Abstract
Autism, a developmental disorder affecting social and communication skills, differs from most the mental handicap in showing a characteristic pattern of poor, intact, and even superior cognitive abilities. This study aims to solve the mismatch of the teaching content and mental health education for autistic children. Inspired by artificial intelligence, an improved neural network matrix factorization (NeuMF) model is designed based on the theory of collaborative filtering, and time data is added to improve the NeuMF by using the K-means clustering algorithm. Several evaluation indexes such as root mean square error (RMSE) and mean absolute error (MAE) are selected to assess the performance of the proposed model. Results show that RMSE and MAE of the improved NeuMF model are 1.251 and 0.625, respectively, which are better than collaborative filtering and traditional neural network factorization models. Moreover, the proposed model is used to recommend the activities of physical education (PE) for developing the intelligence of autistic children. This proves that the optimized model has better performance and can be used to recommend online courses for autistic users. This dynamic personalized curriculum recommendations model can help autistic children recover in a short time.
Highlights
Autism is a developmental disorder with symptoms that appear within the first three years of life
Teachers have collected a large number of useful data in the process of network teaching, and how to use these data to reform education remains a hot topic in a long run. e emergence of artificial intelligence (AI) provides a good idea for constructing the recommendation system [7]
Heinsfeld et al [10] examined the application of deep learning algorithms for the identification of autistic children using the brain activation patterns. e prediction accuracy of this approach reached 70% which showed that machine learning methods are a very promising tool for the assessment of mental disorders
Summary
Received 6 December 2021; Revised 24 December 2021; Accepted 29 December 2021; Published 21 January 2022. Is study aims to solve the mismatch of the teaching content and mental health education for autistic children. An improved neural network matrix factorization (NeuMF) model is designed based on the theory of collaborative filtering, and time data is added to improve the NeuMF by using the K-means clustering algorithm. Several evaluation indexes such as root mean square error (RMSE) and mean absolute error (MAE) are selected to assess the performance of the proposed model. The proposed model is used to recommend the activities of physical education (PE) for developing the intelligence of autistic children. The proposed model is used to recommend the activities of physical education (PE) for developing the intelligence of autistic children. is proves that the optimized model has better performance and can be used to recommend online courses for autistic users. is dynamic personalized curriculum recommendations model can help autistic children recover in a short time
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