The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and chal-lenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios.

Simulations are widely adopted in undergraduate nursing education because they offer low-risk, experiential ways to expose pre-licensure students to clinical environments, and to situate the development of requisite knowledge and skills for patient care. Virtual reality (VR) simulations present novel opportunities for clinical education. Research in this area is burgeoning around questions related to perception about VR modality, adoption of the technology, and educational outcomes VR simulations can help facilitate. In this paper, we demonstrate the application of epistemic network analysis (ENA), a quantitative ethnography (QE) technique, to model how one nursing educator facilitated clinical judgment, and nurtured quality and safety education for nurses' competencies through the use of the Simulation Leaning System with Virtual Reality (SLS with VR). We modeled the discourse obtained from three simulation sessions in October and November 2020, all involving a fundamentals scenario requiring second-year nursing students to practice basic assessment and care management. Our work aims to advance research in healthcare education, particularly nursing education, using immersive learning environments by way of applying theory-backed learning analytic techniques.

Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g. collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g. pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.

This paper reports on an exploratory trial of developing pedagogical visualizations of 16 students' written assignments on literary analysis using two sets of keywords and Epistemic Network Analysis (ENA). The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. Results show that ENA can visually distinguish low, middle and high performing students, but not statistically significantly. Thus, our trial provides a tool for the professor that supports understanding of subject learning and formative assessment.