen, E., & Siegenthaler, E. (2012). The relationship between flexible and self-regulated learning in open and distance universities. The International Review of Research in Open and Distributed Learning, 13(2), 101-123. doi: 10.19173/irrodl.v13i2.1124
- Boer, W. D., & Collis, B. (2005). Becoming more systematic about flexible learning: Beyond time and distance. ALT-J, 13(1), 33-48. doi: 10.1080/0968776042000339781
- Brown, S., & Smith, B. (2013). Research, teaching and learning in higher education. London: Routledge. doi: 10.4324/9781315041568
- Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and expectations. London: Routledge. doi: 10.4324/9780203046098
- Collis, B., & Moonen, J. (2002). Flexible learning in a digital world. Open Learning, 17(3), 217-230. doi: 10.1080/0268051022000048228
- Collis, B., Vingerhoets, J., & Moonen, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia Project. British Journal of Educational Technology, 28(3), 199-217. doi: 10.1111/1467-8535.00026
- Cornelius, S., & Gordon, C. (2008). Universalists, butterflies and changelings: Learners' roles and strategies for using flexible online resources. Proceedings of EdMedia: World Conference on Educational Media and Technology, 2008 (Vol. 1), 4052-4057.
- Cornelius, S., Gordon, C., & Ackland, A. (2011). Towards flexible learning for adult learners in professional contexts: An activity-focused course design. Interactive Learning Environments, 19(4), 381-393. doi: 10.1080/10494820903298258
- Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.
- Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. doi: 10.1007/s11528-014-0822-x
- Gedera, D., Williams, J., & Wright, N. (2015). Identifying factors influencing students' motivation and engagement in online courses. In C. Koh (Ed.). Motivation, leadership and curriculum design (pp. 13-23). Singapore: Springer. doi: 10.1007/978-981-287-230-2_2
- Gillingham, M., & Molinari, C. (2012). Online courses: Student preferences survey. Internet Learning, 1(1), 36-45.
- Glance, D. G., Forsey, M., & Riley, M. (2013). The pedagogical foundations of massive open online courses. First Monday, 18(5). doi: 10.5210/fm.v18i5.4350
- Goodyear, P. (2008). Flexible learning and the architecture of learning places. In M. Spector, D. Merrill, J. vanMerrienboer, & M. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 251-257). Taylor & Francis e-Library.
- Grant, M. M., & Hill, J. R. (2006). Weighing the risks with the rewards: Implementing student-centered pedagogy within high-stakes testing. In R. Lambert & C. McCarthy (Eds.), Understanding teacher stress in an age of accountability (pp. 19-42). Greenwich, CT: Information Age.
- Guest, R. (2005). Will flexible learning raise student achievement? Education Economics, 13(3), 287-297. doi: 10.1080/09645290500073761
- Hannafin, M. J. (1984). Guidelines for using locus of instructional control in the design of computer-assisted instruction. Journal of Instructional Development, 7(3), 6-10. doi: 10.1007/BF02905753
- Harasim, L. (2000). Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education, 3(1), 41-61. doi: 10.1016/S1096-7516(00)00032-4
- Hill, J. R. (2006). Flexible learning environments: Leveraging the affordances of flexible delivery and flexible learning. Innovative Higher Education, 31(3), 187-197. doi: 10.1007/s10755-006-9016-6
- Hung, M. L., Chou, C., & Chen, C. H. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090. doi: 10.1016/j.compedu.2010.05.004
- Jaggars, S. S. (2014). Choosing between online and face-to-face courses: Community college student voices. American Journal of Distance Education, 28(1), 27-38. doi: 10.1080/08923647.2014.867697
- Jochems, W., Van Merriënboer, J. J. G., & Koper, R. (2004). Integrated e-learning: Implications for pedagogy, technology and organization. London: Routledge.
- Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis, 344. NJ: John Wiley & Sons.
- Marton, F., Hounsell, D., & Entwistle, N. J. (Eds.). (1997). The experience of learning: Implications for teaching and studying in higher education. Edinburgh: Scottish Academic Press.
- Narciss, S., Proske, A., & Koerndle, H. (2007). Promoting self-regulated learning in Web-based learning environments. Computers in Human Behavior, 23(3), 1126-1144. doi: 10.1016/j.chb.2006.10.006
- Neville, M. W., Palmer, R., Elder, D., Fulford, M., Morris, S., & Sappington, K. (2015). Evaluating the effects of flexible learning about aseptic compounding on first-year students in a pharmacy skills laboratory. American Journal of Pharmaceutical Education, 79(6), 91. doi: 10.5688/ajpe79691
- Northrup, P. T. (2002). Online learners' preferences for interaction. Quarterly Review of Distance Education, 3(2), 219-26.
- Norusis, M. J. (2012). IBM SPSS statistics 19 statistical procedures companion. Upper Saddle River, NJ: Prentice Hall.
- Reeves, T. C. (1993). Pseudoscience in computer-based instruction: The case of learner control research. Journal of Computer-Based Instruction, 20(2), 39-46.
- Soffer, T., Kahan, T., & Livne, E. (2017). E-assessment of online academic courses via students' activities and perceptions. Studies in Educational Evaluation, 54, 83-93. doi: 10.1016/j.stueduc.2016.10.001
- Wanner, T., & Palmer, E. (2015). Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers & Education, 88, 354-369. doi: 10.1016/j.compedu.2015.07.008
- Willems, J. (2005). Flexible learning: Implications of “when-ever,” “where-ever” and “what-ever.” Distance Education, 26(3), 429-435. doi: 10.1080/01587910500291579
- You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23-30. doi: 10.1016/j.iheduc.2015.11.003