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Record W4220930924 · doi:10.5539/hes.v12n2p54

The Challenges Distance Education Students Experience during Their Education Degree Program in the Faculty of Education at the University of Namibia

2022· article· en· W4220930924 on OpenAlexvenueno aff
Collins Kazondovi, Albert M. Isaacs, Sitali Brian Lwendo

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationInteractivityMedical educationThe InternetHigher educationPsychologyInformation and Communications TechnologyDescriptive statisticsPopulationMathematics educationComputer scienceMedicineMultimediaMathematicsPolitical scienceWorld Wide WebStatistics

Abstract

fetched live from OpenAlex

The main purpose of this study was to identify the challenges distance education students experience during their education degree studies. The specific objectives studied included the following: to investigate the technical and technology limitations, limited interactivity between lecturers and students and among distance education students, and lack of support from administrative staff. The research design employed in this study was the survey research design. The distance education students were asked to complete an online survey to determine their experiences doing their studies on a distance mode. The target population for this study included all distance education students (1675) who are enrolled at the Centre for Open, Distance and eLearning (CODEL) at the University of Namibia in 2020. The questionnaire instrument was administered via google form, 354 responses were received in spreadsheet formats, and chats generated from the responses, the Statistical Package of the Social Sciences (SPSS) file, the descriptive and reliability statistics for all the variables. Based on the findings of this study, this study concludes that the University of Namibia needs to improve slow internet speeds, limited internet access, lecturer student interaction, collaboration between distance education students, lack of computers, among others. Some of the recommendations made by this study include: increased funding in ICT infrastructure for distance education students, better training for lecturers on how to deliver pedagogy and the CODeL administrative staff to better support distance education students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.418
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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