Prediction for Student Academic Performance Using SMNaive Bayes Model

Jia, B; Niu, K; Hou, X; Li, N; Peng, X; Gu, P; Jia, R

Prediction for Student Academic Performance Using SMNaive Bayes Model

Jia, B Niu, K Hou, X Li, N Peng, X

Gu, P Jia, R

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Publisher:: Springer Verlag
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science, 2019, 11888
Issue Date:: 2019-11-15

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Field	Value	Language
dc.contributor.author	Jia, B	en_US
dc.contributor.author	Niu, K	en_US
dc.contributor.author	Hou, X	en_US
dc.contributor.author	Li, N	en_US
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472	en_US
dc.contributor.author	Gu, P	en_US
dc.contributor.author	Jia, R	en_US
dc.date	2019-11-21	en_US
dc.date.issued	2019-11-15	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2019, 11888	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137497
dc.description.abstract	Predicting students academic performance is very important for students future development. There are a large number of students who can not graduate from colleges on time for various reasons every year. Nowadays, a large volume of students academic data has been generated in the process of promoting education informatization from the field of education. It becomes critical to predict student performance and ensure students to graduate on time by taking the best of these data. Machine learning models that predict students performance are widely available. However, some existing machine learning models still have the problem of low accuracy in predicting students performance. To solve this problem, we proposes a SMNaive Bayes (SMNB) model, which integrates Sequential Minimal Optimization (SMO) and Naive Bayes to make the prediction result more accurate. The basic idea is that the model predicts the performance of students professional courses via their basic course performance in the previous stage. In particular, SMO algorithm is leveraged to predict students academic performance of the first step and produces the results of the prediction; Naive Bayes then makes decision about the inconsistent results of the initial prediction; Lastly, the final results of students professional course performance prediction are produced. To test the effectiveness of our proposed model, we have conducted extensive experiments to compare SMNB against four prediction methods. The experimental results demonstrate that the proposed SMNB model is superior to all the compared methods.	en_US
dc.publisher	Springer Verlag	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.ispartof	Advanced Data Mining and Applications	en_US
dc.relation.isbasedon	10.1007/978-3-030-35231-8_52	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Prediction for Student Academic Performance Using SMNaive Bayes Model	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11888	en_US
utslib.location.activity	Dalian, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2019-11-23	en_US
pubs.publication-status	Published online	en_US
pubs.start-date	2019-11-21	en_US
pubs.volume	11888	en_US

Abstract:

Predicting students academic performance is very important for students future development. There are a large number of students who can not graduate from colleges on time for various reasons every year. Nowadays, a large volume of students academic data has been generated in the process of promoting education informatization from the field of education. It becomes critical to predict student performance and ensure students to graduate on time by taking the best of these data. Machine learning models that predict students performance are widely available. However, some existing machine learning models still have the problem of low accuracy in predicting students performance. To solve this problem, we proposes a SMNaive Bayes (SMNB) model, which integrates Sequential Minimal Optimization (SMO) and Naive Bayes to make the prediction result more accurate. The basic idea is that the model predicts the performance of students professional courses via their basic course performance in the previous stage. In particular, SMO algorithm is leveraged to predict students academic performance of the first step and produces the results of the prediction; Naive Bayes then makes decision about the inconsistent results of the initial prediction; Lastly, the final results of students professional course performance prediction are produced. To test the effectiveness of our proposed model, we have conducted extensive experiments to compare SMNB against four prediction methods. The experimental results demonstrate that the proposed SMNB model is superior to all the compared methods.

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http://hdl.handle.net/10453/137497