Institutional barriers to learning in the South African open distance learning context

Abstract

 

The discourses surrounding student success and risk have shifted emphasis from the concept of risk as residing with student attributes, aptitudes and socio-economic history to the concept of contextualized risk, which is a result of the interaction between student, institution and the broader higher education context. This shift in discourse has led to a change in understanding the role that institutions play in “creating” at-risk students through institutional culture, procedures, policies and assumptions about the nature of teaching and learning. Institutional barriers to learning have received relatively little attention in the scholarship of teaching and learning when compared to the wider body of research on student-bound risk factors. Institutional barriers to learning refer to those institutional characteristics that, when combined with the attributes of the student body, create inadvertent barriers to successful study completion. This article makes use of frameworks derived from prior research into the barriers to learning and established models of student support to identify what students perceive as institutionally embedded barriers to teaching and learning in an institution in the South African open, distance and e-learning context.

 

Author Biographies

E.O. Mashile, University of South Africa

Tuition Support and Facilitation of Learning

Executive Director:

 

A. Fynn, University of South Africa

Department of Institutional Research and Business Intelligence,

specialist: Institutional Researcher

M. Matoane, University of South Africa

Directorate: Instructional Support and Services,

Director

References

Aluko, R. and J. Hendrikz. 2012. Supporting distance education students : The pilot study of a tutorial model and its impact on students’ performance. Progressio 34(2): 68–83.

Anderson, T. 2003. Getting the mix right again: An updated and theoretical rationale for interaction. International Review of Research in Open and Distance Learning 4(2). https://doi.org/ 10.19173/irrodl.v4i2.149

Anderson, T. and J. Dron. 2011. Education pedagogy. International Review of of Research in Open and Distance Learning 12(3): 80–97.

Andrews, T., B. Davidson, A. Hill, D. Sloane and L. Woodhouse. 2011. Using students’ own mobile technologies to support clinical competency development in speech pathology. In Models for interdisciplinary mobile learning: Delivering information to students, ed. A. Kitchenham, 247‒264. Hershey, PA: Information Science Reference.

Annand, D. G., K. L. Michalczuk and J. K. Thiessen. 2009. Evaluating the relative efficiencies and effectiveness of the contact centre and tutor models of learner support at Athabasca University. Academic and Professional Development Fund Report 2009‒2010. Athabasca University Library & Scholarly Resources.

Astin, A. W. 1975. Preventing students from dropping out. San Francisco, CA: Jossey-Bass.

Astin, A. W. 1984. Student involvement : A developmental theory for higher education student involvement. Journal of College Student Development 25: 297–308.

Astin, A. W. 1997. How “good” is your institution’s retention rate? Research in Higher Education 38(6): 647–658.

Baijnath, N. 2018. Learning for development in the context of South Africa: Considerations for open education resources in improving higher education outcomes. Journal of Learning for Development 5(2): 87–100.

Bean, J. P. 1980. Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education 12(2): 155–187. https://doi.org/10.1007/BF00976194

Beaudoin, B. and P. Kumar. 2012. Using data to identify at-risk students and develop retention strategies. Washington, DC.

Berge, Z. L., L. Y. Muilenburg and J. Haneghan. 2002. Barriers to distance education and training: Survey results. The Quarterly Review of Distance Education 3(4): 409–418.

Bol, L. and J. K. Garner. 2011. Challenges in supporting self-regulation in distance education environments. Journal of Computing in Higher Education 23(2–3): 104–123. https://doi.org/ 10.1007/s12528-011-9046-7

Borokhovski, E., P. C. Abrami, R. M. Bernard, R. F. Schmid and R. M. Tamim. 2014. A meta-analysis of blended learning and technology use in higher education: from the general to the applied. Journal of Computing in Higher Education 26(1): 87–122. https://doi.org/10.1007/s12528-013-9077-3

Brindley, J. E., C. Walti and O. Zawacki-Richter. 2008. Learner support in open, distance and online learning environments. In BIS. https://doi.org/10.1080/0158791

Brown, M., H. Hughes, M. Keppell, N. Hard and L. Smith. 2013. In their own words : Student stories of seeking learning support. Open Praxis 5(4): 345–354. https://doi.org/10.5944/ openpraxis.5.4.87

Burns, S., J. Cunningham and K. Foran-Mulcahy. 2014. Asynchronous online instruction: Creative collaboration for virtual support. CEA Critic 76(1): 114–131.

