International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

G. Sudhamathy *, N. Valliammal

Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore-641043, India

 

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ABSTRACT

Learning analytics (LA) is a research domain that leverages the analysis of data from the learning process to gain a deeper understanding and enhance learning outcomes. To classify learner performance, a model has been proposed that combines various deep learning techniques, including convolutional neural network (CNN), Long Short-Term Memory (LSTM), and Bayesian models. The integration of these approaches aims to improve the accuracy and effectiveness of performance classification. CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. The effective classification of learners' performance is achieved by combining the strengths of CNN and LSTM, along with the integration of a Bayesian deep learning model. The performance of the proposed model is estimated using the metrics like Accuracy, Precision, Recall and F1-Score. The model showed improvements in Accuracy, Precision, Recall and F1-Score are 98.18%, 97.09%, 96.38% and 95.35% respectively. The proposed model is compared with another existing model such as LSTM and collaborative machine learning (ML) models in terms of performance metrics. The proposed method attained accuracy of 98.18% which is higher than other existing models.


Keywords: Bayesian, Convolutional neural network, Deep learning, Learning analytics, Long short-term memory.


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ARTICLE INFORMATION

Received: 2023-07-24
Revised: 2023-08-21
Accepted: 2023-08-30
Available Online: 2023-11-27


Cite this article:

Sudhamathy, G., Valliammal, N., 2023. The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance. International Journal of Applied Science and Engineering, 20, 2023282. https://doi.org/10.6703/IJASE.202312_20(4).007

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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