Colouring the path from instruction to practice
Bibliographic record
Abstract
Mainstream paper and pencil interventions for Anglophone students with dyslexia emphasize a strategy of analyzing syllables to compensate for irregularities in English letter-sound correspondences. Classroom interventions have developed effective scaffolds for supporting students in analyzing syllables in instructional contexts. However, students typically fail to transfer knowledge to practice contexts (i.e, reading without a tutor). Software has proven to be an effective medium for helping dyslexic students practice basic literacy skills (phoneme awareness and letter knowledge). However, at present, there are no systems specifically designed to support dyslexic students in practicing syllable analysis. Correspondingly, there is a lack of information about which design features would best support dyslexic students in transferring syllable analysis skills from instructional (classroom) to practice (software) contexts. In an attempt to address this gap, we propose two guidelines for software supports of syllable-analysis in dyslexia: 1. Design software that serves as a dual medium for instruction and practice 2. Design scaffolds that serve as dual catalysts for learning and transfer. We realize our guidelines in a prototype software system for syllable analysis that uses colour-coding to direct attention to information during learning and to retrieve learned information during practice.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".