Bibliographic record
Abstract
Writing is an important element of language learning, and an increasing amount of learner writing is taking place in online environments.Teachers can provide valuable feedback by commenting on learner text.However, providing relevant feedback for every issue for every student can be time-consuming.To address this, we turn to the NLP subfield of feedback comment generation, the task of automatically generating explanatory notes for learner text with the goal of enhancing learning outcomes.However, freely-generated comments may mix multiple topics seen in the training data or even give misleading advice.In this thesis proposal, we seek to address these issues by categorizing comments and constraining the outputs of noisy classes.We describe an annotation scheme for feedback comment corpora using comment topics with a broader scope than existing typologies focused on error correction.We outline plans for experiments in grouping and clustering, replacing particularly diverse categories with modular templates, and comparing the generation results of using different linguistic features and model architectures with the original dataset versus the newly annotated one.This paper presents the first two years (the master's component) of a research project for a five-year combined master's and Ph.D program.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".