Integrating Learning Analytics and Recommendation Model to Build Career Recommendation Model

Dr. G. Manikandan; Ms. Vilma Veronica; Ms. S. Hemalatha

doi:10.32628/IJSRST52411228

Authors

Dr. G. Manikandan Professor, Kings Engineering College, Sriperumbudhur, Tamil Nadu, India Author
Ms. Vilma Veronica Assistant Professor, Kings Engineering College, Sriperumbudhur, Tamil Nadu, India Author
Ms. S. Hemalatha Assistant Professor, Kings Engineering College, Sriperumbudhur, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/IJSRST52411228

Keywords:

Learning Analytics, Machine Learning, Recommendation Model, Career Recommendation, ntegrated Learning System, Educational Data Mining, Personalized Learning, Career Pathway Analysis, Skill Profiling, Competency Mapping

Abstract

In the rapidly evolving landscape of education and employment, individuals often face challenges in making informed career decisions that align with their skills, interests, and market demands. This proposed system aims to bridge this gap by integrating learning analytics and a recommendation model to provide personalized and data-driven career recommendations. The system leverages learning analytics, utilizing data from various educational platforms, including academic performance, course completion, and extracurricular activities. This data is processed to identify patterns, strengths, and weaknesses, allowing for a comprehensive understanding of an individual's academic profile.
Simultaneously, a sophisticated recommendation model is employed to analyze the career landscape, considering factors such as job market trends, emerging industries, and required skill sets. Machine learning algorithms within the recommendation model adapt to changing dynamics in the job market, ensuring the system remains up-to-date and relevant. The integration of learning analytics and the recommendation model enables the system to generate personalized career suggestions based on an individual's academic performance, interests, and market demands. These suggestions encompass potential career paths, further educational opportunities, and skill development areas tailored to each user.
Moreover, the system employs explainable AI techniques to provide transparent insights into the reasoning behind each recommendation, fostering user understanding and trust. Additionally, users have the ability to explore and understand the rationale behind the suggested career paths, enabling them to make well-informed decisions.To enhance user engagement and usability, the proposed system features an intuitive interface, accessible through web and mobile platforms. The system also facilitates continuous feedback loops, allowing users to update their profiles and receive real-time recommendations as they progress in their academic and professional journeys.
In summary, the integration of learning analytics and a recommendation model in this proposed system offers a comprehensive and personalized approach to career guidance. By combining academic data analysis with dynamic insights into the job market, the system empowers individuals to make informed decisions, ultimately improving their career trajectory and satisfaction.

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