Portable Technology Use in Special Education Programs and Services in the United States
Bibliographic record
Abstract
In the United States, the Individuals with Disabilities Education Act (IDEA) lists fourteen disabilities for which a student is eligible for special education services: autism, deaf-blindness, deafness, developmental delay, emotional disturbance, hearing impairment, intellectual disability, orthopedic impairment, other health impairment, specific learning disability, speech or language impairment, traumatic brain injury, visual impairment, and multiple disabilities. Services include individualized education programs consisting of assessments, interventions, and other related services. Technologies help level the learning playing field, as they can facilitate the person’s functional academic and social capabilities across settings. Particularly as technological options have increased, there is a greater possibility of matching the technology with the learning need. Thus, the intersection of assistive technology, portable devices, disabilities, K–16 communities, and special education results in the needed topic of portable technologies for formal special education. In addition, the geographic scope is largely the United States, with some Canadian overlap. The term “portable technology” generally denotes a stand-alone device that may be carried easily in one hand, such as a cell phone, small audio or video player, signal device, or laptop computer. Sometimes the terms “mobile” or “handheld” are used instead of “portable.” In educational circles, the term “mobile learning” (or “m-learning”) refers to learning activities in which the learner actively incorporates these portable or mobile devices. Therefore, for the purposes of this bibliography, computer peripherals, wheelchairs, and other appliances (such as cochlear implants) are excluded. The bibliography emphasizes fundamental texts, systematic literature reviews, and scholarly research mainly since 2016. For very similar studies, the most rigorous one was selected for inclusion; furthermore, pilot students’ single-subject cases were avoided. It should be noted that various usage studies constitute the majority of citations. Rigorous assessment concerning the impact of portable technologies on student success is uneven, and, in particular, postsecondary assessment of special education services using portable technologies is limited. Legislative history, often best archived on websites, provides legal context. Although many valuable organizations discuss portable technologies for special education, generally only research-centric ones are included in this bibliography; other resources in the bibliography, such as Grey House Publishing’s Complete Resource Guide for People with Disabilities (cited under Overviews) do list relevant organizations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".