E-Crypto Learning and Applying Data Mining in Education Sector to Improve Learner’s Performance

E.C. Mwakamo¹ , R. Kalimuthu²

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.5 , pp.59-66, May-2021

Online published on May 31, 2021

Copyright © E.C. Mwakamo, R. Kalimuthu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Education is being positively and innovatively enhanced through technology to create new strategies of delivering academic information both online and face to face. Online, Distance and Blended learning are the modern strategies that are and going to impact teaching approaches in academic institutions, relatively through introducing e-learning platforms which are designed as models used to collect and analyze meaningful information online for assessing, analyzing and presenting data patterns taking place among learners. This study proposes an e-learning requirement that focuses on mining student educational data with aim of exploring the behavior of learning, interaction with the platform from data generated by students in an education environment. The data is collected from the event logs or scrapped csv files of student’s information for analysis to determine a model to extract their behavior and predictive patterns in an e-learning environment. The techniques applied in this study are decision tree decision making process of filtering content of data, cluster analysis performing the organization of pattern grouping in collections, and random forest for creating prediction model that closely provides accuracy of student’s success. Elliptic curve and SHA-256 cryptography method is applied to ensure security per the concerns of user confidentiality both for the instructors and student when performing authentication entries into the platform and during chat interactions. Mining of student’s data would help in projecting student’s performance and interaction activities, where the random forest technique surpasses logistic regression and support vector machine classification methods of machine learning predictions. With this approach learning institutions will be able to classify groups of learners with aim of analyzing and directing well informed decisions to help them improve in their learning sessions.

Key-Words / Index Term :
Educational Data Mining (EDM), Classification, Learning management system (LMS), Online learning, Learner’s performance

References :
[1] D.B. Vaghela, P. Sharma, “A data mining approach used in University exams of Engineering learners on the Early prediction of student’s performance”, International Journal of Scientific Research in Multi-disciplinary Studies, Vol:1, Issue 5, 2020.
[2] R. Gautam, D. Pahuja “A review on mining Educational Data”, International Journal of Science and Research, Vol.3 Issue.11, 2014
[3] W. Chou, Elliptic Curve Cryptography and Its Applications to Mobile Devices, Department of Mathematics.
[4] M. Chibambo, “Scurrying Prevalent Issues and Trends of Distance Education in Malawi, to provide a ssuccessful Model for Open and Distance Learning”, Journal of Arts, Sscience and commerce, 2016
[5] R. Hasan, and M. U. Sattar, A. Abbas, K.l U. Sarker, “Using data mining and video learning analytics techniques in predicting performance of students in Higher Education institutions”, 2020
[6] O. S Ahmed, “Cyber security in a Blackboard system for E-Learning used in Light of Educational Quality”, IJCET, 2019
[7] P. Baepler, C. James, “Analysis on Academics and Mining Higher International Education data”, Journal of scholarships for Teaching and Learning, 2010.
[8] S. Krizanic ,“Clustering and decision analysis implemented with decision tree techniqueis used for mining Educational Data”, International Research Journal of Engineering Business Management, 2020.
[9] A. O. Mohamed, M. Omar, “A Requirements Modeling for E-Learning
Management System”, Iinternational Journal of Rresearch Ttechnology and Eengineering, Vol: 7 Issue-6S2, 2019.
[10] K. Dahdouh, A. Dakkak, “Improving Online Education Using Big Data Technologies”, 2020.
[11] A. Sankari, Dr. S. Masih and Dr. Maya Ingle, “A Review on Research Areas in Educational Learning Analytics and Data mining”, International Journal of Scientific & Technology Research, Vol. 8, Issue 12, 2019.
[12] S. Herold, H. Meinel, G. Lorenzen-Zabel, A. “Blessings of open data and technology: E-mobility and Land use monitoring as e-learning examples”, STS Conference, 2019.
[13] A. Mousawi, K. Mershad .l Said, P. Ibrahim,“Using Machine Learning to enhance Students Assessment Moodle Application”, published in May 2020
[14] A. S. Chautan, “Enhancement of Academic decision-making using Clustering and Classification techniques in environments of Higher Education”, International Journal of Scientific Research in Computer Engineering, Vol.8, Issue.2
[15] O. Deperlioglu, A. Kocatepe, “Developing a learner-based management system using web 2.0”, IODL&ICEM Joint Conference and Media Days, 2010.