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Factors That Influence Post-Secondary Students With Disabilities To Adopt Or Reject Assistive Technology For Computer Access

Glenn Goodman, MOT, OTR/L
Associate Professor of Occupational Therapy
Cleveland State University HS 119
2501 Euclid Ave
Cleveland Ohio 44115 (216) 687-2493
Email: g.goodman@csuohio.edu
FAX: (216) 687-9316

The course was implemented spring quarter of 1998. Twenty students enrolled in the course. Sixteen students completed the course. Fourteen students passed the course. One student completed the course one quarter later after requesting an incomplete due to medical complications. Table 1 and Table 2 review the make-up of the students in the class.

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Table 1

Table 2

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The course was 10 weeks long. The following content was covered in the course: (a) introduction to Windows 95 and Microsoft Accessibility Features, (b) overview of assistive technology for computer access, (c) instructions for evaluation using the Assistive Technology Device Predisposition Assessment (Scherer, 1998), and the Decision Tree (Anson, 1997), (c) introduction to Microsoft Word, (d) introduction to Microsoft Excel, (e) introduction to Microsoft Access, (f) introduction to Microsoft Power Point, (g) introduction to Eudora Popmail, and (h) introduction to Internet Explorer and Netscape.

After the course was completed the students participated in a series of 3 interviews scheduled over a one year period to assess whether or not the student actually adopted the assistive technology that was recommended in the class, and to identify factors that influenced the student to adopt or reject the technology. The following data were collected during this one year process: (a) Weekly typing speed and accuracy during the course, (b) weekly progress report of time spent on the computer and in the computer labs on campus during the class, (c) estimate of time spent per week on the computer after the class was completed, (d) survey of professors who taught the students in other courses while the students were in the computer class to assess any noted changes in writing quality or productivity, (e) post-class interviews of the student's partners to assess their perspectives on the value of the course, (f) interview 1 of the students in the class (one month after course completion) focused upon strengths and weaknesses of the class, and asked students to contribute factors that influenced them positively or negatively about the technology, (g) interview 2 of the students in the class (4 months post-class) assessed amount of use of the assistive technology and asked students for member checks of the content of the initial interviews. (The students were also asked to comment on additional factors from the literature that may have influenced them to adopt or reject the technology), (h) interview 3 of the students (one year post-class) utilized Q methodology to assist the students in prioritizing the factors that encouraged them to adopt or reject the assistive technology. These factors were placed into one of four categories based upon the MPT model (Milieu, Person, Technology, and Training) devised by Scherer (Galvin & Scherer,1996; Scherer, 1993).

A summary of the results of the study indicate the following:

1. There were high levels of variability in the demographics of the students in age, level of education, level and type of disability, amount of previous computer and assistive technology experience, and predisposition for technology.
2. The students' opinions varied significantly from rehabilitation professionals' opinions regarding a predisposition for assistive technology as measured by the standardized assessment (ATDPA).
3. Seventy-five percent of the students actually adopted at least some of the assistive technology one year after completing the course.
4. Objective and reported measures of typing speed, typing accuracy, number of pages produced per week, time spent in the computer lab and time spent at home did not reveal a statistically significant difference or what would be considered a practical or useful increase in any of the variables measured. Typing accuracy approached statistical significance in the repeated measures analysis of variance. Several individual students displayed what could be considered meaningful differences for them in these variables. Some examples of these include 3 students who improved typing speed between 7 and 12 words per minute, four students who improved typing accuracy from a beginning level of 17 to 87 percent to ending levels of 93 to 99 percent, two students who showed exceptional dedication through consistently long hours each week in the computer lab, and two students who showed a steady weekly increase in the amount of time they spent at home on the computer.
5. With the exception of two instructors who noted improvements in the quality and quantity of written work, the survey of professors failed to effectively document noticeable changes in academic performance among the students who were taking additional classes that quarter.
6. The students and partners identified several strong points and areas for improvement in the class and the partner experience. Both the students and the partners appreciated the increased knowledge and skills in the area of technology. The affective areas of self-confidence, feelings about the partners, attitudes about the technology, development of patience and rapport with others, and breaking down of stereotypes were outcomes that were also reported by the students and partners. It was hoped that these valuable components of the class would be reported as valued by the students who had this experience.
7. The Q sort identified 3 factors and several consistent items that students indicated were most valued in influencing them to adopt or reject the assistive technology. The training items received the strongest consensus among the students as most influential in assisting them to adopt the assistive technology for the personal computer. Personality characteristics and environmental support were other factors represented in the factor analysis of the 48 items developed for the study.

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The participants mentioned many weaknesses and suggestions for improvement in the design and implementation of the course and the program evaluation. More teaching resources in the way of state of the art computer workstations would clearly improve the learning environment. Better recruitment, training, and support for the lab monitors and student partners would reduce administrative problems during the administration of the course. Errors in data collection and missing data may have affected the outcome of some of the variables evaluated in his study. Efforts to reduce these problems should be made if the class and study were to be repeated.

This experience has provided an outstanding beginning to the development of programs that increase access and empower students with disabilities on the CSU campus to succeed in their student roles. Further expected developments include: (a) enhancement of the computer laboratory with increased hours of operation, (b) efforts to collaborate with the university library and other units on campus to seek funding and resources for program improvement, (c) four additional workstations outfitted with state of the art technology, and (d) repetition of the class at least once every two years to provide the jump start and background necessary for the students with disabilities to independently master the computer skills necessary to survive and thrive as students and future employees.

This project was completed as a doctoral dissertation at Kent State University in the Instructional Technology Program in the College of Education. It was supported by:

Ohio House Bill 790 funds for Capital Improvements

The Ohio Department of Education Goals 2000 Funds

The National Science Foundation - Project Team Funds

The Cleveland State University Center for Teaching and Learning

The Cleveland Foundation

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References

Anson, D. (1997). Alternative computer access: A guide to selection. Philadelphia: F.A. Davis.

Galvin, J.C., & Scherer, M.J. (1996). Evaluating, selecting, and using appropriate assisitive technology. Gaithersburg, MD: Aspen Publications.

Scherer, M.J. (1993). The assistive technology device predisposition assessment: How does it measure up as a measure? Paper presented at the American Congress of Rehabilitation Medicine, Denver: CO.

Scherer, M.J. (1998). Matching person and technology: A series of assessments for evaluating predisposition to and outcomes of technology use in rehabilitation, education, the workplace & other settings. Webster, NY: Institute for Matching Persons and Technology.

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