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Technology-based Assessment in Special Education
by GERALD McCAIN New Mexico State University Las Cruces, N.M. Technology-based assessment in special education has made advances during the last two decades. Whereas the first applications of computer technology for assessment were for scoring student test forms, contemporary uses support many other features and functions. These features include self-administration, software control of item presentation, response evaluation based on conceptual models or algorithms, decision making based on rules and criteria, prescription based on expert knowledge, and direct links between assessment and changes in instruction. Technology-based assessment generally refers to the use of electronic systems and software to assess and evaluate the progress of individual children in educational settings. Thus it encompasses both electronic versions of traditional measurement protocols as well as innovative assessment approaches that employ computers. Examples of approaches in technology-based assessment include: A video-based computer-assisted test able to learn the language preference of the student and automatically switch to it to increase the validity of its measurement; Video segments from popular movies used as elements of a moral dilemma in a real-life, problem-solving test; and Students viewing video segments of peers interacting in various social situations and entering their responses by simply touching a computer screen. In the world of technological evaluation these innovative approaches bring validity and relevancy to the testing procedure. A variety of factors have contributed to the current need for better assessment tools and procedures in our schools. Students being misplaced as a result of poor evaluations is one. Misplacements of students can have devastating results for both student and teacher. Such misplaced students tend to lose interest and drop out of school. Teachers who have misplaced students in their classrooms are often not properly trained to deal with certain behaviors or learning differences. Other factors contributing to changes in assessment practices are partially due to the growing population who qualify for IDEA (Individuals with Disabilities Education Act). Data reported in the 1994 U.S. Dept. of Education's Sixteenth Annual Report to Congress on the Implementation of Individuals With Disabilities Act show a 3.7% overall increase in students receiving special education services during the 1992/93 school year.2 This growth in special education, although not huge, d'es include a significant number of minorities who have different cultural backgrounds and languages. The growing diversity of students in special education warrants new and innovative approaches to assessment practices. Minorities who qualify for special services require culturally relevant assessments, particularly in the areas of language and social behaviors. Culturally Relevant Assessment It is important that minorities be assured of equal representation in special programs. It is well known that ethnic minorities are under-represented in advanced programs for the gifted, such as AEP (Advanced Education Program). Yet, there is an over-representation of certain ethnic minority groups that fall into special education categories such as Intellectually Disabled and Learning Disabled. Studies have shown, for example, that until recently there were approximately twice as many Mexican-American students in classes for the educable mentally retarded (EMR) in the U.S. as would be expected on the basis of proportion in the school population.3,4 As far back as 1916, Miller pointed out that special or "backward" classes furnished "an easy means of disposing of (a non-English-speaking) pupil who, through no fault of his own, is an unsatisfactory member of a regular grade."5 This over-representation of ethnic minority students in classes for the mentally retarded has been attributed to the indiscriminate use of psychological tests, especially IQ tests, combined with the linguistic and cultural orientation of school programs.6 It may also be due to human factors in evaluation regarding personal biases and prejudices, as well as a lack of adequate tools to accurately assess without cultural relevancy. Native languages have been used in individual evaluations, but much of the information is lost during the translation from English to the students' dominant language. This is possibly due to the different cultural dialects and historical backgrounds between evaluators and students. Also, students may perform very well on certain items in their native language, but many items within the testing tools are likely to be either culturally unfamiliar or not even existent in their native language. These students end up with low scores and poor overall results. New technology, however, makes it possible for evaluations to include all items in a student's native language and also to bring in a broader range of culturally relevant items. Expanding Definitions of Learning Another reason for change in our current assessment practices is that we, as educators, know more about the learning process. According to Irvin and Walker, we know considerably more about what to measure than about how to measure it. This notion, with support by the Technology-Related Assistance for Individuals with Disabilities Act of 1988, focuses on assessment tools that move