Problem identification
be able to sense that there is a problem and determine actions that lead to the solution.
Multiple representations
be able to construct representations of problems as discriptive, graphical, algebraic, or other forms of models
be able to move back and forth between representations to aquire or fill in information
Graphical representations
be able to generate graphs from equations
be able to identify the key features of a graphical representation
be able to describe the units for X, Y axis and the function (the "curve", its slope or derivatives)
Algebraic representations
be able to use at least seven different algebraic equations
be able to identify which algebraic equations are possibly the most useful for different environmental data and relationships
be able to relate the parameters in an algebraic to the
Verbal descriptions
be able to make a verbal description that has enough specificity to translate it into a graph, equation, or time-step iterative model
Iterative models - time step
understand how iterative models differ from a simple XY plot of a function
generate simple iterative sequences for growth and other processes
use EXCEL to create a simple iterative model
use STELLA to generate more detailed iterative models
General modeling concepts
using models for problem definition, communication, hypothesis generation and hypothesis testing
setting the boundaries of the model
clarifying the units of all processes and converting between units for energy, mass, time, rates, etc
mass balance and conservation of energy constraints
positive and negative feedback loops
STELLA modeling skills
be able to use the major icons in the modeling environment (stocks, flows, controls, convertors, source/sink clouds)
create outputs with graphs or tables
set the time step, length of the model run, and scale of all parameters being graphed
use X-Y graphs for comparison
transfer model output data from STELLA to Excel for other forms of analysis
analyze (break down to subunits and explain) simple and busier models
explain the underlying equations that are used to generate the relationships and be able to show how those equations (or relationships) are evident in the final output