Quarantined-at-Home Teaching Experience: My E-Learning Plan and Implementation
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
Using my own teaching experience in quarantined-at-home settings, I describe and reflect on my e-learning plan and its implementation. I am teaching two groups of undergraduate students consisting of 80 students. I have taught half of the course content during the first half of the semester in a formal university setting. However, after the novel corona breakout, we are engaged in online teaching. In line with university guidelines and available support, I initiated my e-learning plan based on blended learning and led by the core objectives to maintain accessibility and quality. Using asynchronous and synchronous modes I used common and easily available options to enhance two-way teacher-student communication. The feedback that I received after three weeks of implementation of my e-learning plan proved my understanding of the study context as workable and realistic. My conceptual models about the objectives leading the e-learning plan and the implementation model presented in this article can be helpful for the teachers teaching social sciences for the first time in ‘quarantined’ settings.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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