Difference between revisions of "CS382:Topics Matrix"

From Earlham CS Department
Jump to navigation Jump to search
Line 89: Line 89:
 
|-
 
|-
 
| 10-11
 
| 10-11
| Modeling Predator-Prey Interactions
+
| [[CS382:Predator-Prey|Modeling Predator-Prey Interactions]]
 
| Lynx and rabbits
 
| Lynx and rabbits
 
| Dylan, Matt
 
| Dylan, Matt

Revision as of 10:40, 2 March 2009

When your unit(s) are in a stable location change the Topic column to an actual link. When it's ready to be reviewed update the Status to be "Ready". This is the only signal that the reviewers will look for. When it's being reviewed the status will be "In Review" and then "Done".

Ultimately the matrix should include a column for each section of a unit layout. E.g. there should be a "General Education" column/section, a "Lab" column/section, etc.

Topic Matrix
Week Topic Unit(s) Who Discipline(s) Skill(s) Tool(s) Notes Status
1 What's a Model? Foundations Sam, Mikio Generic TBD TBD
2 Using a Dynamic Model Fire Fitz, Vlado Forestry Critical parameter, parameter sweep Agent modeling, NetLogo
3 Building a Static Model Area Philip, Bryan Generic Accuracy, precision, estimation Mashup Software and physical
4 Visualizing Data Mashup Matt, Nate TBD TBD Mashup Tufte based approach?
5-6 Structural Modeling Bridge Bryan, Dylan Physics Physics Simulation Software and physical?
7-8 Equation Modeling Rocket Vlado, Sam Math, Physics Math, estimation skills and physics TBD Software and physical?
9 Modeling Society People Nate, Philip Sociology TBD Agent modeling, NetLogo
10-11 Modeling Predator-Prey Interactions Lynx and rabbits Dylan, Matt Biology TBD Systems dynamics modeling, NetLogo, and Agent-based
12-13 Chaotic Systems Climate Mikio, Fitz Lots TBD TBD

Context

There are a number of themes that run through the units:

  • Quantitative reasoning
  • Using models, modifying models, developing models
  • Validation and verification
  • Using tools, broadly defined
  • Data -> information -> knowledge
  • Algorithmic thinking
  • Abstraction
  • Computational thinking

The units should have the following attributes, for the pedagogical ones all units should try to adhere to them, for the others we just need to make sure at least one unit covers them.

  • Pedagogical
    • Inquiry based learning
    • Scaffolded
    • Open-ended to a degree
    • Using science to illustrate the diversity and complexity of the world around us
  • Structural
    • Metric system
    • OSX, Windows, Linux whenever possible (lab sizes and locations)
    • Classroom response system - questions for each unit, participation/attendance measured by response rate?

Mechanical and structural stuff:

  • Scales well, say 20-80 students
    • Automated assessment tools
    • Effective use of TAs

Scientific Tools

  • Equation-based modeling
  • Agent-based modeling: Netlogo or AgentSheets
  • Systems-based modeling: Vensim
  • Visualization/visual modeling
  • Mashups and Google Earth

Potentially

  • SecondLife or OpenSim

Foundation Skills

  • Creating a graph
  • Interpreting a graph
  • Basic statistics
  • Estimation
  • Parameter sweep
  • Data collection

Potentially

  • Building a physical model in the real world

Thoughts

Some of these should go somewhere, and some of these should be in every unit.

  • What data do I need to collect, how do I collect it accurately, and then how do I build it
    • Perhaps one unit where they have to go out and collect data to see how hard it really is, how about modeling campus (rectangle and heart)
  • Data collection: sensor nets, lasers