• andreabuttle

Turning Timetable Data into Information

Timetable Data is complicated. The attached picture is version 5. I am not happy with it, but I thought I would share it, hoping that someone else can show me a clearer representation.

I started drawing this as I mapped out an agenda for the SUMS timetabling meeting on data into information. It got complicated though because I am also wondering:

1. how academic staff workload allocation integrates with timetabling

2. what the right measure for curriculum complexity should be.

My initial aim of mapping data to information has brought me some clarity on key metrics. In an earlier version, I had space utilisation rather than Estate fit. I define Estate fit as how well the existing teaching room configuration meets teaching needs. I changed to fit as a necessary and, possibly better, metric. I also did have an internal dialogue as to whether I should use staff experience as a measure, or staff fit (staff as resource or customer of the timetable?). Given staff overloading seriously impacts staff experience, I opted for staff fit but it is open for discussion. This diagram also made me wonder why staff availability constraints are part of timetabling policy rather than the workload allocation model.

My second intention of determining how workload allocation fits in with timetabling is a work in progress. Ever since a head of department talked to me about version 37 of his workload allocation spreadsheet, I have had a desire to dig deeper. Version 37 of an excel spreadsheet screams there must be a better way. Processes, not just data, need to align for this. Initial investigation showed that different subject areas have different views as to whether you timetable and then allocate staff or vice-versa.

Similarly, curriculum complexity is also a work in progress. There is a measure – the percentage of students on a unique pathway – which, if high, indicates there is a problem. That information though, is not moving people to act very quickly. (I could just be impatient!) I do wonder whether there is a better metric which can point more clearly to corrective action. I have in mind something like a network node count i.e. how many times a module is shared by different programmes so that action can be directed at the biggest tangles. If someone reading this is closer to their maths education than me, please help me out. You would also need to look at how many students were associated with the tangle – if it is only a few then the tangle should be removed.

Do I have a worthwhile agenda for my timetabling meeting? Yes. I look forward to an interesting discussion and further enlightenment on November 15 in London. SUMS members will get invites shortly.

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