away from norm-referenced intelligence and academic achievement instruments to a more holistic assessment utilizing technological advances. We know, for example, that the ability to think logically, to use stored knowledge to solve problems, to reason by analogy, and to extend or extrapolate knowledge to new situations are indicators of intelligence. Gardner suggests there are seven distinct intelligences unique to humans: language, logic (math analysis), spatial representation, musical thinking, use of the body, understanding others and understanding self.8 These are all intelligences we can observe and investigate, but how do we go about measuring them? Knowing more about what to measure also includes behavioral correlates of social competence in individual students during evaluation. For instance, a child's ability to "read" social situations is a prerequisite to other skills that lead to social competence. Children with learning disabilities make poor discriminations when presented with social and affective stimulus events. Historically, assessment has been a "weak link" in the chain of conceptualization, training and child performance of socially competent behaviors.9 The most popular methods for such assessments have been peer nomination and ratings, teacher and parent ratings, and direct observations or ratings of a child's behavior during real-life sessions or from videotapes. These provide global measures of a child's generic social competence and teacher/peer acceptance.10 Social competence, as defined by McFall, is a summative term that refers to the quality or adequacy of overall performance on a particular task within a social context as judged by others (teachers, parents, peers).11 The psychometric characteristics of these approaches are generally adequate for some assessment purposes—screening, identification and program evaluation, for example—but they do not clearly identify a child's social competence problems or their source(s).12 Time Factors in Assessment For many educators, the quality of special education depends on the quality of assessment information and its timely application in the classroom, school and community to prescribe practices and monitor programs and services. For instance, the most widely used form of assessment has been, and still is, direct observation of the student in a classroom setting. While effective in referring for special education, it is only one method and should be used as part of the holistic evaluation.13 Direct observations, along with scores from both achievement and norm-referenced tests, are good evaluation methods, but very time consuming. In the meantime lists of referred students pile up only to end up on a waiting list for evaluation, while the semester continues to proceed. This lag time is very discouraging to many classroom teachers, diagnosticians, and students and parents alike. Technology-based assessment can speed up the entire process as computers store and compute test results. Technology-based Assessment Solutions Computers can store and manage large data sets that may then be easily accessed by school personnel and parents; thus technology can facilitate interagency coordination of service.14 Technological trends in assessment practices allow a greater volume of information to be handled by fewer personnel, to better serve the needs of individuals, to comply with ever changing regulations, and to manage and evaluate special education programs.1 Advances in technology have also made it possible to integrate more holistic evaluations of students in different contexts. For instance, when addressing students with physical limitations, technologies such as voice recognition, hand-writing interpreters, pointers, stylus tools and touchscreens enable people to communicate with computers without using keyboards and their rather restrictive fine-motor skill requirements.7 Further, integrating text, graphics, audio and video enables more comprehensive evaluations to take place. With such options available in our schools, special education students will become more familiar and comfortable in the world of technology. Comfort and familiarity with the testing environment and testing tools results in more authentic evaluations.15 Support for a Holistic Approach While computers were first applied in assessment by scoring student test forms, contemporary uses support a variety of other functions. New software features, for instance, enable the student to perform at a comfortable pace and also provide control over the instruments at hand, such as the video segments or preferred language options mentioned earlier. Computers linked to interactive videodiscs, for example, let students learn according to their individual needs and skills.16 Evaluators also benefit from the latest technological features concerning evaluation. Irvin and Walker's study,7 which focused on the uses of technology applications to assess social competence among students labeled as handicapped, suggests that when different abilities are tested in a number of different constructs, the evaluator gains perspective by viewing students in a variety of contexts. This holistic approach formulates a picture of students in everyday life situations that allows a full view of students' abilities. Technological features for holistic assessment include: self-administration, software control of item presentation, response evaluation based on conceptual models or algorithms, decision making