From Needs Analysis to Programme Design: Online Speaking Skills Upskilling Programme for ESL Teachers
Bibliographic record
Abstract
This study observed that many teacher professional development programmes are designed without considering teacher’s voice. The present study addresses this gap by designing an online speaking skills upskilling programme that is based on a group of ESL teachers’ needs. The study employed the mixed methods research approach. Using the convenience sampling, 104 ESL teachers answered an online needs analysis survey. In the qualitative phase, an online interview was carried out with seven teachers who took part in the upskilling programme. Based on the findings, two conclusions can be made. First, for an upskilling programme that focuses on the enhancement of the speaking skills to be successful, psychological constructs such as self-esteem and anxiety need to be considered at the design stage. Second, as the programme was conducted fully online, having synchronous sessions is an important design feature. There are two implications. Firstly, it underscores the importance of integrating psychological considerations into the design of online speaking skills programs for ESL teachers. Secondly, it emphasizes the value of striking a balance between online and synchronous components While the study contributes valuable insights, it is important to acknowledge its limitations. Specifically, it does not explore the impact of improved speaking skills on teachers' classroom practices, and it solely focuses on speaking skills development. Future research could delve into the relationship between teachers' speaking proficiency and their instructional practices. Additionally, exploring the broader benefits of such online upskilling programs on other language skills would be a promising avenue for further investigation.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".