Re-examining Evidence Based Practice in Special Education: A Discussion
Bibliographic record
Abstract
Abstract The Council for Exceptional Children (CEC) recently released updated standards regarding how to determine whether any particular intervention may be deemed an evidence-based practice (EBP). As new criteria regarding the acceptance of any specific intervention as evidence-based become available, the question arises: Would the application of new standards to studies completed under the older guidelines result in changes to past conclusions? The current study examined if changes in EBP standards might change the classification of an exemplar practice that was previously designated as an EBP. In this case, we examined video modeling (VM), an accepted practice regarding skill acquisition in special education as an exemplar practice. In order to determine if the new CEC 2014 standards would impact a previously determined EBP finding, a re-examination of Bellini and Akullian's (2007) frequently cited meta-analysis that focused on VM as an intervention for individuals with autism spectrum disorders (ASD) was conducted. The results revealed that if Bellini and Akullian had conducted their review using updated CEC 2014 standards, VM applied to individuals with ASD would not have been classified as an EBP.
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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 | 0.528 | 0.669 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".