科学成就群体预测因素的随机森林分析——以学校科学活动和学习为中心

IF 0.9 Q3 EDUCATION & EDUCATIONAL RESEARCH
Jeehye Hong, Hyunjung Kim, Hun-Gi Hong
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引用次数: 1

摘要

本研究通过应用随机森林分析的教育数据挖掘(EDM)方法,从2015年国际学生评估项目(PISA)的韩国数据中提取与学生分为三个不同成就组(高、中、低)相关的因素,探索了影响科学成就组预测的科学相关变量。从学生和家长的PISA问卷中收集的57个科学活动和学校学习变量进行了分析。研究发现,学生过去的科学活动、科学教学方法和环境意识等变量在预测科学成绩方面发挥了重要作用。在检验主要变量的偏相关图时,科学活动和教学策略有很大可能改变成就组的预测。本研究的重点是科学相关的情境变量,这些变量可以通过政府政策和科学教师在课堂上的努力来改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Random Forest Analysis of Factors Predicting Science Achievement Groups: Focusing on Science Activities and Learning in School
This study explored science-related variables that have an impact on the prediction of science achievement groups by applying the educational data mining (EDM) method of the random forest analysis to extract factors associated with students categorized in three different achievement groups (high, moderate, and low) in the Korean data from the 2015 Programme for International Student Assessment (PISA). The 57 variables of science activities and learning in school collected from PISA questionnaires for students and parents were analyzed. Variables related to students’ past science activities, science teaching and learning methods, and environmental awareness were found to played important roles in predicting science achievement. When checking partial dependence plots for major variables, science activities and instructional strategies had a high probability of changing the prediction of an achievement group. This study focused on science-related contextual variables that can be improved through government policies and science teachers’ efforts in the classroom.
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来源期刊
AsiaPacific Science Education
AsiaPacific Science Education Social Sciences-Education
CiteScore
1.70
自引率
0.00%
发文量
8
审稿时长
20 weeks
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