Presented at ISEC 2000

Finding Your Way to Effective Technology: When Can Computers Help?

Colin J. Laine - The University of Western Ontario, Canada

Abstract

Given the array of demands made on education - especially in an era of increased integration of special needs learners in our schools, how can teachers, caregivers and employers stay current about technology that "works" for students and employees with diverse learning needs? This session will highlight the Centre for Communicative and Cognitive Disabilities. CCCD is a Canadian university-based (UWO) centre of specialization established in 1985 to improve educational opportunities for individuals with communication impairments. The Centre's research endeavours concentrate on the learning and communication problems, and how computer technology can help bridge the educational gaps that interfere with individual's abilities to reach their full potential. We will demonstrate ongoing ways of accessing information through CCCD. This presentation will include a look at the resources available through our website (http://www.uwo.ca/cccd). The successful computer projects and resources which members of CCCD have investigated and continue to investigate with teachers, caregivers and employers will be shared.

Given the acceleration of educational and employment legislation and policies in many provinces in the past decade, many educators and employers have turned to technology in the hope of making their environments more accessible for individuals with disabilities. Further complicating the inclusion dilemma, has been the explosion of information and the demand that students and employees to be linguistically capable. Thus, our society is demanding learning and working settings be more inclusive concurrent with pressures on those settings to produce individuals with the competencies to deal with an information-burgeoning work world. Given the array of demands made on education - especially in an era of increased integration of special needs learners in our schools, how can teachers, caregivers and employers stay current about technology that "works" for students and employees with diverse learning needs?

Over the past several years, faculty members and graduate educators of the Centre for Communicative and Cognitive Disabilities(CCCD) at The University of Western Ontario have studied effective uses of technology with students with special needs. Through projects and theses, they have investigated, often in their own classrooms, how a particular aspect of technology could benefit students with learning disabilities and other special needs. These researchers recorded gains in the skills and attitudes of most of their students when using computers to facilitate their learning. Through the web site, CCCD continues to share examples of a variety of projects in which teachers have documented marked gains in the learning of students when they were provided with appropriate tools and strategies; it provides an updated directory of software and specialized devices which we have used successfully; and provides links to other resources, both in Canada and globally. This particular paper will focus on one specific area of our research: using assistive technology in developing written expression.

Students with impaired or emergent language skills are hampered by failure to recognize the words they wish to use (Bjaalid, Hoien, & Lundberg, 1995 ; Bruck, 1993). Their written language is improved significantly through intervention in word finding, word fluency, and contextual cueing ( Corrigan & Stevenson, 1994; Wiig & Semmel, 1980 ); and, word-prediction (Heinisch & Hecht, 1993 ; Laine & Follansbee, 1994; MacArthur, 1998 ). If, as Wiig & Semmel (1980) showed, word?finding skills can be assisted considerably through cueing, then how can we effectively assist cueing for low?language learners?

Assistance through word completion or word prediction has been extensively promoted in the past decade as a productivity tool for all; but, does its use help a writer's productivity? Vendors claim that specialty devices or programs can be installed easily and that word-prediction is an agent to improved written expression - "...one component . . . is the promise of increased worker productivity" (Bergeson, 1991, p. 130). Despite vendors' claims of considerable positive results, the results of research into this area are equivocal (cf., Burger, 1997 ; Koester & Levine, 1994a, 1994b ; Sommers, at al 1994; Treviranus & Norris, 1987 ; Venkatagiri, 1994 ). Little research exists regarding the efficacy of implementing specialised access systems to increase personal or professional written productivity for persons with disabilities. This is especially true of word-prediction programs, whether operated manually (by keystroke) or orally (voice-activated). The primary question for this exploratory work was whether or not word-prediction provided more immediate cuing to facilitate word finding & fluency (through word count) and flexibility (through variety of words used) - elements critical to effective written expression (Wiig & Semmel, 1980).

There are two types of predictive dictionary: oral word?prediction and manual word-prediction. Laine & Wilkinson (1997) concluded that oral word-prediction (speech-to-text) requires a higher cognitive functioning level as well as a good language ability for it to be an effective writing tool. This conclusion led us to focus our studies on manual word-prediction (keystroke-to-text) technology to assist word?finding with low?language children and adults. As each keystroke is made, the window containing the word list is modified to predict the word (or phrase) from the main dictionary. Our work has focussed on elementary and secondary language-impaired students in rural and urban schools with a range of learning environments from full regular class inclusion to separate class settings.

