A Data Mining Approach to Understanding Financial Literacy Knowledge and Behavioral Patterns among Tertiary Students
DOI:
https://doi.org/10.21512/comtech.v16i1.12221Keywords:
financial literacy knowledge, behavioral patterns, tertiary studentsAbstract
The research sought to data mine the financial literacy of tertiary students to evaluate and pinpoint deficiencies in their financial knowledge, measure the degree of financial goal-setting and budgeting practices, and determine their primary sources of financial advice. The data were gathered through validated questionnaires and disseminated through surveys. The research focused on tertiary students with 316 valid responses for analysis. The research used data mining techniques under the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to identify students’ financial literacy. Furthermore, A comprehensive analysis using Ms Excel, Statistical Package for Social Sciences (SPSS), and Waikato Environment for Knowledge Analysis (WEKA) reveals significant disparities in financial literacy among various fields of study and course levels. The investigation highlights essential financial behaviors, such as credit card utilization, saving patterns, and budgeting strategies, while revealing deficiencies in formal financial education. The analysis highlights the necessity for specialized financial literacy initiatives in educational programs to bridge knowledge deficiencies and encourage proficient budgeting and goal-setting techniques. The results offer practical guidance for educators, policymakers, and higher education institutions to improve students’ financial well-being, in line with Sustainable Development Goals (SDGs) focused on poverty alleviation and economic development. The research advocates for financial literacy programs in the school curriculum and emphasizes enhancing student participation in workshops. Higher education institutions must provide well-structured financial advice and support services. Lastly, Future studies should delve deeper into socioeconomic factors to improve predictive models and intervention strategies.
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