@article{4371,
  author = {Yubao Shen},
  title = {Building an Intelligent Education Model for Student Profiling Based on Big Data Algorithms},
  journal = {Journal of Intelligent Computing},
  year = {2025},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/jic/2025/16/1/10-18},
  url = {https://www.dline.info/jic/fulltext/v16n1/jicv16n1_2.pdf},
  abstract = {The development of big data technology has driven the pace of teaching innovation and reform. In the
information age, education emphasizes personalized and comprehensive development of students more than
ever before. This paper combines big data algorithms to construct an intelligent education model for classroom
student profiling. The model leverages big data mining algorithms to discover the correlations in student
behavior data. Using classification algorithms based on multi-frequency patterns, the model classifies student
behavior data and constructs multi-frequency pattern trees for students with different academic performance,
reflecting differences in their learning behavior characteristics. Experimental results demonstrate that
applying the intelligent education model based on big data algorithms can effectively provide teachers with
comprehensive and accurate feedback on student behavior characteristics, helping students understand their
learning situations and enabling targeted personalized teaching, significantly improving students’ learning
quality and efficiency.},
}