Teacher's mental health risk assessment based on multi-modal data
Start Date
21-2-2025 3:40 PM
End Date
21-2-2025 4:00 PM
Description
In recent years, mental health among primary and secondary school teachers has gained significant attention. Teachers' mental well-being is crucial for their professional growth, personal development, and impacts students' progress and societal development. Depression, a prevalent psychological disorder, poses a serious threat to teachers' careers and teaching quality. Therefore, developing effective depression risk assessment methods is urgent. This study focuses on constructing a depression risk assessment model for primary and secondary school teachers using multimodal data to provide a scientific basis for early interventions. The research is divided into three parts: constructing a depression risk dataset, extracting and fusing features from different modal data, and building and validating the model. The study recruited 75 teachers through an experimental platform and the snowball sampling method, collecting text, audio, and facial expression data over 15 consecutive working days using video diaries designed based on Beck's cognitive theory of depression. The model labels were based on scores from the CES-D and SWLS scales. After cleaning and excluding irregular recordings, 1,140 diary data were obtained and divided into training and validation sets. Model construction involved using RoBERTa for text, Mel-ResNet for audio, and 3D-ResNet for facial expressions. The Transformer architecture was used for multimodal fusion, and a classification network was utilized for the final depression risk assessment. Expected results show that multimodal fusion models outperform unimodal models in assessment accuracy and robustness. This study theoretically expands the research perspective on depression risk assessment and validates the effectiveness of multimodal technology. Practically, it provides an innovative tool for precise identification of depression risk among teachers, facilitating early psychological interventions. The method holds promise for broader application to diverse populations and scenarios, bringing new opportunities to mental health assessment.
Speaker
Prof XU Jianping
Associate Professor, Beijing Normal University, China
Xu Jianping is a professor at Faculty of Psychology, Beijing Normal University. He serves as Deputy Director of the Institute of Psychological Measurement and Human Resources (Beijing), Coordinator of the Professional Master's Degree Program in Applied Psychology (Zhuhai), and Project Leader for Big Data in Psychology and Behavior research . Additionally, he holds positions in various committees, including as Deputy Director of the Psychological Measurement Professional Committee of the CPS, a member of the Psychology Qualitative Research Professional Committee, a council member of the Public Officials' Mental Health Management Branch of the China Health Management Association. His research focuses on personnel assessment, psychological assessment system design and development, and mental health assessment of students and occupational populations. He has participated in projects such as the Beijing Education Science Planning on Teachers' Professional Growth and the EU Global Teacher Competence Project, and has developed tools for early diagnosis and intervention for teachers' psychological issues. Meanwhile, with a focus on serving national and social needs and promoting the transformation of research results, he has developed numerous assessment tools and talent selection question banks. He has also assisted multiple enterprises, institutions, evaluation, and human resource consulting companies, including the Central Organization Department, the Ministry of Education, the Ministry of Human Resources and Social Security, Daqing Oilfield, and Zhaopin.com, in establishing talent selection modules and providing technical support.
Co-author(s)
Ying WANG; Xinyu ZHANG; Hezhi ZHANG; Hang DONG
Document Type
Keynote speech
Recommended Citation
Xu, J. (2025, February 21). Teacher's mental health risk assessment based on multi-modal data. Presentation presented at the International Conference and Workshop on Health and Well-being in the Digital Era. Lingnan University, Hong Kong.
Teacher's mental health risk assessment based on multi-modal data
In recent years, mental health among primary and secondary school teachers has gained significant attention. Teachers' mental well-being is crucial for their professional growth, personal development, and impacts students' progress and societal development. Depression, a prevalent psychological disorder, poses a serious threat to teachers' careers and teaching quality. Therefore, developing effective depression risk assessment methods is urgent. This study focuses on constructing a depression risk assessment model for primary and secondary school teachers using multimodal data to provide a scientific basis for early interventions. The research is divided into three parts: constructing a depression risk dataset, extracting and fusing features from different modal data, and building and validating the model. The study recruited 75 teachers through an experimental platform and the snowball sampling method, collecting text, audio, and facial expression data over 15 consecutive working days using video diaries designed based on Beck's cognitive theory of depression. The model labels were based on scores from the CES-D and SWLS scales. After cleaning and excluding irregular recordings, 1,140 diary data were obtained and divided into training and validation sets. Model construction involved using RoBERTa for text, Mel-ResNet for audio, and 3D-ResNet for facial expressions. The Transformer architecture was used for multimodal fusion, and a classification network was utilized for the final depression risk assessment. Expected results show that multimodal fusion models outperform unimodal models in assessment accuracy and robustness. This study theoretically expands the research perspective on depression risk assessment and validates the effectiveness of multimodal technology. Practically, it provides an innovative tool for precise identification of depression risk among teachers, facilitating early psychological interventions. The method holds promise for broader application to diverse populations and scenarios, bringing new opportunities to mental health assessment.