Investigating the Efficiency of the Rotation Model in Improving First-Year Undergraduate ESL Learners’ Writing: A Quasi-Experimental Study
Bibliographic record
Abstract
The present study uses a quasi-experimental design to assess the effectiveness of the Rotation Model (RM) on English as a Second Language (ESL) learners’ writing errors among undergraduate students. Few studies have focused on ESL learners' writing errors in inflectional suffixation. To address this gap, the current study investigates the effect of the Rotation Model on ESL learners' writing errors in inflectional suffixation within the Cognitive Load Theory (CLT) framework. The study has collected data from 132 participants. They were divided into two groups. The first group serves as the experimental group (N=66). The experimental group undergoes an intervention through the Rotation Model to improve English writing. The second group is the Control group (N=66). The control received instruction through the grammar-translation method.) Data were collected using pretests and posttests from both groups on two different pictures. The data were analyzed using paired t-tests on SPSS. The analysis revealed that the ESL learners in the experimental group improved their writing by minimizing errors related to inflectional suffixation more than the control group. These findings suggest that the Rotation Model effectively enhances the accuracy of ESL learners' use of inflectional suffixes. The implications of these results underscore the potential of RM as a superior instructional approach over traditional methods in ESL contexts, particularly for complex grammatical structures such as inflectional suffixation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".