Modelling, prediction and classification of student academic performance using artificial neural networks
The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations...
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| Veröffentlicht in: | SN applied sciences 2019-09, Vol.1 (9), p.982, Article 982 |
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| creator | Lau, E. T. Sun, L. Yang, Q. |
| description | The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. Overall, the neural network model has achieved a good prediction accuracy of 84.8%, along with limitations. |
| doi_str_mv | 10.1007/s42452-019-0884-7 |
| format | Article |
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T. ; Sun, L. ; Yang, Q.</creator><creatorcontrib>Lau, E. T. ; Sun, L. ; Yang, Q.</creatorcontrib><description>The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. 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T.</creatorcontrib><creatorcontrib>Sun, L.</creatorcontrib><creatorcontrib>Yang, Q.</creatorcontrib><title>Modelling, prediction and classification of student academic performance using artificial neural networks</title><title>SN applied sciences</title><addtitle>SN Appl. Sci</addtitle><description>The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. 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T.</creatorcontrib><creatorcontrib>Sun, L.</creatorcontrib><creatorcontrib>Yang, Q.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><jtitle>SN applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lau, E. T.</au><au>Sun, L.</au><au>Yang, Q.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling, prediction and classification of student academic performance using artificial neural networks</atitle><jtitle>SN applied sciences</jtitle><stitle>SN Appl. Sci</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>1</volume><issue>9</issue><spage>982</spage><pages>982-</pages><artnum>982</artnum><issn>2523-3963</issn><eissn>2523-3971</eissn><abstract>The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. Overall, the neural network model has achieved a good prediction accuracy of 84.8%, along with limitations.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s42452-019-0884-7</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Academic achievement Accuracy Algorithms Applied and Technical Physics Artificial intelligence Artificial neural networks Back propagation Back propagation networks Bibliometrics Chemistry/Food Science Colleges & universities Data science Discriminant analysis Earth Sciences Educational research Engineering Engineering: Frontiers in Machine Learning: Algorithms and Applications (FMLAA) Entrance examinations Environment Histograms Hypothesis testing Learning Materials Science Modelling Neural networks Neurons Performance evaluation Predictions Quality of education Research Article Statistical analysis Statistical methods Statistics Students Success Teaching University students |
| title | Modelling, prediction and classification of student academic performance using artificial neural networks |
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