Non-cognitive skills : the art of learning to learn well (Part C)

This project deals with the application of data analytics to education, particularly students’ knowledge acquisition process. The target group is students using e-learning platforms. This project involves assessing the mastery level of students for various knowledge skills through machine learning t...

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Main Author: S, Supraja
Other Authors: Andy Khong Wai Hoong
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/67111
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-671112023-07-07T17:57:00Z Non-cognitive skills : the art of learning to learn well (Part C) S, Supraja Andy Khong Wai Hoong School of Electrical and Electronic Engineering DRNTU::Engineering This project deals with the application of data analytics to education, particularly students’ knowledge acquisition process. The target group is students using e-learning platforms. This project involves assessing the mastery level of students for various knowledge skills through machine learning techniques. Bayesian Knowledge Tracing, which is built upon the Hidden Markov Model, is the algorithm being incorporated to develop a model to gauge students’ mastery progression. This algorithm revolves around the manipulation of four main probabilities to calculate every student’s level of knowledge and learning transition for every skill for every attempt made. The method of Brute Force Search is implemented to generate exhaustive combinations, with which two error metrics are utilized to obtain the best fitting parameters to serve as the input for every student for every skill. The results plotted are subsequently regularized to smoothen the errors so as to obtain an accurate reflection of a student’s ability to grasp a skill after several attempts over time. R Studio is being used for implementation of this algorithm. Real-life data from students of Palmview Primary School who have completed several exercises through an e-learning platform is used as input to the model to generate the individualized knowledge tracing results for every student for every skill. Inferences of students’ behaviour and psychological patterns are made to classify different types of students. As such, the application of data analytics to education has great benefits of helping not only students to reflect on their ability, but also teachers to understand their students better. Bachelor of Engineering 2016-05-12T01:17:01Z 2016-05-12T01:17:01Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67111 en Nanyang Technological University 110 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
S, Supraja
Non-cognitive skills : the art of learning to learn well (Part C)
description This project deals with the application of data analytics to education, particularly students’ knowledge acquisition process. The target group is students using e-learning platforms. This project involves assessing the mastery level of students for various knowledge skills through machine learning techniques. Bayesian Knowledge Tracing, which is built upon the Hidden Markov Model, is the algorithm being incorporated to develop a model to gauge students’ mastery progression. This algorithm revolves around the manipulation of four main probabilities to calculate every student’s level of knowledge and learning transition for every skill for every attempt made. The method of Brute Force Search is implemented to generate exhaustive combinations, with which two error metrics are utilized to obtain the best fitting parameters to serve as the input for every student for every skill. The results plotted are subsequently regularized to smoothen the errors so as to obtain an accurate reflection of a student’s ability to grasp a skill after several attempts over time. R Studio is being used for implementation of this algorithm. Real-life data from students of Palmview Primary School who have completed several exercises through an e-learning platform is used as input to the model to generate the individualized knowledge tracing results for every student for every skill. Inferences of students’ behaviour and psychological patterns are made to classify different types of students. As such, the application of data analytics to education has great benefits of helping not only students to reflect on their ability, but also teachers to understand their students better.
author2 Andy Khong Wai Hoong
author_facet Andy Khong Wai Hoong
S, Supraja
format Final Year Project
author S, Supraja
author_sort S, Supraja
title Non-cognitive skills : the art of learning to learn well (Part C)
title_short Non-cognitive skills : the art of learning to learn well (Part C)
title_full Non-cognitive skills : the art of learning to learn well (Part C)
title_fullStr Non-cognitive skills : the art of learning to learn well (Part C)
title_full_unstemmed Non-cognitive skills : the art of learning to learn well (Part C)
title_sort non-cognitive skills : the art of learning to learn well (part c)
publishDate 2016
url http://hdl.handle.net/10356/67111
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