Exploring the use of tablets (iPads) with children and young adults with disabilities in Trinidad
Bibliographic record
Abstract
PURPOSE: This study was conducted to review data gathered during a pilot project which trialed the use of a tablet computer, the iPad. METHODS: Students from a segregated special education school and pre-vocational centre, with a wide range of intellectual and physical disabilities, were previously observed participating in 5-10-min introductory learning sessions with the iPad. This study reviewed quantitative and qualitative data collected during these sessions which included data regarding students' level of engagement and overall ability to learn how to operate the iPad and its applications. RESULTS: Results were positive for level of engagement and ease of use with cause and effect applications. For lower functioning students or students not previously exposed to tablet technology, scores were lower but overall remained high based on the 5-point scaling used in this study. CONCLUSION: Regular use of tablet technology in the classroom with applications appropriate to the level of ability of the student has the potential to enhance engagement in learning as well as maximise independence in the classroom. Implications for Rehabilitation The iPad has the capacity to be used with learners of all different ability levels if applications are selected appropriately and learners are given equal opportunity to access this type of technology. Enjoyment when using the iPad was high overall and this type of technology has the potential to promote more engagement in the learning process. Many applications are easy to use and progress students through step by step increasing the potential for independent learning in the classroom.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".