Author(s)

Shanwen Zhang ¹, Changqing Yu ¹, Xuqi Wang ¹, Dengwu Wang ¹

Affiliation(s)

¹ School of Electronic Information, Xijing University, Xijing, 710123, China

Corresponding Author

Shanwen Zhang

ABSTRACT

Depression prediction and intervention based on education big data, combined with real-time education big data and the influence of COVID-19, we collected college students' education big data, extracted entities related to depression and the relationship between entities, formed the relationship between entities into a knowledge triplet, and then constructed a knowledge map (KG) of depression, predicted depression, and provided intervention words for depression.

KEYWORDS

College students' depression; Knowledge map (KG); Depression prediction; Depression intervention

CITE THIS PAPER

Shanwen Zhang, Changqing Yu, Xuqi Wang, Dengwu Wang, Construction method of college students' depression knowledge map based on education big data. Adult and Higher Education (2023) Vol. 5: 65-69. DOI: http://dx.doi.org/10.23977/aduhe.2023.050412.

REFERENCES

[1] Qu Jianxin. Computational Psychiatry: A New Perspective of Depression Research and Clinical Application [J]. Progress in Psychological Science, 2020, 28 (1): 111-127.
[2] Ji Guangjun, Wang Ning, Wei Dongshuai, et al. Research progress of depression recognition based on artificial intelligence and speech features [J]. Journal of Clinical Psychiatry, 2022,32 (5): 15-417.
[3] Han Jiali, Feng Lei. Research progress in the application of artificial intelligence in the field of depression [J]. Beijing Medical Journal, 2020, 42 (4): 317-322.
[4] Yuan Qinmei, Wang Xing, Shuai Jianwei, et al. Research progress of depression based on artificial intelligence technology [J]. Chinese Journal of Clinical Psychology, 2020, 28 (1): 82+87.
[5] Zha Meng, Ye Ning, Wang Ruchuan, et al. Research on depression prediction based on capsule network model [J]. Computer Technology and Development, 2021, 31 (11): 28-34.
[6] Kim J, Hong J, Choi Y. Automatic Depression Prediction using Screen Lock/Unlock Data on the Smartphone[C]. 18th International Conference on Ubiquitous Robots (UR), 2021.
[7] Dai Z, Zhou H, Ba Q, et al. Improving depression prediction using a novel feature selection algorithm coupled with context-aware analysis [J]. Journal of affective disorders, 2021, 295:1040-1048.
[8] Safayari A, Bolhasani H. Depression diagnosis by deep learning using EEG signals: A Systematic Review [J]. New technologies and equipment in medicine (English), 2021 (4): 16. DOI: 10.20944/reprounts202107.0028.v1.
[9] Squarcina L, Villa F M, Nobile M, et al. Deep learning for the prediction of treatment response in depression[J]. Journal of affective disorders, 2021, 281: 618-622.

Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education