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- All Subjects: engineering
- Creators: Barrett, The Honors College
- Creators: Phelan, Patrick
Using a concept test given prior to the lecture unit, and after, the difference in scores is used to recognize if the concepts presented have actually been comprehended. Used specifically in a lecture unit on solubility and solutions, the concept test tested student’s knowledge of supersaturated, saturated, and unsaturated solutions. With a visual identification and a written explanation, the student’s ability to identify and explain the three solutions was tested.
In order to determine the cause of the change in score from pre- to post-test, an analysis of the change in scores and the effects of question type and solution type was conducted. The significant results are as follows:
The change in score from pre- to post-test was found to be significant, with the only difference between the two tests being the lecture unit and intervention
From pre- to post-test, solution type had a significant effect on the score, with the unsaturated solution being the most easily recognized and explained solution type
Students that felt that the YouTube videos greatly increased their concept comprehension, on average, performed better than their counterparts and also saw a greater increase their score from pre- to post-test
Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.
Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.