Group 2

Eugene Mahmoud
Dan Burleson
Paul Watta
Mahalakshmi Nagulapati
Maryam Heshmatzadeh
Conroy Robinson
Abhishek Appaji

Learning Goals Discussion

  • What lense are learning goals?
    • Focused on activities 
    • MATLAB grader (more automatic grading)
    • MATLAB Grader with Live Script?
    • Activity focus 
    • California state wide - Matlab courses for transfer students (course objectives for discipline specific) 
    • What do you want students to do for the class? 
    • Focused on project 
  • What are good general learning outcomes in science/computation courses? 
    • Understanding algorithms and converting them into a code
    • Design algorithms and flowcharts to facilitate programming and problem solutions.

       
    • Be able to model a real-life problem properly by imposing the constraints and using computational tools available
    • Students will be able to write programs and use tools to analyze data sets in a variety of formats and identify issues where data-cleaning methods should be employed.
    • Students will be able to write programs and use tools to visualize data in a variety of ways to gain insight into the structure or nature of the data.

       
    • Students will be able to write technical reports which clearly explain the problem at hand and then explain how it was solved, including the effective use of mathematics, figures, and tables.
    • Design and document computer programs carefully and completely to facilitate analysis and debugging by another programmer, and to anticipate and resolve errors.

       
    • See MATLAB (or programming language generally) as a tool and not something scary.
    • Introduce students to MATLAB as a computing tool that can be used in the different Engineering Disciplines
    • Students should be able to use MATLAB to simplify problem-solving.
  • What are examples of learning outcomes in a course you teach?
    • Students will be able to apply the appropriate machine learning algorithm and evaluate performance.
    • Students will be able to set up and solve linear systems of equations in MATLAB.
    • Students will be able to simplify polynomial expressions using MATLAB commands.
    • Apply numeric techniques and computer simulations to solve engineering-related computational problems.
    • Solve one-dimensional and multidimensional unconstrained and constrained nonlinear programming problems using both gradient and non-gradient algorithms.
    • I teach introduction to programming class CSCI10, where one complete assignment is to learn basic programming in MATLAB. If they finish all the MATLAB on-ramp exercises they get 10% of the total grade.  Students learn basic programming skills through this assignment.

Final Report Out:

Summary:

  • Eugene Mahmoud
    • Worked on Learning Objectives related to teaching activities
    • Putting values into engineering system
    • playing around with centroid (autocad shapefiles)
  • Dan Burleson
  • Paul Watta: 
    • Created live script to access openAI and get a response in the script. Needed to the openAPT key
      • part of course learning generative AI tools
      • ability expand to a project where students create use interface to change prompt.
  • Mahalakshmi Nagulapati: 
    • Work on matrices and matrix math work.  Explored MATLAB capabilities.
    • Using MATLAB onramp in programming lab. 
    • Goal to use MATLAB in other courses in undergraduate level (linear algebra, numerical analysis)
  • Maryam Heshmatzadeh: Future assessment (better), be creative in MATLAB grader
    • Here is the assessment I modified:

      This Assignment on MATLAB Grader is related to "Trigonometric Interpolating Polynomials" in Discrete Fourier Transform.

      Students work individually on the exercise after receiving a lecture on the related theory. They will create sampled data from a given continuous function and complete a pre-structured DFT MATLAB script in the Learner platform to find the coefficients in interpolating trig polynomials, ak and bk and reconstruct the original data.

      The output is the reconstructed data and it should be in good agreement with the original continuous function.The student will also learn how changing the sampling frequency affects the accuracy of the reconstruction. They will put the Nyquist criteria into test for themselves to understand its logic better.

      Learning goals:

      • 1- Understand the concept of Trigonometric Polynomial Approximation.
      • 2- Create a discrete sampled set from a given continuous function with the specified sampling rate.
      • 3- Use proper MATLAB functions/commands to apply related theory.
      • 4- Create proper plots to compare the reconstructed data to the original data.
      • 5- Analyze the effect of sampling frequency on the accuracy of the reconstructed data.
      • 6- Find the minimum required sample points to effectively reconstruct the highest frequency component included in the approximation.

        and here is how the exercise looks like on MATLAB Grader:

        Assessment Description.pdf (Acrobat (PDF) 355kB Oct29 24)

        I will be working on creating better  assessments for my exercises on MATLAB Grader.

 

  • Conroy Robinson: 
    • Worked on learning outcomes and background in the teaching activity. Usually done verbally but explicitly added to activity. 
    • Feedback on background: 
    • Future: Connect with fellow participants 
  • Abhishek Appaji:
    • Worked on Teaching activities based on comments. Accessing data from public data sets
    • Explored toolboxes - wants more customizations. 
    • Future: make a GUI