June 14, 2015
June 14, 2015
June 17, 2015
NSF Grantees Poster Session
26.178.1 - 26.178.13
Object Mining by Co-robot Learning Systems in Engineering Laboratory Environments For Enhanced Student Feedback AbstractThe objective of this paper is to test the hypothesis that co-robot learning systems classifyobjects within an engineering laboratory environment with accuracies comparable to humanobservations. The term co-robot refers to a class of robots that work side by side humans, ratherthan being completely autonomous and isolated. In the process, there is an exchange ofinformation both from the human and co-robot with the ultimate goal of creating a symbioticlearning relationship, where both humans and co-robots learn from one another while performingspecific tasks of interest.Universities spend millions of dollars each year constructing new laboratory facilities ormaintaining existing ones. However students typically only have access to them during “normalbusiness hours”, as the time constraints of instructors and teaching assistants limit the availabilityof these resources. A successful integration of co-robots has the potential to expand theavailability of laboratory facilities by providing students with real time performance feedback,comparable to that of an instructor or teaching assistant. However, in the context of engineeringeducation, there exists a knowledge gap in terms of whether the integration of these co-robotsystems would have a meaningful impact on enhancing students’ performance during laboratoryactivities. In order for co-robots to provide meaningful performance feedback to students, theymust themselves acquire knowledge about the laboratory environments themselves. Specifically,co-robot learning systems must be able to detect objects within a laboratory environment thatstudents may use during laboratory activities. This would enable the co-robot systems to providefeedback to students while they used these objects.Technology advancements in computer vision and machine learning techniques are creatingopportunities for STEM researchers to integrate commercial, off-the-shelf technologies in thedesign and development co-robot systems in STEM education classrooms. In this work, theauthors employ the Microsoft Kinect to serve as the computer vision system to observe objects inthe laboratory environment. An object detection algorithm is proposed, based on machinelearning principles to enable the co-robot system to detect objects in the laboratory environmentand classify them, based on the knowledge existing in its knowledge repository. N-fold crossvalidation is employed to determine the co-robot’s object classification accuracy, compared to abaseline rate set by an instructor.The knowledge gained from this research has broad impacts within engineering education andbeyond, as researchers seek novel technology solutions that have the potential to transform themanner in which students learn and receive feedback, towards more customized modes of STEMeducation delivery.
Tucker, C., & Kumara, S. (2015, June), An Automated Object-Task Mining Model for Providing Students with Real Time Performance Feedback Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23517
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