A personalized machine learning–based system to evaluate students’ skillset and analyze the gap between academia and industry for engineering students

Abstract

Purpose

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

Design/methodology/approach

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

Findings

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

Research limitations/implications

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

Practical implications

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

Social implications

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Originality/value

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

Citation

Thavasi, S. and Revathi, T. (2024), "A personalized machine learning–based system to evaluate students’ skillset and analyze the gap between academia and industry for engineering students", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-01-2024-0053