CS382:GenEds

General Education Alignment

Helpful Total Geneds Coverage Table, Fig. 18c.

Unit	Foundations of Modelling	Static Modeling	Fire	Visualization	Structural Modeling	Rocket Modeling	Computational Sociology and Agent Based Modeling	Predator Prey ( Lynx Hare )	Chaos	CS382:End-Notes
ARa	Complete	Complete	None	None	Partial		Complete	Complete	Complete
ARb	Complete	Complete	Partial	None	Complete		Complete	Partial	Partial
ARc	Complete	None	Partial	None	No		Complete	Partial	Complete
QRa	Complete	None	Complete	Complete	Not really		Partial	Complete	Complete
QRb	Partial	Partial	Complete	Partial	Not really		Partial	Complete	Complete
QRc	Complete	Complete	Complete	Partial	Not really		None	Complete	Complete
QRd	Partial	Partial	Complete	None	Not really		None	Complete	Partial
QRe	Complete	Complete	Complete	None	Yes		None	Complete	Complete
QRf	Complete	Complete	Complete	None	Yes		Partial	Complete	Partial
QRg	None	Partial	Complete	Partial	Yes		Complete	Complete	None
SIa	Complete	Complete	Partial	None	Yes		Complete	Complete	Complete
SIb	Complete	Complete	Complete	None	Yes		None	Complete	Complete
SIc	Complete	Complete	Complete	Complete	Yes		Complete	Partial	Partial

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: Complete. 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: Complete. The concepts covered in the static model are intentionally abstract, and rely on the lab activity to ground those abstract concepts in a practical application.
  • Fire: None. This unit deals almost entirely will quantitative reasoning, and would be hard to expand into the abstract world.
  • Visualization: None.
  • Structural Modeling: Partial. 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. None.Does not apply; this unit is purely quantitative.
  • Computational Sociology and Agent Based Modeling: Complete. Agents -> abstract models
  • Predator Prey ( Lynx Hare ): Complete. The entire unit is about students learning exactly what you can do with a system dynamics model. It would be impossible to teach that without discussing the properties of the model and what operations you could perform on it.
  • Chaos: Complete. They are handling abstract model of chaos and applying it in metaverse.
  • CS382:End-Notes:

• They provide experience in generalizing from specific instances to appropriate classes of abstract models.
  • Foundations of Modelling: Complete. 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: Complete. The static model is an abstract framework that we're contextualizing using an activity that grounds the abstraction in something as concrete as 'the heart'.
  • Fire: Partial. 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: None.
  • Structural Modeling: Complete. 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. None.Again it does not apply/support.
  • Computational Sociology and Agent Based Modeling: Complete. discussion of boids, sugarscape and agent based modeling as a whole
  • Predator Prey ( Lynx Hare ): Partial. The way the unit works is by teaching students about the class of model, and has them build a specific one as part of a lab activity.
  • Chaos: Partial. From specific instances of weather data, they generalize chaos model.
  • CS382: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.
  • Foundations of Modelling: Complete. See above about the lab. Also we apply word problems in the form of fermi-problems encouraging students to make and defend measurements and create numeric results.
  • Static Modeling: None. This unit isn't geared towards this as far as I can see.
  • Fire: Partial. The Verification/Validation/Accreditation process teaches the students how to take the procedures they learn using models and apply them to every aspect of scientific discovery. The fire unit attempts to teach students proper use of scientific models to insure that the questions they want answered are being answered by the model they're using.
  • Visualization: None.
  • Structural Modeling: No. this unit isn't geared towards this as far as I can see.
  • Rocket Modeling: Analysis of this unit's support or not for this item. None.Does not support it; it could but we have different focus.
  • Computational Sociology and Agent Based Modeling: Complete. the emergent behavior is a process of abstract manipulation; comparing emergent behavior back against the real world is "converting solutions back into concrete results"
  • Predator Prey ( Lynx Hare ): Partial. The lab activity involves constructing a model, manipulating the data that goes in, and then collecting results, but not in order to satisfy a concrete problem.
  • Chaos: Complete. They provide experience in solving weather forecasting problem. The process is abstraction of weather data, manipulation, and Analysis. They formalize real-world short-term/long-term climate model problem in words.
  • CS382:End-Notes:

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: Complete. The discussion of vetting materials requires and creates an understanding of how to interpret quantitative information. In addition the lab teaches students how to generate quantitative information. This unit briefly touches on visualization of data
  • Static Modeling: None. The students will work with tabular data to get a feel for the balance between accuracy and precision
  • Fire: Complete. 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: Complete. They will be doing many graphs and tables in this Unit.
  • Structural Modeling: The students use tables to organize bridge data. The focus of this section, however is not on interpreting tabluar data.
  • Rocket Modeling: Analysis of this unit's support or not for this item. Complete.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: Partial. 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: Complete. They have formulas of attractor, generate graph of climate change, and analyze big table of climate data.
  • CS382:End-Notes:

• Representing mathematical ideas symbolically, graphically, numerically and verbally.
  • Foundations of Modelling: Partial. The exercises in lab as well as the fermi problems beget skill in representing data numerically and verbally. We discuss representing data graphically and symbolically but do not go into detail in this unit.
  • Static Modeling: Partial. Yes. Well... maybe not verbally. The groups will be using the abstract notion of a static model to solve a real world problem.
  • Fire: Complete. 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: Partial. This unit definitely attempts to represent something graphically, but I don't think quite in the way that they mean.
  • 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. CompleteIt 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: Partial. 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: Complete. Lorentz attractor is representing chaos mathematically, symbolically, graphically, numerically, and verbally.
  • CS382:End-Notes:

