The Effect of Using Automated Essay Evaluation on ESL Undergraduate Students’ Writing Skill
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Advances in Natural Language Processing (NLP) have yielded significant advances in the language assessment field. The Automated Essay Evaluation (AEE) mechanism relies on basic research in computational linguistics focusing on transforming human language into algorithmic forms. The Criterion® system is an instance of AEE software providing both formative feedback and an automated holistic score. This paper aims to investigate the impact of this newly-developed AEE software in a current ESL setting by measuring the effectiveness of the Criterion® system in improving ESL undergraduate students’ writing performance. Data was collected from sixty-one ESL undergraduate students in an academic writing course in the English Language department at Princess Norah bint Abdulruhman University PNU. The researcher employed a repeated measure design study to test the potential effects of the formative feedback and automated holistic score on overall writing proficiency across time. Results indicated that the Criterion® system had a positive effect on the students’ cores on their writing tasks. However, results also suggested that students’ mechanics in writing significantly improved, while grammar, usage and style showed only moderate improvement. These findings are discussed in relation to AEE literature. The paper concludes by discussing the implications of implementing AEE software in educational contexts.</p><p><span><br /></span></p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it