Related Concept Videos
01:19Autism Spectrum Disorder
464
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
464
01:25Learning Disabilities
296
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
Dyslexia
Dyslexia is a...
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Related Experiment Video
Updated: Oct 5, 2025
06:37
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
4.3K
Deep learning-based school attendance prediction for autistic students.
Mohammed Jarbou1, Daehan Won2, Jennifer Gillis-Mattson3
1Department of Systems Science and Industrial Engineering, Binghamton University-State University of New York, Binghamton, NY, 13902, USA.
Scientific Reports
|January 27, 2022
Summary
Predicting school absenteeism (SA) for autistic students is crucial. Deep learning models, specifically Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP), accurately forecast short- and long-term SA using past attendance data.
Area of Science:
- Neuroscience
- Education Technology
- Machine Learning
Background:
- Autism Spectrum Disorder (ASD) is a neurodevelopmental condition impacting social communication and behavior.
- Autistic students often face challenges in daily functioning, making consistent school attendance vital for accessing educational and therapeutic support.
- School absenteeism (SA) in autistic students is linked to adverse outcomes, including school dropout, necessitating early prediction and intervention strategies.
Purpose of the Study:
- To develop and evaluate a deep learning framework for predicting short- and long-term school absenteeism in autistic students.
- To identify effective machine learning algorithms for forecasting SA, considering the heterogeneity within the autistic population.
- To demonstrate that past attendance patterns are more predictive of future SA than individual or demographic characteristics.
Main Methods:
- A deep learning framework was developed utilizing Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) algorithms.
- The models were trained and validated using historical school attendance data of autistic students.
- Performance was evaluated by comparing the predictive accuracy and recall of LSTM and MLP against other machine learning approaches.
Main Results:
- Individual and demographic characteristics were found to be non-predictive of SA in autistic students.
- The LSTM algorithm significantly improved short-term SA prediction accuracy by 20% and recall by 13%.
- The MLP algorithm enhanced long-term SA prediction accuracy and recall by 5%.
Conclusions:
- Deep learning models, particularly LSTM and MLP, offer a promising approach for accurately predicting school absenteeism in autistic students.
- Utilizing past attendance data is a more effective strategy for SA prediction than relying on student demographics.
- Early and accurate prediction of SA can facilitate timely interventions, potentially mitigating negative academic consequences for autistic students.