Paraphrasing Strategy in EFL Ecuadorian B1 Students and Implications on Reading Comprehension
Bibliographic record
Abstract
Reading comprehension in Ecuadorian students has been mostly managed at a literal comprehension level, leaving out inferential and critical comprehension. This is because most of the articles students read require a high level of literacy and a good domain of comprehension strategies. One of these strategies is paraphrasing; therefore, the purpose of this research was to analyze the effects of paraphrasing and its implications on reading comprehension skills in English as a foreign language. This study was developed in B1 students enrolled at the 6th level of English at Linguistics Competence Department at Universidad Nacional de Chimborazo UNACH with a sample of 50 students. A base-line pre-test and a posttest to an experimental and control group were applied. The project implementation took ten sessions and students learned the techniques to effectively paraphrase and the pitfalls they should avoid when applying this strategy. The analysis of T-student test yielded that the experimental group outperformed the control group. The main results showed that once students learn the techniques and correctly apply them, it helped them out to go beyond the literacy level, applying an authentic reading comprehension of the text. Pedagogical implications about paraphrasing and reading comprehension are presented in the discussion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".