CS382:GenEds

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General Education Alignment

Helpful Total Geneds Coverage Table, Fig. 18c.
[[[CS382:Unit-foundation-templated|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:staticmodel-outline|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:fire|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:Unit-mashup|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:structural-outline|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:equation-outline|% scrape(page, '^= *(.*?) *=') %]]] [[[Cs382:unit-compsoc|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:Predator-Prey|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:chaos_templated|% scrape(page, '^= *(.*?) *=') %]]] [[[CS382:End-Notes|% scrape(page, '^= *(.*?) *=') %]]]
ARa Yes Done Sort of Little of this Done Yes
ARb Yes Done not really Little of this Done Yes
ARc Done not really Little of this Done No
QRa Yes Done not really Little of this Done Yes
QRb Yes Done Not really Little of this Done Yes
QRc Oh yeah Done Not really Nada Done Yes
QRd Oh yeah Done Not really Nope Done No
QRe Oh yeah Done Nope Done Yes
QRf Oh yeah Done Yes Done Yes
QRg Oh yeah Done Yes Done Yes
SIa Done Yes Done Yes
SIb Done None Done No
SIc Done Yes Done No

Analytical Reasoning Requirement

Abstract Reasoning

From the [Catalog Description] Courses qualifying for credit in Abstract Reasoning typically share these characteristics:

  • They focus substantially on properties of classes of abstract models and operations that apply to them.
    • Foundations of Modelling: Yes, The entire unit centers around learning how to create and use abstract models. We work on first what they are and then how to use them.
    • Static Modeling: Yes. Well... maybe not verbally. The groups will be using the abstract notion of a static model to solve a real world problem.
    • Fire: This unit deals almost entirely will quantitative reasoning, and would be hard to expand into the abstract world.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Sort of. This lab is more concrete. This unit will go early in the semester so it will apply some of the more abstract ideas presented earlier.
    • Rocket Modeling: Analysis of this unit's support or not for this item. Does not apply; this unit is purely quantitative.
    • Computational Sociology and Agent Based Modeling: Agents -> abstract models
    • Predator Prey ( Lynx Hare ): Analysis of this unit's support or not for this item.
    • <Chaos>: Yes.
    • End-Notes:
  • They provide experience in generalizing from specific instances to appropriate classes of abstract models.
    • Foundations of Modelling: Yes, The lab provides hands on experience in generalizing and extrapolating from a specific small scale problem to a larger instance of that problem. The lab further focuses on getting students to put together a toolkit of techniques to create simple abstract models.
    • Static Modeling: Yes. Well... maybe not verbally. The groups will be using the abstract notion of a static model to solve a real world problem.
    • Fire: Parameter sweeping (one of the primary goals of this unit) can be used in almost every instance of computational simulations. In this sense it can be expanded from this specific model to others, yet it is more of a quantitative method of analysis than it is abstract.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Yes, because we're showing how structures and bridges, specifically apply to the abstract model parameters described in the What is a static model and what is a dynamic model units.
    • Rocket Modeling: Analysis of this unit's support or not for this item. Again it does not apply/support.
    • Computational Sociology and Agent Based Modeling: discussion of boids, sugarscape and agent based modeling as a whole
    • Predator Prey ( Lynx Hare ): Analysis of this unit's support or not for this item.
    • <Chaos>: Yes.
    • End-Notes:
  • They provide experience in solving concrete problems by a process of abstraction and manipulation at the abstract level. Typically this experience is provided by word problems which require students to formalize real-world problems in abstract terms, to solve them with techniques that apply at that abstract level, and to convert the solutions back into concrete results.


Quantitative Reasoning

From the [Catalog Description] General Education courses in Quantitative Reasoning foster students' abilities to generate, interpret and evaluate quantitative information. In particular, Quantitative Reasoning courses help students develop abilities in such areas as:

