A Qualitative Study on The Function of Information and Communication Technology Utilization in Teaching Students with Intellectual Disabilities: Implications for Techniques of Teaching/Job Coaching
Bibliographic record
Abstract
Background: Recently, the use of Information and Communication Technology (ICT) in education has been promoted in Japan. However, teachers do not yet fully understand using ICT for persons with intellectual disabilities. Objective: This study clarifies the support functions of ICT as perceived by teachers involved in special needs education (support for students with intellectual disabilities). Methods: We conducted in-depth interviews with five teachers involved in special-needs education. We qualitatively analyzed the interview data obtained using content analysis. Results: From the interviews, 53 ICT-utilization episodes were obtained. These episodes were categorized into 13 categories of utilization and seven aspects of support: understanding, communication, behavior, meta-cognition, environment, memory, and opportunities. Conclusion: ICT is a beneficial tool to compensate for intellectual disabilities and help students with intellectual disabilities learn better. We believe that teachers can better assist students with intellectual disabilities by successfully matching individual needs with the ICT features. The results obtained in this study will be helpful while considering support for persons with intellectual disabilities, not only in education but also in vocational training, employment support, and telework support for persons with intellectual disabilities.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".