Blue Sky Opportunities

Here I imagine how the timetabling data could be combined with other data sources to unlock insights. This does not mean that any of this should be done. It also does not mean that it can’t be done very effectively using non-graph solutions. It is very much blue-skies thinking in that it is not constrained by the realities of data, systems, technology, or policy.

If any of these ideas are taken forward, the usual data governance, ethics and privacy considerations need to be addressed, in addition to the technical and practical challenges like data quality, integration, and scalability.

Modules and Library Resources:

  • Data: Integrate module data with library resources such as reading lists, e-resources, and physical collections.
  • Insights: Identify the most frequently accessed resources for each module and assess their impact on student learning outcomes. Make recommendations for additional resources or support services.
  • Action: Enhance module delivery by aligning resources with learning objectives, providing targeted support, and improving access to relevant materials.

Student Travel and Engagement:

  • Data: Include student term-time addresses and combine with attendance and engagement data. Combine with public transport data, e.g. bus and train schedules.
  • Insights: Understand the impact of travel distances on student engagement and academic performance. Identify potential transport barriers for specific student groups or areas. This is particularly relevant to Bristol where there is a large student population and accommodation challenges.
  • Action: Optimise timetable scheduling to minimise travel distances, particularly for students with long commutes. Explore targeted transportation solutions or support services.

Equitable Access and Outcomes Analysis:

  • Data: Combine timetable data with various pre and post student datasets, like SES, POLAR, Free Lunch data, and graduate outcomes.
  • Insights: Identify if timetable structures disproportionately impact specific student groups. Does this impact their academic performance or future prospects?
  • Action: Use insights to proactively address disparities in access and outcomes by adjusting timetables, providing targeted support, or informing policy changes.

Self-Serve Timetable Changes and Student Behaviour:

  • Data: Analyse student self-serve timetable changes1 and compare original and modified schedules.
  • Insights: Gain insights into student preferences and identify common reasons for timetable modifications. This can inform timetable design and optimise scheduling processes - this could be particularly useful to inform the Timetable Quality Index.
  • Action: Address common issues identified through timetable adjustments, such as frequent clashes or gaps in scheduling. Optimise the self-serve process based on student behaviour.

Facilities Optimisation and Space Utilisation:

  • Data: Integrate estates/facilities datasets like master location data, wifi hits, etc. with timetable and occupancy data.
  • Insights: Identify underutilised spaces and peak usage times. Optimise space allocation and improve campus resource management. Understand flow of students and staff around campus.
  • Action: Adjust timetables to balance space utilisation, explore flexible learning environments, and inform future campus development plans.

Student Clustering and Community Building:

  • Data: Incorporate student demographic (with appropriate data privacy safeguards)
  • Insights: Identify clusters of students with shared characteristics or interests. This can inform targeted support services, social events, and community-building initiatives.
  • Action: Foster a sense of belonging by connecting students with similar backgrounds or interests. Tailor support services to meet the needs of specific student groups.

Module and Timeslot Recommendations:

  • Data: Combine timetable, enrolment, student performance, and feedback data.
  • Insights: Develop a recommender system to suggest module combinations and timeslots based on student interests, past performance, and peer recommendations.
  • Action: Personalise the student experience and improve module selection.

Unpopular Activity Analysis:

  • Data: Analyse attendance data and identify unpopular activities, correlating them with time, location, staff, and other properties.
  • Insights: Understand the factors contributing to low attendance and identify potential areas for improvement.
  • Action: Adjust timetable scheduling, explore alternative teaching approaches, or provide additional support to address identified issues.

Identifying Anomalies and Opportunities:

  • Data: Apply graph algorithms and machine learning techniques to identify patterns, anomalies, and trends in combined datasets.
  • Insights: Discover hidden opportunities for improvement, detect potential issues proactively, and gain deeper insights into complex relationships between various factors.
  • Action: Address identified issues, optimise processes, and leverage opportunities for innovation and continuous improvement.

Footnotes

  1. Students are able to make certain changes to their timetables, at activity and module level. These changes are recorded and can be analysed.↩︎