Data Analytics

Strategically Managing Allocation of Faculty Lines and Types Across Departments and Colleges – A Risk Based Model

Ensuring that faculty resources are most effectively applied across the colleges and departments of the university is a very important responsibility of the Provost/VPAA. These decisions can have a big impact on a department and are therefore very closely watched, and politically very sensitive. By studying this article and its linked resources, and by using the associated shared files, you will be able to:
• Explain how and why the available budget for hiring faculty positions can vary throughout and between years.
• Explain the impact of having a high number of adjuncts, or many faculty working overloads, can have on a department and how measuring this can help determine changing the number of full-time positions is justified.
• Explain how departmental course scheduling efficiency can be measured and factors that affect this measure, including how course caps are set.
• Make a case to automatic fund and schedule additional sections of courses experiencing student demand, in efficiently scheduled departments.
• Determine the breakeven enrollment number for courses beyond which it is profitable for an additional section to run.
• Develop a free market-based model for faculty hiring in which adjuncts/overloads initially absorb demand and are then converted to full-time positions.
• Develop a growth-rate based assignment of faculty appointment types within departments to manage risk.
• Develop guidelines for the appointment rank of new faculty based on strategically targeted distributions.
• Explain how accreditation standards may affect faculty hiring.
• Develop a spreadsheet-based model for predicting and prioritizing allocation of new and vacant faculty lines to departments across the university.
• Explain the constraints and the factors which must be considered when making faculty line allocation decisions.
• Develop a plan and a transparent process for making decisions on line allocation that is based on data but balanced by considerations of human impact.

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