• Using mathematical and statistical ideas to solve problems in a variety of contexts.
  • Foundations of Modelling: Complete. the lab design is geared toward teaching how to solve counting and statistical problems in multiple contexts.
  • Static Modeling: Complete. Yes. described above.
  • Fire: Complete. While this unit deals almost entirely with a single tool, the idea of parameter sweeping is necessary in every form of simulation and is thus applicable in a wide range of contexts.
  • Visualization: Partial. Looks at using statistical ideas to solve problems in the single context of Visualization.
  • Structural Modeling: This is one context where we're using mathematical and statistical ideas.
  • Rocket Modeling: Analysis of this unit's support or not for this item. Partially.Still not sure how big will variety be but what is sure that math and statistical ideas will be used to solve problems.
  • Computational Sociology and Agent Based Modeling: None.
  • Predator Prey ( Lynx Hare ): We are using mathematics to solve problems.
  • Chaos: Complete. They require mathematical and statistical ideas to solve data mining problem of climate data.
  • CS382:End-Notes:

• Using simple models such as linear dependence, exponential growth or decay, or normal distribution.
  • Foundations of Modelling: Partial. we discuss the creation of these models however the ones that students use in this unit are likely to not fulfill these.
  • Static Modeling: Partial. Maybe...Not too sure about this one.
  • Fire: Complete. 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: None.
  • Structural Modeling: Not really.
  • Rocket Modeling: Analysis of this unit's support or not for this item. Partially.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: None. 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: Partial. Chaos model will never be simple. But Lorenz attractor abstract chaos theory a lot.
  • CS382:End-Notes:

• Understanding basic statistical ideas such as averages, variability and probability.
  • Foundations of Modelling: Complete. we introduce statistics in the context of models and discuss their usefulness
  • Static Modeling: Complete. Yes. To fill in some of the gaps in their data, students will need to be prepared to formulate estimatations.
  • Fire: Complete. 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: None.
  • 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. Complete.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: None.
  • Predator Prey ( Lynx Hare ): Only so much as these ideas would be helpful in a particular model.
  • Chaos: Complete. Students will develop those skills through analyzing weather data.
  • CS382:End-Notes:

• Making estimates and checking the reasonableness of answers.
  • Foundations of Modelling: Complete. 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: Complete. Yes, students are asked to make estimates to effectivily interpret the data they collect.
  • Fire: Complete. 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: None.
  • 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. " Complete.Yes, estimation and confirmation are important part of the unit.
  • Computational Sociology and Agent Based Modeling: Partial. 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: Partial. Students just can review their estimate because of actual weather and professional's weather prediction.
  • CS382:End-Notes:

• Recognizing the limitations of mathematical and statistical methods.
  • Foundations of Modelling: None. Yet I need to add this in the introductory portion
  • Static Modeling: Partial. A portion of the lectures will be devoted to explaining the limitations of static models and modeling the real world in general.
  • Fire: Complete. 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: Partial. Visualization does speak to the limitations of both visualization itself and the model a visualization represents.
  • 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. Complete.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: Complete. 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: None. This unit require unlimited amount of calculation for accurate forecast.
  • CS382:End-Notes:

Scientific Inquiry Requirement

From the [Catalog Description] Scientific inquiry:

• Develops students' understanding of the natural world.
  • Foundations of Modelling: Complete. this unit lays the framework for students to explore the natural world through counting and modeling.
  • Static Modeling: Complete. The students are making static models of the natural world.
  • Fire: Partial. 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: None.
  • Structural Modeling: Yes.
  • Rocket Modeling: Analysis of this unit's support or not for this item. Complete.'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: Complete. 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: Complete. This unit develops students' understanding of chaotic flow of climate change.
  • CS382: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: Complete. one of the major take-away points of this unit is how to develop a scientific knowledge of a system. In order to test hypotheses students need to build models and apply them to the real world
  • Static Modeling: Complete. Students will define a new framework for describing their environment in a static model.
  • Fire: Complete. 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: None.
  • Structural Modeling: Yes
  • Rocket Modeling: Analysis of this unit's support or not for this item. Complete.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 ): Complete. Experimentation is used when developing models.
  • Chaos: Complete. This unit strengthens students' knowledge of the scientific way of reading weather data and developing weather forecasting theory.
  • CS382:End-Notes:

• Emphasizes and provides first-hand experience with both theoretical analysis and the collection of empirical data.
  • Foundations of Modelling: Complete. 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: Complete. Yes. The students are collecting data and developing an effective way to represent that data to describe a physical space.
  • Fire: Complete. The lab portion of this unit is exactly this: gathering numerical data in order to provide the basis for some sort of conclusion.
  • Visualization: Complete. Deals with collection of data from data sources and theoretical analysis of how to visualize it.
  • Structural Modeling: Yes. Students will understand how physical models and computational models can be used to simulate the same structures and processes.
  • Rocket Modeling: Analysis of this unit's support or not for this item. Complete.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: Complete. Models = theoretical. Analyzing one's own social circle, for example, is empirical collection.
  • Predator Prey ( Lynx Hare ): Partial. We certainly provide some experience with theoretical analysis but not much empirical data will be collected.
  • Chaos: Partial. Theoretical analysis of chaos model will derive infinite variation. Students have to guess the result from the collection of empirical data of weather.
  • CS382:End-Notes: