TY  - JOUR
AU  - Weng, Shaoyuan
PY  - 2026
DA  - 2026/03/26
TI  - A Perspective on Student Behavior Analytics via Approximate Fast Clustering and Deep Learning
JO  - ICCK Transactions on Educational Data Mining
T2  - ICCK Transactions on Educational Data Mining
JF  - ICCK Transactions on Educational Data Mining
VL  - 2
IS  - 1
SP  - 33
EP  - 37
DO  - 10.62762/TEDM.2026.361754
UR  - https://www.icck.org/article/abs/TEDM.2026.361754
KW  - approximate fast clustering
KW  - deep learning
KW  - student behavior analysis
KW  - educational data mining
AB  - The rapid digitization of education has generated massive, heterogeneous student behavioral data, creating both opportunities and methodological challenges for educational data mining. This perspective paper discusses an emerging paradigm that integrates approximate fast clustering algorithms with deep learning techniques to enable scalable, high-resolution student behavior analysis. By leveraging approximate computation, clustering efficiency can be significantly improved without sacrificing analytical fidelity, while deep learning models facilitate the extraction of high-level representations from multimodal behavioral data. The synergy between these approaches could enable robust applications such as anomaly detection, performance prediction, and personalized intervention. In addition, key research directions are highlighted, including mixed-type feature modeling, interpretability, and real-time adaptive analytics, which are critical for advancing intelligent education systems.
SN  - 3070-5843
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Weng2026A,
  author = {Shaoyuan Weng},
  title = {A Perspective on Student Behavior Analytics via Approximate Fast Clustering and Deep Learning},
  journal = {ICCK Transactions on Educational Data Mining},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {33-37},
  doi = {10.62762/TEDM.2026.361754},
  url = {https://www.icck.org/article/abs/TEDM.2026.361754},
  abstract = {The rapid digitization of education has generated massive, heterogeneous student behavioral data, creating both opportunities and methodological challenges for educational data mining. This perspective paper discusses an emerging paradigm that integrates approximate fast clustering algorithms with deep learning techniques to enable scalable, high-resolution student behavior analysis. By leveraging approximate computation, clustering efficiency can be significantly improved without sacrificing analytical fidelity, while deep learning models facilitate the extraction of high-level representations from multimodal behavioral data. The synergy between these approaches could enable robust applications such as anomaly detection, performance prediction, and personalized intervention. In addition, key research directions are highlighted, including mixed-type feature modeling, interpretability, and real-time adaptive analytics, which are critical for advancing intelligent education systems.},
  keywords = {approximate fast clustering, deep learning, student behavior analysis, educational data mining},
  issn = {3070-5843},
  publisher = {Institute of Central Computation and Knowledge}
}