USING A HANDS-ON ROBOTICS PROJECT TO AFFECT SKILL DEVELOPMENT IN A CONTROL ANALYSIS COURSE
MetadataShow full item record
This study aims to assess the impact on skill development of a hands-on experimentation and learning device within the undergraduate aerospace control analysis curriculum at Georgia Institute of Technology. The Transportable Rotorcraft Electronics Control System (TRECS) take-home lab kit was used as a hands-on learning treatment on 37.5% (n=24) of the Fall 2020 Control Analysis course taught by Dr. Chance McColl. The other students (n=40) in the course were taken as a control group. A Likert scale skill evaluation survey was performed to determine which skills are developed while using the TRECS. The response distributions and an accompanying Mann Whitney U-test can be found in the results section. On the topic of optimal control algorithms, which are extensively covered in the course lecture material and applied in the TRECS project, Users and Nonusers reported significantly (p=0.10) increased response and Users were found to have significantly (p=0.10) improved beyond Nonusers. Response distributions for topics including PID control, embedded software, and other electronics were not found to change significantly throughout the course, despite the application of the TRECS treatment or the presence of the topic in the course curriculum. The other goal of this research was to propose an improved study which addresses the limitations to this dataset such as small sample sizes, self-reports, sole focus on development of course-specific subject matter and selection bias from the lack of random assignment of the treatment. The recommendations for a future study are aimed to improve trustworthiness, increase transferability, and incorporate multiple verification elements including the development of a new skill assessment that could evaluate students’ application-level understanding of course concepts.
Showing items related by title, author, creator and subject.
Mehta, Nishant A. (Georgia Institute of Technology, 2013-05-15)Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to machine learning, and it ...
Berlind, Christopher (Georgia Institute of Technology, 2015-07-22)Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to ...
Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances Chu, Vivian (Georgia Institute of Technology, 2018-01-09)The real world is complex, unstructured, and contains high levels of uncertainty. Although past work shows that robots can successfully operate in situations where a single skill is needed, they will need a framework that ...