Scale of Adaptive Information Technology Accessibility for Postsecondary Students with Disabilities (SAITAPSD): A Preliminary Investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it