Cho, M. H. 2012. Online student orientation in higher education: A developmental study. Educational Technology Research and Development 60(6): 1051–1069. https://doi.org/10.1007/s11423-012-9271-4

Cho, S. K. and Z. L. Berge. 2002. Overcoming barriers to distance training and education. Education at a Distance [USDLA Journal] 16(1): 1–12.

Costelo, A. B. and J. Osborne. 2005. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical. Practical Assessment Research & Evaluation 10(7): 1–9. https://doi.org/10.1234/2013/999990

De Hart, K. and J. Venter. 2013. Comparison of urban and rural dropout rates of distance students University of South Africa Research method. Perspectives in Education 31(1): 66–76.

Distefano, C., M. Zhu and D. Mîndrilă. 2009. Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation 14(20): 1–11. https://doi.org/10.1.1.460.8553

Dzubian, C. D. and E. C. Shirkey. 1974. When is a correlation matrix appropriate for factor analysis? Psychological Bulletin 81(6): 358–361.

Elkaseh, A., K. W. Wong and C. C. Fung. 2015. A review of the critical success factors of implementing e-learning in higher education 22(2).

Garrison, D. R., T. Anderson and W. Archer. 2000. Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education 2–3: 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6

Goold, A., J. Coldwell and A. Craig. 2010. An examination of the role of the e-tutor. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.1060

Granić, A. and M. Ćukušić. 2007. Universal design within the context of e-learning. Access: 617–626.

Guri-rosenblit, S. 2012. Open/distance teaching universities worldwide: Current challenges and future prospects. Magazyn Edukacji Eektronicznej 2(4): 4–12. http://wyrwidab.come.uw.edu.pl/ ojs/index.php/eduakcja/article/viewFile/80/50

Irani, T., C. Scherler, M. Harrington and R. Telg. 2001. Overcoming barriers to learning in distance education: The effects of personality type and course perceptions on student performance. National Agricultural Education Research Conference, 13 December 2004.

Ives, C. and M. M. Pringle. 2013. Moving to open educational resources at Athabasca University: A case study. International Review of Research in Open and Distance Learning. https://doi.org/10.19173/irrodl.v14i2.1534

Jolliffe, I. and D. J. Bartholomew. 2006. Latent variable models and factor analysis. In Applied Statistics Vol. 38. https://doi.org/10.2307/2347739

Lee, S. J., S. Srinivasan, T. Trail, D. Lewis and S. Lopez. 2011. Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. Internet and Higher Education 14(3): 158–163. https://doi.org/10.1016/j.iheduc.2011.04.001

Liebenberg, H., Y. Chetty and P. Prinsloo. 2012. Student access to and skills in using technology in an open and distance learning context. The International Review of Research in Open and Distance Learning 13(4): 250–268. https://doi.org/10.19173/irrodl.v13i4.1303

Machika, P. 2013. The alignment of institutional and student commitment to student needs. Progressio 35(1): 91–103.

Mashile, E. O. and M. C. Matoane. 2016. Leadership in ODL institutions: An Ubuntu perspective. In Open Distance Learning through the philosophy of Ubuntu, 108–123. New York: Nova Science Publishers, Inc.

Matoane, M. C. and E. O. Mashile. 2013. Key considerations for successful e-tutoring : Lessons learnt from an institution of higher learning in South Africa. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2013, ed. T. Bastiaens and G. Marks, 863–871.

Means, B., Y. Toyama, R. Murphy and M. Baki. 2013. Effectiveness of online and blended learning. Teachers College Record 115(030303): 1–47. https://doi.org/10.3991/ijac.v3i2.1322

Mills, R. 2008. Looking back, looking forward: What have we learned? In Learner support in open, distance and online learning environments, 29–38.

