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Katya Hill, Ph.D.,
CCC-SLP, Assistant Professor
Center for Assistive Technology Education And Research (C.A.T.E.R.)
Edinboro University of Pennsylvania
102, Compton, Edinboro University of Pennsylvania,
Edinboro, Pennsylvania 16444
Historic approaches to the AAC assessment place teams in the position of being bombarded by an endless list of features and devices from which to choose. Teams frequently resort to a checklist method and trial-and-error strategies to make the most appropriate decisions about AAC systems. At an ASHA AAC Leadership Conference, Sharon Glennen (2000) referred to the idiosyncratic nature of our assessment methodology. Using these methods means that an augmented communicator is likely to receive three different AAC solutions from three different teams.
The AAC Language-based Assessment Model conceptualizes an alternative to a technology and feature-focused assessment and intervention process (Hill, 2001). The model serves as a metaphor for building success on a strong foundation. Starting at the bottom, each level of the process should be completed and supported with evidence before moving up to the next level. A multi-disciplinary team approach is recommended with each level of decision being led by an individual with the appropriate professional knowledge and experience. Quantitative performance measurement is essential. Success may only be reached through the application of a structured and scientific approach to assessment and intervention.
The Goal: The first element of a strong foundation is agreement on the goal of AAC. For people who rely on AAC, success in life can be rather directly a function of the ability to communicate. Full interpersonal communication substantially enhances an individual’s potential for education, employment, and independence. Therefore, it is imperative that only the most effective interactive communication is the goal of the AAC intervention process.
Language Models: The next component of the model is knowledge of normal language development and normal language use. Use of language requires both linguistic competence (understanding the rules), and linguistic performance (using these rules). The basic rules of language apply to AAC. The methods used to achieve interactive communication should be considered in terms of 1) knowledge of normal language development, 2) knowledge of normal language use, and 3) the evidence on AAC performance by individuals who rely on AAC systems.
AAC Language Representation Methods: The third component of the model is knowledge of the three basic methods used to represent language on AAC systems. They are single meaning pictures, alphabet-based systems, and semantic compaction (Romich, Vanderheiden, & Hill 2000). It is important to know the attributes of these methods in order to present evidence to the consumer to make informed decisions.
Outcomes: Determining desired outcomes is based on the previous foundational steps of the model. Teams should not develop outcomes before choosing language representation methods, but need to develop outcomes at this point in the process in order to evaluate technology features. Outcome measures are objective criteria, usually developed during the assessment and recommendation process that can be used to judge the effectiveness of both devices and services. The selection of performance summary measures should coincide with the development of outcomes. Until recently, AAC practitioners have relied on traditional qualitative data gathering methods to collect evidence and measure outcomes. Today, the use of automated performance monitoring facilitates the quantitative analysis of performance data (Hill & Romich 2000a).
For teams in the AAC assessment process, outcomes cannot be developed until the language representation methods have been chosen. First, the language representation methods determine whether an outcome is achievable. For example, use of word prediction to access extended vocabulary cannot be an achievable outcome on a system only supporting single meaning pictures. For the outcome mentioned above, use of performance monitoring tools would indicate the frequency of use for word prediction along with the vocabulary accessed using this method. Second, consideration of language development and communicative competence should be reflected in the identified outcomes. Again, observational methods and performance summary measures can be gathered that reflect a language approach to the assessment and intervention processes. Finally, the outcomes identified by a team working with an individual relying on AAC should reflect their long-term commitment to the goal of AAC.
Technology Models: A set of specifications for the features of the AAC system will guide the selection process. Many technology considerations can be made. These decisions should be weighed against how technology considerations influence the chosen language representation methods, selection rate, and communication rate. Typically, these considerations can be divided into two categories: required features and desirable features. Decisions must be made relative to physical access, output modes, mounting, and other areas. Quantitative measurement of selection rate in bits per second (Romich, Hill, & Spaeth, 2001) assures that this component of communication rate is optimized. The traditional feature match process is applied at this level as long as teams make recommendations that have a positive impact on effective communication. Approaches that avoid feature choices based on personal preferences that have little functional application to the chosen language representation methods should be encouraged. Features such as automated language activity monitoring (LAM) allow evidence-based practice to occur and should be included whenever possible. (Hill & Romich, 2000b)
AAC Devices: Consider only AAC devices that meet the requirements determined in the previous steps. First include those devices that support the chosen language representation method(s). From those, choose the devices that have the required technology features that maximize selection rate and communication rate. The final step is to choose the devices that have other desirable traits. Performance measurement data can be used to support the team’s technology solution and provide a starting point for intervention (Hill & Romich 2001b). The use of this information can strengthen the proposal for funding and intervention. The consideration of cost before this final step suggests a compromise in the personal achievement of the individual and is thus inappropriate.
Therapy: Most people who rely on AAC can benefit from the ongoing services of speech-language pathologists and other professionals. Evidence-based practice using observational methods, baseline data collection, language activity monitoring, and automated performance measurement tools yields the most effective results. This includes logging of communication and the editing, coding, analysis, and reporting of summary measures based on language sampling in controlled contexts and natural environments over time. In addition to regular therapy, this information can provide a scientific process for characterizing performance or communicative competence, and a valuable record of progress. Today’s growth in telerehabilitation services and distance learning is opening up new venues and options for intervention. For example, the AAC Institute web site www.aacinstitute.org includes a language sample library as a resource for evidence on performance differences among individuals with various profiles and among various AAC systems.
Hill, K. (2001). Achieving success in AAC: Assessment and intervention. AAC Institute Press.
Hill, K., & Romich, B. (2000a). A proposed standard for AAC and writing system data logging for clinical intervention, outcomes measurement, and research. RESNA ’99 Proceedings, (pp. 22-24). Arlington, VA: RESNA Press.
Hill, K., & Romich, B. (2000b). AAC Best Practice Using Automated Language Activity Monitoring. “Proceedings of the Ninth Biennial Conference of ISAAC,” pp. 761-763.
Hill, K., & Romich, B. (2001b). AAC Clinical summary measures for characterizing performance. CSUN Conference Proceedings, Los Angeles, CA. www.csun.edu/cod/conf2001/proceedings/0098hill.html
Glennen, S. (2000). AAC assessment: Myths and realities. AAC Leadership Conference, American Speech Language Hearing Association. Sea Island, Georgia.
Romich, B.A., Hill, K.J., & Spaeth, D.M. (2001). AAC selection rate measurement: A method for clinical use based on spelling. RESNA ’01 Proceedings, (pp. 52-54). Arlington, VA:RESNA Press.
Romich, B.A., Vanderheiden, G.C., & Hill, K.J. (2000). Augmentative Communication in the Biomedical Engineering Handbook second edition. Bronzino, J.D., editor. Pp. 144-1 through 8. CRC Press. Boca Raton, FL.
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