An Investigation into EFL Students’ Perceptions towards the Integration of Automated Writing Evaluation in L2 Writing Classrooms: A Case Study
Bibliographic record
Abstract
Research has confirmed the effectiveness of integrating AWE (Automated Writing Evaluation) into L2 writing instruction and pedagogy. Yet, little research on students’ perceptions of integration has been conducted in terms of learning and assessment. To address the gap, the current study aimed to explore the perceptions of students regarding the integration of automated writing evaluation (AWE) systems. The research is grounded in the context of a case study, which provides a detailed examination of student attitudes and experiences with AWE tools within a specific educational setting. Data was gathered from classroom observation and a semi-structured interview with one focal participant. Key findings reveal that while the student acknowledged the convenience and immediate feedback provided by AWE systems, they also expressed concerns about the potential for reduced human interaction and the limitations of automated feedback in capturing the nuances of language learning. This case study contributes to the broader discourse on technology in writing instruction by providing empirical evidence on EFL students’ perceptions of AWE systems. It offers insights for educators, policymakers, and technologists on how to design and implement AWE tools that are responsive to the needs and perceptions of L2 writers.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".