Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/11884
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dc.contributor.authorBuranasing A.
dc.date.accessioned2021-04-05T03:01:22Z-
dc.date.available2021-04-05T03:01:22Z-
dc.date.issued2020
dc.identifier.issn20103689
dc.identifier.other2-s2.0-85086096491
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/11884-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086096491&doi=10.18178%2fijiet.2020.10.7.1416&partnerID=40&md5=dd340dcbd2e48e02185df77640fa14d1
dc.description.abstractComputer science is the study of computers and computational systems which computer scientists deal mostly with application software and system software. Although knowing how to program is essential to the study of computer science, but it is only one element of the field. For example, software development uses various skills and techniques which are included in various subjects of a general computer science course. This paper focuses on senior students in computer science course who would like to assess the efficiency of their computer science skill in order to improve themselves. Moreover, the model also helps in the recruitment of new staff so that the companies would be able to assess the efficiency of newly graduated students or inexperienced candidates. This is because the lack of skill and inefficiency could cause problems to the hiring companies since they would have to invest time and money into training the new staff. This model can solve this problem by evaluating the performance and define the skills that must be improved directly. The result of the model is satisfactory, the average accuracy from experiment testing of confusion matrix is 89.33%. © 2020 by the authors.
dc.titleEfficiency assessment of undergraduate students based on academic record using deep learning methodology
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationInternational Journal of Information and Education Technology. Vol 10, No.7 (2020), p.511-515
dc.identifier.doi10.18178/ijiet.2020.10.7.1416
Appears in Collections:Scopus 1983-2021

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