Evaluation feedback information for optimization of mental health courses with deep learning methods

  • Wenhua Liu
  • Yijie ZhangEmail author


In order to give full play to the value of mental health education courses in colleges and universities and help students understand the relevant knowledge, thus helping to alleviate their own psychological problems, this paper uses the evaluation feedback of mental health courses in colleges and universities to conduct in-depth learning, and further optimize the mental health education courses in colleges and universities. College students, as a seemingly relaxed group, actually bear tremendous pressure. Many pressures, such as study, life, emotion, interpersonal communication, and graduate employment, have seriously affected the mental health of college students. Increasingly prominent mental health problems have attracted the attention of colleges and universities. Domestic colleges and universities have gradually opened mental health education courses, aiming at helping students find breakthroughs in the problem, thereby promoting students’ physical and mental development and adapting to the current and developing social environment with the positive and normal psychological state. However, the existing mental health education curriculum in colleges and universities cannot play its role effectively. The general evaluation criteria make the teaching activities less effective, and the students fail to understand the content of the mental health education curriculum well. Therefore, this paper uses the evaluation feedback information of the mental health curriculum in colleges and universities to conduct an in-depth study, optimize the mental health curriculum, and improve the mental health of colleges and universities. The teaching mode of health course enables students to have a deeper understanding of the content of mental health education. It can really help students alleviate psychological pressure and solve psychological problems, which is conducive to the healthy development of universities and society.


Deep learning Course optimization Evaluation feedback Deep confidence network 


Compliance with ethical standards

Conflict of interest

All Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Conley CS, Shapiro JB, Kirsch AC et al (2017) A meta-analysis of indicated mental health prevention programs for at-risk higher education students. J Couns Psychol 64(2):121CrossRefGoogle Scholar
  2. David EJR, Okazaki S, Saw A (2015) Bicultural self-efficacy among college students: initial scale development and mental health correlates. J Couns Psychol 56(2):211–226CrossRefGoogle Scholar
  3. de Jesús RJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309CrossRefGoogle Scholar
  4. Deng S, Huang L, Xu G et al (2017) On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst 28(5):1164–1177CrossRefGoogle Scholar
  5. Donisch K, Bray C, Gewirtz A (2016) Child welfare, juvenile justice, mental health, and education providers’ conceptualizations of trauma-informed practice. Child Maltreatment 21(2):125–134CrossRefGoogle Scholar
  6. Eisenberg D, Hunt J, Speer N et al (2011) Mental health service utilization among college students in the United States. J Nerv Ment Dis 199(5):301–308CrossRefGoogle Scholar
  7. Fardoust S, Kanbur R, Luo X et al (2018) An evaluation of the feedback loops in the poverty focus of world bank operations. Eval Program Plan 67:10–18CrossRefGoogle Scholar
  8. Giap CN, Son LH, Chiclana F (2018) Dynamic structural neural network. J Intell Fuzzy Syst 34(4):2479–2490CrossRefGoogle Scholar
  9. Gloria CT, Steinhardt MA (2016) Relationships among positive emotions, coping, resilience and mental health. Stress Health 32(2):145–156CrossRefGoogle Scholar
  10. Guo J, Guo S, Yu Y (2016) Design and characteristics evaluation of a novel teleoperated robotic catheterization system with force feedback for vascular interventional surgery. Biomed Microdevice 18(5):76CrossRefGoogle Scholar
  11. Halpern-Manners A, Schnabel L, Hernandez EM et al (2016) The relationship between education and mental health: new evidence from a discordant twin study. Soc Forces 95(1):107–131CrossRefGoogle Scholar
  12. Jie J, Wang J, Du J (2016) Exploration and effect of innovative implementation mode of college mental health curriculum—from the perspective of developmental counseling. Sci Educ Lit 9:140–142Google Scholar
  13. Jing L, Wu X, Gu X (2017) Research on the deep learning online course activity design: based on case analysis to the open university of UK. J Distance Educ 35(2):56–65Google Scholar
  14. Li S, Liu G, Tang X et al (2017) An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis. Sensors 17(8):1729CrossRefGoogle Scholar
  15. Lorencatto F, Gould NJ, McIntyre SA et al (2016) A multidimensional approach to assessing intervention fidelity in a process evaluation of audit and feedback interventions to reduce unnecessary blood transfusions: a study protocol. Implement Sci 11(1):163CrossRefGoogle Scholar
  16. Lughofer E, Meda-Campaña JA, Páramo LA et al (2018) Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models. J Intell Fuzzy Syst 35(2):2585–2596CrossRefGoogle Scholar
  17. Meyrose AK, Klasen F, Otto C et al (2018) Benefits of maternal education for mental health trajectories across childhood and adolescence. Soc Sci Med 202:170–178CrossRefGoogle Scholar
  18. Mo J, Zhang T, Yuan H et al (2016) Application of deep learning in the teaching practice of image processing technology course. Educ Teach BBS 9:115–116Google Scholar
  19. Peek HS, Richards M, Muir O et al (2015) Blogging and social media for mental health education and advocacy: a review for psychiatrists. Curr Psychiatry Rep 17(11):88CrossRefGoogle Scholar
  20. Rubio JJ, Ricardo Cruz D, Elias I, Ochoal G (2019) ANFIS system for classification of brain signals. J Intell Fuzzy Syst 37(3):4033–4041CrossRefGoogle Scholar
  21. Shearer RL, Gregg A, Joo KP (2015) Deep learning in distance education: are we achieving the goal? Am J Distance Educ 29(2):126–134CrossRefGoogle Scholar
  22. Shone N, Ngoc TN, Phai VD et al (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell 2(1):41–50CrossRefGoogle Scholar
  23. Thornicroft G, Mehta N, Clement S et al (2016) Evidence for effective interventions to reduce mental-health-related stigma and discrimination. Lancet 387(10023):1123–1132CrossRefGoogle Scholar
  24. Wilson J, Czik A (2016) Automated essay evaluation software in English language arts classrooms: effects on teacher feedback, student motivation, and writing quality. Comput Educ 100:94–109CrossRefGoogle Scholar
  25. Yang L, Ma Y (2017) Innovative thinking on college mental health education. Coll Educ 5:90–91Google Scholar
  26. Zhang Q, Zhu SC (2018) Visual interpretability for deep learning: a survey. Front Inf Technol Electron Eng 19(1):27–39CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Psychology and Mental HealthHuaihe Hospital of Henan UniversityKaifeng CityChina

Personalised recommendations