Development of transcript-Driven IT Specialization Recommendation System using ML
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Date
2024
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Faculty of Engineering and Natural Science
Abstract
Recommendation systems in education are pivotal for guiding students through their academic and career paths. However, traditional systems often fail to address the unique challenges and rapid changes within the Information Technology (IT) sector. This study proposes a machine learning-driven approach to enhance the precision and personalization of IT career guidance.This research develops a sophisticated machine learning model using a variety of algorithms, including Random Forest, Logistic Regression and Decision Trees, to analyze and process detailed student transcripts. The study aims to predict and align students’ IT specializations with both their capabilities and market demands. A robust validation framework, including cross-validation and algorithm comparison, ensures the accuracy and reliability of the recommendation system. The model demonstrates a high degree of predictive accuracy, outperforming traditional recommendation systems. It effectively identifies individual strengths and market opportunities, providing tailored recommendations that improve educational outcomes and job market readiness. Integrating machine learning with educational recommendation systems offers a promising avenue for addressing the specialization needs within the IT sector. By leveraging detailed transcript data and advanced predictive analytics, the proposed system aligns educational paths with professional demands, enhancing student employability and meeting industry needs.
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Myrzabayeva A / Development of transcript-Driven IT Specialization Recommendation System using ML / 2024 / Computer Science - 7M06102