  • Using and interpreting formulas, graphs and tables.
    • Foundations of Modelling: Yes, The discussion of vetting materials requires and creates an understanding of how to interpret quantitative information
    • Static Modeling: Yes. Well... maybe not verbally. The groups will be using the abstract notion of a static model to solve a real world problem.
    • Fire: This unit is intended to teach the student how to gather data using a specific tool and analyze that data to come to some conclusion.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: not really. or maybe... If we use the pasco solution, the beam strength will be documented, and the students can perform basic calculations to figure out whether beams will break under a certain load.
    • Rocket Modeling: Analysis of this unit's support or not for this item. It does support this it; as I described above- the unit requires and will develop math, and science skills so it will also include certain number of formulas, graphs and certainly tables.
    • Computational Sociology and Agent Based Modeling: Little of this - the point is to avoid formulas (initially), but graphs and tables come up when analyzing model results.
    • Predator Prey ( Lynx Hare ): In this unit we have students create and examine formulas for modeling the relationships between the different parts of the systems. We also have them draw diagrams for representing their model and then use graphs and tables to analyze their results.
    • <Chaos>: Yes.
    • End-Notes:
  • Representing mathematical ideas symbolically, graphically, numerically and verbally.
    • Foundations of Modelling: Partially, The exercises in lab as well as the fermi problems beget skill in representing data numerically and verbally
    • Static Modeling: Yes. Well... maybe not verbally. The groups will be using the abstract notion of a static model to solve a real world problem.
    • Fire: The student will need to create a lab write-up in which they express why they went about collecting the necessary amount of data. They will also need to include examples of said data and an explanation of what conclusion(s) can be drawn from that data.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: The models provide a framework for visualizing physical (mathematical) constraints.
    • Rocket Modeling: Analysis of this unit's support or not for this item. It does support this it; as I described above- the unit requires and will develop math, and science skills so it will also include certain number of formulas, graphs and tables which will also have their symbolical, graphical and numerical representations.
    • Computational Sociology and Agent Based Modeling: Little of this. We're working with people, not numbers.
    • Predator Prey ( Lynx Hare ): Systems dynamics is at it's core representing systems symbolically and mathematically.
    • <Chaos>: Yes.
    • End-Notes:
  • Using mathematical and statistical ideas to solve problems in a variety of contexts.
  • Using simple models such as linear dependence, exponential growth or decay, or normal distribution.
    • Foundations of Modelling: Partially, we discuss the creation of these models however the ones that students use in this unit are likely to not fulfill these.
    • Static Modeling: Oh yeah...
    • Fire: This wildfire model clearly shows how the number of burned trees is directly dependent on certain features of the forest (density, wetness, etc) and how minor changes in those features can dramatically change the outcome.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Not really.
    • Rocket Modeling: Analysis of this unit's support or not for this item. Probably we will meet linear dependence in this unit; following the graphs of the various bottle pressure bottles, etc.
    • Computational Sociology and Agent Based Modeling: This could be worked in, but isn't there now.
    • Predator Prey ( Lynx Hare ): In this unit we look at the concepts of linear and exponential growth and decay, among others.
    • <Chaos>: No.
    • End-Notes:
  • Understanding basic statistical ideas such as averages, variability and probability.
    • Foundations of Modelling: Yes, we introduce statistics in the context of models and discuss their usefulness
    • Static Modeling: Oh yeah...
    • Fire: At the end of this unit the student should be able to understand that one cannot make sufficiently accurate conclusions about a model with only a single data set.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Yes, because the traversal (where we test the bridge by simulating a car driving over it) is a deterministic process. Maybe we could introduce the difference between probabilistic and deterministic.
    • Rocket Modeling: Analysis of this unit's support or not for this item. It will certainly contain all the above mentioned ideas cause we are talking about predicting events, simulating models and analyzing predicted events.
    • Computational Sociology and Agent Based Modeling: Nope.
    • Predator Prey ( Lynx Hare ): Only so much as these ideas would be helpful in a particular model.
    • <Chaos>: Yes.
    • End-Notes:
  • Making estimates and checking the reasonableness of answers.
    • Foundations of Modelling: Yes, in both the lab work and the other problems for the students to solve this unit requires strong support for all assertions that students make
    • Static Modeling: Oh yeah...
    • Fire: In the beginning of the lab portion of this unit, the student should take a guess at what the result will be when the density of the forest is varied. After running a number of trials, they should be able to easily assess the accuracy of they're answer as well as the reasonableness of their results from the lab.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Yes, because students will try to build different types of bridges and determine the 'reasonableness' of their solutions by the simulated test outcome.
    • Rocket Modeling: Analysis of this unit's support or not for this item. "Yes, estimation and confirmation are important part of the unit.
    • Computational Sociology and Agent Based Modeling: In a more abstract way than most mathiness.
    • Predator Prey ( Lynx Hare ): Experimentation is used when developing models and there is an analysis part of our lab.
    • <Chaos>: Yes.
    • End-Notes:
  • Recognizing the limitations of mathematical and statistical methods.
    • Foundations of Modelling: Not yet, I need to add this in the introductory portion
    • Static Modeling: Oh yeah...
    • Fire: The student should clearly note that this model of wildfires is far from indicative of how they actually happen. It should be stressed that this model is simply proof of concept for showing the profound effect a single variable can have on the overall results.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: yes, because the physical model will not account for all variables, such as wind.
    • Rocket Modeling: Analysis of this unit's support or not for this item. Even if numbers of paper have power to predict and evaluate something; real life experiment can always show more than numbers.
    • Computational Sociology and Agent Based Modeling: This is fulfilled - the point of ABM is the limitation of mathematical and statistical methods.
    • Predator Prey ( Lynx Hare ): When comparing SD to Agent based we will go into the relative strengths and weaknesses of SD in general and as compared to Agent based modeling.
    • <Chaos>: Yes.
    • End-Notes:


