The Dartmouth/Thayer approach to engineering problem solving is a framework for
bringing problems of the "real world" into the classroom. Students solve these
problems by proceeding through a problem-solving cycle, step by carefully documented step.
If they discover that the solution they are working on is, in fact, unviable, they examine
their paper trail and move back only so far as they need, perhaps only a single step.
When students have gone the full round of the problem-solving cycle, they look at the
original problem and decide whether their solution is specific enough or whether they need
to iterate the cycle.
The problem can involve any kind of decision making from a social problem,
"Students are taking too long to get into the lunchroom", to a complex one such
as how to create an experiment to demonstrate sonoluminescence (light generated by sound).
Each step of the problem-solving cycle is clear:
 State the Problem: look at the problem
carefully
Redefine the Problem: redefine it to eliminate bias
Identify Constraints and Set General Specifications:
refine and add specifications
Identify Alternative Solutions
Analyze the alternatives
Select the Most Viable Alternative
Iterate the cycle until you find a solution
[A more detailed explanation]
The process of homing in on the best possible solution is framed by a series of
problem-solving matrices. The columns of a matrix are headed by the specifications, the
rows by the ideas for alternative solutions.
Alternatives can be ranked on a simple scale of good, bad, and neutral (+, -, and 0) or
a more sophisticated scale that gives additional weight to the most important
specifications. The best solution is the one that garners the most points by satisfying
the most specifications.
[sample matrix]
The first round of the problem-solving cycle narrows the focus of the
original problem.
Then it's time to start another round.
Redefine the problem to develop a more specific
solution
Generate tighter specifications
Brainstorm more focused alternatives
Analyze the alternatives and use the matrix to select
the one that best narrows the focus of the problem
Reiterate the process until the problem is solved, using
a new matrix for each major decision
[matrices for an iterated problem]
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