An Empirical Study on the Validity of the AES Systems Juku and iWrite for Continuation Writing Task Assessment
Bibliographic record
Abstract
The automatic English scoring (AES) systems are coming to the forefront of English learners’ minds with their speed, accuracy and personalized feedback. However, fewer researchers have studied the validity of AES systems in assessing narrative texts such as continuation writing tasks. Therefore, this paper empirically investigates the scoring between two AES systems, Juku and iWrite, and the difference in scoring validity between these two systems and the teacher. This study mainly uses a quantitative method. The subjects of the study were the continuation writing tasks scores of 30 senior high school students in a Chinese middle school. Each task was scored by a professional teacher, Juku and iWrite, all with a perfect score of 25. Then the scores were statistically analyzed using SPSS 26.0. The results of the analysis showed that (1) iWrite was more consistent and correlated with manual scoring than Juku. (2) In terms of mean scores, the manual scores were significantly higher than the Juku and iWrite scores. (3) In terms of discrimination, the system scores were not as good as the manual scores, but the latter were more subjective. (4) In terms of accuracy and stability, the AES systems were higher than manual scoring. Therefore, learners can use the AES scoring system as a reference and practice narrative writing based on the system’s feedback on grammar and the teacher’s feedback on plot and content.
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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 | 0.041 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".