An Examination of Differences between the Mean Indicator Ratings by Different Stakeholders in Distance Education Programme
Bibliographic record
Abstract
The continued rapid growth of distance education programmes in higher education has brought concerns regarding how stakeholers perceive quality in distance education. The study examined the differences between the mean indicator ratings by different stakeholders in a distance learning programme. The study adopted a case study research design to collect data from 320 students, 56 facilitators and 24 administrative staff selected randomly from the Institute of Distance Learning, Kwame Nkrumah University of Science and Technology in Ghana. The data collected through questionnaires were analysed using the statistical package for the social sciences (SPSS) software, version 20. Mean indicator rating analysis revealed that students’ highest perception of quality was on support services and the lowest was academic integrity and institutional prestige. Whilst both facilitators and administrators rated support services as the highest, infrastructure scored the lowest. The results of the study therefore, revealed common benchmarks and quality indicator (support services) that all parties deem important in designing, implementing, and evaluating distance education programmes. Respondents noted the lack of appropriate tools and media; unavailability of reliable technology and technological plan; ineffective communication and co-ordination; and, time constraints as some of the quality challenges for distance education at the Institute. The study recommends monitoring and evaluation of service delivery for distance learning programmes to ensure fitness for purpose, value for money and customer satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".