Recommender system to ensure academic success

In recent years, the application of recommender systems has been widely implemented among web services such as e-commerce, e-government [1], e-learning [2], [3], [4], among many others [5]. Recommender system has been utilised as a tool to predict and suggest relevant items to users. In the area of...

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Main Author: M Heriz Putra Yusoff
Other Authors: Andy Khong W H
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166943
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1669432023-07-07T17:42:41Z Recommender system to ensure academic success M Heriz Putra Yusoff Andy Khong W H Chua Hock Chuan School of Electrical and Electronic Engineering AndyKhong@ntu.edu.sg, EHCHUA@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering In recent years, the application of recommender systems has been widely implemented among web services such as e-commerce, e-government [1], e-learning [2], [3], [4], among many others [5]. Recommender system has been utilised as a tool to predict and suggest relevant items to users. In the area of education, previous studies have researched the Intervention recommendation system which plays a significant role in advising students for their future, such as forecasting student academic performance based on past data, suggesting student’s curricula [2], student career forecasting [7], and graduation forecast on time [8]. At Nanyang Technological University (NTU), student care managers (SCM) ensure students have access to the assistance they require when they face difficulties in achieving academic success. To decrease assessment time for timely intervention while alleviating shortage of SCMs, a recommender system is implemented. This paper implements the use of Knowledge Graph Attention Network (KGAT) as a solution for intervention suggestion strategy and framework to enhance academic success in NTU. The KGAT presents a powerful deep learning model that has been proposed to enhance the recommendation system by incorporating the graph structure of the consumer-article interactions. The KGAT model is based on message propagation and the attention mechanism, allowing the model to learn the significance of the different parts of the knowledge graph and achieve distant-dimensional relationships to make precise, varied, and explainable suggestions. The KGAT model has shown to outperform the state-of-the-art recommendation models on several datasets. It has also shown to be effective in capturing the complex relationships between the consumer and article nodes in the knowledge graph. This model can be easily map and applied in the area of intervention recommender strategy to support student’s academic success in NTU, where students are the users and intervention recommendation are the items. Bachelor of Engineering (Information Engineering and Media) 2023-05-19T11:57:16Z 2023-05-19T11:57:16Z 2023 Final Year Project (FYP) M Heriz Putra Yusoff (2023). Recommender system to ensure academic success. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166943 https://hdl.handle.net/10356/166943 en A3045-221 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Software::Software engineering
spellingShingle Engineering::Computer science and engineering::Software::Software engineering
M Heriz Putra Yusoff
Recommender system to ensure academic success
description In recent years, the application of recommender systems has been widely implemented among web services such as e-commerce, e-government [1], e-learning [2], [3], [4], among many others [5]. Recommender system has been utilised as a tool to predict and suggest relevant items to users. In the area of education, previous studies have researched the Intervention recommendation system which plays a significant role in advising students for their future, such as forecasting student academic performance based on past data, suggesting student’s curricula [2], student career forecasting [7], and graduation forecast on time [8]. At Nanyang Technological University (NTU), student care managers (SCM) ensure students have access to the assistance they require when they face difficulties in achieving academic success. To decrease assessment time for timely intervention while alleviating shortage of SCMs, a recommender system is implemented. This paper implements the use of Knowledge Graph Attention Network (KGAT) as a solution for intervention suggestion strategy and framework to enhance academic success in NTU. The KGAT presents a powerful deep learning model that has been proposed to enhance the recommendation system by incorporating the graph structure of the consumer-article interactions. The KGAT model is based on message propagation and the attention mechanism, allowing the model to learn the significance of the different parts of the knowledge graph and achieve distant-dimensional relationships to make precise, varied, and explainable suggestions. The KGAT model has shown to outperform the state-of-the-art recommendation models on several datasets. It has also shown to be effective in capturing the complex relationships between the consumer and article nodes in the knowledge graph. This model can be easily map and applied in the area of intervention recommender strategy to support student’s academic success in NTU, where students are the users and intervention recommendation are the items.
author2 Andy Khong W H
author_facet Andy Khong W H
M Heriz Putra Yusoff
format Final Year Project
author M Heriz Putra Yusoff
author_sort M Heriz Putra Yusoff
title Recommender system to ensure academic success
title_short Recommender system to ensure academic success
title_full Recommender system to ensure academic success
title_fullStr Recommender system to ensure academic success
title_full_unstemmed Recommender system to ensure academic success
title_sort recommender system to ensure academic success
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166943
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