An analysis of the professional preferences and choices of computer engineering students
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
The revelation of the preferences and choices of students is critical for understanding which areas of the discipline they aim. Especially for final-year undergraduate students, job prospects are an important concern. In this study, the graduation project choices of the final-year undergraduate students in the department of computer engineering were investigated in light of their impact on both the industry and academia. A total of 1,693 course grades of 94 final-year undergraduate students were retrieved from the Student Information System. These course grades were utilized as the features of the employed machine learning algorithms alongside the features that were deduced from them. In addition, the popularities of the graduation project topics on both GitHub and IEEE were investigated to reveal their impact on the industry and academia. To this end, we proposed an experimental study, using 14 machine learning techniques, that predicts the topics of the graduation projects that the final-year undergraduate students chose. According to the experimental results, the accuracy of the proposed model was calculated to be as high as 100 % when it was utilized with the Bayes Net, Kleene Star, or J48 algorithm. This experimental result confirms the efficiency of the proposed model. Finally, the insights gained from the data are discussed to shed light on the reasons for the choices of the graduation projects as well as their relationships with the courses.
Description
Keywords
computer engineering, data mining, machine learning, prediction, student analysis
Journal or Series
Computer Applications In Engineering Education
WoS Q Value
Q3
Scopus Q Value
Q1
Volume
28
Issue
4
