Validity Arguments for Automated Essay Scoring of Young Students’ Writing Traits
Bibliographic record
Abstract
Machines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant, especially in formative assessment contexts where feedback is key. Yet the extent to which writing traits can be validly scored by machines remains under-researched, especially in the K-12 context. The present study investigated the validity of machine learning (ML) models designed for students in grades 3–6 to score three writing traits: task fulfillment, organization and coherence, and vocabulary and expression. The study utilized an argument-based approach, focusing on two primary inferences: evaluation and explanation. The evaluation inference investigated human-machine score alignment, the ability for the models to detect off-topic and gibberish responses, and the consistency of human-machine score alignment across grades and language backgrounds. The explanation inference investigated the relevance of features used in the models. Results indicated that human-machine score alignment was sufficient for all writing traits; however, validity concerns were raised regarding the models’ performances detecting off-topic and gibberish responses and the consistency across sub-groups. Implications for language assessment professionals and other educators were discussed.
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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 | 0.239 | 0.680 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".