Go to previous article
Go to next article
Return to 2002 Table of Contents
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 6444
Prentke Romich Company & University of Pittsburgh
1022, Heyl Road
Wooster, Ohio 44691
Phone: 330-262-1984 extn 211
The goal of AAC intervention is to provide the supports and services that result in the most effective communication possible for the individual. Various AAC modalities such as natural speech, vocalizations, gestures, sign, and/or eye gaze may be employed for overall communicative competence. However, communicative effectiveness using assistive technology is accomplished through spontaneous novel utterance generation (SNUG). For service providers, supporting an individual moving toward achieving the goal requires a systematic language approach to assessment and intervention following the principles of evidence-based practice. According to the ASHA Scope of Practice (2001) "speech-language pathology involves...using instrumentation...to observe, collect data, and measure parameters of communication...in accordance with the principles of evidence-based practice."
Conventional methods of AAC system performance monitoring are based on personal observation and video or audio recording with subsequent observation, timing, and/or transcription. Moving an augmented communicator toward communicative competence requires observation of the individual in real-life situations and the collection of performance data (Light, 1998). However, only 10% of speech-language pathologists report that they use language sampling and observations as part of their standard data collection battery (Beck, 1996). In addition, the cost of this approach is high because of the human time investment. Consequently, in the past professionals seldom collected data on the actual daily environmental use of AAC systems by consumers.
Automated data logging and the language activity monitor (LAM) are beginning to contribute significantly to the efforts to collect objective data regarding the daily communication activity of users of AAC devices. Recent developments in LAM tools allow service providers and consumers to obtain and analyze AAC language samples on a routine basis (Hill 2000). LAM tools fall into two areas. 1) The LAM function records the language data. 2) Language analysis computer programs provide for the semi-automation of the processes of editing the data into utterances, analyzing the data, and reporting specific information. The LAM makes quantitative data on semantic and syntactic diversity readily available for clinical use. In addition, since LAM data include language event time stamps, the identification of language representation method (LRM) usage, communication rate (in words per minute), and selection rate (in bits per second) is possible.
The LAM function creates a record of the time of day and content of each language event, the generation of one or more letters or words. A recent enhancement is the addition of a mnemonic code to represent the way in which the language event was generated (single meaning picture, word selection from a menu, etc.) Non-language events, such as device operation functions, also can be recorded. Standardization of the reporting protocol assures compatibility with various recording, editing, and analysis programs.
The LAM function is built into modern high performance AAC systems. For older systems with a serial port representation of language events, a computer program (PC-LAM) or an add-on LAM device can be used.
The LAM data can be uploaded to a computer by attaching the AAC system (or LAM Device) to a computer. Using the LAMterm program, the logfile can be uploaded to the computer. Once in the computer, the logfile can be opened by editing and analysis software. Hill (2000, 2001) has documented the operational transcription procedures required to edit logfiles. A first step in the process is the editing of the logfile into utterances. This function includes the stripping away of the time stamps and the non-language data. Typically, this process requires some manual intervention since most people who rely on AAC do not use sentence terminators (".", "?", and "!") in their routine spoken communication. The editor features two side by side windows on the computer screen. This facilitates reference to the raw time stamped data during the editing task. The time stamps can offer useful clues as to which words are part of the same utterance.
The development of an editing and analysis program has been a recent and ongoing task. The program produces a number of quantitative summary measures. These include number of utterances, total number of words, number of different word roots, mean length of utterance in both words and morphemes, average and peak communication rate, selection rate, a rate index, and errors. The program also generates word lists ordered alphabetically or by frequency of occurrence. Other analysis programs, such as Systematic Analysis of Language Transcripts (SALT) (Miller & Chapman, 1983), from The University of Wisconsin are available that would yield additional information that could be of value.
The most significant implication of language activity monitoring is in the area of clinical intervention. Building communicative competence of individuals using AAC requires procedures for specifying target skills and completing baseline observations to document performance. The LAM allows for the collection of baseline and performance data and the generation of a performance measurement report (Hill & Romich 2001). In addition, through the use of the LAM the clinical intervention process can yield better results in less time at a lower cost. Here are some examples of how LAM is being used clinically.
