Development of Guidance Record Management System with Exploratory Data Algorithm for Predicting Academic Performance
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Abstract: This study produces a web-based Guidance Record Management System (GRMS) designed to simplify record-keeping in guidance offices. It allows students to submit and manage their records, which counselors can access for sessions. The system also includes exploratory data analysis (EDA) features for data-driven decision-making. The admin dashboard presents visualizations and insights using EDA with Python and a Decision Tree algorithm, offering valuable information on student behavior and academic performance.
The system is built using the PHP framework Laravel, CSS and Tailwindcss for styling, JavaScript, Python's Pandas library, along with Matplotlib and Seaborn for data visualization, MySQL as the database, and Apache as the server. It is evaluated according to the ISO 25010 standard, which provides a framework for assessing software quality, ensuring that the system meets key requirements for functionality, reliability, usability, and performance.
The study successfully identified the respondents' genders as male and female. Evaluation results reveal that both male and female users, as well as technical respondents, strongly agree on the acceptability and usability of the Guidance Record Management System. Male users provided an overall average mean of 3.4, while male technical respondents rated it slightly higher at 3.5. Similarly, female users and technical respondents both rated the system with an average mean of 3.5. These results indicate a consistent positive reception across both groups, affirming the system’s effectiveness and ease of use for its intended audience.
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