Asee peer logo

Deep Learning and Artificial Intelligence: Project Collaboration Across Classes

Download Paper |

Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computing and Information Technology Division Technical Session 1

Tagged Division

Computing and Information Technology

Tagged Topic

Diversity

Page Count

13

DOI

10.18260/1-2--34370

Permanent URL

https://peer.asee.org/34370

Download Count

193

Request a correction

Paper Authors

biography

Franz J. Kurfess California Polytechnic State University, San Luis Obispo Orcid 16x16 orcid.org/0000-0003-1418-7198

visit author page

Franz J. Kurfess is a professor in the Computer Science and Software Engineering Department, California Polytechnic State University, San Luis Obispo, where he teaches mostly courses in Artificial Intelligence, Human-Computer Interaction, and User-Centered Design. Before joining Cal Poly, he was with Concordia University in Montreal, Canada, the New Jersey Institute of Technology, the University of Ulm, Germany, the International Computer Science Institute in Berkeley, CA, and the Technical University in Munich, where he obtained his M.S. and Ph.D. in Computer Science.

His main areas of research are Artificial Intelligence and Human-Computer Interaction, with particular interest in the intersection of the two fields.

visit author page

biography

Maria Pantoja California Polytechnic State University, San Luis Obispo

visit author page

Maria Pantoja
Computer Engineering
Computer Science & Software Engineering
Office: 14-211
Phone Number: 805-756-1330
Email: mpanto01@calpoly.edu
Homepage:
https://cpe.calpoly.edu/faculty/mpanto01/
Biography
B.S., Universidad Politecnica de Valencia, Spain
Ph.D., Santa Clara University

Research Interests
High Performance Computing
Neural-Electronics
Parallel Computing

visit author page

biography

Irene Humer California Polytechnic State University, San Luis Obispo Orcid 16x16 orcid.org/0000-0003-2647-4813

visit author page

Ph. D. Electrical Engineering and Information Technology, Vienna University of Technology
M. S. Physics, University of Vienna
M. S. Education Physics and Mathematics, University of Vienna

Research Interests: Computer Science Education, Physics Simulation, Applied Computing

visit author page

Download Paper |

Abstract

Working in collaborative environments is an essential skill for computing professionals. In our program, students have significant team experience from previous classes; almost all of our classes in Cal Poly’s Computer Science, Software Engineering and Computer Engineering programs have lab sections, and students start with team-based work early. However, they mostly work within small teams of about five members that are stable throughout a term. To stretch the students’ collaborative skills and enhance their experience with more fluid team configurations, we experimented with creating teams across two related senior-level classes within Computer Science, but with different perspectives and approaches. For one particular project, shark spotting in drone video footage, the teams also collaborated with a group of Marine Biologists from the Shark Lab at CSU Long Beach, who provided the video material and acted as a customer for three teams across the two classes. This exposed our students to collaborators among different fields, with their own terminology, goals, work methods and practical approaches. Our paper reports on the initial experiment during the Fall 2019 term, involving two sections of an Artificial Intelligence class and one section of a Deep Learning class. We are planning to continue this collaboration in the future.

Kurfess, F. J., & Pantoja, M., & Humer, I. (2020, June), Deep Learning and Artificial Intelligence: Project Collaboration Across Classes Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34370

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2020 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015