Design of a Data-Driven Career Guidance System for Secondary School Students in Minna Local Government, Niger State
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- NGN 5000
Background of the Study
Career guidance plays an essential role in shaping the future of secondary school students by helping them make informed decisions about their academic and professional paths. In Minna Local Government, Niger State, traditional career guidance methods are often inadequate due to limited resources and outdated counseling practices. A data-driven career guidance system offers a modern solution by leveraging data science techniques to analyze student performance, interests, and labor market trends (Adebola, 2023). Such a system integrates various data sources including academic records, standardized test scores, and extracurricular activities to generate personalized career recommendations. By employing predictive analytics and machine learning algorithms, the system can identify patterns and forecast career opportunities that align with individual student profiles (Ibrahim, 2024). This approach not only enhances the accuracy of guidance but also empowers students to make strategic decisions about their education and career development. Furthermore, real-time data analytics allow the system to update recommendations as new information becomes available, ensuring that the advice remains relevant and reflective of current industry trends. The interactive nature of the system engages students by providing detailed profiles of various career paths, including necessary qualifications, salary expectations, and potential growth opportunities. Additionally, the system facilitates communication between students, counselors, and educators, thereby creating a collaborative environment for career planning. Despite its potential, challenges such as data privacy, integration of heterogeneous data sources, and ensuring the reliability of predictive models must be addressed. This study aims to design and develop a data-driven career guidance system tailored for secondary school students in Minna, evaluating its effectiveness in enhancing career decision-making processes and ultimately contributing to improved educational and professional outcomes (Chinwe, 2025).
Statement of the Problem
Secondary school students in Minna often face uncertainty regarding their future career choices due to inadequate and generic career counseling services. Traditional guidance methods are largely based on anecdotal advice and limited assessments, which do not capture the multifaceted nature of modern career opportunities (Olufemi, 2023). This results in misinformed decisions, underemployment, and a gap between student aspirations and market realities. Furthermore, the lack of personalized career guidance contributes to low student motivation and engagement, ultimately affecting academic performance. While data-driven systems have the potential to provide tailored career advice, their adoption in secondary schools is limited by insufficient technological infrastructure and concerns about data privacy. There is also a challenge in integrating various data sources—from academic records to labor market information—to generate accurate career recommendations. These challenges underscore the need for a robust system that leverages data analytics to offer personalized, dynamic, and evidence-based career guidance. This study seeks to address these issues by designing a comprehensive system that combines predictive analytics with real-time data updates to guide students toward viable career paths. The research will focus on identifying key indicators that influence career choices and developing a model that predicts suitable career options based on individual profiles. By bridging the gap between student potential and market demands, the proposed system aims to enhance career readiness and improve overall educational outcomes.
Objectives of the Study:
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To design a data-driven career guidance system that integrates student and market data.
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To evaluate the system’s effectiveness in providing personalized career recommendations.
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To propose strategies for improving the integration of data analytics in secondary school career guidance.
Research Questions:
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How can data science be utilized to provide personalized career guidance to secondary school students?
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What key indicators influence the accuracy of career recommendations?
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How can the system be effectively integrated into existing school counseling services?
Significance of the Study
This study is significant as it demonstrates the potential of data-driven solutions to transform career guidance in secondary schools. By providing personalized and timely career recommendations, the system aims to empower students to make informed decisions about their futures, bridging the gap between education and employment opportunities. The findings will offer valuable insights for educators and policymakers seeking to modernize career counseling practices, ultimately contributing to improved student outcomes and workforce readiness (Adebola, 2023).
Scope and Limitations of the Study:
The study is limited to the design and evaluation of a data-driven career guidance system for secondary school students in Minna Local Government, Niger State, and does not extend to other educational levels or regions.
Definitions of Terms:
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Data-Driven Career Guidance: The use of data analytics to generate personalized career recommendations.
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Predictive Analytics: Techniques used to forecast future outcomes based on historical data.
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Career Readiness: The preparedness of students to enter the workforce based on their skills and qualifications.
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