Project-based Learning via Blackboard Discussion Board for Vocabulary Acquisition of Saudi EFL Learners
Bibliographic record
Abstract
The global pandemic of COVID-19 affected all learning systems worldwide. It shifted the attention to alternative online learning platforms, including discussion boards, blogs, and other tools that necessitate collaborative teamwork. The current research employed project-based learning (PBL) via the blackboard discussion board to improve vocabulary acquisition for Saudi EFL learners and to determine the advantages and difficulties associated with learning. The researchers used a quantitative research design with equivalent groups of 60 female students from King Khalid University divided into two study groups: 30 for the experimental group taught through project-based learning via discussion board and 30 for the control group taught traditionally. Pre- and post-vocabulary tests were administered to the two groups, and a questionnaire of four dimensions about the advantages, skills, effects, and challenges encountered by students through learning were post-administered to the experimental group. Results proved that project-based learning employed through the discussion board enhanced students' receptive and productive vocabulary knowledge. The questionnaire analysis revealed that the PBL increased teamwork skills and responsibility. It motivated students' problem-solving skills and autonomous learning. Despite these merits, there were some challenges, such as the instability of the internet connection, lack of time, and some students' hesitation to deliver presentations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".