CS86 Science, Computing Tools, and Instrumentation

Lesson Plan (Draft)

Week 1

Content Area:

Internet concept Ė e-mail, www, telnet, ftp, usenet. Role of computer technology instrumentation on learning science.

Learning Technique:

Concept of collaborative learning, problem-based learning, project-based learning, and inquiry-based learning.

Hands-on Experience:

Computer boot process and shutdown process; Access World Wide Web (WWW), e-mail; Access Mathcad 7.0/MathConnex.

Reading Assignment(s):

Mathcad 7.0 User Manual: Chapter 2.

Exercise(s):

    1. Visit the web sites of NASA, USGS, EPA, FAA, Bureau of Census, and NYU Genome project to get a glimpse on their scientific studies related to our daily life.
    2. Identify and sketch a possible science study related to our daily life.

Learning Objective(s):

    1. Experience on the activities of scientific study and their relationship to our daily life.
    2. Practical skill on using the lab facilities and the electronic resource center.

Week 2

Content Area:

Concept of constants, variables, operators, and functions; Expansion and factorization of mathematical expressions. An illustration using Lorenzís equations for modeling weather patterns.

Learning Technique:

Concept of patterns; Mathematical structures of patterns; Pattern analysis.

Hands-on Experience:

Mathcad 7.0/MathConnex overview; Mathcad calculation tools.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapters 1, 7, 8, 11 and 12.

Exercise(s):

    1. Patterns in number sequences.
    2. Describing mathematical characteristics of number sequences.
    3. Deriving mathematical structures of number sequences as patterns in the form of mathematical expressions.

Learning Objective(s):

    1. Differences among constants, variables, operators, and functions.
    2. Functional behaviors of mathematical structures in terms of mapping concept.
    3. Relationship between patterns and mathematical structures.
    4. Practical skill on using Mathcad to manipulate numbers, number sequences, and mathematical expressions.

Week 3

Content Area:

Concept of linear and non-linear functions; Basics of differentiation and Taylor series. A case study on projectile motion in Physics.

Learning Technique:

Concept of perception structures; Pattern synthesis.

Hands-on Experience:

Mathcad 7.0/MathConnex graphing tools; Microsoft Excel.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapters 7, 13 and 17.

Exercise(s):

    1. Visualization of projectile motion.
    2. Manipulating equations of projectile motion.
    3. Deriving data of projectile motion from equations.
    4. Re-deriving equations of projectile motion by applying Taylor series to the data of projectile motion.

Learning Objective(s):

    1. Concept of (linear and non-linear) functions.
    2. Relationships among data, functions and Taylor series.
    3. Concept of perceptual structures of data patterns through visualization, and the equivalence of mathematical structures of patterns in functional form.
    4. Practical skill on using Mathcad graphing tools for data/function visualization.

Weeks 4 - 5

Content Area:

Statistical mean and variance; Basic concept of correlation and linear regression. A case study on the social phenomenon of attitudes towards employee insurance coverage in specific labor force sectors.

Learning Technique:

Concept of dependency structure; Pattern inference.

Hands-on Experience:

Mathcad statistics tool; Internet tool such as ftp and www.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapter 14.

Exercise(s):

    1. Accessing through the web the population survey data using the DES (Data Extraction System) of the Bureau of Census, U.S. Department of Commerce.
    2. Retrieve selected survey data using DES and ftp.
    3. Conduct statistical analysis on the data downloaded from the DES.

Learning Objective(s):

    1. Basics of statistics including concept of statistical mean, variance, linear regression and correlation.
    2. Practical skill on internet tools such as WWW, ftp.
    3. Practical skill of using Mathcad statistics tool.

Weeks 5 - 6

Content Area:

Basic concept of causal relationship; Concept map and Concept of probability model; Concept of inference for answering queries based on observations. A case study using an aviation model of flight control in aerodynamic engineering.

Learning Technique:

Inquiry based learning; Concept of dependency structure; Pattern inference.

Hands-on Experience:

Mathcad statistics tool; Bayesian inference tool.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapter 14.

Exercise(s):

    1. Develop a dependency model among parameters of projectile motion.
    2. Develop a dependency model among attributes of population survey data.
    3. Construct "what-if" query-response scenario for inference.

Learning Objective(s):

    1. Understand statistical correlation as one aspect of causality.
    2. Understand the relationship between linear regression and correlation.
    3. Practical skill on using Bayesian inference tool.

Week 7

Content Area:

Concept of experimental science: Hypothesis formulation; Experiment design; Control experiment; Data collection and analysis; Hypothesis (in)validation; Evaluation and Assessment; Reproducibility. A case study on the photosynthesis experiment in biochemistry.

Learning Technique:

Concept formulation; Pattern synthesis.

Hands-on Experience:

Mathcad electronic book; Cut-and-paste electronic documents.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapters 2 (revisited), 5, 6.

