Towards enhanced assessment question classification: a study using machine learning, deep learning, and generative AI
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
This study aims to benchmark the performance of machine learning (ML), deep learning (DL), and generative AI (GenAI) models in categorising assessment questions based on Bloom’s Taxonomy. Previous studies have lacked comprehensive investigations into the performance of these approaches. Further, the GenAI remains unexplored, offering a promising avenue for groundbreaking explorations. Therefore, we explore the effectiveness of various ML models by incorporating domain-specific term weighting and utilising word embeddings. The study also analyses the performance of Recurrent Neural Networks (RNNs) and Convolutional Neural Network (CNN) with and without bidirectional connections, as well as an approach that combines RNNs and CNN. Furthermore, we evaluate several transformer-based models by fine-tuning them alongside GenAI models text-davinci-003, gpt-3.5-turbo, PaLM2, and Gemini Pro in zero-shot classification settings. The results demonstrate that ML models outperformed DL models, achieving a best accuracy of 0.871 and F1 score of 0.872. Additionally, domain-specific term weighting is found to be superior to word embeddings. Furthermore, most ML and DL models performed better than GenAI models, with GenAI models achieving a best accuracy of 0.618 and a best F1 score of 0.627. Therefore, the outcome suggests considering the ML models with domain-specific term weighting as benchmark models in future research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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