Study Progression and Success of Autistic Students in Higher Education: A Longitudinal, Propensity Score-Weighted Population Study

Research output: PhD ThesisPhD-Thesis - Research and graduation internal

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Abstract

Study Progression and Success of Autistic Students in Higher Education A Longitudinal, Propensity Score-Weighted Population Study In this PhD thesis, we aimed to improve understanding of the study progression and success of autistic students in higher education by comparing them to students with other disabilities and students without disabilities. We studied their background and enrollment characteristics, whether barriers in progression existed, how and when possible barriers manifested themselves in their student journey, and how institutions should address these issues. We found autistic students to be different from their peers but not worse as expected based on existing findings. We expect we counterbalanced differences because we studied a large data set spanning seven cohorts and performed propensity score weighting. Most characteristics of autistic students at enrollment were similar to those of other students, but they were older and more often male. They more often followed an irregular path to higher education than students without disabilities. They expected to study full time and spend no time on extracurricular activities or paid work. They expected to need more support and were at a higher risk of comorbidity than students with other disabilities. We found no difficulties with participation in preparatory activities. Over the first bachelor year, the grade point averages (GPAs) of autistic students were most similar to the GPAs of students without disabilities. Credit accumulation was generally similar except for one of seven periods, and dropout rates revealed no differences. The number of failed examinations and no-shows among autistic students was higher at the end of the first semester. Regarding progression and degree completion, we showed that most outcomes (GPAs, dropout rates, resits, credits, and degree completion) were similar in all three groups. Autistic students had more no-shows in the second year than their peers, which affected degree completion after three years. Our analysis of student success prediction clarified what factors predicted their success or lack thereof for each year in their bachelor program. For first-year success, study choice issues were the most important predictors (parallel programs and application timing). Issues with participation in pre-education (absence of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors of second-year success and delays in the second and final year of their bachelor program. Additionally, academic performance (average grades) was the strongest predictor of degree completion within three years. Our research contributes to increasing equality of opportunities and the development of support in higher education in three ways. First, it provides insights into the extent to which higher education serves the equality of autistic students. Second, it clarifies which differences higher education must accommodate to support the success of autistic students during their student journey. Finally, we used the insights into autistic students’ success to develop a stepped, personalized approach to support their diverse needs and talents, which can be applied using existing offerings.
Original languageEnglish
QualificationPhD
Awarding Institution
  • Vrije Universiteit Amsterdam
Supervisors/Advisors
  • Begeer, Sander , Supervisor
  • Bhulai, Sandjai, Supervisor
  • Krabbendam, Lydia, Co-supervisor
  • Meeter, Martijn, Co-supervisor
Award date8 Jun 2022
Place of PublicationAmsterdam
Publisher
Print ISBNs9789464582185
Electronic ISBNs9789464582182
DOIs
Publication statusPublished - 8 Jun 2022

Keywords

  • autism, autism spectrum disorder, study progression, study success, retention, degree completion, higher education, propensity score weighting, structural equation modelling, machine learning

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