TY  - JOUR
AU  - Zhu, Xiaowen
PY  - 2026
DA  - 2026/09/28
TI  - A Review of Recent Advances in Academic Performance Prediction within Educational Data Mining
JO  - ICCK Transactions on Educational Data Mining
T2  - ICCK Transactions on Educational Data Mining
JF  - ICCK Transactions on Educational Data Mining
VL  - 2
IS  - 2
SP  - 56
EP  - 62
DO  - 10.62762/TEDM.2026.918140
UR  - https://www.icck.org/article/abs/TEDM.2026.918140
KW  - educational data mining
KW  - academic performance prediction
KW  - machine learning
KW  - deep learning
KW  - model adaptation
AB  - With the rapid advancement of artificial intelligence and smart education, academic performance prediction has emerged as a critical research direction within educational data mining. This paper provides a systematic review of recent progress in this field, focusing on technological evolution, application scenarios, model adaptation, emerging trends, and persistent challenges. The development of prediction technologies has progressed through three stages: statistical methods, machine learning, and deep learning. These approaches have been widely applied to grade prediction, academic risk and dropout prediction, knowledge tracing, and personalized learning recommendation. Different models vary significantly in their data scale requirements, interpretability, and suitability for specific tasks. Current research is shifting toward interpretability, multimodal data fusion, privacy preservation, and lightweight deployment. Despite these advances, challenges remain, including data imbalance, cold-start problems, and limited model interpretability. To better support precision teaching and personalized education, future research should prioritize interpretability, actionability, and deployability.
SN  - 3070-5843
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Zhu2026A,
  author = {Xiaowen Zhu},
  title = {A Review of Recent Advances in Academic Performance Prediction within Educational Data Mining},
  journal = {ICCK Transactions on Educational Data Mining},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {56-62},
  doi = {10.62762/TEDM.2026.918140},
  url = {https://www.icck.org/article/abs/TEDM.2026.918140},
  abstract = {With the rapid advancement of artificial intelligence and smart education, academic performance prediction has emerged as a critical research direction within educational data mining. This paper provides a systematic review of recent progress in this field, focusing on technological evolution, application scenarios, model adaptation, emerging trends, and persistent challenges. The development of prediction technologies has progressed through three stages: statistical methods, machine learning, and deep learning. These approaches have been widely applied to grade prediction, academic risk and dropout prediction, knowledge tracing, and personalized learning recommendation. Different models vary significantly in their data scale requirements, interpretability, and suitability for specific tasks. Current research is shifting toward interpretability, multimodal data fusion, privacy preservation, and lightweight deployment. Despite these advances, challenges remain, including data imbalance, cold-start problems, and limited model interpretability. To better support precision teaching and personalized education, future research should prioritize interpretability, actionability, and deployability.},
  keywords = {educational data mining, academic performance prediction, machine learning, deep learning, model adaptation},
  issn = {3070-5843},
  publisher = {Institute of Central Computation and Knowledge}
}