Assessing Students for Industry Readiness using Classification Methods
Hossain Molla J, Basak SK, Obaidullah SM, Alam PA, Goto T, Sen S (2023)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2023
Publisher: Association for Computing Machinery
Series: ACM International Conference Proceeding Series
City/Town: New York, NY
Pages Range: 25-29
Conference Proceedings Title: ACIT '22: Proceedings of the 9th International Conference on Applied Computing & Information Technology
Event location: Virtual, Online
ISBN: 9781450397605
Abstract
Although Placement of students is an intrinsic requirement for the students worldwide majorly for professional as well as for higher degree courses. However, often the students are not aware about their skill levels that are considered as the industry readiness parameters. The Institutions often take care of the students to upgrade the skill level to meet the requirement of the Industry. In order to analyze the students based on the several parameters of industry readiness intelligent methods are required to assess them. In this research work, different classification methods are applied on the existing placement data to evaluate whether a student will get a job or not. Accuracies of these methods are compared using a real life data set. This analysis will help the Institutes to take the decision how long the training programs will continue. The result of the classification methods have been improved further using Random oversampling.
Involved external institutions
India (IN)
Japan (JP)How to cite
APA:
Hossain Molla, J., Basak, S.K., Obaidullah, S.M., Alam, P.A., Goto, T., & Sen, S. (2022). Assessing Students for Industry Readiness using Classification Methods. In Kensei Tsuchida, Takaaki Goto, Hiroki Nomiya, Yuhki Kitazono (Eds.), ACIT '22: Proceedings of the 9th International Conference on Applied Computing & Information Technology (pp. 25-29). Virtual, Online, US: New York, NY: Association for Computing Machinery.
MLA:
Hossain Molla, Jakir, et al. "Assessing Students for Industry Readiness using Classification Methods." Proceedings of the 9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022, Virtual, Online Ed. Kensei Tsuchida, Takaaki Goto, Hiroki Nomiya, Yuhki Kitazono, New York, NY: Association for Computing Machinery, 2022. 25-29.
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