The Impact of Virtual Learning on Students’ Engagement in the Saudi EFL Context: A Connectivism-Theoretic Perspective
Bibliographic record
Abstract
Drawing on the connectivism learning theory (CLT), this paper probes the extent to which virtual learning (VL) influences students’ engagement (SE) in the Saudi EFL context. More specifically, this paper tests the hypothesis of whether or not VL improves SE in terms of eight learning variables: class attendance, class participation, the acquisition of the four language skills, learning anxiety, learning motivation, learning self-efficacy, willingness to communicate, and learning autonomy. This paper adopts a mixed-method approach, represented by both quantitative and qualitative methods of analysis and constituting two methodological instruments: a questionnaire and an interview. The sample consists of 256 EFL majors who are studying English at Prince Sattam bin Abdulaziz University, Saudi Arabia, and 14 EFL teachers who are teaching English at the same university. Three main findings are reported in this study: first, there is a positive attitudinal perception by the participants concerning the impact of VL on SE; second, VL increases students’ attendance, enhances their class participation, learning motivation, learning self-efficacy, learning autonomy, and willingness to communicate, and it decreases students’ learning anxiety; and third, SE is influenced by VL in terms of both productive and receptive language skills; positively, with regard to speaking and listening; and negatively, in terms of reading and writing. Pedagogically, the results of this paper necessitate reconsidering the efficiency with which new technologies are used in learning and teaching, and it is further anticipated to offer promising potential for the possibility of whether or not to completely depend on virtual learning in the near future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".