Huang Ying, Zhou Ying, Chen Jihe, Wu Danyan
School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China.
The Faculty of Education, Southwest University, Chongqing 400715, China.
J Intell. 2024 Sep 26;12(10):93. doi: 10.3390/jintelligence12100093.
The PISA 2022 literacy assessment highlights a significant decline in math performance among most OECD countries, with the magnitude of this decline being approximately three times that of the previous round. Remarkably, Hong Kong, Macao, Taipei, Singapore, Japan, and Korea ranked in the top six among all participating countries or economies, with Taipei, Singapore, Japan, and Korea also demonstrating improved performance. Given the widespread concern about the factors influencing secondary-school students' mathematical literacy, this paper adopts machine learning and the SHapley Additive exPlanations (SHAP) method to analyze 34,968 samples and 151 features from six East Asian education systems within the PISA 2022 dataset, aiming to pinpoint the crucial factors that affect middle-school students' mathematical literacy. First, the XGBoost model has the highest prediction accuracy for math literacy performance. Second, 15 variables were identified as significant predictors of mathematical literacy across the student population, particularly variables such as mathematics self-efficacy (MATHEFF) and expected occupational status (BSMJ). Third, mathematics self-efficacy was determined to be the most influential factor. Fourth, the factors influencing mathematical literacy vary among individual students, including the key influencing factors, the direction (positive or negative) of their impact, and the extent of this influence. Finally, based on our findings, four recommendations are proffered to enhance the mathematical literacy performance of secondary-school students.
2022年国际学生评估项目（PISA）的素养评估凸显了大多数经合组织国家数学成绩的显著下降，下降幅度约为上一轮的三倍。值得注意的是，中国香港、中国澳门、中国台北、新加坡、日本和韩国在所有参与国家或经济体中排名前六，其中中国台北、新加坡、日本和韩国的成绩也有所提高。鉴于人们普遍关注影响中学生数学素养的因素，本文采用机器学习和SHapley加法解释（SHAP）方法，对PISA 2022数据集中来自六个东亚教育系统的34968个样本和151个特征进行分析，旨在找出影响中学生数学素养的关键因素。首先，XGBoost模型对数学素养表现的预测准确率最高。其次，确定了15个变量为全体学生数学素养的重要预测指标，特别是数学自我效能感（MATHEFF）和预期职业地位（BSMJ）等变量。第三，数学自我效能感被确定为最具影响力的因素。第四，影响数学素养的因素在个体学生之间存在差异，包括关键影响因素、其影响的方向（正向或负向）以及这种影响的程度。最后，基于我们的研究结果，提出了四项建议，以提高中学生的数学素养表现。
