A neural network students' performance prediction model (NNSPPM)
In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic p...
| 发表在: | 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2013 |
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| 格式: | Conference paper |
| 语言: | English |
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2013
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| 在线阅读: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894188408&doi=10.1109%2fICSIMA.2013.6717966&partnerID=40&md5=20ad4ef647d924178c64b769d14b2a1a |
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Arsad P.M.; Buniyamin N.; Manan J.-L.A. |
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Arsad P.M.; Buniyamin N.; Manan J.-L.A. 2-s2.0-84894188408 A neural network students' performance prediction model (NNSPPM) 2013 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2013 10.1109/ICSIMA.2013.6717966 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894188408&doi=10.1109%2fICSIMA.2013.6717966&partnerID=40&md5=20ad4ef647d924178c64b769d14b2a1a In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic achievement at semester eight. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Students' results for the fundamental subjects in the first semester were used as independent variables or input predictor variables while CGPA at semester eight was used as the output or the dependent variable. The study was done for two different entry points namely Matriculation and Diploma intakes. Performances of the models were measured using the coefficient of Correlation R and Mean Square Error (MSE). The outcomes from the study showed that fundamental subjects at semester one and three have strong influence in the final CGPA upon graduation. © 2013 IEEE. English Conference paper |
| author |
2-s2.0-84894188408 |
| spellingShingle |
2-s2.0-84894188408 A neural network students' performance prediction model (NNSPPM) |
| author_facet |
2-s2.0-84894188408 |
| author_sort |
2-s2.0-84894188408 |
| title |
A neural network students' performance prediction model (NNSPPM) |
| title_short |
A neural network students' performance prediction model (NNSPPM) |
| title_full |
A neural network students' performance prediction model (NNSPPM) |
| title_fullStr |
A neural network students' performance prediction model (NNSPPM) |
| title_full_unstemmed |
A neural network students' performance prediction model (NNSPPM) |
| title_sort |
A neural network students' performance prediction model (NNSPPM) |
| publishDate |
2013 |
| container_title |
2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2013 |
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| doi_str_mv |
10.1109/ICSIMA.2013.6717966 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894188408&doi=10.1109%2fICSIMA.2013.6717966&partnerID=40&md5=20ad4ef647d924178c64b769d14b2a1a |
| description |
In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic achievement at semester eight. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Students' results for the fundamental subjects in the first semester were used as independent variables or input predictor variables while CGPA at semester eight was used as the output or the dependent variable. The study was done for two different entry points namely Matriculation and Diploma intakes. Performances of the models were measured using the coefficient of Correlation R and Mean Square Error (MSE). The outcomes from the study showed that fundamental subjects at semester one and three have strong influence in the final CGPA upon graduation. © 2013 IEEE. |
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| language |
English |
| format |
Conference paper |
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scopus |
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Scopus |
| _version_ |
1828987882923098112 |