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

Purpose: An increasing number of autistic students in the United States are seeking post-secondary education. In response, some post-secondary institutions have established Autism Support Programs (ASP) to address the comprehensive needs of this population. There is little up-to-date, comprehensive information about which institutions host these programs, what types of services they offer, and what is required to access them.

Methods: Expanding on previous research, we introduce a new method, which utilizes established data science techniques, to identify ASPs at post-secondary institutions in the U.S. Our technique also allows us to identify the characteristics of the ASPs, including admissions requirements, cost, structure, and supports offered.

Results: Results highlight our method is more efficient and more robust than previous methods from the literature. For example, we identify 49 schools hosting ASPs that were not identified in past literature searches. We report on the characteristics of identified ASPs such as application process, most common supports and program cost.

Conclusion: The bi-directional change in the number of ASPs shows that this is an evolving field, requiring automated tools to enable regular updates to data. Although it is promising that a relative handful of U.S. schools have established these programs, a large majority of post-secondary institutions have not, and for those that host them, barriers to access exist, including the necessity of an ASD diagnosis, coupled with up-front and ongoing costs.