Effectiveness of the Automated Writing Evaluation Program on Improving Undergraduates’ Writing Performance
Bibliographic record
Abstract
Automated Writing Evaluation program (AWE) has gained increasing ground in ESL/EFL writing instruction because of its instructional features, such as the instant automated writing score system and the diagnostic corrective feedback in real-time for individual written drafts. However, there is little known about how the automated feedback provided by the AWE program can impact students’ writing performance in an authentic classroom and how to make the most of it to improve students’ writing performance effectively, especially for ESL/EFL undergraduate students. This paper attempts to offer an overview of the investigation of the effectiveness of automated feedback via a literature review. According to the inclusion and exclusion criteria, eleven articles published in the past five years were finally included for the analytical synthesis. The literature review matrix for the synthesis reveals the research gaps of the previous literature in the levels of the effectiveness of the automated feedback, including the lack of the design of delayed post-test, writing performance in terms of writing traits, and students’ writing strategies regarding the use of AWE program. The conclusion highlights the need for future research by bridging the gaps of exploring the long-term internalized impact of the embedded use of automated feedback and an advanced teaching method on improving both students’ overall writing performance and analytic writing scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".