Utilizing Depth Sensors for Analyzing Multimodal Presentations

Abstract

Body language plays an important role in learning processes and communication. For example, communication research produced evidence that mathematical knowledge can be embodied in gestures made by teachers and students. Likewise, body postures and gestures are also utilized by speakers in oral presentations to convey ideas and important messages. Consequently, capturing and analyzing non-verbal behaviors is an important aspect in multimodal learning analytics (MLA) research. With regard to sensing capabilities, the introduction of depth sensors such as the Microsoft Kinect has greatly facilitated research and development in this area. However, the rapid advancement in hardware and software capabilities is not always in sync with the expanding set of features reported in the literature. For example, though Anvil is a widely used state-of-the-art annotation and visualization toolkit for motion traces, its motion recording component based on OpenNI is outdated. As part of our research in developing multimodal educational assessments, we began an effort to develop and standardize algorithms for purposes of multimodal feature extraction and creating automated scoring models. This paper provides an overview of relevant work in multimodal research on educational tasks, and proceeds to summarize our work using multimodal sensors in developing assessments of communication skills, with attention on the use of depth sensors. Specifically, we focus on the task of public speaking assessment using Microsoft Kinect. Additionally, we introduce an open-source Python package for computing expressive body language features from Kinect motion data, which we hope will benefit the MLA research community.