Outlining the Design Space (or, what can $40,000 buy you?)

Your budget is $40,000 / year.  You have access to a wealth of scientific personnel from a major research university – undergraduates, graduate students and faculty.  You’d like to turn that money and access into a reinvigorated mathematics, science and engineering education at the K-12 level.  Let’s treat this as a design problem, starting by making a concrete list of our resources, constraints and objectives:

Resources:

• $40,000 / yr
• Access to Tier I research university students and faculty
• Access to cutting-edge technical research
• Access to current education “best-practices”
• Access to a local K-12 school district

Constraints:

• Quantifiable results must be achieved within 2 years to retain funding
• School district is failing, student achievement is poor, and district resources are nil.
• Student population is predominantly minority.  University population is predominantly white. 

This is the lay of the land.  Like many design tasks, here we have a poorly posed objective.  Knowing how designs without clear goals tend to wander, let’s try to tighten up that objective statement a bit:

Objective:

• Leverage program access to university technical resources and personnel to increase K-12 student STEM interest and exposure.
• Measure student STEM exposure as number of students exposed to hands-on work with university research-related topics for some minimum rate of exposure and minimum duration, i.e., number of hours/week and weeks/year.  Select a minimum criterion and justify it.
• Demonstrate retention of participating students year over year.
• Develop other metrics of success which can be quantified and evaluated.  Justify the relevance of these metrics. 

This is still somewhat poorly posed, but it’s a little better.  For example, the measure of exposure is somewhat arbitrarily chosen by me.  Basically, you need to cut down your design space somehow, and since I think the chance of raising interest correlates with increased exposure, I linked them in that first objective.  Then I said that I want a program that grabs kids for a certain amount of time, does it consistently every week and over a long span of weeks, and potentially keeps them involved for multiple years.

That last objective is a catch-all to recognize that these objective statements still need some improvement.  Luckily, design optimization is an iterative process, so we’ll be revisiting these objectives.  But first, in my next post I’ll start brainstorming solutions and see what I can come up with.