Student Career Prediction Using Advanced Machine Learning Techniques

Author:

Sripath Roy K,Roopkanth K,Uday Teja V,Bhavana V,Priyanka J

Abstract

As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This paper mainly concentrates on the career area prediction of computer science domain candidates.  

Publisher

Science Publishing Corporation

Subject

Hardware and Architecture,General Engineering,General Chemical Engineering,Environmental Engineering,Computer Science (miscellaneous),Biotechnology

Cited by 23 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computer-Assisted Career Guidance Tools for Students’ Career Path Planning: A Review on Enabling Technologies and Applications;Journal of Information Technology Education: Research;2024

2. Student Career Prediction Using Machine Learning;2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech);2023-12-23

3. Implementing Convolutional Neural Networks for Career Prediction: A Case Study on Twelfth Grade Students;2023 International Conference on Emerging Research in Computational Science (ICERCS);2023-12-07

4. Study on Predicting University Student Performance Based on Course Correlation;Journal of Education and Educational Research;2023-10-01

5. Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model;Education and Information Technologies;2023-08-19

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