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A Decision Support System of Selecting Groups (Science/ Business Studies/ Humanities) for Secondary School Students in Bangladesh

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dc.contributor.author Hasan, Rifat
dc.contributor.author Ovy, Md. Khairul Alam
dc.contributor.author Nishi, Ifrat Zahan
dc.contributor.author Hakim, Md. Azizul
dc.contributor.author Hafiz, Rubaiya
dc.date.accessioned 2021-08-23T07:28:36Z
dc.date.available 2021-08-23T07:28:36Z
dc.date.issued 2020-10-15
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6039
dc.description.abstract As education is the only way to turn a person into human resource, every country tries to give her citizens proper scope of bringing out their inner ability by offering the appropriate education. According to the education system of Bangladesh, an 8 th grade completing student has to choose a group (science, Business Studies, humanity) for further studies. This group will be his/her initial highway for higher education. But it is a matter of sorrow that, in Bangladesh this crucial event is done by some rumors and some traditional old school ways, which are mostly wrong and destructive. From the perspective of this country, the only way of choosing those groups is previous result. Of course, result is one of the most important attribute, but it should not be the only thing. Again in this country, Science is thought to be superior than other groups. That's why, parents have the tendency to impose this group to their children without knowing their ability and interest and leads them towards an uncertain future. Therefore, the aim of this paper is to build a model of group selection by analyzing some random attributes of higher level students who have already gone through this event of selecting groups with the help of data mining and some machine learning algorithms, so that a newly 9 th grade student could have the proper direction of selecting a group which is best for him/her. For the purpose of experimentation we have used three machine learning algorithms: Naïve Bayes, Sequential Minimal Optimization (SMO) and Random Forest. Among these algorithms Random forest gives the best prediction result with an accuracy of 84.9%. en_US
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Education en_US
dc.subject Business en_US
dc.subject Machine learning algorithms en_US
dc.subject Prediction algorithms en_US
dc.subject Machine learning en_US
dc.subject Data mining en_US
dc.title A Decision Support System of Selecting Groups (Science/ Business Studies/ Humanities) for Secondary School Students in Bangladesh en_US
dc.type Article en_US

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