Continual learning in knowledge tracing

The key to building a more sustainable world is high-quality education. The recent COVID-19 pandemic has sparked a surge in online education, allowing students and teachers to learn and teach from the comfort of their own homes.This has led to large amount of student learning activities data bein...

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Main Author: Sujanya, Suresh
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157960
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author Sujanya, Suresh
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Sujanya, Suresh
author_sort Sujanya, Suresh
collection NTU
description The key to building a more sustainable world is high-quality education. The recent COVID-19 pandemic has sparked a surge in online education, allowing students and teachers to learn and teach from the comfort of their own homes.This has led to large amount of student learning activities data being collected. Knowledge Tracing (KT), which aims to monitor learners’ evolving knowledge state and evaluate their growing knowledge acquisitions, is a crucial and vital component in online learning. The learning assessments depend on the ability of a student to learn and master a skill based on the history of their performance. However, due to data privacy concerns, it is difficult to combine the learners’ data from multiple schools, and the learning of newer tasks leads to forgetting of the older ones. Hence, this work explores the feasibility of developing these models while preserving the confidentiality of learners’ data and customizing the learning experiences within their schools. This study is conducted using a portion of the ASSISTments dataset (2009) in a continual learning framework adapting the Self Attentive Knowledge Tracing (SAKT) algorithm. The outcomes achieved by learning sequentially in a task-incremental setting are better than pooling all the data together. Keywords: Knowledge Tracing, Continual learning, catastrophic forgetting.
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spelling ntu-10356/1579602023-07-04T17:51:41Z Continual learning in knowledge tracing Sujanya, Suresh Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research EPNSugan@ntu.edu.sg Engineering::Electrical and electronic engineering The key to building a more sustainable world is high-quality education. The recent COVID-19 pandemic has sparked a surge in online education, allowing students and teachers to learn and teach from the comfort of their own homes.This has led to large amount of student learning activities data being collected. Knowledge Tracing (KT), which aims to monitor learners’ evolving knowledge state and evaluate their growing knowledge acquisitions, is a crucial and vital component in online learning. The learning assessments depend on the ability of a student to learn and master a skill based on the history of their performance. However, due to data privacy concerns, it is difficult to combine the learners’ data from multiple schools, and the learning of newer tasks leads to forgetting of the older ones. Hence, this work explores the feasibility of developing these models while preserving the confidentiality of learners’ data and customizing the learning experiences within their schools. This study is conducted using a portion of the ASSISTments dataset (2009) in a continual learning framework adapting the Self Attentive Knowledge Tracing (SAKT) algorithm. The outcomes achieved by learning sequentially in a task-incremental setting are better than pooling all the data together. Keywords: Knowledge Tracing, Continual learning, catastrophic forgetting. Master of Science (Computer Control and Automation) 2022-05-16T09:52:47Z 2022-05-16T09:52:47Z 2022 Thesis-Master by Coursework Sujanya, S. (2022). Continual learning in knowledge tracing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157960 https://hdl.handle.net/10356/157960 en application/pdf Nanyang Technological University
spellingShingle Engineering::Electrical and electronic engineering
Sujanya, Suresh
Continual learning in knowledge tracing
title Continual learning in knowledge tracing
title_full Continual learning in knowledge tracing
title_fullStr Continual learning in knowledge tracing
title_full_unstemmed Continual learning in knowledge tracing
title_short Continual learning in knowledge tracing
title_sort continual learning in knowledge tracing
topic Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/157960
work_keys_str_mv AT sujanyasuresh continuallearninginknowledgetracing