E-learning Practices in North Cyprus Universities: Benefits, Drawbacks and Recommendations for Effective Implementation
Bibliographic record
Abstract
The nature of higher education is changing in the world today. Rising tuition fees, reduced budgets, and an increasing need for distance education (New Media Consortium, 2007) are pushing educational institutions to reinvestigate how education is delivered. In line with this shifting context, e-learning is being practiced more and more frequently in higher education, providing modern and stimulating occasions for not only educational institutions but also students (Wagner, Hassanein and Head, 2008). Despite the relative benefits of e-learning in higher education, there are some challenges for disorganized, technology concentrated institutions, when attemping to put distance learning courses into practice. This article, therefore, aims at defining the concept of e-learning, providing an overview of e-learning in relation to higher education, expounding types of e-learning, listing benefits and drawbacks of e-learning, summarizing e-learning practices in North Cyprus universities and making recommendations for successful implementation of e-learning in North Cyprus higher education context.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".