• Home
  • Project
    Introduction
    • Introduction
    • Background and Motivation
    • What is a Good Timetable?
    • Project Aims and Scope
  • Graph Data
    Model
    • Graph vs Relational Data Models
    • Graph Data Model for Timetabling
    • Early Insights
    • Model Expansion
    • Graphing Time
  • Data
    Pipeline
    • ETL Overview
    • Approach
    • Configuration and Logging
    • Extract
    • Transform
    • Google Drive Load
    • Neo4j Load
    • Reflection
  • Timetable
    Metrics
    • Timetable Metrics
    • Metric Aggregations
    • Implementing Metrics
    • TQI Summary
  • Final
    Thoughts
  • Appendices
    & Extras
    • Appendix Table of Contents
    • References
    • Acknowledgements
  • Word
  1. Timetable Metrics
  2. Metric Aggregations
  • Home
  • Project Introduction
    • Introduction
    • Background and Motivation
    • What is a Good Timetable?
    • Project Aims and Scope
  • Graph Data Model
    • Graph vs Relational Data Models
    • Graph Data Model for Timetabling
    • Early Insights
    • Model Expansion
    • Graphing Time
  • Data Pipeline
    • ETL Overview
    • Approach
    • Configuration and Logging
    • Extract
    • Transform
    • Google Drive Load
    • Neo4j Load
    • Reflection
  • Timetable Metrics
    • Timetable Metrics
    • Metric Aggregations
    • Implementing Metrics
    • TQI Summary
  • Final Thoughts
  • Appendices
    • Random Graph Generator
    • Technology Stack
    • Configuration
    • Anonymisation
    • ETL Summary and Code
      • ETL Summary
      • ETL Code
      • Config and Misc
      • Extract-SQL
      • Extract
      • Google Drive Load
      • Transform
      • Neo4j Load
    • Neo4j & Cypher Code
      • Cypher Queries
      • Creating Nodes and Relationships
      • Deleting Nodes and Relationships
      • General Queries
      • Count Queries
      • Hard (timetabling) Constraints
      • Student Clashes
      • Soft Constraints
      • Rooms and Spaces
      • Perspectives
      • Blue Skies Opportunities
  • Supervision
    • Supervision
    • Notes Example 1
    • Notes Example 2
    • Notes Example 3
  • References
  • Acknowledgements
  1. Timetable Metrics
  2. Metric Aggregations

Metric Aggregations

With the individual metrics calculated, the next step is to aggregate these into meaningful scores at different levels. This could be at the student level, programme level, department level, or even at the room or building object.

The metrics used and their weightings will depend on the use-case and the priorities of the institution. For example, a student-level score could be used to identify students with particularly poor timetables, while a programme-level score could be used to compare the quality of timetables across different programmes, and a room-level score could be used to identify rooms that are underutilised or overbooked or are otherwise unsuitable.

This allows for a more nuanced understanding of timetable quality and can help identify areas for improvement.

Student-level

Each student node can have a quality score reflecting their individual timetable experience based on assigned activities and associated penalties.

Programme-level

By aggregating student scores within a programme, we gain insights into the overall quality experienced by students in that programme.

Other groupings

Scores can be aggregated at various levels, such as by department, room type, or time slot, to identify potential areas for improvement.

Timetable Metrics
Implementing Metrics

Copyright 2024, Petter Lövehagen

 

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