Computer Modeling in Science  

Table Of Contents

Course Description:

This course will prepare high school science teachers to use computer models as a means of attaining many of the Maryland State Science Core Learning Goals. Current research emphasizes the importance of student-constructed knowledge as the means to the mastery of concepts. Computer models, whether student-built or pre-built, allow the student to experience the effects that a single variable can have on a complex system. Interacting with a model gives the student the opportunity to construct his/her own knowledge about a system. Building and testing a model reinforces the concepts seen through data collected in the wet labs and the theory presented in the textbook. Participants in this course will learn the theory behind system dynamics, they will learn how to use computer models relevant to their subject areas, and they will experience building their own models. To evaluate student learning during a modeling activity, performance assessments and associated rubrics will also be developed.

Course Objectives:

At the completion of this course, the participants will be able to:

I. Describe the components, dynamics and applications of a system.
  1. Identify whether a causal feedback loop is reinforcing or stabilizing.
  2. Identify whether system behavior is goal-setting, goal-seeking, self-referencing or stimulus-response.
  3. Identify real world applications for a system.
II. Analyze the behavior of models specific to their disciplines.
  1. Identify examples of equilibrium, homeostasis, oscillation.
  2. Identify examples of models that exhibit behavior that is constant, linear, exponential, s-shaped, periodic, or overshoot-and-collapse.
  3. Identify the influence of delay on a system.
III. Design and build a dynamic model of a system.
  1. Define the purpose of the model.
  2. Determine the key variables in the model.
  3. Determine the manner in which information flows between key variables.
  4. Quantify the flows with equations and graphs.
  5. Determine methods of quantifying soft variables.
  6. Compare model results to real world knowledge of the system.
  7. Refine the model by incorporating additional, relevant information.
IV. Develop and use performance assessments and rubrics for a system.
  1. Use a given scoring tool to evaluate student responses to questions from a selected modeling activity.
  2. Apply the Common Themes from the AAAS Benchmarks to develop questions and an associated scoring tool for a modeling activity for their classroom.
  3. Apply Maryland State Science Core Learning Goal #1 to develop questions and an associated scoring tool for a modeling activity for their classroom.
V. Identify computer models that they will use in their classroom.
  1. Select at least four modeling activities to use with their students.
  2. Describe the pre- and post-model activities they will do with their students.
VI. Demonstrate techniques for model usage with students.
  1. Practice teach a model to a subset of participants who are unfamiliar with the model.
  2. Compile a list of expected difficulties and suggestions for dealing with them.

Course Outline and Schedule:

Session Topic Reading Assignment
1 What is Systems Thinking? Roberts, Ch. 1
Hannon, Ch. 2		 Causal loop diagram of a system relevant to science
2 System Dynamics Fisher Articles
Roberts, Ch. 6, 7, 11		 Quantify the information flows in the causal loop diagram from Session 1
3 What is STELLA? How Does it Work? The Money Model Calculation of output from a simple model
4 Building a First Model Nuclear Decay
Simple Kinematics		 Critique of model
5 Intermediate Model I Deer Population
Kinetics		 Critique of model
6 Intermediate Model II Enzyme Reaction Rates
Flow		 Critique of model
7 Intermediate Model III Pan Water Cycle
Rock Cycle 		Critique of model
8 Intermediate Model IV Free Fall
Projectile Motion		 Critique of model
9 Advanced Model I Glucose-Insulin
Peppered Moth		 Critique of model
10 Advanced Model II Tailgating
Universal Gravitation		 Critique of model
11 Advanced Model III Carbon Cycle Critique of model
12 Planning & Building a Model Intro to Systems, Ch. 8 Model Description
13 Testing & Refining a Model Model De-bugging Hints Model print-out
14 Performance Assessments and Rubrics Common Themes from
AAAS Benchmarks
Maryland Science CLG		 Questions and scoring tool for a computer model
15

Practice Teach a Model

 Designing a Lesson Plan Summary of teaching hints

Assignments:

Participants will demonstrate their understanding of the use of computer models as a means of attaining both concept and process core learning goals by completing the following projects:

1. Participants will use causal loop diagrams to identify feedback in a system.

2. Participants will quantify the flow of information in a system.

3. Participants will calculate and analyze the output of a STELLA model.

4. Participants will critique a variety of models and student activities pertinent to their subject area.

5. Participants will design and build their own STELLA model of a dynamic system.

6. Participants will develop performance assessments and rubrics tied to Maryland Science CLG and AAAS Benchmarks for a modeling activity.

7. Participants will develop a mini-lesson to teach one of the modeling activities.

Evaluation and Grading:

Participants will be evaluated by the instructor on specific performance based competencies for each project. On-going corrective and constructive feedback will be provided by the instructor and peer coaches. Refinement of assignments will be encouraged until competencies are demonstrated by achieving a minimal score of 3 on each task.

The following 4-point scoring tool will be utilized to demonstrate competencies in Assignment 5 (Building a STELLA Model). Rubrics for other assignments will be similar.

Outstanding (4):

The participant, using analysis, has developed a deep understanding of the system being modeled. The system diagram and information flows accurately represent the key parameters in the system. The equations specifying the relationships between the parts of the system demonstrate a comprehensive integration of the available data and known theory regarding the system. The model has been thoroughly tested against a variety of conditions.

Good (3):

The participant, using analysis, has developed a good understanding of the system being modeled. The system diagram and information flows accurately represent many of the key parameters in the system. The equations specifying the relationships between the parts of the system demonstrate a mostly complete integration of the available data and known theory regarding the system. The model has been tested against a variety of conditions.

Fair (2):

The participant has developed a basic understanding of the system being modeled. The system diagram and information flows accurately represent only part of the key parameters in the system. The equations specifying the relationships between the parts of the system demonstrate an incomplete integration of the available data and known theory regarding the system. The model has been tested against a few conditions.

Poor (1):

The participant has exhibited a lack of understanding of the system being modeled. The system diagram and information flows are inaccurate representations of the key parameters in the system. The equations specifying the relationships between the parts of the system demonstrate a lack of understanding of the available data and known theory regarding the system. The model has not been tested.

Instructional Resources:

TEXTS:

American Association for the Advancement of Science (1996) Benchmarks in Science

Few, Arthur (1996) System Behavior and System Modeling, University Science Books, Sausalito, CA

Hannon, Bruce and Ruth, Matthias (1994) Dynamic Modeling, Springer-Verlag New York, Inc.

Hannon, Bruce and Ruth, Matthias (1997) Modeling Dynamic Biological Systems, Springer-Verlag New York, Inc.

High Performance Systems (1996) Introduction to Systems Thinking, Hanover, NH

Maryland Virtual High School (1998) CoreModels Activities for High School Science, Montgomery County Public Schools and the National Science Foundation

Roberts, Nancy, Andersen, D., Deal, R., Garet, M., and Shaffer, W. (1983) Introduction to Computer Simulation, Addison Wesley

System Dynamics in Education Project (1994) Road Maps, A guide to Learning System Dynamics, MIT

Waters Center (1998) Demo Dozen, Trinity College of Vermont

ARTICLES:

Fisher, Diana (1994) "Generic Processes" Creative Learning Exchange, http://sysdyn.mit.edu/cle/home.html

Fisher, Diana (1994) "From Exponential to Convergent to Logistic Models Using STELLA" Creative Learning Exchange, http://sysdyn.mit.edu/cle/home.html


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