GIFTS: Situational Learning of MATLAB Using Data Collection and Analysis Modules Based on Upper-Level Engineering Lab Experiments

Brian Patrick O'Connell

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Conference: 14th Annual First-Year Engineering Experience (FYEE) Conference
Location: University of Tennessee in Knoxville, Tennessee
Publication Date: July 30, 2023
Start Date: July 30, 2023
End Date: August 1, 2023
Page Count: 2
DOI: 10.18260/1-2--44842
Permanent URL: https://peer.asee.org/44842
Download Count: 126

Paper Authors

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Brian Patrick O'Connell Northeastern University orcid.org/0000-0002-6626-256X

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Dr. O'Connell is an associate teaching professor in the First-Year Engineering program at Northeastern University. He studied at the University of Massachusetts at Amherst in 2006 then worked in industry as a Mechanical Engineer working on ruggedized submarine optronic systems. He returned to academia in 2011 at Tufts University planning to work towards more advanced R&D but fell for engineering education and educational technologies. His research now focuses on developing engineering technologies and learning environments, specifically makerspaces, to support engineering education at many levels. He's also heavily involved with his local FIRST Robotics Challenge team as a mentor.

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Abstract

All College of Engineering students at [blanked] participates in the first-year engineering courses in mixed discipline cohorts. Since these courses service students of all majors, the curriculum includes a range of engineering tools they will need in the following years. One of those tools is MATLAB due to its use in many upper-level lab courses across all disciplines. Historically, the curriculum for MATLAB has focused on introductory programming skills and MATLAB syntax taught through simple examples such as geometry calculators, games, and visualization of provided data sets. In those future labs, MATLAB will interface with external data collection hardware or other data sources, collect and organize the data, perform analysis, and present the data and results to support their conclusions. From a programming experience standpoint, a script to play a game of Tic-Tac-Toe and a script to assist with a complex experiment to answer a research question differ significantly in the algorithms used and vary fundamentally in logic development. A [grant blanked] produced a new situational learning curriculum to bridge that gap between the student’s initial experience with MATLAB and how they will use it in future courses and professionally. A series of MATLAB modules inspired by typical lab experiments provide students with hands-on experience in data collection, organization, analysis, and visualization. In addition, the modules lead students to apply their MATLAB knowledge to real-world problems and develop algorithmic thinking skills relevant to their future use of the tool. The initial implementation of the curriculum took place in a Fall 2022 Honors-level first-year engineering course. The data-collection aspect of the modules was well received, and with detailed guidance, they successfully analyzed large datasets and visualized the results. However, students struggled when the assignments became more open-ended, allowing them much more freedom in what research questions they might address and how they would do so. Even if given a list of possible questions, many students became stagnant and could no longer understand “What they were supposed to do” in that context. Overall, the curriculum provided a beneficial situational learning experience for the students, with multiple means of engaging with and learning the material. The type of algorithmic thinking they experienced was much closer to their future use of the design tool. They not only engaged with MATLAB but engaged with experimental practices that they will utilize alongside MATLAB in the future. While there were some challenges with the open-ended assignments, the initial implementation suggests that this instructional approach could improve student learning outcomes in engineering education.

Citation
Format

O'Connell, B. P. (2023, July), GIFTS: Situational Learning of MATLAB Using Data Collection and Analysis Modules Based on Upper-Level Engineering Lab Experiments Paper presented at 14th Annual First-Year Engineering Experience (FYEE) Conference, University of Tennessee in Knoxville, Tennessee. 10.18260/1-2--44842

TY - CPAPER
AB - All College of Engineering students at [blanked] participates in the first-year engineering courses in mixed discipline cohorts. Since these courses service students of all majors, the curriculum includes a range of engineering tools they will need in the following years. One of those tools is MATLAB due to its use in many upper-level lab courses across all disciplines. Historically, the curriculum for MATLAB has focused on introductory programming skills and MATLAB syntax taught through simple examples such as geometry calculators, games, and visualization of provided data sets. In those future labs, MATLAB will interface with external data collection hardware or other data sources, collect and organize the data, perform analysis, and present the data and results to support their conclusions. From a programming experience standpoint, a script to play a game of Tic-Tac-Toe and a script to assist with a complex experiment to answer a research question differ significantly in the algorithms used and vary fundamentally in logic development.
A [grant blanked] produced a new situational learning curriculum to bridge that gap between the student’s initial experience with MATLAB and how they will use it in future courses and professionally. A series of MATLAB modules inspired by typical lab experiments provide students with hands-on experience in data collection, organization, analysis, and visualization. In addition, the modules lead students to apply their MATLAB knowledge to real-world problems and develop algorithmic thinking skills relevant to their future use of the tool.
The initial implementation of the curriculum took place in a Fall 2022 Honors-level first-year engineering course. The data-collection aspect of the modules was well received, and with detailed guidance, they successfully analyzed large datasets and visualized the results. However, students struggled when the assignments became more open-ended, allowing them much more freedom in what research questions they might address and how they would do so. Even if given a list of possible questions, many students became stagnant and could no longer understand “What they were supposed to do” in that context.
Overall, the curriculum provided a beneficial situational learning experience for the students, with multiple means of engaging with and learning the material. The type of algorithmic thinking they experienced was much closer to their future use of the design tool. They not only engaged with MATLAB but engaged with experimental practices that they will utilize alongside MATLAB in the future. While there were some challenges with the open-ended assignments, the initial implementation suggests that this instructional approach could improve student learning outcomes in engineering education.

AU - Brian Patrick O'Connell
CY - University of Tennessee in Knoxville, Tennessee
DA - 2023/07/30
PB - ASEE Conferences
TI - GIFTS: Situational Learning of MATLAB Using Data Collection and Analysis Modules Based on Upper-Level Engineering Lab Experiments
UR - https://peer.asee.org/44842
DO - 10.18260/1-2--44842
ER -