Deep learning-based school attendance prediction for autistic students

Abstract

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by deficits in social communication and interaction as well as the presence of repetitive, restricted patterns of behavior, interests, or activities. Many autistic students experience difficulty with daily functioning at school and home. Given these difficulties, regular school attendance is a primary source for autistic students to receive an appropriate range of needed educational and therapeutic interventions. Moreover, school absenteeism (SA) is associated with negative consequences such as school drop-out. Therefore, early SA prediction would help school districts to intervene properly to ameliorate this issue. Due to its heterogeneity, autistic students show within-group differences concerning their SA. A comprehensive statistical analysis performed by the authors shows that the individual and demographic characteristics of the targeted population are not predictive factors of SA. So, we used the students’ recent previous attendance to predict their future attendance. We introduce a deep learning-based framework for predicting short-and long-term SA of autistic students using the Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) algorithms. The adopted algorithms outperform other machine learning algorithms. In detail, LSTM increased the accuracy and recall of short-term SA prediction by 20% and 13%, while the same scores of long-term SA prediction increased by 5% using MLP.

The Relationship between Interpreting Anxiety and Learning Motivation Assisted by Long Short-Term Memory Neural Network Algorithms

“…The experimental results show that compared with traditional methods, LSTM based prediction has higher accuracy and better performance. Jarbou et al [13] trained a large amount of historical data using LSTM and multi-layer perceptron (MLP) algorithms to predict short-term and long-term absenteeism among students with autism. The experimental results show that both algorithms have high prediction accuracy.…”

Translating sophisticated data analytic strategies regarding school attendance and absenteeism into targeted educational policy

“…less social capital; Dronkers et al, 2017), autism (e.g. maladaptive behavior; Jarbou et al, 2022), preschoolers (e.g. physical and emotional development vulnerabilities; Draidi Areed et al, 2022), and substance use (e.g.…”

Revealing underlying factors of absenteeism: A machine learning approach

“…When exploring other studies where machine learning was used to explore absenteeism, we found a few examining the relationship between asthma and absenteeism ( Lary et al, 2019 ) predictive modeling of student performance ( Ng et al, 2021 ), and attendance autistic students ( Jarbou et al, 2022 ). More is surely available, but these give a glimpse into many types of opportunities for exploration using this method.…”