A common approach to intervention is a weekly therapy session. Sessions frequently include the review and practice of old vocabulary, the introduction of new vocabulary, instruction on system use, and modeling of normal communication interaction. The AAC clinician frequently has little or no quantitative information on what happens in the natural environment between sessions. Using LAM, the first step in the therapy session is the uploading of LAM data. For a quick search, this file then can be pasted into a word processor and the FIND function can be used to look for uses of words covered in the previous session. They can be counted and the context within which they were used can be assessed.
Identification of vocabulary usage and the language representation method(s) being employed can be useful in guiding therapy. Tullman & Hurtubise (2000) used the LAM to collect and analyze the vocabulary usage patterns of a preschooler. Various pilot studies have been conducted that identify the language representation method(s) and vocabulary diversity of school-age children and persons with ALS using AAC systems. The most effective users of AAC systems have been documented to use semantic compaction for between 90% and 100% of communication (Hill 2001). Observation of much lower use of semantic compaction offers a clear opportunity for improvement.
The AAC Institute, established in 2000, is a not-for-profit charitable organization providing information and services valued by many of the stakeholders who have an interest in the field of AAC. The AAC Institute is dedicated to advancing the communication of people who rely on AAC. It promotes the goal of AAC, the AAC Rules of Commitment, and evidence-based AAC practice. This mission is accomplished through service delivery, research, information dissemination, and education. Geographic scope is worldwide. For the people who rely on AAC and for those who have the responsibility of providing services to them, timely dissemination of evidence or data is critical for effective clinical practice.
In order to facilitate widespread application of accumulated automated performance data from developed research and commercially available sources, an AAC database of language samples is being established by the AAC Institute. The web-based AAC Language Sample Library (LSL) provides the field with a systematic and efficient database to support clinical intervention, outcomes measurement, and research activities. Similar databases are maintained for language samples of persons who use natural speech (MacWhinney 1996; Miller 1983). The AAC language sample library allows for 1) readily available samples for clinicians to use for comparison purposes, 2) readily available samples for consumers to use for comparison purposes, 3) data to support the identification and measurement of AAC outcomes, and 4) data to support AAC research.
Beck, A.R. (1996). Language assessment methods for three age groups of children. Journal of Children's Communication Development. 17(2): p. 51-66.
Higginbotham, D.J., Lesher, G.W., & Moulton, B.J., (1999). Development of a voluntary standard format for augmentative communication device logfiles. In Proceedings for the RESNA '99 Annual Conference. Arlington, VA: RESNA Press.
Hill, K. (2000). AAC performance monitoring: Tools for quantitative research. International Society for Augmentative and Alternative Communication (ISAAC) Research Symposium. Washington, DC. August.
Hill, K. (2001). The development of a model for automated performance measurement and the establishment of performance indices for augmented communicators under two sampling conditions. Unpublished dissertation. University of Pittsburgh. Pittsburgh, Pennsylvania.
Hill, K. J. & Romich, B. A. (1999). AAC Language Activity Monitoring and Analysis for Clinical Intervention and Research Outcomes. Proceedings of Technology and Persons with Disabilities. CSUN, Los Angeles, California.
Hill, K., & Romich, B. (2001). Clinical summary measures for characterizing AAC performance. In Proceedings of the C-SUN Conference, Los Angeles, California.
MacWhinney, B. (1996). The CHILDES system. American Journal of Speech-Language Pathology 5: 5-14.
Miller, J. F. & Chapman, R.S. (1983). Systematic analysis of language transcripts (SALT). San Diego, College Hill Press.
Miller, J., & Chapman, R., (1991) SALT: A computer program for the Systematic Analysis of Language Transcripts. University of Wisconsin: Madison, WI.
Tullman, J., & Hurtubise, C. (2000). Language activity monitoring on a young child using a VOCA. In Proceedings of the ISAAC Annual Conference, Washington, DC.
Go to previous article
Go to next article
Return to 2002 Table of Contents
Return to Table of Proceedings