Scale of Adaptive Information Technology Accessibility for Postsecondary Students with Disabilities (SAITAPSD): A Preliminary Investigation
Bibliographic record
Abstract
The responses of 81 Canadian junior and community college students with disabilities were used to develop and evaluate the Scale of Adaptive Information Technology Accessibility for Postsecondary Students with Disabilities (SAITAPSD). This is an 18-item self-administered tool that evaluates computing accessibility for and by students with various disabilities. The scale, a companion to the service provider version of the measure (Fossey et al., 2005), contains a total score and three empirically derived subscales: Adaptive Computer Availability and Support, Perceived Computer Competency, and New Computer Technologies. Results indicated that the three subscales account for 50% of the variability in total scores. Psychometric data showed good temporal stability and internal consistency for both the subscales and the total score. Validity data showed strong relationships between scores and key criterion variables as well as other measures of obstacles and facilitators to academic success. The scale may be used to evaluate an institution’s information technology (IT) accessibility, provide empirical data to influence IT policy, and pinpoint areas of strength as well as areas for improvement, all from the perspective of students with disabilities. Recently, we reported on the development of a scale to evaluate the accessibility of campus computing intended for disability service providers to complete (Fossey et al., 2005). Here we present a companion measure, designed for completion by students with various disabilities. The student measure had to meet a variety of criteria: including easy for students with all types of disabilities to complete; reflective of the changing landscape in the use of information and computer technologies on campus (e.g., eLearning); meaningful to rehabilitation centers to assist them in making needed adaptive hardware (e.g., foot mouse) and software (e.g., software that reads material on the screen) available for their clientele; and helpful as a tool for advocating with campus administration and staff regarding the importance of acquiring and implementing computer technologies accessible to all learners. The measure focuses on the availability and accessibility of adaptive computer technologies in a variety of locations on as well as off campus. Accessibility in this context refers to a range of situations such as whether computers with adaptive technologies are available in general use computer labs; whether eLearning (e.g., course web pages, CD-ROMs) used by faculty is accessible to all learners; and whether learners receive adequate training in how to use needed adaptive software/hardware (Goodman, Tiene, & Luft, 2002).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".