The Impact of Instructors’ Perceptions of E-Learning on the Quality of Online Teaching: A Case Study of the French Language Instructors at the University of Bahrain During COVID-19 Pandemic
Bibliographic record
Abstract
French language instructors at the University of Bahrain faced many challenges in adapting their teaching practices during the sudden transition to online teaching due to the COVID-19 pandemic. In this case study, we explore the French language instructors’ perceptions of e-learning and their attitudes toward technology during the pandemic as well as their students’ perceptions of the quality of their online teaching. The objective of this study is to analyze the relationship between instructors’ perceptions and teaching performance. We conducted the study during the beginning of the sudden change to online teaching and administered online survey-based data collection instruments to gather information about French language instructors’ perceptions and undergraduate students’ satisfaction with the quality of French language online teaching. One year later, we completed data collection with semi-structured interviews of the instructors’ perceptions and online teaching experience. The findings indicated that despite the abrupt switch to online teaching, instructors showed a prominent level of technology acceptance. However, the results indicated effective online teaching was highly correlated to instructors’ pedagogical preparedness and self-efficacy level. Consequently, this study outlines key factors influencing the efficacy of e-learning, including pedagogical preparedness, instructors’ self-efficacy, and information and communications technology literacy. In addition, in this study we propose implications for instructors’ preparation and development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".