CHALLENGES AND OPPORTUNITIES POSTGRADUATE DOCTORAL STUDENTS FACED IN OPEN DISTANCE E-LEARNING

Rehabeam K. Shapaka

Abstract


Using qualitative multiple case study research design, this article expounds into the challenges and opportunities the postgraduate doctoral students faced in open distance e-learning. Interview schedule and field notes were used to collect data in which a criterion-based purposive sampling technique was used to select 40 postgraduate doctoral students, finished doctorate studies at open distance e-learning in Khomas, Omusati, and Oshana Regions in Namibia. Typological analysis, thematic analysis, content analysis using Atlas.ti and MAXQDA were used to analyse data. The study found that critical challenges are faced by postgraduate doctoral students inhibiting their doctoral studies completion, however a variety of learning opportunities exists that enhance their ability to facilitate the doctoral degrees studies completion. The study found that variety of mode of supervision exists to enhance postgraduate doctoral students’ ability to increase their doctoral degrees studies completion. The study found that a number of factors determine the choice of the postgraduate doctoral studies aiming to promote, advance postgraduate doctoral degrees studies completion. The study found that a variety of factors determines the choice of open distance e-learning aim to advance postgraduate doctoral students’ ability to enhance postgraduate doctoral degrees studies completion. Challenges and opportunities play a very critical role in the research supervision of postgraduate doctoral degrees, which affect completion of postgraduate doctoral studies. Postgraduate doctoral students should use open distance e-learning to facilitate teaching, learning, and assessment of postgraduate doctoral degrees to promote and enhance postgraduate doctoral degrees towards achieving the completion as part of terminal-dissemination process.


Keywords


postgraduate research supervision, postgraduate studies, student support, supervision models

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References


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DOI: http://dx.doi.org/10.46827/ejoe.v11i1.6717

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