Using learner profiling technique to predict college students’ tendency to choose elearning courses: A two-step cluster analysis
DOI:
https://doi.org/10.55420/2693.9193.v5.n2.211Keywords:
college students, distance education enterprise, elearning, affinity for technology, separation of school life and personal lifeAbstract
Profiling elearning students is becoming a common practice in the field. In this phase of the investigation, we plan to (a) follow up on the recommendation for further research we stated in an earlier study on learner preference in types of elearning courses and (b) explore plausible patterns (profiles) based on two learner characteristics/behaviors (i.e., perceived distance between social life and school life and perceived affinity for technology) and their relationship with choices of learning environments where students learn most. Results suggested that (a) the probability of a student in favor of elearning was 1.29 times more likely when the student was LDAA, as opposed to HDLA, and (b) the probability of a student in favor of elearning was 1.26 times more likely when the student was HDHA, as opposed to HDLA. Implications of the results are discussed.
Metrics
References
Archer, E., Chetty, Y. B., & Prinsloo, P. (2014). Benchmarking the habits and behaviours of successful students: A case study of academic-business collaboration. The International Review of Research in Open and Distance Learning, 15(1), 62-83.
Baxter, J. (2012). Who am I and what keeps me going? Profiling the distance learning student in higher education. The International Review of Research in Open and Distance Learning, 13(4), 107-129.
Baxter, J. A., & Haycock, J. (2014). Roles and student identities in online large course forums: Implications for practice. The International Review of Research in Open and Distance Learning, 15(1), 20-40.
Jelfs, A., & Richardson, J. T. E. (2013). The use of digital technologies across the adult life span in distance education. British Journal of Educational Technology, 44(2), 338-351.
Jones, S. J. (2012). Technology review: The possibilities of learning analytics to improve learner-centered decision making. Community College Enterprise, 18(1), 89-92.
Onyancha, O. B. (2010). Profiling students using an institutional information portal: A descriptive study of the Bachelor of Arts degree students, University of South Africa. South African Journal of Libraries & Information Science, 76(2), 153- 167.
Pan, C., Sivo, S., García, F., Goldsmith, C., & Cornell, R. A. (2014, October). Technology and me--what do students think? Paper presented at the 64th International Council for Educational Media (ICEM 2014) Conference, Eger, Hungary.
Purnell, K., McCarthy, R., & McLeod, M. (2010). Student success at university: Using early profiling and interventions to support learning. Studies in Learning, Evaluation, Innovation & Development, 7(3), 77-86.
?chiopu, D. (2010). Applying two step cluster analysis for identifying bank customers’ profile. BULETINUL, 62(3), 66-75.
Shih, M.-Y., Jheng, J.-W., & Lai, L.-L. (2010). A two-step method for clustering mixed categorical and numeric data. Tamkang Journal of Science and Engineering, 13(1), 11-19.
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177-193.
Yukselturk, E., & Top, E. (2013). Exploring the link among entry characteristics, participation behaviors and course outcomes of online learners: An examination of learner profile using cluster analysis. British Journal of Educational Technology, 44(5), 716-728.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Open Access Policy Statement
HETS Online Journal has adopted an open access policy and provides immediate access to its content free of charge to the reader. The journal does not pass on the cost of publication or submission of manuscripts, known as an Article Processing Charge (APC), to authors.
HOJ is licensed under CC-BY-NC-SA.
