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Predicting Students' Academic Performance with Decision Tree and Neural Network

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Date Issued:
2019
Abstract/Description:
Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their datasets and computational results. Then, it demonstrates how the synthetic student dataset is generated, analyzes some input attributes from the dataset such as gender and high school GPA, and delivers with some visualization results to determine which classification methods approaches are the most efficient. After testing the data with decision tree classifiers and neural networks methodologies, it concludes the effectiveness of both approaches in terms of the model evaluation performance as well as discussing some of the most promising future work of this research.
Title: Predicting Students' Academic Performance with Decision Tree and Neural Network.
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Name(s): Feng, Junshuai, Author
Jha, Sumit Kumar, Committee Chair
Zhang, Wei, Committee Member
Zhang, Shaojie, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their datasets and computational results. Then, it demonstrates how the synthetic student dataset is generated, analyzes some input attributes from the dataset such as gender and high school GPA, and delivers with some visualization results to determine which classification methods approaches are the most efficient. After testing the data with decision tree classifiers and neural networks methodologies, it concludes the effectiveness of both approaches in terms of the model evaluation performance as well as discussing some of the most promising future work of this research.
Identifier: CFE0007455 (IID), ucf:52680 (fedora)
Note(s): 2019-05-01
M.S.
Engineering and Computer Science, Computer Science
Masters
This record was generated from author submitted information.
Subject(s): data mining -- machine learning -- data science -- decision trees -- neural networks -- edm -- R
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007455
Restrictions on Access: public 2019-05-15
Host Institution: UCF

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