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Predicting knowledge level of learners using Machine Learning Algorithms

Belagur Ramesh, Sachin (2019) Predicting knowledge level of learners using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The evolution of Technology in the last decade has witnessed ground-breaking innovations and one of the major innovations being in the e-learning landscape. Over the years we have witnessed how online learning has morphed into an easily accessible learning platform. Currently with extensive research on areas concentrated on offering accurate and tailored information to the user this area has seen plenty of new developments and has promising returns on providing quality and targeted learning. This research aims at understanding the user of an e-learning platform which is essentially vital to deliver accurate and quality learning in this age where the attention span of humans has seen a drastic drop and the online world is driven by distractions of a million types. At the stage of discovering insights this research uses a set of machine learning algorithms pre-dominantly being classification algorithms, which predict the users’ level of knowledge and the resulting outcome. Linear discriminant Analysis, classification and Regression trees, Support vector machine, Random forests and K Nearest Neighbor have been used to mine for insights. On a high level this research found that Random Forest algorithm was more efficient and accurate in comparison to other algorithms, achieved a 62% accuracy in predicting the knowledge level of the user.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

L Education > LC Special aspects / Types of education > E-Learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 14 Oct 2019 09:07
Last Modified: 14 Oct 2019 09:07
URI: http://trap.ncirl.ie/id/eprint/3860

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