The Problems of Summary Writing Encountered by Thai EFL Students: A Case Study of the Fourth Year English Major Students at Naresuan University
Bibliographic record
Abstract
The purposes of this study were to examine the quality of summary writing and type of problems made by Thai EFL English major students and also elicit opinions regarding problems encountered by these EFL learners in summary writing. 67 pieces of summaries written by fourth year English major students who enrolled in a research report writing course (205426) in the first semester of academic year 2021 at Naresuan University were collected and analyzed. It was found that the quality of summary writing produced by these EFL writers was viewed as “fair”, and there were no significant differences among all dimensions. They were all regarded as “fair”. Each dimension revealed the main problems mostly encountered. Concerning their opinions about summary writing problems, they thought that they found it difficult to find the main ideas of the text and finally lost focus. In addition, having difficulty in choosing vocabulary to replace the words from the original source when paraphrasing and having little time for revising were also their main problems. Pedagogical implications for L2 learning and teaching summary writing are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".