Exercise(s):

    1. Retrieve an electronic book discussing the use of statistical correlation to analyze possible relationship between dew point and fog condition.
    2. Prepare an electronic book report based on the article "Physika" by Hans Christian Von Baeyer (The Sciences, New York Academy of Sciences, Jan/Feb 1998) and additional material(s). The report should focus on the historical development of Lorenzís equations that model weather patterns and the impact on the study of chaos.
    3. Deposit the electronic book report in the Mathcad collaboratory server.

Learning Objective(s):

    1. Principle of experimental science.
    2. Practical skill on retrieving, preparing, and distributing electronic books.

Week 8

Content Area:

Modeling concept revisit; Concept of invariant properties and dynamic behaviors; Two kinds of abstraction: summary and elaboration; A case study on analyzing invariant properties and dynamic behaviors of fractals using Fibonacci-based patterns and Mandelbrot set.

Learning Technique:

Concept abstraction; Pattern analysis and synthesis.

Hands-on Experience:

Cut-and-paste technique for data management; Data import and export using OLE component objects; MathConnex modules.

Reading Assignment(s):

Supplementary material; Mathcad 7.0 User Manual: Chapters 4 and 19.

Exercise(s):

    1. Experiment MathConnex module for Fibonacci-based fractal visualization.
    2. Prepare an electronic data log book on fractal visualization experiment.
    3. Deposit the electronic data log book in the Mathcad collaboratory server.

Learning Objective(s):

    1. Concept of OLE for DDE (Dynamic Data Exchange).
    2. Concept of abstracting invariant property of a science study.
    3. Practical skill on manipulating MathConnex modules.

Weeks 9 - 10

Content Area:

Concept of OLE2 components and COM technology; Concept of object Pascal programming using Delphi. An illustration on ActiveX component with an OLE container for telnet to access official FAA aviation weather data.

Learning Technique:

Collaborative learning; System design concept.

Hands-on Experience:

Telnet; Integrating electronic workbook, aviation weather data on wind speed and direction, and MathConnex projectile motion module using OLE container concept.

Reading Assignment(s):

Supplementary material.

Exercise(s):

    1. Prepare an electronic workbook for aviation weather data collection from the internet.
    2. (In)validate Lorenzís equations for modeling weather patterns using collected data in an embedded environment of Mathcad.
    3. Optional for extra credits: Modify MathConnex module for projectile motion to incorporate wind from real weather data.

Learning Objective(s):

    1. Concept of COM and OLE.
    2. Basic programming concept from the system design perspective.
    3. Practical skill on telnet.
    4. Practical skill on system design and integration.

Week 11

Content Area:

Concept of computer controlled instrumentation. A case study on collecting data of noise pollution.

Learning Technique:

Inquiry based learning; System analysis.

Hands-on Experience:

Operation of computer controlled noise sensors, humidity sensors, and wind detectors.

Reading Assignment(s):

Supplementary material.

Exercise(s):

    1. Prepare an electronic data log book for collecting data from humidity sensors, and wind detectors, and aviation related noise pollution from noise sensors.
    2. (To be determined) Possible field trip to weather and air traffic control center (ARTCC) at Long Island.

Learning Objective(s):

    1. Practical skill on computer controlled instrumentation.
    2. Relationships among computing tools, instrumentation, and science.

Week 12

Experiment 1: A psychology experiment on short-term memory model

Brief description

Students will acquire short-term memory latency time on computer controlled numeric visual display objects of different length.

Students will analyze the data to determine the validity of Sternberg short-term memory model.

Week 13

Experiment 2: A multi-disciplinary experiment on studying the effect of wind (speed and direction) on noise pollution caused by aircraft over Queens College.

Brief description

Students will use PC-instrumentation to acquire weather data and data of noise pollution due to aircraft flying over Queens College campus to/from La Guardia airport.

Students will analyze the data to determine any relationship between the wind direction/speed and average noise pollution.

Week 14

Wrapping up.

 

Course activities and grading policy

This course emphasizes regular participation on class activities and collaborative learning. 2 or 3 students will be grouped together for course activities. Weekly activities of the course consist of 2 hours of classroom meeting and 2 hours of laboratory activities for hands-on experience. In addition, it will also be an optional 2-hour weekly forum. Each week a new topic related to science, computing tools and instrumentation will be discussed. This optional weekly forum is an excellent opportunity for students of different backgrounds and levels to share with each other their experience.

In additional to the 2-hour classroom and 2-hour lab meeting, it will also be an open lab session of at least 4 hours for students to use the lab facilities outside the course hours. The schedule of the open lab session will be scheduled and discussed at the first lecture.

Each student is required to maintain a log book to record the weekly activities including, but not limited to, the meeting(s)/seminar(s)/field trip(s) attended, the work done, problems encountered, progress, and learning experience. Each student has to submit this logbook in electronic form (as an electronic book) at the end of the semester. This logbook will account for 10% of the final grade.

Activities

1. Classroom and lab activities participation (20 points)

2. Logbook (10 points)

3. Weekly exercise (20 points)

4. First experiment (10 points)

5. Second experiment (10 points)

6. Final exam + Practical test (30 points)

Extra credits: 20 points with a maximum of 10 points counted towards final grade.