Scientific Inquiry Requirement

From the [Catalog Description] Scientific inquiry:

  • Develops students' understanding of the natural world.
    • Foundations of Modelling: Yes, this unit lays the framework for students to explore the natural world through counting and modeling.
    • Static Modeling:
    • Fire: After the completion of this unit, the student should understand the world's dependence on a surprisingly small number of variables even though this model is far from accurate.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: Modeling physical structures is important because the natural world is comprised of physical structures.
    • Rocket Modeling: Analysis of this unit's support or not for this item. The students are making a model which is going to resist gravity, but also be affected by natural happenings like air drag, and possible weather features.
    • Computational Sociology and Agent Based Modeling: Yes. Agent based methodology informs a way of thinking about natural processes that differs from more typical techniques - the ideas of emergent behavior are present in every day life.
    • Predator Prey ( Lynx Hare ): They can model natural systems including our example, Predator-Prey models.
    • <Chaos>: Yes.
    • End-Notes:
  • Strengthens students' knowledge of the scientific way of knowing - the use of systematic observation and experimentation to develop theories and test hypotheses.
    • Foundations of Modelling: Yes, one of the major take-away points of this unit is how to develop a scientific knowledge of a system.
    • Static Modeling:
    • Fire: The entirety of this lab is to change a variable, observe the results, and repeat, eventually leading to having enough data to make reasonable theories on the model.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: The students will determine the most appropriate method to build a simulated bridge through both lecture content and trial and error. They will test their models by building physical models of their virtual structures.
    • Rocket Modeling: Analysis of this unit's support or not for this item. The students will have chance to pre-use computer simulators and software which are going to possibly give them ideas to develop their own ideas about the model and predictions of occurrences throughout the lab.
    • Computational Sociology and Agent Based Modeling: None.
    • Predator Prey ( Lynx Hare ): Experimentation is used when developing models.
    • <Chaos>: No.
    • End-Notes:
  • Emphasizes and provides first-hand experience with both theoretical analysis and the collection of empirical data.
    • Foundations of Modelling: Yes, the lecture emphasizes analysis of data and the basics of how to collect it. The lab focuses on the collection of empirical data.
    • Static Modeling:
    • Fire: The lab portion of this unit is exactly this: gathering numerical data in order to provide the basis for some sort of conclusion.
    • Visualization: Analysis of this unit's support or not for this item.
    • <Structural Modeling>: The students collect the empirical data by synthesizing lecture content and trial and error. They (potentially in groups) will each devise different models to solve the same problem.
    • Rocket Modeling: Analysis of this unit's support or not for this item. The lab will be the main medium of experiencing the real model evolving; and cause of that will be collection of data and students analysis of ones.
    • Computational Sociology and Agent Based Modeling: Yes. Models = theoretical. Analyzing one's own social circle, for example, is empirical collection.
    • Predator Prey ( Lynx Hare ): We certainly provide some experience with theoretical analysis but not much empirical data will be collected.
    • <Chaos>: No.
    • End-Notes:
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