Impact of a Multi-factor Digital Intervention on Reading and Affect Skills of Students with Reading Difficulties
Bibliographic record
Abstract
Learning to read is a basic human right.However, not all children learn to read successfully.Thus, early diagnosis and targeted interventions are crucial for the 5 to 10% of children who have reading difficulties despite receiving adequate instruction.The focus of this thesis was to test whether participation in a 13-week computerized game-based reading intervention, Neuralign©, resulted in improvements in reading skills and affect for children in Grades 2 to 8 with reading difficulties or other challenges.Children were randomly assigned to be in the Neuralign© or waitlist group.Analyses of reading outcomes and reading affect after the intervention were conducted using multiple regression, controlling for pretest performance and cognitive skills.Results showed no evidence that Neuralign© influenced children's post-test scores on reading or affect skills.The current study adds to the literature about computer-based reading interventions by testing Neuralign© and provided useful direction for development of the intervention.principal investigator, Dr. Heather Douglas, for their consistent support throughout the completion of this project.Their patience, insight, attention to detail, and support enabled me to step out of my comfort zone and accomplish more than what I expected.I feel truly grateful and honoured for having the opportunity of being a mentee under Dr. LeFevre and Dr. Douglas.I am positive that the skills they have taught me will benefit me in my career in the future.Thank you.Second, I would like to extend my heartfelt gratitude
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".