摘要
大学生学业倦怠的主要表现有生理耗竭与心理疲劳、学习动机丧失与学习兴趣减退、行为上的懈怠与逃避、情绪消极与人际关系紧张、认知偏差与自我效能感降低、学业失败与自我否定等.诱发大学生学业倦怠的因素有个体心理因素、外部环境因素、教育评价因素及社会文化因素,大数据技术在大学生学业倦怠干预中可以发挥重要作用,具有精准识别学业倦怠、动态监测学业变化、制定个性化干预策略、多方参与协同干预、效果评估与反馈调整等功能.
Abstract
The main manifestations of college students'academic burnout include physical exhaustion and mental fatigue,loss of learning motivation and decline in interest,behavioral slackening and avoidance,negative emotions and interpersonal tension,cognitive bias and reduced self-efficacy,academic failure and self-denial,etc.The factors inducing college students'academic burnout are deeply analyzed and summarized into individual psychological factors,external environmental factors,educational evaluation factors,and socio-cultural factors.Big data technology plays an important role in intervening in college students'academic burnout,which can be used for precise identification of academic burnout,dynamic monitoring of academic changes,personalized intervention strategy formulation,multi-party participation and collaborative intervention,as well as effect evaluation and feedback adjustment.
NETL
NSTL
万方数据