Through the “Camera Lens”: How Do Students Grasp the Future of Learning via Online Platforms?
Bibliographic record
Abstract
During the COVID-19 pandemic, the academic world came into contact with a virtual learning environment that allows students and educators to leave the boundaries of space and time and maintain academic interactions at unrestricted times and sites. After three semesters of remote learning, there is a feeling that the return to universities and to closed spaces will deter students and that they will prefer remote learning. Studying from home spares valuable time, time otherwise wasted in traffic jams, as well as petrol and other expenses. Remote learning gives a feeling of freedom, comfort, flexible time, and a better sense of control over one’s studies than in the classroom. The current study, conducted about one year after the outbreak of the pandemic, examined students’ background variables: gender, years of schooling, marital status, financial and employment status – with the goal of exploring the association between these variables and students’ preference for either face-to-face or digital teaching. It is evident from the research findings that after this experience of e-Learning neither of these two methods shows a clear advantage over the other. Was the e-Learning experience during the crisis a one-time, incidental event? Or perhaps, in light of the crisis, academic institutions should prepare for a different type of learning, one that combines face-to-face with digital learning? The study illuminates an issue that is confronting educational institutions in general and academic institutions in particular, i.e., preparations for teaching and learning in the post-crisis world, after the considerable upheaval to which we were subjected.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".