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This paper proposes an automated learning system that provides students with real time performance feedback during engineering laboratory assignments by discovering associations between objects that students interact with, and the manner of interaction. Technological advancements in computer vision and machine learning techniques are creating opportunities for STEM researchers to integrate commercial, off-the-shelf technologies in the design and development of automated learning systems in STEM classrooms. In this work, the authors employ the Microsoft Kinect to serve as the computer vision system to observe objects in the laboratory environment and how students utilize those objects. Machine learning metrics are utilized to quantify the veracity of the object-student associations generated by the proposed automated feedback system. The knowledge gained from this research has broad impacts within engineering education and beyond, as researchers seek novel technology solutions that have the potential to transform the manner in which students learn and receive feedback, towards more customized modes of STEM education delivery.

An Automated Object-Task Mining Model for Providing Students with Real Time Performance Feedback