Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
Design in Engineering Education Division (DEED) Technical Session 1
Design in Engineering Education Division (DEED)
Diversity
9
10.18260/1-2--43395
https://peer.asee.org/43395
167
My name is Lucas Buccafusca. I am currently a teaching faculty at Johns Hopkins University in Electrical and Computer Engineering.
I received my Ph.D. in Industrial and Systems Engineering at the University of Illinois at Urbana-Champaign, earned my Masters in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017 and my Bachelor's degree in Electrical and Computer Engineering in 2013 from the University of Colorado at Boulder.
My pedagogical research interests are on improving the quality of collegiate classroom environments through the use of nontraditional techniques and active participation by instructors. These include the use of failure as a teaching tool, humor and empathy as a means of connecting with students, and gamification.
My technical research interests are Distributed Control, Learning, Distributed Optimization and Nonlinear Systems. Applications of my research are primarily used for Wind Farm arrays.
In the field of data science, advancements in the field of machine learning have led to programs developing high-level reasoning, intricate data understanding, and groundbreaking predictive models. Machine Learning (ML) research aims at making a program ‘learn,’ that is, develop models and techniques with known information to be able to handle future problems. Traditionally, this is done by increasing the quantity and quality of input data and training the learner in more effective ways to interpret that information. This has a direct parallel to the collegiate classroom, as instructors aim to inspire mastery over a topic to their students through a variety of methods (homework problems, examinations, projects, etc.) and teach them the corresponding skillsets from feedback on these assignments. Machine Teaching (MT) research, on the other hand, aims at making the teacher more productive by using their own cognitive models to improve the quality of the data holistically. Again, this has a corresponding counterpart to current teaching pedagogies; the instructor decides on the details of an assignment from their own knowledge and experience with the end goal of having students retain the information and apply it to future problems. This paper identifies how the various innovations, lessons, and conclusions discovered in the field of artificial intelligence can enhance the quality of a collegiate classroom experience and improve student performance.
Buccafusca, L. (2023, June), Learning from Machine Learning and Teaching with Machine Teaching: Using Lessons from Data Science to Enhance Collegiate Classrooms Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43395
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