Revisiting the Effectiveness of Blackboard Learning Management System in Teaching English in the Era of COVID-19
Bibliographic record
Abstract
The study carries out a detailed review of the overall impact of deploying the Blackboard online platform in the EFL teaching-learning process. In pursuing this aim, this study has followed the narrative literature approach, using analytical and comparative techniques as primary research methods. Numerous studies have been analysed thoroughly to conclude whether these technology-oriented tools directly affect the EFL teaching-learning process. The study also provides a definitive opinion regarding the usefulness of blackboard technology. The analysis of literature pointed out that EFL classes were positively influenced when Blackboard technology was utilised. Blackboard technology’s advantages in EFL were found to outnumber their disadvantages. However, technical challenges remain in integrating this technology successfully into modern classrooms. It should also be noted that while such technology-based teaching tools are a step in the right direction, they should not be considered as a perfect replacement for time-tested teacher-student classroom interactions that happen organically in classrooms. Additional preparation is also required from both teachers and students to make a meaningful contribution to such technology-oriented classes. Particularly, teachers need much training, encouragement, and support to move towards further advanced and collaborating pedagogies online.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".