What drives the performance of Chinese urban and rural secondary schools: A machine learning approach using PISA 2018

《What drives the performance of Chinese urban and rural secondary schools: A machine learning approach using PISA 2018》

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作者单位: Research Institute of Economics and Management, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan 611130, People's Republic of China;Research Institute of Economics and Management, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan 611130, People's Republic of China

摘要: Despite Chinese students' excellence in international academic assessment, a clear achievement gap exists between urban and rural areas. This study examines factors driving school-level academic performance disparity between urban and rural areas. Micro-level data from the 2018 Programme for International Student Assessment survey and a machine learning method are employed. Intermediate learning outcomes and student characteristics (e.g., class size, grade repetition rate, parents' socioeconomic status and emotional support, students' learning time, and credibility assessment skills) are essential in the difference in academic performance between the best and worst schools in urban areas. However, in rural areas, school characteristics such as complexity of admission policies, quality of learning environment, and number of computers are important. These findings highlight the need for tailored policies in both urban and rural areas.