Early warning of moral responsibility deficiency in personalized education: a machine learning approach
Yue Li, Yao Wang

TL;DR
This paper explores how machine learning can help detect moral responsibility issues in college students to improve personalized education.
Contribution
A deep learning-based early warning model is proposed to detect moral responsibility deficiencies in college students.
Findings
College students generally have good moral responsibility but show issues like unclear moral understanding and unstable moral emotions.
Significant differences in moral responsibility exist based on factors like political appearance, academic performance, and participation in activities.
An early warning model using deep learning is developed to detect moral responsibility deficiencies in real time.
Abstract
Moral responsibility is not only an important component of the moral standard system, but also a significant indicator for measuring the moral consciousness and moral level of one person. Our contemporary society has the characteristics of differentiation and individuation. The individual society inevitably causes the impact to the university student thought, simultaneously also proposed the new challenge to the university moral education work, therefore, making it urgent to strengthen the cultivation of moral responsibility among modern college students. This paper empirically investigates the current situation of ethical responsibility of college students using self-administered questionnaires and individual interviews. The study found that the overall situation of college students’ moral responsibility is good, but there are some problems, such as the unclear understanding of…
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Taxonomy
TopicsIdeological and Political Education · Psychology of Moral and Emotional Judgment · Advanced Technologies in Various Fields
