David Williamson Shaffer’s research while affiliated with University of Wisconsin–Madison and other places

Background: Few studies have connected nursing students' performance in simulations to clinical compe-tencies exemplified in The Essentials and the NCJMM. This retrospective study examined the effectiveness of screen-based virtual patient simulations (VPS) in facilitating ten undergraduate prelicensure students' practice of clinical judgment, person-centered care, and professionalism. Methods: Multimodal transcripts capturing patient interactions, exam actions, and documentation in a gerontology and mental health scenario were analyzed using Transmodal Ordered Network Analysis (T/ONA). We examined the strength and order of connections students made to recognizing cues, analyzing cues, diagnosing and prioritizing hypotheses, generating solutions, evaluating outcomes, subjective assessment, therapeutic communication, and care-management and coordination. Findings: Both scenarios afforded opportunities for students to gather comprehensive data and make evidence-based and patient-centered care decisions. However, students' patterns of connection making were significantly different for virtual patients who presented palliative care versus mental health care needs. Conclusion: The assurance for nursing students' practice readiness can be realized through careful implementation of diverse assignment types and scenarios in VPS. T/ONA provides a novel and systematic approach for studying student behaviors in simulations.

In highly immersive, team-based healthcare simulations, students must collaborate effectively to complete open-ended learning tasks. These often require students to move among multiple locations within the physical learning space, 1 requiring the development of effective communication tactics to manage complex team dynamics and multiple concurrent dialogue segments. Analysing such distributed team dialogue using only audio data poses significant challenges as it is hard to distinguish how teams temporarily break into smaller groups. This study investigates the integration of audio and spatial data to model embodied team communication tactics in highly-dynamic healthcare simulation scenarios. We employ ordered network analysis (ONA) to examine the order of communication behaviours across coded co-located dialogue segments of teams of nursing students. We also explore the potential of ONA to enhance teaching through a qualitative assessment of its usability with teachers. Results reveal that high-performing teams employ effective team tactics by prioritising primary tasks (i.e., deteriorating patients) and performing critical tasks timely and correctly, while low-performing teams struggle to develop prioritisation tactics (i.e., focus-ing on stable patients) and with team coordination. Interviews with teachers found high-level information in ONA networks interpretable but faced challenges with sequential details and contextualisation. This research offers insights into improving team-based learning and assessment in healthcare education.

A highly authentic learning setting is likely to trigger positive motivational and emotional reactions due to its emphasis on promoting the acquisition of knowledge that is connected and transferable to real-world phenomena outside the learning environment. However, a high level of authenticity is usually accompanied by a high level of complexity due to the complexity inherent in the real world. This complexity can be overwhelming for learners and can hamper or even prevent cognitive learning outcomes. Consequently, to help learners cope with this complexity, they need some kind of instructional support. By building a high level of support into the learning setting in order to promote cognitive learning outcomes, the level of authenticity and thereby the effects of authenticity on motivational outcomes may, however, in turn be reduced. In the present conceptual paper, we refer to this tension between authenticity and complexity, on the one hand, and instructional support, on the other hand, as the “authenticity dilemma”. Based on existing empirical evidence from previous studies, we (1) outline this dilemma, (2) discuss ways to reconceptualize it, and (3) derive implications regarding the conditions and effects of authentic learning. Finally, we discuss the findings of the studies included in the special issue “Perspectives on Authentic Learning” through the lens of the authenticity dilemma.

This chapter provides a tutorial on conducting epistemic network analysis (ENA) and ordered network analysis (ONA) using R. We introduce these two techniques together because they share similar theoretical foundations, but each addresses a different challenge for analyzing large-scale qualitative data on learning processes. ENA and ONA are methods for quantifying, visualizing, and interpreting network data. Taking coded data as input, ENA and ONA represent associations between codes in undirected or directed weighted network models, respectively. Both techniques measure the strength of association among codes and illustrate the structure of connections in network graphs, and they quantify changes in the composition and strength of those connections over time. Importantly, ENA and ONA enable comparison of networks both visually and via summary statistics, so they can be used to explore a wide range of research questions in contexts where patterns of association in coded data are hypothesized to be meaningful and where comparing those patterns across individuals or groups is important.

To help young people understand socio-environmental systems and develop the confidence that meaningful action can be taken to address socio-environmental problems, young people need interactive simulations that enable them to take consequential actions in a familiar context and see the results. This can be achieved through reduced-form models with appropriate user interfaces, but it is a significant challenge to construct a system capable of producing educational models of socio-environmental systems that are localizable and customizable but accessible to educators and learners. In this paper, we present iPlan, a free, online educational software application designed to enable educators and middle- and high-school-aged learners to create custom, localized land-use simulations that can be used to frame, explore, and address complex land-use problems. We describe in detail the software application and its underlying computational models, and we present robust evidence that the accuracy of iPlan simulations is appropriate for educational contexts and preliminary evidence that educators are able to produce simulations suitable for their pedagogical goals and learner populations.

Background Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking. Methods In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples. Results Data were visualized with Epistemic Network Analysis to analyze code co-occurrences in the three aligned data streams, and with Qualitative/Unified Exploration of State Transitions to examine the order in which participants in our two subsamples encountered online content. Conclusions This paper describes our methods and planned analyses elaborated with mock figures. Quantifying qualitative data, aligning data streams, and representing all information in a tabularized dataset allows us to group data according to various participant attributes and employ data visualization techniques to pinpoint patterns therein.