Date of Graduation

8-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Curriculum and Instruction (PhD)

Degree Level

Graduate

Department

Curriculum and Instruction

Advisor/Mentor

Elizabeth Lorah

Committee Member

Christine Holyfield

Second Committee Member

Christian Goering

Keywords

preference assessment;preservice training;speech-language pathologist;technology

Abstract

Identifying preferred stimuli is an initial step in many evidence-based educational programs for young children. Preference assessments, such as the Multiple Stimulus Without Replacement (MSWO), provide an empirically validated way of identifying and ranking these stimuli. Traditional methods of training professionals to implement MSWO often require the presence of an expert trainer, involve lengthy instructional time, require additional training to transfer skills into the clinical or classroom setting, and necessitate follow-up training to maintain skills over time. Intelligent agent technology may overcome these challenges by providing professionals with easily accessible, consistent instruction. The purpose of the current study was to compare the use of intelligent agent technology with pen and paper self-instructional methods in training preservice speech-language pathologists to implement MSWO with young children. The results demonstrate significant increases in implementation fidelity for two out of five participants and slight increases for the remaining three during the intelligent agent condition. Additionally, the participants collectively scored the results of the MSWO incorrectly nearly half of the time while using traditional methods. In contrast, all participants were able to score and interpret the results accurately during every session using intelligent agent technology. There was a significant reduction in duration of implementation for two participants, a moderate reduction for two participants, and a slight reduction for the remaining participant while using intelligent agent technology. Results of the follow-up survey suggest that all participants found intelligent agent technology had a higher treatment acceptability and was more effective at producing socially significant outcomes than traditional methods. Recommendations for clinicians and future research are discussed.

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