"Assisting Writers with Assistive Technology"
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Maggie CollinsMaggie Collins is earning her Ph.D. in Rhetoric and Writing at Bowling Green State University. Her research interests include writing program administration, writing assessment, and composition pedagogy. Before attending BGSU, she attended DePaul University where she earned her M.A. in Writing, Rhetoric, and Discourse while working at DePaul University’s University Center for Writing-based Learning as a peer writing tutor. ContentsAssistive Technology Overview Cont. Framework for AT in Writers Centers |
Assistive Technology OverviewAs previously mentioned, assistive technology is a tool that heightens one’s productivity or capabilities (Hetzroni & Shrieber, 2004). In “Assistive Technology for Postsecondary Students with Learning Disabilities: An Overview,” Marshall Raskind and Eleanor Higgins (1998), learning disability experts, provide a summary of assistive technology and its use in postsecondary education. For written language, they argue word processing, spell checking, proofreading programs, outlining/brainstorming, speech recognition, speech synthesis/screen reading, and word prediction are AT that can help LD students with writing (1998). However, this list is slightly more extensive than others as most researchers consider text-to-speech software, word processors, speech recognition software, and organizational tools as the main forms of assistive technology (Urquhart Engstrom, 2005). It is crucial to note that AT comes in high-tech and low-tech forms; tools do not have to be computer software—they can be as simple as colored pens and rulers, which can be employed in various ways to help individuals process and write material. Raskind and Higgins (1998) cited a three-year study by the Center on Disability at CSUN on students with learning disabilities who used various types of AT. During the first year, one study was on speech synthesis/screen review, or text-to-speech, assistive technology; this software refers to a synthetic voice reading what is on the screen to users, which some users may use to hear their mistakes and correct them (Raskind & Higgins, 1998). The study determined the effectiveness of the tool’s ability to increase “students’ efficiency at proofreading written compositions” by comparing students’ successes with AT, with a human reader, and without assistance (Raskind & Higgins, 1998, pg. 35). They concluded that the students found substantially more errors while using AT than when they proofread with a human reader or without assistance—the subjects used the technology to find more specific types of errors, like typographical mistakes and incorrect “capitalization, spelling, and usage” (Raskind & Higgins, 1998, pg. 35). In this study, the AT always did better than no assistance; however, human readers helped the students find mechanical grammar errors better than the AT (Raskind & Higgins, 1998). CSUN’s study provided great results to prove AT’s effectiveness for LD students, but some may disagree with the reason why that form of AT helped the students. Diane Campbell, author of “Assistive Technology and Universal Instructional Design: A Postsecondary Perspective” (2004) and director of the B.E.S.T. Program in Boston, would argue that the students in CSUN’s study improved their scores because they “learn primarily through oral means” (pg. 170), which would allow the students to listen and create or edit their drafts during their composing processes. Focusing on learning preferences or styles suggests that non-LD writers would benefit from AT the same way as LD writers. Raskind and Higgins (1998) also believe all people would improve their skills with AT, but they revolve their research back to people with LDs by asserting that these programs would be better for students with “written language deficits” (pg. 32). |