Research Article
Design of Student Portrait Model Based on Educational Big Data Mining
@INPROCEEDINGS{10.4108/eai.26-5-2023.2337321, author={Wenwen Yin and Jiatong Ju and Youquan Gao and Kun Fan and Jinkai Chen and Zhaohui Zheng}, title={Design of Student Portrait Model Based on Educational Big Data Mining}, proceedings={Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26--28, 2023, Nanjing, China}, publisher={EAI}, proceedings_a={ICEKIM}, year={2023}, month={9}, keywords={association mining student portraits educational data mining}, doi={10.4108/eai.26-5-2023.2337321} }- Wenwen Yin
Jiatong Ju
Youquan Gao
Kun Fan
Jinkai Chen
Zhaohui Zheng
Year: 2023
Design of Student Portrait Model Based on Educational Big Data Mining
ICEKIM
EAI
DOI: 10.4108/eai.26-5-2023.2337321
Abstract
The correlation between student behavior and academic development is a key focus of school education work in the context of big data. This article designs a diversified and integrated education big data association mining model. Firstly, the behavioral data of middle school students in supermarkets, canteens, psychology, and education are collected and synthesized into a data table according to their student IDs. Then, student labels are extracted based on the scale standards and partition functions. Then, based on the FP growth association algorithm, the degree of association and differences in behavior performance, consumption level, and academic level among different student groups are studied, Finally, the tree hole text and logistic regression model were used to construct student portraits, predict psychology, and academic trends, respectively. The experimental results indicate that the constructed student portrait can effectively describe students' academic and life characteristics, providing a basis for educators to provide personalized care and support to students to a certain extent.
