Difference between revisions of "CS382:Topics Matrix"

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This is meant to be a rough draft of an idea Charlie and I talked about: that we should have a n-by-n matrix of different ideas that we want to make sure we hit.  Since an n-by-n matrix is hard to display (especially on a wiki!) it turned into a list instead.  Once this is finished, we should make sure that at least one unit addresses each one of these topics.
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__NOTOC__
  
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Initial "dimensions" of the matrix:
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<center>
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* [[#Scientific Disciplines|Scientific disciplines]]
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{| class="wikitable" border="1"
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* [[#Scientific Tools|Scientific tools]]
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|+ Topic Matrix
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* [[#Guideline Skill Sets|Skill sets]] from the [[CS382:attributes-guidelines|attribute guidelines]]
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|-
 +
! Week
 +
! Topic
 +
! Unit(s)
 +
! Who
 +
! Discipline(s)
 +
|-
 +
| 1
 +
| [[CS382:Unit-foundation-templated|What's a Model?]]
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| Foundations
 +
| Sam, Mikio
 +
| Generic
 +
|-
 +
| 2
 +
| [[CS382:staticmodel-outline|Building a Static Model]]
 +
| Area
 +
| Philip, Bryan
 +
| Generic
 +
|-
 +
| 3
 +
| [[CS382:fire|Using a Dynamic Model]]
 +
| Fire
 +
| Fitz, Vlado
 +
| Forestry
 +
|-
 +
| 4
 +
| [[CS382:Unit-mashup|Visualizing Data]]
 +
| Mashup
 +
| Matt, Nate
 +
| TBD
 +
|-
 +
| 5-6
 +
| [[CS382:structural-outline|Structural Modeling]]
 +
| Bridge
 +
| Bryan, Dylan
 +
| Bridge Building
 +
|-
 +
| 7-8
 +
| [[CS382:equation-outline|Equation Modeling]]
 +
| Rocket
 +
| Vlado, Sam
 +
| Math, Physics
 +
|-
 +
| 9
 +
| [[Cs382:unit-compsoc|Agent Based Modeling and Computational Sociology]]
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| People
 +
| Nate, Philip
 +
| Sociology
 +
|-
 +
| 10-11
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| [[CS382:Predator-Prey|Modeling Predator-Prey Interactions]]
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| Lynx and rabbits
 +
| Dylan, Matt
 +
| Biology
 +
|-
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| 12-13
 +
| [[CS382:chaos_templated|Chaotic Systems]]
 +
| Climate
 +
| Mikio, Fitz
 +
| Lots
 +
|-
 +
| 14
 +
| [[CS382:End-Notes|End Notes]]
 +
| Wrap-up and review
 +
| TBD
 +
| Lots
 +
|}
 +
</center>
 +
'''
  
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=== Scientific Disciplines ===
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= Concepts, Techniques and Tools =  
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* Biology
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There are a number of recurring themes, standard scientific techniques, and tools for doing science both in the real world and with a computing system contained in this course.  Many of them are seen at more than one point. 
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* Chemistry
 
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* Physics
 
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* Geology (earthquakes)
 
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* Environmental science
 
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* One of the social sciences
 
  
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Potentially:
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The plan is to introduce all of them at the start of the course, call them out whenever we encounter them during the course, and then review all of them as a group at the end of the course.
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* Astronomy
 
  
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=== Scientific Tools ===
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== Concepts ==
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* Equation-based modeling
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* Using models, modifying models, developing models
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* Agent-based modeling: Netlogo or AgentSheets
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* Data -> information -> knowledge
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* Systems-based modeling: Vensim
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* Algorithmic thinking
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* Visualization/visual modeling
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* Computational thinking
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* Mashups and Google Earth
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* Abstraction
 +
* Accuracy vs precision
  
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Potentially
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== Techniques ==
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* SecondLife or OpenSim
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* Validation and verification
−
 
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* Interpreting a graph
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=== Foundation Skills ===
 
 
* Creating a graph
 
* Creating a graph
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* Interpreting a graph
 
 
* Basic statistics
 
* Basic statistics
 
* Estimation
 
* Estimation
Line 35: Line 96:
 
