Big Data on Exploring Influencing Factors of the Reading Achievement between China and Finland

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Yi Huang, Yan Zhang, Haijian Hu


This paper is based on Big Data analysis in computer science to find the significant influencing factors in reading achievement in China and Finland and differentiate factors between the two countries so as to find the implications and suggestions for the government policy and then reach the education equality and educational core competition. Reading is the main subject in PISA 2018. In 2018, about 60,000 students from 79 participating nations and economies finished the evaluation, accounting for around 32 million 15-year-olds. In all, 12058 samples in China( B-S-J-Z) and 5496 samples in Finland participate in the PISA 2018. The Plausible Value in Cognitive Process Subscale of Reading entails locate information, understand, evaluate, and reflect. Reading achievement is related to student-level, school-family level and country level. We use student-level constructs to measure every construct including GRADE, MISS, POSS, READINT, CLSSIZE, SELF, SES, SEX, READACH and design a model of three-level HLM in Big Data of factors influencing reading achievement. The Big Data statistics shows the PISA outcome has unstable level in PISA in 2012, 2015, and 2018 and the outcome cannot be applied to other places in China because it just includes four cities, B-S-J-Z. Also, big data also reflects the higher teaching level and teaching staffs in China and Finland than OECD Average although we cannot get all the data of regions in two countries.

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