Little is known empirically about the productive interaction between this technology and the user with disabilities; its role in learning; or, its impact on independence and productivity. Many of the manual word-prediction systems (e.g., Aurora, Co-Writer, E-Z Writer, TextHELP!) have attempted to incorporate increasingly large dictionaries; phrase & word lists; prefix and suffix panels; and larger phrase prediction based on either usage or through grammar rules. Such refinements do not necessarily lead to an increase in productivity (Koester & Levine, 1994; Venkatagiri, 1994). Similarly, much of the research on oral word-prediction has focussed on its technical aspects - especially the merits of continuous speech systems compared to discrete speech systems. Continuous-speech systems may react too quickly for the low-functioning user to discern and correct errors "on-the-run".

Thus, providing a language-impaired student with a sophisticated speech recognition system may in fact create more of a barrier than an assistance. Given our client base, we need a system that will encourage the user to find and use words; and to be able to use specific tools as needed rather than have them loaded continuously (albeit transparently). These differences seemed significant enough to warrant two discrete sets of studies.

Issues

Several issues from the literature focussed our attention in relating word-prediction and written expression.

1. Written language in language-delayed students is improved significantly through intervention in Word Finding, Fluency & Flexibility (Wiig & Semmel, 1980).

2. The use of cues in intervention facilitate or improve word recall and retrieval (e.g., German, 1991; Hutchinson, 1983; MacArthur, 1998; McGregor, 1989; Wing, 1990). However, the word-finding efforts of language delayed individuals result in idiosyncratic patterns of expression in spontaneous speech and writing.

3. Such interventions facilitate language use better if used across the curriculum and in non language-class environments (Wiig & Semmel, 1980).

4. Using word-processing on computers is directly related to significant growth in writing ability and motivation for writing in children with severe learning disabilities (Crealock & Sitko, 1990).

5. There is substantial literature supporting the use of (voice) feedback with word-processing (e.g., Cochran-Smith, 1991; Macarthur, 1998; Morocco & Neuman, 1986; Sitko, Sitko & McBride, 1991) to improve writing with adolescents with severe learning and language disabilities.

6. Students are effective in using simple fact-retrieval especially when it was menu-driven (Edyburne, 1991).

These issues have reinforced our belief that different modes of text creation would affect the writing among low-functioning individuals with communication impairments.

The critical task is to get past the wall of words. Placing a word-list in the hands of the writer so that words or phrases emerge as the letters unfold should cue the writer by predicting what word might be wanted; adding an aural cue would be especially useful to visually dysfunctional writers. Maintaining a flow of ideas is one of the keys to effective language-use, problem?solving and good written expression. So, conceiving the device as a writing "toolkit", which would allow users to switch on any of the tools as they need them, should provide the most effective, individualised environment. For the technology to be effective, it is critical to ensure that the cueing device is appropriate to the needs that have been assessed (Heinisch & Hecht, 1993). For a student with very low language facility, a manual word prediction system provides more effective cueing than a sophisticated ("sexy") speech?recognition system. Also we have found that such students have a greater sense of control over slower manual word?prediction than they do over speech?recognition systems.

Discussion and Conclusions

Reflecting on the original issues, results of our preliminary studies do point to a better understanding of how assistive technology impacts on the classroom and on the workplace. The technology and the training have resulted in significant improvements to the work environment for the clients.

Several researchers in writing (eg., McGregor, 1989; Smith, 1975; Wiig & Semmel, 1980) have demonstrated that word-generation and engaged time are key to written-language development. We found that the students stayed on-task for a greater length of time using computers for writing over pencil and paper. They remained marginally longer on task using WriteAway2000 than they used another word-processor. While the difference between the two computer programs was not statistically significant, the students were, according to the teacher more focussed on the task of composing written work while using WriteAway2000. Word-generation (fluency) has been demonstrated as a precursor to effective written expression.