* Data collection
 
* Data collection
  
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Potentially
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== Tools ==
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* Building a physical model
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* Spreadsheet; {Open, Neo} Office
 +
* Plotting; Sigma plot, gnuplot (?!), PlotDrop.  Why not just use the spreadsheet's tool?
 +
* Equation-based modeling; spreadsheet?
 +
* Agent-based modeling; NetLogo or AgentSheets
 +
* Systems-based modeling; Vensim
 +
* Visualization/visual modeling
 +
* Mashups and Google Earth
  
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=== Thoughts ===
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Potentially:
−
Some of these should go somewhere, and some of these should be in every unit.
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* SecondLife or OpenSim
 +
 
 +
= Overall Context =
 +
* 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?
 +
 
 +
= General Education =
 +
* [[CS382:Total-geneds|Total Geneds]]
 +
* Consider moving the scraped pages here from the main page for gen eds, labs, etc.
 +
 
 +
= Items to be sorted =
 +
* Quantitative reasoning
 +
* Using tools, broadly defined
 +
 
 +
* Mechanical and structural stuff:
 +
** Scales well, say 20-80 students
 +
*** Automated assessment tools
 +
*** Effective use of TAs
  
 
* What data do I need to collect, how do I collect it accurately, and then how do I build it
 
* 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)
 
** 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
 
* Data collection: sensor nets, lasers
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=== Course Structure - D R A F T ===
 
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# What's a model (foundational material that isn't spread-out or requires more than one pass) - no lab
 
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# Using a simple dynamic model - fire
 
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# Building a simple static model - model area of parts of campus
 
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# Structural modeling - bridge building (software and physical?)
 
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# Equation modeling - water rockets and relationship between launch angle and distance traveled
 
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# Agent modeling - social systems
 
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# Chaotic systems - climate model
 
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# Systems dynamics - viral spread
 
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# Visualizing data - mashup
 
−
 
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Too many, loose 2?
 

Latest revision as of 14:10, 29 April 2009


Topic Matrix
Week Topic Unit(s) Who Discipline(s)
1 What's a Model? Foundations Sam, Mikio Generic
2 Building a Static Model Area Philip, Bryan Generic
3 Using a Dynamic Model Fire Fitz, Vlado Forestry
4 Visualizing Data Mashup Matt, Nate TBD
5-6 Structural Modeling Bridge Bryan, Dylan Bridge Building
7-8 Equation Modeling Rocket Vlado, Sam Math, Physics
9 Agent Based Modeling and Computational Sociology People Nate, Philip Sociology
10-11 Modeling Predator-Prey Interactions Lynx and rabbits Dylan, Matt Biology
12-13 Chaotic Systems Climate Mikio, Fitz Lots
14 End Notes Wrap-up and review TBD Lots

Concepts, Techniques and Tools

There are a number of recurring themes, standard scientific techniques, and tools for doing science both in the real world and with a computing system contained in this course. Many of them are seen at more than one point.

The plan is to introduce all of them at the start of the course, call them out whenever we encounter them during the course, and then review all of them as a group at the end of the course.

Concepts

  • Using models, modifying models, developing models
  • Data -> information -> knowledge
  • Algorithmic thinking
  • Computational thinking
  • Abstraction
  • Accuracy vs precision

Techniques

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

Tools

  • Spreadsheet; {Open, Neo} Office
  • Plotting; Sigma plot, gnuplot (?!), PlotDrop. Why not just use the spreadsheet's tool?
  • Equation-based modeling; spreadsheet?
  • Agent-based modeling; NetLogo or AgentSheets
  • Systems-based modeling; Vensim
  • Visualization/visual modeling
  • Mashups and Google Earth

Potentially:

  • SecondLife or OpenSim

Overall Context

  • 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?

General Education

  • Total Geneds
  • Consider moving the scraped pages here from the main page for gen eds, labs, etc.

Items to be sorted

  • Quantitative reasoning
  • Using tools, broadly defined
  • Mechanical and structural stuff:
    • Scales well, say 20-80 students
      • Automated assessment tools
      • Effective use of TAs
  • 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
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