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5. Examining the Effects of Theory of Mind and Social Skills Training on Social Competence in Adolescents with Autism.

Author

Ma, Weina, Mao, Jieyu, Xie, Yu, Li, Simeng, and Wang, Mian

Subjects
autism, multiple baseline designs, social competence, social skills, theory of mind
Abstract

Individuals with autism spectrum disorders (ASD) have impairment in interpreting emotional communication and the mental states of others, which limits their social competence. Mounting evidence has suggested that theory of mind (ToM) is a vital strategy to enhance social communication and interaction skills of children with ASD. However, very little research has looked at how ToM and social skills training affect social competence in adolescents with autism. This study examined the effectiveness of an intervention program, ToM-SS, which integrated the ToM and social skills training to improve the social competence of three adolescents with autism. A multiple baseline across behaviors design was adopted to evaluate the participants learning outcomes and demonstrated a functional relationship between intervention and skill mastery. Results show that the intervention produced substantial improvements in students acquisition of ToM (e.g., seeing leads to knowing and identifying desire-based and context-based emotions) and targeted social skills (e.g., praising others, expressing emotion and seeking help). Feedback and comments from teachers and parents also indicate good social validity of the intervention program.

Published
2023

8. Analysis of the Characteristic Transformation and Innovative Development of Street Stall Economy : --Take Chaoshan Area Jieyang City as an Example

Author

Li, Simeng, Wu, Yajing, Shi, Rongyu, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Wang, Zhikai, editor, Zhang, Guijie, editor, Ganesan, R., editor, Zulkafli, Abdul Hadi, editor, and Yin, Teh Sin, editor

Published
2024
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9. Think Twice before Seeking Private Supplementary Tutoring in Mathematics: A Data Set from China Questioned Its Effectiveness

Author

Ren, Ping, Dou, Zhongchen, Wang, Xici, Li, Simeng, and Wang, Lidong

Abstract

Private supplementary tutoring (PT) has expanded significantly and attracted widespread public interest in China in recent years. It deserves systematic empirical research and reflection, given its crucial theoretical and practical implications. Based on the data of a large-scale survey and testing project, this study examined its effects on the students' academic achievements in mathematics. The results revealed that PT in the current semester did not have any significant positive effect on the students' academic achievement in mathematics. This suggested that households' huge financial investment in PT failed to achieve the desired result. Accordingly, these findings imply that parents should carefully select private tutoring for their children, and the government should provide more comprehensive professional guidelines to regulate the private tutoring industry.

Published
2023
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28. A New Modal Autoencoder for Functionally Independent Feature Extraction

Author

Guo, Yuzhu, Pan, Kang, Li, Simeng, Han, Zongchang, Wang, Kexin, and Li, Li

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Autoencoders have been widely used for dimensional reduction and feature extraction. Various types of autoencoders have been proposed by introducing regularization terms. Most of these regularizations improve representation learning by constraining the weights in the encoder part, which maps input into hidden nodes and affects the generation of features. In this study, we show that a constraint to the decoder can also significantly improve its performance because the decoder determines how the latent variables contribute to the reconstruction of input. Inspired by the structural modal analysis method in mechanical engineering, a new modal autoencoder (MAE) is proposed by othogonalising the columns of the readout weight matrix. The new regularization helps to disentangle explanatory factors of variation and forces the MAE to extract fundamental modes in data. The learned representations are functionally independent in the reconstruction of input and perform better in consecutive classification tasks. The results were validated on the MNIST variations and USPS classification benchmark suite. Comparative experiments clearly show that the new algorithm has a surprising advantage. The new MAE introduces a very simple training principle for autoencoders and could be promising for the pre-training of deep neural networks.

Published
2020

43. Using technology-based learning tool to train facial expression recognition and emotion understanding skills of Chinese pre-schoolers with autism spectrum disorder

Author

Zhang, Shudong, Xia, Xuenan, Li, Simeng, Shen, Lili, Liu, Junli, Zhao, Libo, and Chen, Chunhui

Subjects
Brain Disorders, Autism, Clinical Research, Behavioral and Social Science, Intellectual and Developmental Disabilities (IDD), Mental Health, Mind and Body, Clinical Trials and Supportive Activities, Pediatric, Mental health, Autism Spectrum Disorders, emotion skills, facial expression, recognition, Specialist Studies in Education, Rehabilitation
Abstract

Objectives: Given the pervasiveness of emotional and behavioral deficits of individuals with Autism Spectrum Disorders (ASD), there is a pressing need for effective interventions to address their difficulties on Facial Expression Recognition and Emotion Understanding (FER/EU). Qunatiandi, a structured, app-based intervention program that is designed for Chinese children with ASD was utilized in this study. Three young children (two girls and one boy; age M = 4.94 years) completed an 8-week one-on-one intervention in a rehabilitation center setting. It was hypothesized that the three children would show greater progress in their FER/EU. Methods: In this study, a modified multiple probe across the program phases design was used; the dependent variable was the percentage of unprompted correct receptive identification responses for FER/EU tests during instruction and probes. Data were taken during baseline, the endpoint of instruction sessions, and a maintenance stage followed by intervention termination. Results: Findings of the study revealed that all participants demonstrated significant improvements in social acuity, gains were mostly achieved on emotion distinguishing and understanding (above 80% of accuracy). A similar scoring pattern was also found in the maintenance probe phase. Conclusion: The study is one of only a few early intervention projects to improve FER/EU skills for children with ASD using an app-based intervention. The results demonstrated that children with ASD can experience increases in emotion distinguishing and understanding skills. Maintenance probe results showed that the interventional effect can be maintained for a period of time after intervention.

Published
2019

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