ESL Pre-service Teachers’ Perceptions on the Use of Paragraph Punch in Teaching Writing
Bibliographic record
Abstract
The development of Information and Communication Technologies (ICTs) provides broad opportunities in teaching English in ESL countries. Given the rapid development in computer applications, it is important to look at how these applications can be used in language teaching specifically for writing skills. The purpose of this paper is to investigate the pre-service teachers’ perceptions of a writing software called ‘Paragraph Punch’ as a tool for assisting beginner writers. This software is designed to help learners of English as a second language to develop and organise paragraphs in essay writing. This paper provides an overview of the development of computer-assisted language learning (CALL) over the years, and the background and features of Paragraph Punch. Data for this study have been gathered from third-year TESL students in a state university in Malaysia using a questionnaire survey to elicit their views on the use of Paragraph Punch as a potential writing tool. The descriptive analysis of the data showed that the (i) respondents have a positive view towards Paragraph Punch as a potential writing tool, (ii) Paragraph Punch is more suited for beginner writers, and (iii) the software can still be improved in terms of interactivity and layout to enhance writing. The findings have been discussed with regard to ESL writing.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".