Comprehensive Analysis of a Recommender System for Career Guidance
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Date
2024
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Faculty of Engineering and Natural Science
Abstract
Choosing a suitable specialty is crucial for students, influenced by personal interests, academic performance, and career prospects. However, many struggle due to a lack of clear guidance and information overload. This research proposes a recommendation system to help students choose appropriate specialties and elective courses based on their academic performance and grades. The study focuses on IT students, as this field offers a wide range of specialties and career opportunities. It utilizes machine learning techniques, including reinforcement learning algorithms, to analyze academic data and provide personalized recommendations. The study compares traditional machine learning algorithms like Decision Tree, Support Vector Machine, and Random Forest with reinforcement learning algorithms such as Q-learning and Deep Q-Network. The methodology involves data collection, preparation, and feature engineering, followed by implementing various classifiers to build the recommendation system. Results indicate that Q-learning achieves the highest accuracy in recommending specialties, outperforming other algorithms. However, traditional machine learning algorithms also show competitive performance, suggesting both approaches can be effective. This research contributes to educational technology by offering a practical solution to help students make informed academic and career decisions. Future work includes enhancing the recommendation system with real-time data and user feedback mechanisms to improve its effectiveness and usability.
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Imankulova A / Comprehensive Analysis of a Recommender System for Career Guidance / 2024 / Computer Science - 7M06102