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Blockchain Framework for Learner Performance Prediction using Life-Brain Storm-based Light GBM Coupled Neural Network

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Title Blockchain Framework for Learner Performance Prediction using Life-Brain Storm-based Light GBM Coupled Neural Network
 
Creator Xue, Yunlan
Singh, Vineeta
Singh, Suruchi
Kant, Kamal
Pandey, Saurabh
Kumar, Alok
Alam, Mohd. Shah
Tiwari, Shesh Mani
Joshi, Kapil
Kaushik, Vandana Dixit
 
Subject Blockchain
Deep-learning
e-khool LMS
E-learning
Performance prediction model
 
Description 652-668
E-learning is one of the dominant applications of digital techniques in the educational platform. Tutors can effectivelytailor their instruction to each student by using the automatic identification of the student's learning styles. Nowadays Deep learning techniques provide the preferable predictive model in the e-learning platform. Hence, this research article provides the prediction of the learner’s performance by using the Life-Brain Storm (Life-BS) based LightGBM coupled Neural Network (NN). A significant part of the research lies in the tuning of the hyper-parameters using the proposed Brain rule selection algorithm, which boosts the accuracy of the classifier. Furthermore, by lowering the dimensionality of the data, the feature extraction approach is developed in this study to reduce the computational complexity of the prediction framework. The suggested Life-BS-based LightGBM coupled NN model is shown to be effective by the experimental assessment, which yielded the lowest RMSE as well as the MSE for courses 1, 2, and 3, respectively. In addition, the evaluation metrics such as MAE and Kappa scores achieve better results for course-1, course-2, and course-3 respectively. Use of blockchain, including kappa score also in performance metrics along with Life-Brain Storm based LightGBM coupled Neural Network proposed learner performance prediction model are the keypoints of the presented work.
 
Date 2024-06-07T09:41:22Z
2024-06-07T09:41:22Z
2024-06
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/64043
https://doi.org/10.56042/jsir.v83i6.9904
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(6) [June 2024]