Group 1

  • Chao Wang, Electrical Engineering, Arizona State University
  • John Rogers, Mechanical Engineering, Benedictine College, Atchison, Kansas.
  • Hongmei Chi, Computer Science, Florida A&M University
  • Silvio Simani - Computer Science (Robotics & Automation), University of Ferrara
  • Wagdy Mahmoud - Computer Engineering/Computer Science
  • Zekeriya Aliyazicioglu - Electrical Engineering, California State University Pomona

Discussion

Learning Outcomes

  • What are good general learning outcomes in science/computation courses? 
    • Support ABET outcomes
    • Use computation to learn about the discipline
    • Analyze data, visualize data, interpret data, and draw conclusions
    • Compare predictions to actual outcomes (compare simulation to experimental results)
    • Explain the assumptions and limitations of analysis
    • Explain why simulations are not always accurate--e.g. pendulum model with small angle assumption 
    • Recognize the data pattern and learn to identify the models for their dataset
  • What are examples of specific learning outcomes on a course you teach?
    • Use and select appropriate tools and technical skills to collect and analyze data from a variety of sources, to describe and predict the behavior of designs, and to justify design decisions based on appropriate models.
    • Electric Circuits course 
      • Interpret and compare experimental data with theoretical predictions, identifying deviations and understanding possible sources of error.
      • Analyze transient responses in RC and RL circuits during charging and discharging by plotting in MATLAB.
      • Analyze the efficiency of AC power transfer in electrical systems.
    • Identify the proper tools (such as MATLAB, or Python library) for their own datasets
    • Audio processing course: 
      • Detect events in audio data (e.g. exceed a threshold sound level)
      • Record audio from a microphone as a computer file
      • Develop algorithms to generate audio files, e.g. Morse Code 
    • Automatic Control courses (B= Bachelor's level; M=Master's level)

B1. Apply the proper design tools

B2. Analyze the achieved results

M1. Design the solution that is implemented

M2. Evaluate the achieved performances

Assessment

  • How do you assess student performance and learning? 
    • Learning to write tests in MATLAB grader
    • Create questions in MATLAB Grader to assess each learning objective.
    • Still missing the integration between the MATLAB grader and Simulink (for Simulink scheme verification)
  • What challenges do you have assessing student performance relative to your planned learning outcomes?
    • JR: Difficulty articulating goals in the reverse design method (goals--assessment--activity)
    • Verification of the correct design of a Simulink scheme
  • What techniques or tools can you adopt to help? 
    • MATLAB grader
    • Simulink design
    •