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Auteur Martijn MEETER |
Documents disponibles écrits par cet auteur (1)
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Predicting academic success of autistic students in higher education / Theo BAKKER in Autism, 27-6 (August 2023)
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Titre : Predicting academic success of autistic students in higher education Type de document : Texte imprimé et/ou numérique Auteurs : Theo BAKKER, Auteur ; Lydia KRABBENDAM, Auteur ; Sandjai BHULAI, Auteur ; Martijn MEETER, Auteur ; Sander BEGEER, Auteur Article en page(s) : p.1803-1816 Langues : Anglais (eng) Mots-clés : academic achievement;autism;higher education;predictive modeling;propensity score weighting;student retention Index. décimale : PER Périodiques Résumé : 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 SummaryWhat 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 policyThese 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. En ligne : http://dx.doi.org/10.1177/13623613221146439 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=509
in Autism > 27-6 (August 2023) . - p.1803-1816[article] Predicting academic success of autistic students in higher education [Texte imprimé et/ou numérique] / Theo BAKKER, Auteur ; Lydia KRABBENDAM, Auteur ; Sandjai BHULAI, Auteur ; Martijn MEETER, Auteur ; Sander BEGEER, Auteur . - p.1803-1816.
Langues : Anglais (eng)
in Autism > 27-6 (August 2023) . - p.1803-1816
Mots-clés : academic achievement;autism;higher education;predictive modeling;propensity score weighting;student retention Index. décimale : PER Périodiques Résumé : 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 SummaryWhat 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 policyThese 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. En ligne : http://dx.doi.org/10.1177/13623613221146439 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=509