IJIET 2020 Vol.10(7): 511-515 ISSN: 2010-3689
doi: 10.18178/ijiet.2020.10.7.1416

Efficiency Assessment of Undergraduate Students Based on Academic Record Using Deep Learning Methodology

Arthit Buranasing

Abstract—Computer 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%.

Index Terms—Artificial intelligence, deep learning, student performance prediction, recommender system.

Arthit Buranasing is with Srinakharinwirot University, Thailand (e-mail: arthit.bur@hotmail.com).

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Cite:Arthit Buranasing, "Efficiency Assessment of Undergraduate Students Based on Academic Record Using Deep Learning Methodology," International Journal of Information and Education Technology vol. 10, no. 7, pp. 511-515, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net