The ARGUE Mnemonic: A Strategy to Improve Argumentative Essay Writing with Students who have Visual Impairments
Bibliographic record
Abstract
Writing can be a challenging task for many students. This study offered 10 ninth-grade students (8 females, 2 males) with visual impairments located in the Western United States the opportunity to learn a mnemonic strategy for argumentative-essay writing called ARGUE: Analyze data; Review keywords and create sentences; Generate a plan; Use your thoughts to say an oral draft; and Express your ideas in typed text. Action research methods were employed in the study from April through May over twenty-one 65-minute sessions. All students argumentative essay writing skills increased in content and quality by the end of the intervention’s timeline, except for one. Another student had an increase for content but not quality. Three participants had the highest end-of-intervention gains. They also had the largest gains in number of words written. From the results of repeated measures t-tests, there was a significant difference in the scores for content in baseline and application conditions. All student participants felt others would benefit from using ARGUE and that the mnemonic was effective as is. Limitations and future research are also 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".