Weaknesses in Emergency Remote Teaching in Higher Education Within the Context of the ODL Learning Component in Turkey
Bibliographic record
Abstract
In critical situations caused by crises such as a pandemic, emergency remote teaching (ERT) practices might not be effective because they depend mostly on on-the-spot decision-making. On the other hand, open and distance learning (ODL) has its own dynamics and is a well-planned system. In order to put quality ODL plans into practice in crisis situations, contingency plans, created before any crises, are required. Past crises ought to be examined in order to cope with future crises effectively. This study aims to identify weaknesses in ERT practices in higher education within the context of the learning component of ODL system by focusing on COVID-19 and using it as an example of a past crisis. Exploratory case study was the method used. The study group consisted of 14 faculty and 14 learners from 14 higher education institutions. Qualitative data were collected via semi-structured interviews and documents. The data were analyzed using descriptive and content analysis. Research findings revealed that ERT has many weaknesses in several themes within the context of the learning component of the ODL system; these include teaching method, course structuring, and e-learning materials, among others. In light of the findings, it can be concluded that many factors influence challenges in ERT. Accordingly, to be able to move from ERT to ODL in the next crisis, these weaknesses need to transform into solutions in advance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".