Muilenburg, L. Y. and Z. L. Berge. 2005. Student barriers to online learning: A factor analytic study. Distance Education 26(1): 29–48. https://doi.org/10.1080/01587910500081269

Naveed, Q. N., A. Muhammed, S. Sanober, M. R. N. Qureshi and A. Shah. 2017. Barriers effecting successful implementation of E-learning in Saudi Arabian Universities. International Journal of Emerging Technologies in Learning 12(6): 94–107. https://doi.org/10.3991/ijet.v12i06.7003

Odum, M. 2011. Factor scores, structure and communality coefficients: A primer. Annual Meeting of the Southwest Educational Research Association. http://ir.obihiro.ac.jp/dspace/handle/ 10322/3933

Osborne, J. 2014. Best practices in exploratory factor analysis. In Best practices in quantitative methods, ed. J. Osborne, A. B. Costello and J. T. Kellow, 86–99. https://doi.org/10.4135/9781412995627.d8

Sayed, M. and F. Baker. 2014a. Blended learning barriers: An investigation, exposition and solutions. Journal of Education and Practice 5(6): 81–85. http://iiste.org/Journals/ index.php/JEP/article/view/11212

Sayed, M. and F. Baker. 2014b. Blended learning barriers: An investigation, exposition and solutions. Journal of Education and Practice 5(6): 81–85.

Selwyn, N. 2011. Digitally distanced learning: A study of international distance learners’ (non)use of technology. Distance Education 32(1): 85–99. https://doi.org/10.1080/01587919.2011.565500

Stewart, S., D. H. Lim and J. Kim. 2015. Factors influencing college persistence for first-time students. Journal of Developmental Education 38(3): 12–20.

Subotzky, G. and P. Prinsloo. 2009. Towards a conceptual model of retention and success in distance education: The case of the University of South Africa. NADEOSA Annual Conference. Pretoria, South Africa, 17‒18 August.

Subotzky, G. and P. Prinsloo. 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. https://doi.org/10.1080/01587919.2011.584846

Swail, W. S. 1995. The development of a conceptual framework to increase student retention in science, engineering and mathematics programms at minority institutions of higher education. George Washington University.

Swail, W. S. 2007. The art of student retention. (Vol. 1). Austin, Texas: Educational Policy Institute.

Swail, W. S., K. E. Redd and L. W. Perna. 2003. Retaining minority students in a framework for success. In ASHE-ERIC Higher Education Report (Vol. 30). https://doi.org/10.1002/aehe.3002

Sweet, R. 1986. Student dropout in distance education: An application of Tinto’s model. Distance Education 7(2): 201–213. https://doi.org/10.1080/0158791860070204

Tait, A. 2018. Education for Development: From Distance to Open Education. Journal of Learning for Development 5(2): 101–115. http://www.jl4d.org/index.php/ejl4d/article/view/294/313

Tait, A. W. 2014. From place to virtual space: Reconfiguring student support for distance and e-learning in the digital age. Open Praxis 6(1): 5–16. https://doi.org/10.5944/openpraxis.6.1.102

Tedre, M., M. Apiola and J. C. Cronje. 2011. Towards a systemic view of educational technology in developing regions. IEEE AFRICON Conference. https://doi.org/10.1109/AFRCON. 2011.6072012

Tinto, V. 1975. Dropout from higher education : A theoretical synthesis of recent research. Review of Educational Research 45(1): 89–125.

Tinto, V. 1982. Limits of theory and practice in student attrition. The Journal of Higher Education 53(6): 687–700. https://doi.org/10.2307/1981525

Tinto, V. 2010. Higher education: Handbook of theory and research. (Vol. 25). https://doi.org/10.1007/ 978-90-481-8598-6

Xiao, J. 2017. Learner-content interaction in distance education: The weakest link in interaction research. Distance Education 38(1): 123–135. https://doi.org/10.1080/01587919.2017.1298982

Yasmin, D. 2013. Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Distance Education 34(2): 218–231. https://doi.org/10.1080/ 01587919.2013.793642

Zuccaro, C. 2010. Statistical Alchemy – the misuse of factor scores in linear regression. International Journal of Market Research 52(4): 511–531. https://doi.org/10.2501/s1470785309201429

Published
2020-05-30
How to Cite
Mashile, E.O., A. Fynn, and M. Matoane. 2020. “Institutional Barriers to Learning in the South African Open Distance Learning Context”. South African Journal of Higher Education 34 (2), 129-45. https://doi.org/10.20853/34-2-3662.
Section
General Articles