Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing
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
An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describe and evaluate particular language features of Coh-Metrix for a novel AES program that would score junior and senior high school students’ essays from their large-scale assessments. Specifically, we studied nine categories of Coh-Metrix features for developing prompt-specific AES scoring models for our sample. We developed the models by capitalizing on the nine features’ informativeness as a function of dimensionality reduction. We used a three-staged scoring framework. The machine scores were validated against a “gold standard” of ratings, that is, those assigned by two human raters. The nine language features reliably captured the construct of the students’ writing quality. We performed a secondary analysis to see how the scoring models performed in relation to other, already established AES systems, and there was no systematic pattern of scoring discrepancy. However, for essays with widely divergent human ratings, the scoring models were disadvantaged owing to the inherent unreliability of the human 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.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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