Feasibility and Effectiveness of a Computer-Assisted Instructional System Implemented by Teachers for Students on the Autism Spectrum With Intellectual Disabilities in China
Bibliographic record
Abstract
The purpose of this study was to evaluate the feasibility and effectiveness of computer-assisted instruction (CAI) implemented by special education teachers in a school setting in China. Feasibility was evaluated by procedural fidelity of teacher implementation and a social validity questionnaire. Effectiveness was measured by the acquisition of bidirectional naming through multiple exemplar instruction implemented in CAI for students on the autism spectrum with intellectual disabilities. Bidirectional naming is a key ability in early language development that allows children to expand their knowledge through incidental learning (Greer et al., 2011). Three students on the autism spectrum with intellectual disabilities (7 years of age; 1 female, 2 males) and their teachers (27–29 years of age; female) participated in this study. Using a single-case design--multiple probes across participants with pre- and post-assessments, all three students demonstrated improvements at post-instruction bidirectional naming assessments. Teachers implemented CAI with a high level of fidelity; they gave it high ratings for acceptability and feasibility. They were satisfied with CAI and student learning outcomes. All three students reported that they enjoyed learning with CAI. The implications and potential utilities of incorporating CAI in special education classrooms in China 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".