Bibliographic record
Abstract
This study aims at investigating students’ perceptions of flipping EFL classes with the blackboard system. A course was designed to give students an opportunity to experience flipped learning context in developing their reading skills. 49-second year, English department students participated in the project during a complete academic semester consisting of 15 weeks at the university of Al-Jouf, KSA. A detailed questionnaire was prepared and used to enquire students’ perceptions. Results of the study revealed that the majority of the participants were willing to use the flipped learning model with blackboard system. The participants provided mixed reasons for this willingness including getting marks, better learning, better communication with the instructor, and having fun. The results also indicated that the majority of participants perceived flipped learning with the blackboard system as a beneficial learning context. The most perceived benefits included improved pronunciation of new vocabulary, facilitating the acquisition of new vocabulary, preparing students for class work, increasing students’ time practicing reading at home, reading silently more often, better communication with the instructor and submitting homework easily and quickly. The study also revealed that participants faced some problems when using blackboard in the flipped learning model. Most of these problems were technical and could be overcome with proper training on the use of the system itself. The study recommends the integration of flipped learning in EFL classes. The study also suggests further investigation of the topic with different courses especially theoretical courses taught to university students in English departments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".