University Exam Scheduling

Dataset Description:

This dataset simulates university data essential for developing and optimizing exam scheduling algorithms using machine learning methodologies. It includes detailed information on students, instructors, courses, classrooms, and timeslots, facilitating comprehensive analysis and algorithmic development for educational planning.

Key Features:

Students: Unique identifiers, personal details, program enrollment, and academic year.
Instructors: Identification, contact information, and department affiliation.
Courses: Course specifics such as ID, title, department, credit allocation, and detailed descriptions.
Classrooms: Facilities information including ID, building designation, room numbers, capacity metrics, and room type categorization.
Timeslots: Timeframes categorized by ID, day of the week, commencement, and conclusion timings.
Intended Use:

Educational Research: Suitable for academics and researchers focusing on optimizing educational scheduling systems.
Machine Learning Development: Ideal for developing algorithms to automate and enhance exam scheduling processes.
Educational Policy Planning: Supports decision-making processes in educational institutions for better resource utilization and scheduling efficiency.
License: CC0 - Public Domain Dedication

Update Frequency: One-time dataset creation