Development of analytics tools for e-Learning

Students are constantly classified based on factors such as their personal backgrounds, grades, and their behaviour in school. With the rise of data analytics and machine learning, such classifications are now extended into the form of prediction models. Machine learning applications have become inc...

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Main Author: Chua, Eileen Pei Fang
Other Authors: Chua Hock Chuan
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/75241
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-752412023-07-07T16:44:04Z Development of analytics tools for e-Learning Chua, Eileen Pei Fang Chua Hock Chuan School of Electrical and Electronic Engineering DRNTU::Engineering Students are constantly classified based on factors such as their personal backgrounds, grades, and their behaviour in school. With the rise of data analytics and machine learning, such classifications are now extended into the form of prediction models. Machine learning applications have become increasingly common in education today due to the sheer amount of information available through online systems. Currently, we want to find out whether there are any links between student background, e-Learning usage, and student performance. In this project, we are aiming to build a prediction model based on based student background data, weekly e-Learning, and student class performance. The main objective is find out how well final grades can be determined before the final examination. Furthermore, we also want to understand the impact of different background factors and e-Learning on the students’ performance so that useful and actionable insights can be obtained. Classification algorithms such as K-Nearest Neighbours, Support Vector Machines, Decision Trees, and Random Forest, covering linear, non-linear, and ensemble methods were used in this project. Various prediction models were developed and tested, and we were able to predict students’ grades with a maximum mean accuracy of 56% near the end of the course. Through this project, we were also able to identify trends based on background factors on student performance to complement the insights with the prediction model so that the results and findings of this project can be implemented in future classes. Bachelor of Engineering 2018-05-30T05:30:55Z 2018-05-30T05:30:55Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75241 en Nanyang Technological University 61 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Chua, Eileen Pei Fang
Development of analytics tools for e-Learning
description Students are constantly classified based on factors such as their personal backgrounds, grades, and their behaviour in school. With the rise of data analytics and machine learning, such classifications are now extended into the form of prediction models. Machine learning applications have become increasingly common in education today due to the sheer amount of information available through online systems. Currently, we want to find out whether there are any links between student background, e-Learning usage, and student performance. In this project, we are aiming to build a prediction model based on based student background data, weekly e-Learning, and student class performance. The main objective is find out how well final grades can be determined before the final examination. Furthermore, we also want to understand the impact of different background factors and e-Learning on the students’ performance so that useful and actionable insights can be obtained. Classification algorithms such as K-Nearest Neighbours, Support Vector Machines, Decision Trees, and Random Forest, covering linear, non-linear, and ensemble methods were used in this project. Various prediction models were developed and tested, and we were able to predict students’ grades with a maximum mean accuracy of 56% near the end of the course. Through this project, we were also able to identify trends based on background factors on student performance to complement the insights with the prediction model so that the results and findings of this project can be implemented in future classes.
author2 Chua Hock Chuan
author_facet Chua Hock Chuan
Chua, Eileen Pei Fang
format Final Year Project
author Chua, Eileen Pei Fang
author_sort Chua, Eileen Pei Fang
title Development of analytics tools for e-Learning
title_short Development of analytics tools for e-Learning
title_full Development of analytics tools for e-Learning
title_fullStr Development of analytics tools for e-Learning
title_full_unstemmed Development of analytics tools for e-Learning
title_sort development of analytics tools for e-learning
publishDate 2018
url http://hdl.handle.net/10356/75241
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