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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.
Tucker et al. (Wed,) studied this question.