A Proposed Hybrid CNN-RNN Architecture for Student Performance Prediction


  • Sam Yit Xiong School of Computer Sciences, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, MALAYSIA
  • Esraa Faisal Malik Gasim School of Management, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, MALAYSIA
  • Chew Xin Ying School of Computer Sciences, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, MALAYSIA https://orcid.org/0000-0001-5539-1959
  • Khaw Khai Wah School of Management, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang, MALAYSIA https://orcid.org/0000-0003-2646-6477
  • Lee Ming Ha Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Sarawak, MALAYSIA https://orcid.org/0000-0003-2096-5865


Convolutional Neural Network, Recurrent neural network, CNN-RNN, Hybrid Deep Learning, Education, Student Performance Prediction


Education is one of the important factors for the development of a country, thus, an early prediction system to predict the student performance is needed. With such system, education institutes would have a strong capacity to detect slow learners and investigate the major variables impacting their academic performance and preventing students from dropping out of school owing to poor marks or failing the exam. There are several studies that uses traditional machine learning to predict student performance, however, hybrid deep learning in predicting student performance has never been reported. Whereas machine learning requires feature selection, deep learning models may conduct automatic feature selection in the training model. However, it may suffer from the curse of dimensionality as it collects more and more features from student data. This paper proposes a hybrid deep learning model with combination of Convolutional Neural Network (CNN) and Recurrent neural network (RNN), i.e. CNN-RNN, where CNN captures the local dominant features and reduce the curse of dimensionality and RNN obtains the semantic correlation between features. The results of the experiments indicate that the hybrid CNN-RNN prediction model performs better than deep learning model by 3.16%, where the accuracy increased from 73.07% in ANN to 79.23% in hybrid CNN-RNN.


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Overall process of the proposed hybrid model




How to Cite

S. Y. . Xiong, E. F. M. . Gasim, C. . Xin Ying, K. K. . Wah, and L. M. . Ha, “A Proposed Hybrid CNN-RNN Architecture for Student Performance Prediction”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 347–355, Oct. 2022.



Research Article