Personalized Career-Path Recommender System for STEM Students
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Date
2024
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Publisher
Faculty of Engineering and Natural Science
Abstract
This dissertation introduces a Personalized Career-Path Recommender System (PCRS) designed to help high school students in Kazakhstan, particularly those interested in STEM (Science, Technology, Engineering, and Mathematics) fields. The system uses the Myers-Briggs Type Indicator (MBTI) personality types and students’ academic performance to offer personalized recommendations for university specializations. The research addresses the common challenges faced by students, such as high dropout rates and frequent changes in majors, often due to the lack of structured career guidance. To tackle these issues, the study collected a variety of data, including students’ demographics, academic records, and personal attributes, as well as detailed profiles of university majors. Advanced machine learning techniques, including content-based filtering, collaborative filtering, fuzzy logic, and hybrid approaches, were used to process this data and generate accurate recommendations. The effectiveness of the PCRS was tested with real data from students at SDU University. The results show that the system can provide relevant and personalized career guidance, significantly improving students’ decision-making processes and satisfaction with their chosen specializations. By combining MBTI personality assessments with academic performance data, this research offers a fresh approach to educational technology and career counseling. The insights and methods developed in this study can be adapted for use in other regions facing similar challenges, ultimately helping more students make informed and satisfying career choices.
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Keywords
Personalized Education, Career Guidance, STEM Education, Machine Learning
Citation
Zhalgassova Zh / Personalized Career-Path Recommender System for STEM Students / 2024 / 7M06102 - Computer Science