Student Reality Engine

Discover what you're actually asking your students to do

Reality Check

The evidence is clear: Student workloads have increased to an average of 33.4 hours per week (Times Higher Education, 2023), while 68% now work part-time jobs, double the rate of previous years. Yet credit hours alone are poor predictors of actual workload. QAA guidance recommends transparency and realistic breakdowns, with assessment representing just 20-30% of module hours.

This tool shows you what your module expectations look like in the context of a real student's life across multiple modules, with clear RAG (Red/Amber/Green) status indicators.

Understanding Credit Systems

Important: All credit systems represent total learning time including contact hours, independent study, reading, and assessment preparation - not just contact time.

  • UK Credits (CATS/SCOTCAT): 1 credit = 10 hours total learning time (120 credits = 1 year)
  • ECTS (European): 1 credit = 25 hours total learning time (60 ECTS = 1 year)
  • Conversion: 1 ECTS = 2 UK credits (60 ECTS = 120 UK credits per year)
  • US/Canada Credits: 1 credit = ~45 contact hours + 90 hours independent study = 135-140 hours total per semester

Note: Some UK institutions interpret credit hours as 50-minute "nominal hours" rather than full clock hours.

Enter your module's credit value (any international system)
Please enter between 1 and 120 credits
Number of weeks students actively study (excluding exam/assessment weeks)
Compare student study time to standard employment hours (35h = typical 9-5 job)
Total time students spend on essential reading across the entire semester
Please enter between 0 and 600 hours
Lectures, seminars, tutorials, labs across entire semester
Please enter between 0 and 240 hours
Research, writing, preparation time across entire semester
Please enter between 0 and 480 hours
Group work, additional tutorials, field work, etc. across entire semester
Please enter between 0 and 240 hours

This Workload Calculator tool was designed by David Calum Millar collaboratively with a custom agent called Morna, running on Claude (Anthropic), who is trained on the Creative Compass & Learning Design Compass frameworks by David Calum Millar, then developed using Claude Code. The final design, all editorial decisions, and responsibility for the tool’s content rest with the author. Special thanks to the invaluable critical feedback from Gavin Innes, Dr. Piotr Jaworski, and Dr. Egor Kiselev.