based on rules and criteria, prescription based on expert knowledge, and direct links between assessment and changes in instruction.1 Other examples of advances in, and proliferation of, technology that will help students with disabilities gain greater independence and inclusion into regular classrooms and society are: speech recognition systems, electronic communications, personal computers, robots and artificial intelligence.17 Conclusion Emerging trends in technology-based assessment will continue impacting the lives of students with disabilities well into the 21st century. Growth and improvement in special education assessment is inevitable as technology is increasingly used to assist evaluation of students; this is especially vital as student population grows more diverse. In addition, further improvements in assessing children with disabilities may suggest new directions for more effective curriculum and instruction development. More comprehensive evaluations lead to more appropriate placements, which in turn, result in more suitable learning environments. Technology can make large differences in placement criteria by enabling more holistic pictures of students to be acquired before placement decisions are finalized. The ability to store and retrieve large amounts of pertinent information will result in more appropriate placements of students than was previously possible. Technology gives us new opportunities to view students using a more well-rounded academic evaluation. It also facilitates integration of multiple assessments. Skill deficits that occur during different tasks and social situations are more quickly and easily identified. Utilizing technology to improve assessment practices is a movement in the right direction for schools. Knowing more about what to assess gives us new insights on the how of assessment methods. The future of technology-based assessment is wide open, with endless possibilities to assist educators in evaluating students in special education placements, curriculum development and student instruction. The infinite possibilities of technology shines new light on assessment procedures and the resulting opportunities for students with disabilities are only now beginning to be realized and fully understood. Gerald McCain was a special education teacher at Alameda Elementary in the Las Cruces Public Schools for four years. He is currently pursuing a doctorate in bilingual special education at New Mexico State University. E-mail:firstname.lastname@example.org This article emerged from research conducted with Dr. Karin Wiburg, professor of educational technology at New Mexico State University. References: 1. Greenwood, C. & Rieth, H. J. (1994), "Current Dimensions of Technology-Based Assessment in Special Education," Exceptional Children, 61 (2), pp. 105-113. 2. U.S. Dept. of Education (1994), Sixteenth Annual Report to Congress on the Implementation of Individuals With Disabilities Act, Washington, DC: Office of Special Education & Rehabilitation Services, Office of Special Education Programs, Division of Innovations & Development. 3. Williams, J.C. (1968), Improving Educational Opportunities for Mexican-American Handicapped Children, Washington, DC: Office of Education (EDO18326). 4. Gaarder, A.B. (1977), Bilingual Schooling and the Survival of Spanish in the United States. Rowley, MA: Newbury House. 5. Miller, H. (1916), The School and The Immigrant, Cleveland, OH: Survey Committee of the Cleveland Foundation. 6. Mercer, J. R. (1973), Labeling the Mentally Retarded, Los Angles: University of California Press. 7. Irvin, L. K. & Walker H. M. (1993), "Improving Social Skills Assessment of Children With Disabilities: Construct Development and Applications of Technology," Journal of Special Education Technology, 12 (1), pp. 63-70. 8. Gardner, H. (1983), Frames of Mind: The Theory of Multiple Intelligences, New York: Basic Books. 9. Gresham, F. & Reschly, D. (1988), "Issues in the Conceptualization, Classification and Assessment of Social Skills in the Mildly Handicapped," Advances in School Psychology, Vol. 6, T. R. Kratochwill (Ed.), New York: Lawrence Erlbaum. 10. Walker, H.M., Stieber, S. & O'Neill, R.E. (1990), "Middle School Behavioral Profiles of Anti-Social and At-Risk Control Boys: Descriptive and Predictive Outcomes," Exceptionality, 1, pp. 61-77. 11. McFall, R. (1982), "A Review and Reformulation of the Concept of Social Skills," Behavioral Assessment, 4, pp. 1-33. 12. Dodge, K. (1986), "A Social Information-Processing Model of Social Competence in Children," Minnesota Symposium In Child Psychology, M. Perlmutter (Ed.), Hillsdale, NJ: Lawrence Erlbaum. 13. Cheng, L.L., Ima, K. & Labovitz, G. (1994), "Assessment of Pacific Islander Students for Gifted Programs," Addressing Cultural and Linguistic Diversity in Special Education: Issues and Trends, pp. 30-45. 14. Montague, M. (1992), A Model for Service Delivery in the Year 2010, [project paper], Washington, DC: COSMOS. 15. DeLeon, J. (1991), Lecture: Special Education Law; workshop on special education law at New Mexico State University. 16. Sawyer, R.J. & Zantal-Wiener, K. (1993), "Emerging Trends in Technology for Students With Disabilities: Considerations for School Personnel," Teaching Exceptional Children, 26 (1), pp. 70-77. 17. Hales, R. M. & Carlson, L.B. (1992), Issues and Trends in Special Education, Lexington: University of Kentucky.
This article originally appeared in the 08/01/1995 issue of THE Journal.