In this study, Fluency (word count) increased for all students from notebooks to word-processor to WriteAway2000; but word variety (Flexibility) was not found to increase significantly. We did see an increase in the variety and complexity in the words the students used in their journals and in their general written language activities. Given the severity of the students' language delay, one might expect their variety of word use to occur more slowly. The number of spelling errors decreased while using WriteAway2000, but they did not significantly decrease when the students used their notebooks. The majority of spelling errors appeared to fall into the category of "typos" (neighbouring keys; repeat key errors; capitalization errors) - which may indicate the students did not proof-read their work before submitting it to the teacher.

We also found that the greatest acceleration in written expression has been experienced by those students with the more diverse communication abilities; where the teachers were fully conversant with the program and used it themselves; where the teachers have incorporated the writing tool as "normal" for everyone in the class regardless of whether or not they had been identified as exceptional and in need of specialised technology; and where the teachers encouraged the students to use any tools as they thought appropriate. Thus any of the classes might have twenty or thirty students using WriteAway2000 with as many variations in the preferences (tools) being set, but their not showing on the computer screens nor determining the teachers' responses to the students' compositions.

Summary

The work on word-prediction technologies in one example of the research work of members of CCCD. This work is summarised and disseminated on the CCCD website, in articles and books and through various conferences and colloquia. Our major concern at this point is the effect that assistive technology systems have had on written composition and problem?solving. They tend to demand a micro?focus, thus different metacognitive and problem?solving strategies have to be considered when training potential users. To be effective, we believe that these systems are not self?teaching tools. Any accommodation that helps persons with disabilities is nothing without an accurately planned, individualised training program. We found the following points also critical to successful implementation:

While the answers to the questions and ideas are somewhat speculative, we maintain that the use of assistive technologies related to writing are directly associated with the improvement of our clients' written expression.

References

Bjaalid, I?K.; Hoien, T.; Lundberg, I. (1995) A comparison of components in word recognition between dyslexic and normal readers. Scandinavian Journal of Educational Research. Vol 39(1) 51?59.

Bruck, M. (1993). Word recognition and component phonological processing skills of adults with childhood diagnosis of dyslexia. Special Issue: Phonological processes and learning disability. Developmental Review, 13(3) 258?268.

Burger, S. R. (1997). Spontaneous communication in augmentative and alternative communication (AAC): A comparison of dynamic display and Minspeak®. Lafayette, IN: College of Wooster, Purdue University.

Corrigan, R., & Stevenson, C. (1994). Children's causal attributions to states and events described by different classes of verbs. Cognitive Development, 9, 235?256.

Heinisch, B., & Hecht, J. (1993). Predictive word processors: A comparison of six programs. TAM Newsletter, 8, 4-5,8-9.

Information Services Inc., (1998). WriteAway2000: The all-in-one toolkit promoting written expression and literacy. St. John's Newfoundland: Author.

Koester, H. H. , & Levine, S. P. (1994). Learning and performance of able-bodied individuals using scanning systems with and without word-prediction. Assistive Technology, 6, 42-53.

Laine, C. J., & Follansbee, R. (1994). Using word-prediction technology to improve the writing of low-functioning hearing-impaired students. Child Language Teaching and Therapy, 11, 283-297.

Laine, C. J., & Wilkinson, W. (1997). Improving employment opportunities for persons with disabilities through training: A case study using speech recognition systems. Training & Development.

MacArthur, C. A. (1998). Word Processing with Speech Synthesis and Word Prediction: Effects on the Dialogue Journal Writing of Students with Learning Disabilities. Learning Disability Quarterly, 21, 2, p151?66.

Sommers, R. K. et al. (1994). Word skills of children normal and impaired in communication skills and measures of language and speech development. Journal of Communication Disorders, 27, 223-240.

Treviranus, j. & Norris, L. (1987). Predictive programs: Writing tools for severely physically disabled students. Proceedings of the 10th RESNA conference. Pp 130-132.

Venkatagiri, H. S. (1994). Effect of window size on rate of communication in a lexical prediction AAC system. Augmentative and Alternative Communication, 10, 105-112.

Wiig, E. H., & Semmel, E. M. (1980). Language assessment and intervention for the learning disabled. Columbus, OH: Merrill.

Index