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Applying Data Science Practices to Identify Characteristics of Postsecondary Autism Support Programs From Their

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A new data science method efficiently identifies Autism Support Programs (ASPs) at U.S. colleges. While promising, many institutions lack these programs, and access barriers like diagnosis and costs persist for autistic students.

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Area of Science:

  • Higher Education
  • Autism Spectrum Disorder
  • Disability Support Services

Background:

  • Increasing enrollment of autistic students in post-secondary education.
  • Establishment of Autism Support Programs (ASPs) by some institutions.
  • Lack of comprehensive, up-to-date information on ASPs, their services, and access requirements.

Purpose of the Study:

  • To introduce a novel data science method for identifying U.S. post-secondary Autism Support Programs (ASPs).
  • To characterize identified ASPs, including admissions, costs, structure, and support services.
  • To address the need for updated, comprehensive data on ASPs.

Main Methods:

  • Utilized established data science techniques to identify ASPs.
  • Expanded on previous research methodologies.
  • Enabled identification of ASP characteristics: admissions, cost, structure, and supports.

Main Results:

  • The developed method is more efficient and robust than prior approaches.
  • Identified 49 previously uncatalogued ASPs.
  • Detailed characteristics of identified ASPs, including application processes, common supports, and program costs.

Conclusions:

  • The field of ASPs is dynamic, necessitating automated tools for data updates.
  • A significant majority of post-secondary institutions do not offer ASPs.
  • Barriers to ASP access include mandatory ASD diagnosis and associated financial costs.