Training Special Education Teachers in China to Deliver Bidirectional Naming Instruction in a Computer-Assisted Instructional System
Bibliographic record
Abstract
The study sought to evaluate effects of self-directed training on teachers' ability to implement computer-assisted instruction (CAI) designed to teach bi-directional naming (BiN), a skill that involves incidental learning, to children with developmental delays. Three special education teachers in China participated in this fully online study. A single case design with multiple probes across participants was used. At baseline, we conducted 1.5-2 hours of online lecture-based training about BiN and its teaching procedures, followed by evaluation of teacher implementation using picture cards. In the intervention condition, we provided teachers access to the web-based CAI BiN program and its accompanying manual in order for them to learn the system and evaluate their implementation of BiN instruction. Compared to baseline, all three teachers implemented BiN instruction with greater accuracy and in less time in the intervention condition. Implications for training special education teachers to use CAI to teach BiN for students with developmental delays are 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".