Mental State Perception Based On Student Behavior Data

With the development of China's economy and society,the people's living standards have improved significantly.However,the accompanying academic pressures,emotional problems,family problems,and employment pressures have made it not uncommon for Chinese college students to commit suicides due to psychological problems.Due to the current lack of awareness of mental illness and the low willingness to take the initiative to see a doctor,it is imperative to find and intervene students with psychological problems in a timely manner.The arrival of the era of big data has provided us with feasibility for our research goals,not only providing data support from the data level,but also providing analytical methods from the technical level.Based on the behavioral data generated by students on campus,this paper explores the features associated with psychological state and constructs an algorithm model for sensing the abnormal psychology of students,and assists psychologists in timely psychological intervention for students with psychological abnormalities,and provides new ideas for psychologists.This paper analyzes the student assessment scale data,expounds the psychological meaning of each assessment dimension,and measures the correlation between the dimen-sions through the Pearson correlation coefficient,and finds that there is a strong correla-tion between depression and the potential risk of suicide,anxiety and other psychological states.This paper describes the characteristics of students'psychological state in two as-pects,namely static features that do not change greatly with time and dynamic features that change with time.The feature of these two aspects respectively represent the static factors affecting people's psychology and personality,and the acquired living environment and behavior that affects psychology and emotion.Specifically,static features include some basic attributes of students,which are at-tributed to the state of depression through hypothesis testing and regression analysis.Dy-namic features are constructed from students'behavioral data generated from the school,which can be associated with changes in student behavior,including student consumption features,student behavior features and social relationship features.Through the above-mentioned eigenvectors combined with the student assessment scale data,the design includes experiments such as depression classification,depression multi-classification and depression score regression,using different label data and evalu-ation indicators for different experiments,and adopting different algorithm models.Hori-zontally contrasting effects between models through different algorithm models and com-pare the performance of the same model in different parameters by the method of grid search.The final experimental results are:the F₁value of the depression dichotomous model can reach 0.9;the F_macroof the depression multi-classification model can reach above 0.85,and the F_microcan reach 0.95 or more;the depression score returns.The model's MSE is optimally 3.4,and MAE is around 1.0.Among them,the student con-sumption features are the most important to the model,followed by the student behavior features.The above experiments show that the predictability of students'psychological state and the relationship between students'psychological and behavioral data are very signifi-cant,indicating that students'behavioral data can be used to perceive their psychological state in real time,and students with abnormal psychological status can be alerted.Psychol-ogy teachers and experts can intervene in time for students with psychological problems.