基于多维数据挖掘的学生学习画像构建

Construction of student learning portrait based on multi⁃dimensional data mining

以多维数据为基础,应用数据挖掘技术预测学生学习习惯并构建学习画像。通过收集273名学生的数据,涵盖人际关系、个性特征和健康状况等因素,经过数据预处理,成功建立了基于支持向量机、K近邻、多层感知器的多维数据挖掘模型,实现对学生学习习惯的预测。研究结果展现了全方位的学生画像,包括时间管理、学习方法等方面。该研究证实了数据驱动决策在教育领域的重要性,为提供个性化教育方案提供了科学依据。

Based on multidimensional data,this study aims to apply data mining technology to predict students’learning habits and build learning profiles.By collecting data from 273 students,including interpersonal relationships,personality traits,and health status,and after data preprocessing,a multidimensional data mining model based on support vector machines,K‑nearest neighbors,and multilayer perceptrons was successfully established,enabling prediction of students’learning habits.The research results present a comprehensive student profile,including aspects such as time management and learning methods.This study confirms the importance of data‑driven decision‑making in the field of education and provides a scientific basis for offering personalized education plans.