Volume 30, Issue 2, 2021
DOI: 10.24205/03276716.2020.4037
Analysis of Situation of Life Education for College Students based on Genetic Simulated Annealing Algorithm
Abstract
Background: In recent years, incidents of suicide, murder, wounding, and other life injuries among college students have occurred from time to time. The occurrence of these incidents further reflects the disregard for life of college students and the life education of college students needs to be improved and strengthened. Therefore, it is necessary to study the life education of contemporary college students. This paper studies the popularization of life education among college students and the main factors leading to college students’ miscarriages and the students’ outlook on life, and identifies high-risk groups with weak life awareness in order to give reasonable and practical suggestions to improve the school’s life education.
Method: Firstly, we study the status quo of life education of college students, including: college students' outlook on life, college students' cognition of life education, and the popularization of college life education. Secondly, we use the fuzzy C-means clustering method of genetic simulated annealing algorithm to identify the suicidal tendency of college students, classify and manage the education of students and improve the efficiency of life education in colleges. Finally, we summarize the problems reflected in the questionnaire and give countermeasures and suggestions.
Results: The life education among college students is not universal, and most students lack of life education. The outlook on life of contemporary college students generally presents a positive, optimistic and cheerful attitude, with very few negative expressions. When facing pressure from life and study, college students are often in a painful and helpless state, and may make some irrational behaviors when unable to resolve them. Factors affecting college students’ tendency to commit suicide mainly include: mental illness, love failure, study pressure, family reasons, and social pressure.
Conclusion: Colleges should use clustering algorithms to depict student portraits, hierarchically manage students' education based on classification results, allocate educational resources reasonably and manage students efficiently.
Keywords
life education, genetic simulated annealing algorithm, Logistic regression, student portraits