Competency-Based Education in RN to BSN Programs.

BACKGROUND: The call for nursing education reform has never been louder. National organizations recognize the urgent need to prepare nursing students to practice competently, moving away from traditional teaching and curricula and to competency-based education (CBE) strategies to prepare future nurses for independent clinical practice. PROBLEM: The 2021 American Association of Colleges of Nursing Essentials do not account for the competencies achieved in registered nurse (RN) associate degree and diploma programs. This presents a challenge for RN to bachelor of science in nursing (BSN) program administrators and faculty when designing curricula to meet the new Essentials. APPROACH: The Essentials crosswalk created by the National RN-Baccalaureate Faculty Forum serves as a foundational guide for the development of the template models discussed in this article. CONCLUSION: This article provides a template of instructional models for implementing CBE in RN to BSN programs.

Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments.

Interactive visualization can support fluid exploration but is often limited to predetermined tasks. Scripting can support a vast range of queries but may be more cumbersome for free-form exploration. Embedding interactive visualization in scripting environments, such as computational notebooks, provides an opportunity to leverage the strengths of both direct manipulation and scripting. We investigate interactive visualization design methodology, choices, and strategies under this paradigm through a design study of calling context trees used in performance analysis, a field which exemplifies typical exploratory data analysis workflows with Big Data and hard to define problems. We first produce a formal task analysis assigning tasks to graphical or scripting contexts based on their specificity, frequency, and suitability. We then design a notebook-embedded interactive visualization and validate it with intended users. In a follow-up study, we present participants with multiple graphical and scripting interaction modes to elicit feedback about notebook-embedded visualization design, finding consensus in support of the interaction model. We report and reflect on observations regarding the process and design implications for combining visualization and scripting in notebooks.

Scalable Comparative Visualization of Ensembles of Call Graphs.

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey, a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. We demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.