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Journal of Intelligent Systems and Internet of Things
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Title

Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique

  Rahul Sharma 1 ,   Shiv Shakti Shrivastava 2 ,   Aditi Sharma 3 *

1  Department of Computer Science and Engineering, Rabindranath Tagore University, Raisen, (M.P.), India
    (sharma.rahul5656@gmail.com)

2  Department of Computer Science and Engineering, Rabindranath Tagore University, Raisen, (M.P.), India
    (shivshakti18@gmail.com)

3  Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India; IEEE Senior Member, Symbiosis Institute of Technology, Pune, India
    (aditi.sharma@ieee.org)

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Doi   :   https://doi.org/10.54216/JISIoT.100203

Received: April 07, 2023 Revised: July 02, 2023 Accepted: October 05, 2023

Abstract :

Data analysis is an essential component of decision support in various industries that includes industrial and educational institutions. This research proposes Data Mining (DM) techniques to improve the efficiency of higher education (HE) institutions. DM has a substantial impact on different higher education activities including student performances, management of student’s life cycle, selection of courses, monitoring of retention rate, grants & funds management by using technique’s such as clustering, decision trees (DT), and association. Educational Data Mining (EDM) is an interdisciplinary study topic that focuses on getting DM to the fields of education by leveraging methods from (ML) statistics, (DM), and (DA) to get important insights from educational sets of data. EDM is critical in transforming raw data into useful information, allowing for a greater knowledge of students and their academic settings, as well as promoting better teacher assistance and ESD (Educational System Decisions). The study's goal is to provide a complete overview of EDM (Educational Data Mining), highlighting its various applications and benefits in the context of higher education.

Keywords :

EDM (Educational Data Mining); DM (Data mining) techniques; Data processing methods; Knowledge discovery in databases (KDD); Learning analytics (LA); EDM tools , and Visualizations tools were all examples of data mining strategies (DMS);

References :

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Cite this Article as :
Style #
MLA Rahul Sharma , Shiv Shakti Shrivastava , Aditi Sharma. "Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique." Journal of Intelligent Systems and Internet of Things, Vol. 10, No. 2, 2023 ,PP. 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
APA Rahul Sharma , Shiv Shakti Shrivastava , Aditi Sharma. (2023). Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
Chicago Rahul Sharma , Shiv Shakti Shrivastava , Aditi Sharma. "Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique." Journal of Journal of Intelligent Systems and Internet of Things, 10 no. 2 (2023): 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
Harvard Rahul Sharma , Shiv Shakti Shrivastava , Aditi Sharma. (2023). Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
Vancouver Rahul Sharma , Shiv Shakti Shrivastava , Aditi Sharma. Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 10 ( 2 ): 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
IEEE Rahul Sharma, Shiv Shakti Shrivastava, Aditi Sharma, Predicting Student Performance Using Educational Data Mining and Learning Analytics Technique, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 10 , No. 2 , (2023) : 24-37 (Doi   :  https://doi.org/10.54216/JISIoT.100203)
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