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  1. Home
  3. Research Domains
  5. Education
  7. Specialist Studies In Education
  9. Special Education And Disability
  11. Assistive Technology Adoption In Special Education: A Logistic Regression On Teacher, Classroom, And Student Factor In Taiwan.
  1. Home
  3. Research Domains
  5. Education
  7. Specialist Studies In Education
  9. Special Education And Disability
  11. Assistive Technology Adoption In Special Education: A Logistic Regression On Teacher, Classroom, And Student Factor In Taiwan.

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Assistive technology adoption in special education: A logistic regression on teacher, classroom, and student factor in Taiwan.

Hsin-Yi Kathy Cheng1,2, Wei-Ting Shen1, Yu-Chun Yu3

  • 1Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, TaoYuan, Taiwan.

Assistive Technology : the Official Journal of RESNA
|January 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Assistive technology (AT) adoption in special education is influenced by teacher experience and classroom type. More experienced teachers and those in self-contained classrooms are more likely to use AT, improving educational outcomes.

Keywords:
Assistive technologydisabilityinclusive classroomsspecial education

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Area of Science:

  • Special Education
  • Educational Technology
  • Rehabilitation Science

Background:

  • Assistive Technology (AT) plays a crucial role in supporting students with disabilities.
  • Understanding factors influencing AT adoption is vital for effective implementation in special education settings.
  • Teacher experience, classroom environment, and student needs are key considerations for AT integration.

Purpose of the Study:

  • To investigate the factors influencing the adoption of assistive technology (AT) among special education teachers in Taiwan.
  • To identify the relationship between teacher characteristics, classroom types, student disability categories, and AT utilization.
  • To explore the impact of AT adoption on teaching effectiveness and educational outcomes for students with disabilities.

Main Methods:

teacher’s preparation
  • Logistic regression analysis was employed to analyze data from 702 special education teachers in Taiwan.
  • Key predictors examined included teacher experience, classroom type, and student disability categories.
  • Utilization rates for different types of AT (mobility, communication, positioning) were calculated.

Main Results:

  • Mobility aids (91.5%) had the highest utilization, followed by communication (69.8%) and positioning aids (58.0%).
  • Teachers with over 10 years of experience (OR=1.486) and those in self-contained classrooms (OR=1.472) showed higher AT adoption likelihood.
  • Student disabilities significantly influenced AT use; mobility aids correlated with physical disabilities, while communication devices were more common for intellectual/emotional disabilities.

Conclusions:

  • Teacher experience and classroom setting are significant predictors of assistive technology adoption in special education.
  • Ease of use and paraprofessional support influence teachers' willingness to adopt AT.
  • Increased AT adoption positively correlates with teaching effectiveness, underscoring the need for targeted professional development and resource allocation.