ICT616 Data Resources Management: Student Performance Report

This report presents an analytical study of student performance data obtained from the UCI Learning website, focusing on students from two Portuguese schools. The data encompasses demographics, social aspects, school-related features, and grades. The analysis compares variables and factors, investigates differences between Mathematics and Portuguese language datasets, and examines the association of G3 grades with G1 and G2 grades. Predictive modeling using regression analysis is performed in RapidMiner software to explore the predictability of student performance based on various attributes. The research objectives include comparing variables, investigating model variability, and analyzing the relationship between different grading periods, providing a comprehensive overview of the factors influencing student achievement.

An analytical report of
‘Student Performance’ Data
Name of the University:
Name of the Student:
Course ID:

Data Source and Information
• The data is collected from online source (UCI Learning website,
‘University of California’).
• The data is gathered by Paulo Cortez (University of Minho,
Portugal). Now it is freely available in repository of UCI learning
website.
• The data approaches the achievement of students of two
Portuguese schools.
• The data attributes include students’ demographics, social
approach, school related features and student grades.
• The number of instances of the data set is 649 and total number
of attributes is 33.

Data Description
• 1. ‘School’: Name of the school of the student.
• 2. ‘Sex’: Sex of the student.
• 3. ‘Age’: Age of the student.
• 4. ‘Address’: Home address type of the student.
• 5. ‘Famsize’: Family size of the students.
• 6. ‘Pstatus’: Cohabitation status of the parents.
• 7. ‘Medu’: Education of mother.
• 8. ‘Fedu’: Father’s education.
• 9. ‘Mjob’: Job of mother.
• 10. ‘Fjob’: Job of father.
• 11. ‘Reason’: Reason to choose the school.
• 12. ‘Guardian’: Guardian of the student
• 13. ‘Travel-time’: Travelling time from home to school.
• 14. ‘Study-time’: Weekly study time.
• 15. ‘Failures’: Number of past class failures.

Data Description
• 16. ‘Schools-up’: Extra educational support is available or not.
• 17. ‘Famsup’: Family educational support is available or not.
• 18. ‘Paid’: Extra paid classes within the course of the subject or not.
• 19. ‘Activities’: Extra-curricular activities of the students is present or not.
• 20. ‘Nursery’: Attended or not in nursery school.
• 21. ‘Higher’: Whether the student wants to take or not higher education.
• 22. ‘Internet’: Whether internet access is available or not.
• 23. ‘Romantic’: Whether student is in romantic relationship or not.
• 24. ‘Famrel’: Quality of family relationships.
• 25. ‘Freetime’: Free time after school.
• 26. ‘Goout’: Going out with friends.
• 27. ‘Dalc’: Workday alcohol consumption.
• 28. ‘Walc’: Weekend alcohol consumption.
• 29. ‘Health’: Current health status.
• 30. ‘Absenses’: Number of school absences.

Research Objectives
• Comparison of variables and factors are accomplished in
this analysis.
• Common differences regarding Mathematics and
Portuguese language data set and the differences of
predictors and their predictability are investigated in this
analysis.
• Variability of models of two different data sets are
investigated in this analysis.
• Investigating the association of G3 with G1 and G2 that
correspond to the 1st and 2nd period grades of the
students.

Research Curriculum
• Predictive Modelling Using Regression Analysis.
• Analysis conducted in RapidMiner software.

Analysis and discussion
Mathematical Grades Prediction Models

Analysis and discussion
Portuguese Grades Prediction Models

Thank You