Usability Testing of the Teacher Help for Learning Disabilities Program: An eHealth Intervention for Teachers
Bibliographic record
Abstract
Use of evidence-based interventions for learning disabilities (LDs) in the classroom is limited by several factors such as teachers’ knowledge of LDs and access to interventions. eHealth interventions (i.e., interventions delivered via the Internet) have the potential to be a powerful tool in overcoming barriers to implementing evidence-based strategies within the classroom. The current article describes the development and usability testing of Teacher Help for LD, an eHealth professional development program that assists classroom teachers in providing evidence-based interventions to students with LDs. Specialists in LDs (n = 18), consisting of individuals within the educational and health systems, were asked to evaluate the usability of the Teacher Help for LD intervention and provide their feedback. Results from both quantitative and qualitative data suggest that participants were very satisfied with the program and found the program highly usable. Results will help to prepare and modify the program for use with regular classroom teachers in an upcoming cluster randomized controlled trial across Canada.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".