Exploring the Design of Teaching Mental Health of College Students Based on Data-Driven Learning

Ying Fang, Na Li, Sang-Bing Tsai and Yuchen Li

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: Chinese contemporary college students, this generation has been pampered since childhood, growing up under the wings of their parents, most of them are flowers in the hothouse, now stepping into the university and carrying the double expectations of society and family. With the rapid development of modern technology and social culture, people in modern society are facing fierce competition, frequent stress, fast pace, and unprecedented psychological pressure, which has a significant impact on human health. Therefore, the construction of a university psychological wellness education model has become the focus of theoretical research. As a new type of mental health teaching and learning model, data-driven learning (DDL) not only provides learners with rich, diverse, and realistic mental health data, but also creates an ideal learning environment for learners due to its corpus-based teaching and learning characteristics. This paper explores the design of DDL-based mental health teaching and learning for students, combining theoretical research, and empirical analysis from the actual university, and constructing a comprehensive system of psychological wellness education in college while building a local system of DDL-based psychological wellness education. The experimental results show that the Q-learning algorithm and SA-Q algorithm have no environmental triggering mechanism, while the data-driven control algorithm has the step number of formula 24, 19, and 17, respectively, thus reaching the optimal path. Therefore, in the data-driven psychology classroom, students change from participating in activities according to the course teaching objectives and mental health teachers’ requirements to inquiry-based learning and interactive experiences that focuses on problem solving and task completion.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3882540

DOI: 10.1155/2022/3882540

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