ABSTRACT
Extended Reality (XR) technologies such as head-mounted displays are deemed beneficial for the collaboration of co-located as well as distributed people. As such, XR technologies appear particularly promising for supporting distant and hybrid teaching which became highly relevant during the Covid-19 pandemic. Despite the potential awarded to such technologies, practical applications are still very rare. In order to investigate the impediments to the practical adoption of XR technologies, the respective systems should be evaluated in real-world settings. Existing evaluation tools are, however, not suited for this purpose. In this paper, we explain why today's evaluation tools such as questionnaires, observation, and performance measurements are not sufficient for evaluating long-time, exploratory, and collaborative tasks that are typical in educational settings. To address this gap, we follow a top-down approach: Based on an existing model of user acceptance, we specify the variables that are to be optimized by HCI research and outline the potential of wearable-based measuring instruments to quantitatively assess these parameters. Eventually, we point out related research gaps that should be addressed by future research.
ABSTRACT
Extended Reality (XR) technologies such as head-mounted displays are deemed beneficial for the collaboration of co-located as well as distributed people. As such, XR technologies appear particularly promising for supporting distant and hybrid teaching which became highly relevant during the Covid-19 pandemic. Despite the potential awarded to such technologies, practical applications are still very rare. In order to investigate the impediments to the practical adoption of XR technologies, the respective systems should be evaluated in real-world settings. Existing evaluation tools are, however, not suited for this purpose. In this paper, we explain why today’s evaluation tools such as questionnaires, observation, and performance measurements are not sufficient for evaluating long-time, exploratory, and collaborative tasks that are typical in educational settings. To address this gap, we follow a top-down approach: Based on an existing model of user acceptance, we specify the variables that are to be optimized by HCI research and outline the potential of wearable-based measuring instruments to quantitatively assess these parameters. Eventually, we point out related research gaps that should be addressed by future research. © 2022, IFIP International Federation for Information Processing.