Sample Learning Outcomes



  1. By 10/31/2018 I will develop an understanding of the process of designing and printing 3D objects.

  2. By 11/15/2018 I will learn how to use image analysis to calculate Young’s Module of an object and calculate the uncertainty of those calculations.

  3. By 12/14/2018 I will have an understanding of how to design, test, and present the results.


S.M.A.R.T. = Specific, Measureable, Attainable, Relevant, Time Bound

Specific: The first set of goals are not specific to the project or what is going to be achieved.

Measurable: The revised goals can track progress, such as what has been learned over the internship.

Attainable: All of the revised goals are doable within the set timeframe.

Relevant: The first set of goals do not explain how or why it is important that they are achieved. The new goals apply themselves to future projects and his/her degree.

Time Bound: Both goals set a “due date” in order in ensure that the goals are met in a timely manner.


  1. By 11/15/2018 I will apply my current knowledge of learning programming languages, running programs, and troubleshooting to learn the entire process of 3D printing which includes design, set up, programming and printing to conduct my first 3D printing project. As an electrical engineer, this skill-set is going to be relevant to the project in the future when I will need to design and create my own circuit board.  

  2. By 11/31/2018 I will first learn MATLAB then apply MATLAB to my current understanding of the Uncertainty Principle to create the code for MATLAB to perform calculations to determine Young’s Modulus from experimental images and then perform Image Analysis and calculate the uncertainty of those calculations

  3. By 12/31/2018 I will submit a paper for publication in “Measurement Science and Technology” by applying the skills I learned in my Circuit Design and Digital Logic. I will design, test and present the results by examining the previous proposal titled “Nondestructive Evaluation of Young’s Modulus Using Elastica Geometry,” identify inaccuracies in the paper, and include a larger dataset (the last dataset included one example, where the minimal number of data sets needs to be anywhere between 3 and 20)

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