Due to the COVID-19 pandemic, many institutions of higher education had to close their campuses and shift to online education. Here, we investigate how stay-at-home orders impacted students. We investigated results obtained by 15,125 bachelor students at a large Dutch research university during a semester in which the campus was closed and all education had shifted online. Moreover, we surveyed 166 students of the bachelor of psychology program of the same university. Results showed that students rated online education as less satisfactory than campus-based education, and rated their own motivation as having gone down. This was reflected in a lower time investment: lectures and small-group meetings were attended less frequently, and student estimates of hours studied went down. Lower motivation predicted this drop in effort. Moreover, a drop in motivation was related to fewer credits being obtained during stay-at-home orders. However, on average students reported obtaining slightly more credits than before, which was indeed found in an analysis of administered credits. In a qualitative analysis of student comments, it was found that students missed social interactions, but reported being much more efficient during online education. It is concluded that whereas student satisfaction and motivation dropped during the shift to online education, increased efficiency meant results were not lower than they would normally have been.
Background: Autistic individuals' enrollment in universities is increasing, but we know little about their study progress over time. Many of them have poor degree completion in comparison to students with other disabilities. However, longitudinal studies on study progression over time of autistic students (AS) in comparison to their peers are absent. It is essential to study AS outcomes during the first year, controlling against the results of students without disabilities. Methods: This preregistered population study examined first-year progression and retention within the same area of study of autistic bachelor students (n = 96; age M = 20.0 years, 95% confidence interval [CI] 18.0-21.0) in comparison to students without disabilities (n = 25,001; age M = 19.0 years, 95% CI 18.0-20.0), enrolled in the same area of study at a major Dutch university. To control for substantial differences in sample sizes and differences in demographics or prior education, we applied propensity score weighting to balance outcomes. We analyzed progression and retention, examining the average grades, the number of examinations, resits, no shows, the credit accumulation in each period, and the average retention after the first year. Results: Over the course of the first bachelor year, AS received grades similar to students with no disabilities. We found no statistical differences in the number of examinations, resits, and no shows. Credit accumulation was generally similar during the academic year except for one of seven periods, and retention within the same area of study revealed no differences. Conclusions: This study shows that AS have similar success rates compared with students with no disabilities but could benefit from additional support on test-taking. Improved insights can enable universities to develop appropriate and timely support for often-talented students, improve first-year retention, and advance degree completion.
Individuals with autism increasingly enroll in universities, but researchers know little about how their study progresses over time towards degree completion. This exploratory population study uses structural equation modeling to examine patterns in study progression and degree completion of bachelor’s students with autism spectrum disorder (n = 101) in comparison to students with other recorded conditions (n = 2,465) and students with no recorded conditions (n = 25,077) at a major Dutch university. Propensity score weighting is applied to balance outcomes. The research shows that most outcomes (grade point average, dropout rates, resits, credits, and degree completion) were similar across the three groups. Students with autism had more no-shows in the second year than their peers, which affected degree completion after 3 years. The overall performance of autistic students appeared to be adequate and comparable to their peers. However, addressing participation and inclusivity is vital to improve academic support for students with autism. These insights can enable universities to develop appropriate and timely support for all talented students to progress in their studies and complete their degrees.
Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students ( N = 101) in comparison to students with other health conditions ( N = 2465) and students with no health conditions ( N = 25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students. Laymen Summary What is already known about the topic? Autistic youths increasingly enter universities. We know from existing research that autistic students are at risk of dropping out or studying delays. Using machine learning and historical information of students, researchers can predict the academic success of bachelor students. However, we know little about what kind of information can predict whether autistic students will succeed in their studies and how accurate these predictions will be. What does this article add? In this research, we developed predictive models for the academic success of 101 autistic bachelor students. We compared these models to 2,465 students with other health conditions and 25,077 students without health conditions. The research showed that the academic success of autistic students was predictable. Moreover, these predictions were more precise than predictions of the success of students without autism. For the success of the first bachelor year, concerns with aptitude and study choice were the most important predictors. Participation in pre-education and delays at the beginning of autistic students’ studies were the most influential predictors for second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance in high school was the strongest predictor for degree completion in 3 years. Implications for practice, research, or policy These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
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