Cao Canxi, Zhang Tongxin, Xin Tao
Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
Front Psychol. 2024 Feb 14;15:1329724. doi: 10.3389/fpsyg.2024.1329724. eCollection 2024.
Scientific literacy is a key factor of personal competitiveness, and reading is the most common activity in daily learning life, and playing the influence of reading on individuals day by day is the most convenient way to improve the level of scientific literacy of all people. Reading engagement is one of the important student characteristics related to reading literacy, which is highly malleable and is jointly reflected by behavioral, cognitive, and affective engagement, and it is of theoretical and practical significance to explore the relationship between reading engagement and scientific literacy using reading engagement as an entry point. In this study, we used PISA2018 data from China to explore the relationship between reading engagement and scientific literacy with a sample of 15-year-old students in mainland China. 36 variables related to reading engagement and background variables (gender, grade, and socioeconomic and cultural status of the family) were selected from the questionnaire as the independent variables, and the score of the Scientific Literacy Assessment (SLA) was taken as the outcome variable, and supervised machine learning method, the XGBoost algorithm, to construct the model. The dataset is randomly divided into training set and test set to optimize the model, which can verify that the obtained model has good fitting degree and generalization ability. Meanwhile, global and local personalized interpretation is done by introducing the SHAP value, a cutting-edge machine model interpretation method. It is found that among the three major components of reading engagement, cognitive engagement is the more influential factor, and students with high reading cognitive engagement level are more likely to get high scores in scientific literacy assessment, which is relatively dominant in the model of this study. On the other hand, this study verifies the feasibility of the current popular machine learning model, i.e., XGBoost, in a large-scale international education assessment program, with a better model adaptability and conditions for global and local interpretation.
科学素养是个人竞争力的关键因素，阅读是日常学习生活中最常见的活动，发挥阅读对个人日益增长的影响是提高全民科学素养水平最便捷的途径。阅读参与度是与阅读素养相关的重要学生特征之一，它具有很强的可塑性，由行为、认知和情感参与共同体现，以阅读参与度为切入点探究其与科学素养之间的关系具有理论和实践意义。在本研究中，我们使用来自中国的PISA2018数据，以中国大陆15岁学生为样本，探究阅读参与度与科学素养之间的关系。从问卷中选取36个与阅读参与度及背景变量（性别、年级以及家庭的社会经济和文化地位）相关的变量作为自变量，将科学素养评估（SLA）得分作为结果变量，采用监督机器学习方法——XGBoost算法构建模型。将数据集随机分为训练集和测试集以优化模型，这可以验证所得到的模型具有良好的拟合度和泛化能力。同时，通过引入前沿的机器模型解释方法SHAP值进行全局和局部个性化解释。研究发现，在阅读参与度的三个主要组成部分中，认知参与度是更具影响力的因素，阅读认知参与度水平高的学生在科学素养评估中更有可能获得高分，这在本研究模型中相对占主导地位。另一方面，本研究验证了当前流行的机器学习模型即XGBoost在大规模国际教育评估项目中的可行性，具有较好的模型适应性以及进行全局和局部解释的条件。
