More efficient processes for creating automated essay scoring frameworks: A demonstration of two algorithms
Bibliographic record
Abstract
Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms. The purpose of this study is to compare the effectiveness and the performance of two AES frameworks, each based on machine learning with deep language features, or complex language features, and deep neural algorithms. More specifically, support vector machines (SVMs) in conjunction with Coh-Metrix features were used for a traditional AES model development, and the convolutional neural networks (CNNs) approach was used for more contemporary deep-neural model development. Then, the strengths and weaknesses of the traditional and contemporary models under different circumstances (e.g., types of the rubric, length of the essay, and the essay type) were tested. The results were evaluated using the quadratic weighted kappa (QWK) score and compared with the agreement between the human raters. The results indicated that the CNNs model performs better, meaning that it produced more comparable results to the human raters than the Coh-Metrix + SVMs model. Moreover, the CNNs model also achieved state-of-the-art performance in most of the essay sets with a high average QWK score.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".