e-Tutor: A Multilingual Open Educational Resource for Faculty Development to Teach Online
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="2">The situation in Ukraine poses severe problems to the higher education system and to students in Eastern Ukraine. Many students and academicians had been compelled to leave their university buildings and move westwards. Hence, they are forced to substitute face-to-face teaching with distance learning, often on a large scale, but within a short span of time and with limited resources. While technical/technological infrastructure often exists, know-how about conducting online teaching and respective faculty development is often found to be lacking. Within the framework of a project funded by the Swiss National Science Foundation (SNSF), a faculty development program developed in Turkey as an Open Educational Resource (e-Tutor) was adopted in three languages (English, Ukrainian, and Russian) to support qualifying university staff in teaching online. e-Tutor comprises of 14 modules, each with various content, covering different aspects of online teaching. In the following note, we briefly present the program along with the context, target group/aims, concept, genesis, initial experiences, and further development.</p>
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.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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 it