Administrators' Perceptions of Motives to Offer Online Academic Degree Programs in Universities
Bibliographic record
Abstract
Although the number of online academic degree programs offered by universities in Turkey has become increasingly significant in recent years, the current lack of understanding of administrators’ motives that contribute to initiating these programs suggests there is much to be learned in this field. This study aimed to investigate administrators’ perceptions of motives for offering online academic degree programs in universities in Turkey in terms of online associate degree programs, online master's degree programs, online bachelor's degree completion programs, and online bachelor's degree programs. A qualitative research method was employed for this study. Semi-structured interviews were conducted with 16 administrators from different universities’ distance education centers in Turkey and thematic analysis was applied to the data. The research found that administrators’ motives for offering online academic degree programs mainly involve in answering to the high demand of prospective students. Six major themes were identified with regard to influencing factors for administrators’ motives: demands for programs, mission to support education, readiness of infrastructure, teaching staff as well as applicability of content, overcoming the shortage of classroom space and teachers, obtaining revenue, and gaining prestige.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".