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Journal : Journal of Applied Data Sciences

Quantitative Analysis of Educational Techniques for Psychological Development in Vocational Students in China Li, Shuang; Sangsawang, Thosporn; Thepnuan, Narumom; Pigultong, Matee; Punyayodhin, Sulaganya; Darboth, Kanokwan
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.173

Abstract

The research of objective were to: 1) examines environment, educational system, teacher-student relationship, self-awareness, and other aspects affecting Chinese vocational school students' psychological quality., and 2) development Psychological Quality for Vocational School Students in China Model for address unique psychological challenges and foster personal development in vocational education. Populations and sampling group were stents tests 7,000 Zigong, Rong County, and Dujiangyan vocational and technical students. The questionnaires used a percentage- based scoring standard, with a score below 50 indicating “strongly disagree,” 51 to 70 indicating “neutral,” 71 to 90 indicating “moderately agree,” and 91 to 100 indicating “strongly agree.” Data processing affects Zigong, Rong County, and Dujiangyan Chinese vocational school students' mental health. Statistical percentage of students picking each option. Guttman half coefficient was .802 after Split-Half Method testing of the data, indicating good split-half reliability and internal consistency. The questionnaire reveals how survey questions, sample size, and data processing affect Chinese vocational school students' mental health. The questions asked Zigong, Rong County, and Dujiangyan vocational and technical school students about mental health. 4,768 people completed 6,458 surveys. After deleting 97 low-reliability questionnaires with similar answers to seven consecutive items, 4,671 were valid. The Countermeasure Developing Model in China enhances the psychological quality of vocational school students by implementing multi-level therapy, methodical mental health education, and a supportive learning environment.
Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement Shan, Linlin; Jiang, Guisong; Li, Shuang; Zhao, Shuai; Luo, Kunjie; Zhang, Long; Li, Yi
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.67

Abstract

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.