University Instructors’ Perceptions toward Online Teaching at the Onset of the COVID-19 Outbreak in Lebanon: A Descriptive Study
Bibliographic record
Abstract
Purpose: The study aims at understanding to what extent university instructors are ready for the sudden shift from face-to-face teaching to online teaching and how they perceive the usefulness and feasibility of this new modality of teaching. Methodology/Approach/Design: Faculty members from the nine campuses of the largest private university in Lebanon were invited to participate in the completion of a survey, made available in English and Arabic. The survey was completed by 692 respondents. Descriptive analyses were performed by summarizing the count and percentage of responses within each category. Results: Analyses showed that university instructors possess the infrastructure for online teaching. Moreover, they reported positive perceptions about their readiness to teach online and about the feasibility and usefulness of online teaching. However, instructors reported that online teaching was deficient in assessment, teaching large classrooms, and delivering the practical components of the courses they taught. Practical Implications: Findings suggest that instructors require formal training on how to integrate pedagogy with technology. Originality/Value: Since online instruction is new in Lebanon, the study findings can help universities and other educational institutions direct their efforts in their endeavor to improve their online experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".