There are several phases involved in this methodology such as information gathering, analysis, design, development, testing, and feedback. Agile methodology then be modified into information gathering, analysis, design, development, testing, and feedback to meet the activities carried out throughout developing the system.
Collect and build student’s profile and gather information
Based on the student’s profile, design the algorithm system that describes the procedures of the assessment.
Encourage students to reflect on their learning process and provide feedback that guides them to higher levels of understanding and application.
Predictive Analysis System
We apply Machine Learning algorithms such as decision trees and linear regression to predict student performance, using data from academic records and assessments. This approach enables the identification of patterns and trends, informing personalized educational strategies through continuous model refinement with new insights.
Evaluate a student's skills and academic performance by considering their coursework, projects, extracurricular activities, major, and school enrollment details, along with their career goals and aspirations.
Customize assessment tests for students based on their profiles, focusing on five key areas: cultural adaptability, language communication, cognitive ability, technical skills relevant to their major or career, and overall career readiness.
Utilize institutional and industry data to conduct predictive analysis, enabling the evaluation and monitoring of each student's performance and trends.
Develop a personalized assessment system using an algorithm that tailors tests to each student's profile and institutional data, incorporating adaptive testing and providing detailed feedback and reports for self-improvement.
Integrate diverse data sources and apply machine learning algorithms for predictive analysis of student performance and career success, with a focus on continuous system improvement through regular updates and feedback.
Conduct pilot tests of the system with a select group of students for initial feedback and adjustments, followed by a full-scale implementation across the wider student body, accompanied by continuous support and updates.
Continuously monitor and evaluate the system's effectiveness in predicting student performance and career outcomes, while establishing a feedback loop with students and educators for ongoing refinement.