[This corrects the article DOI: 10.1016/j.ipm.2022.103093.].

In the midst of the COVID-19 pandemic, the use of non-face-to-face information and communication technology (ICT) such as kiosks has increased. While kiosks are useful overall, those who do not adapt well to these technologies experience technostress. The two most serious technostressors are inclusion and overload issues, which indicate a sense of inferiority due to a perceived inability to use ICT well and a sense of being overwhelmed by too much information, respectively. This study investigated the different effects of hybrid technostress—induced by both inclusion and overload issues—on the cognitive load among low-stress and high-stress people when using kiosks to complete daily life tasks. We developed a ‘virtual kiosk test’ to evaluate participants’ cognitive load with eye tracking features and performance features when ordering burgers, sides, and drinks using the kiosk. Twelve low-stress participants and 13 high-stress participants performed the virtual kiosk test. As a result, regarding eye tracking features, high-stress participants generated a larger number of blinks, a longer scanpath length, a more distracted heatmap, and a more complex gaze plot than low-stress participants. Regarding performance features, high-stress participants took significantly longer to order and made more errors than low-stress participants. A support-vector machine (SVM) using both eye tracking features (i.e., number of blinks, scanpath length) and a performance feature (i.e., time to completion) best differentiated between low-stress and high-stress participants (89% accuracy, 100% sensitivity, 83.3% specificity, 75% precision, 85.7% F1 score). Overall, under technostress, high-stress participants experienced cognitive overload and consequently decreased performance; whereas, low-stress participants felt moderate arousal and improved performance. These varying effects of technostress can be interpreted through the Yerkes-Dodson law. Based on our findings, we proposed an adaptive interface, multimodal interaction, and virtual reality training as three implications for technostress relief in non-face-to-face ICT.

Boredom is … a vital problem for the moralist, since half the sins of mankind are caused by the fear of it. (Bertrand Russell, “The Conquest of Happiness,” 1930).Humans are essentially storytellers...

Video-based learning (VBL) is widespread; however, there are numerous challenges when teaching and learning with video. For instructors, creating effective instructional videos takes considerable time and effort. For students, watching videos can be a passive learning activity. Artificial intelligence (AI) has the potential to improve the VBL experience for students and teachers. This half-day workshop will bring together multi-disciplinary researchers and practitioners to collaboratively envision the future of VBL enhanced by AI. This workshop will be comprised of a group discussion followed by a presentation session. The goal of the workshop is to facilitate the cross-pollination of design ideas and critical assessments of AI approaches to VBL.

While video becomes increasingly prevalent in educational settings, current research has yet to investigate what feedback instructors need regarding their students' engagement and learning despite video technologies being equipped to provide viewing analytics and collect student feedback. In this paper we investigate instructors' requirements from video analytics. We used a Grounded Theory Approach and interviewed 16 instructors who teach using video to determine the advantages for using video in their teaching and the different requirements for analytics and feedback in their existing practice. Based on our analysis of the interviews, we found three categories of information that instructors want to inform their teaching. Instructors are looking to see if their students have watched their videos, how much they understood in those videos, and how useful the videos are to the students. These categories provide the foundations and design implications for instructor-centric educational video analytics interfaces.

Pointing is a fundamental building block in human communication. While it is ubiquitous in our daily interactions within the real world, it is difficult to precisely interpret a virtual agent's pointing direction to the physical world, considering its complex and subtle gesture cues, such as the movements of the human hand and head. Fish Tank Virtual Reality (FTVR) display has the potential to provide accurate pointing cues as it creates a compelling 3D spatial effect by rendering perspective-corrected vision. In this paper, we conducted a study with pointing cues of three levels (Head-only, Hand-only, and Hand+Head) to evaluate how the head and hand gesture cues affect observers' performance in interpretation of where a virtual agent is pointing in a spherical FTVR display. The results showed that the hand gesture significantly helps people interpret the pointing both accurately and quickly for fine pointing (15°), with 19.4% higher accuracy and 1.42 seconds faster than the head cue. The combination of the head and hand yielded a small improvement on the accuracy (4.4%) with even slightly longer time (0.38 seconds) compared to the hand-only cue. However, for coarse pointing (30°), head cue appears to be sufficient with the accuracy of 90.2%. The result of this study provides guidelines on cues selection for designing pointing in the virtual environment.