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| Harnessing Technological Tools to Improve Behavior Analytic Service Delivery |
| Monday, May 27, 2024 |
| 8:00 AM–8:50 AM |
| Convention Center, 100 Level, 113 A |
| Area: AUT/DDA; Domain: Service Delivery |
| Chair: Emily Dowling (University of Florida) |
| CE Instructor: Brenna R Griffen, Ph.D. |
| Abstract: Behavior analysts work closely with a variety of individuals to help implement behavioral interventions with high treatment fidelity. Training these individuals can be highly resource intensive, may require lengthy training, and can be difficult or impossible in some settings. By utilizing smart technology to promote effective training, it is possible to minimize resources needed to train individuals, provide easily accessible instruction, and can be applied in remote settings. Smart technology may have the added benefit of producing training with long term maintenance and high treatment integrity. The current investigations evaluated the use of the smart technology platform Guidance, Assessment, and Information System (GAINS) for behavioral procedures including a multiple stimulus without replacement (MSWO) preference assessment, daily living task, and discrete trial training. The purpose of these investigations was to assess whether treatment fidelity could be increased by using GAINS to train individuals to implement behavioral procedures compared to using traditional training methods. |
| Instruction Level: Basic |
| Keyword(s): Smart technology, Training, Treatment fidelity |
| Target Audience: Registered Behavior Technicians (RBTs), Board Certified Assistant Behavior Analysts (BCaBAs), and Board Certified Behavior Analysts (BCBAs) |
| Learning Objectives: At the conclusion of the presentation, participants will be able to: 1) Attendees will learn about the utility of technological tools in ABA 2) Attendees will learn how technological tools can support reliable implementation of behavioral interventions |
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Enhancing Behavioral Interventions for Children With Autism Spectrum Disorder (ASD) With a Smart App for Caregivers and Staff |
| EMILY DOWLING (University of Florida), Corina Jimenez-Gomez (University of Florida), Aliya Yagafarova (Auburn University), Donald A. Hantula (Temple University), John T Nosek (Guiding Technologies) |
| Abstract: Behavior analysts work closely with caregivers, teachers, paraprofessionals, and direct care staff to address the needs of clients. This involves supporting them in the implementation of behavioral interventions, which can be very resource intensive and may be challenging in some settings. Further, ensuring sustained procedural fidelity may require additional monitoring and re-coaching of procedures. A smart technology platform that coaches and guides the implementation of behavioral interventions may be useful for minimizing resources required to support caregivers and direct care staff. By utilizing smart technology, more resources can be provided to individuals in remote settings, allowing underserved communities to access behavior analytic services. This tool also may aid in maintaining high, long-term procedural fidelity and maintenance. In a series of studies, we evaluated the functionality of a smart technology platform, Guidance, Assessment, and Information System (GAINS), to support the correct implementation of interventions by staff and caregivers working with children with autism. Specifically, this presentation will showcase evaluation of correct implementation of discrete trial teaching by behavioral technicians who had limited experience was evaluated with listener responding, intraverbal, and imitation procedures. Findings support the utility of the GAINS platform for improving the procedural fidelity with which behavioral protocols were implemented. |
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Training Professionals to Conduct Preference Assessments With Artificial Intelligence and Traditional Methods |
| BRENNA R GRIFFEN (Louisiana State University-Shreveport), Elizabeth R. Lorah (University of Arkansas), Nicolette Sammarco Caldwell (The University of Arkansas) |
| Abstract: Training professionals to implement preference assessments, such as Multiple Stimulus Without Replacement (MSWO), traditionally requires the presence of an expert, involves lengthy instructional time, requires additional training in natural settings, and necessitates follow-up for skill maintenance. Artificial Intelligence, such as the GAINS (Guidance Assessment and Information System) application, may overcome these challenges by providing professionals with easily accessible, consistent instruction. The current study used an alternating treatment design to compare the use of GAINS with pen and paper self-instructional methods in training five preservice speech-language pathologists to implement MSWO. The results demonstrate significant increases in implementation fidelity for two out of five participants and slight increases for the remaining three while using GAINS. Additionally, when using traditional methods, participants collectively scored the results of the MSWO incorrectly nearly half of the time. These errors never occurred using GAINS. Further, the use of GAINS resulted in a moderate to significant reduction in duration of implementation for four participants. During the follow-up survey, all participants indicated that GAINS had a higher treatment acceptability and was more effective at producing socially significant outcomes. Use of technology has the potential to significantly reduce training time and improve outcomes for direct service staff. |
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Expert Guiding Technology to Help Individuals With Developmental Challenges Build Life and Vocational Skills |
| John T Nosek (Guiding Technologies), ELIZABETH CALDWELL (Chimes Delaware), Matthew Tincani (Temple University) |
| Abstract: A multiple baseline SCED across four paired participants of Direct Service Providers (DSPs) and adults with IDD with a diagnosis of autism (consumers) was conducted. Chimes staff identified the task of doing the laundry as a very useful and challenging task. As part of their job responsibilities, Direct Service Providers (DSPs) are tasked with helping consumers learn to do the laundry. Dependent variables were 1) DSP Program Fidelity Performance, the percentage of teaching steps correctly implemented, and 2) Consumer Performance, the percentage of Independent completion of the 18 steps of a task analysis to sort and wash clothes. The Independent Variable was expert guiding technology to support instructors in providing Task Analysis instruction while collecting data on consumer performance. In baselines, DSP program fidelity was low and consumers failed to learn the task. With the expert guiding technology, DSP program fidelity improved and consumer independent performance improved. Average Inter Observer Agreement (IOA) (> 25% sessions) between primary and secondary observers for both DSP Program Fidelity and Consumer Performance was 91% to 99%. Three of four DSPs completed usability and social validity surveys (average 4.2/5 with 1 = strongly disagree, 5 = strongly agree). |
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