Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.09.26.509545

Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis

Jacobs, C.·Foote, G.·Williams, M.
View original source ↗Export metadata ↓

Abstract

BackgroundImmersive technology is becoming more widespread in simulation-based medical education with applications that both supplement and replace traditional teaching methods. There is a lack of validated measures that capture user experience to inform of the technology utility. We aimed to establish a consensus of items and domains that different simulation experts would include in a measure for immersive technology use. MethodsA 3-stage modified Delphi using online software was conducted to support the conceptual framework for the proposed measure. The first round was informed by prior work on immersive technology in simulation. In the first round, participants were asked to describe what we could measure in simulation-based education and technology. Thematic analysis generated key themes that were presented to the participants in the second round. Ranking of importance in round 2 was determined by mean rank scores. The final round was an online meeting for final consensus discussion and most important domains by experts were considered. ResultsA total of 16 simulation experts participated in the study. A consensus was reached on the ideal measure in immersive technology simulation that would be a user questionnaire and domains of interest would be: what was learnt, the degree of immersion experienced, fidelity provided, debrief, psychological safety and patient safety. No consensus was reached with the barriers that this technology introduces in education. ConclusionsThere is varied opinion on what we should prioritise in measuring the experience in simulation practice. Importantly, this study identified key areas that aids our understanding on how we can measure new technology in educational settings. Synthesising these results in to a multidomain instrument requires a systematic approach to testing in future research

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jacobs, C., Foote, G., Williams, M.. 2022-09-27. Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis. https://doi.org/10.1101/2022.09.26.509545

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗
bioRxiv2024

Retrospective Analysis of the Effects of BWF Interdisciplinary Postdoctoral to Faculty Transition Awards on Future Funding Success

Established by the Burroughs Wellcome Fund (BWF) in 2001, the Career Award at the Scientific Interface (CASI) is a career development award for scientists with doctoral training in the physical/mathematical/computational sciences or engineering conducting postdoctoral research in the biological sciences. The goal of the program is to support early career scientists interested in pursuing an independent research career with an interdisciplinary focus. In order to assess the benefit of the CASI award on recipients, the authors undertook a retrospective analysis of the funding data for CASI recipients to evaluate success against matching cohorts. These cohorts included applicants who succeeded to the final interview stage but were ultimately unsuccessful (interviewed), applicants who submitted proposals but did not make it to the final interview stage (proposal declined), and a randomly selected dataset of researchers from a comparable program, the highly competitive Pathway to Independence Award (K99/R00) from the National Institutes of Health (NIH). The results indicate that CASI recipients outperformed unsuccessful applicants and their K99/R00 counterparts in federal grant rates and overall grant dollars. The authors conclusion affirms that the CASI mechanism and BWF support successfully achieve the objective of invigorating the careers of young investigators, resulting in tangible downstream long-term effects.

scientific communication and education↗
bioRxiv2024

Mapping the Learning Curves of Deep Learning Networks

There is an important challenge in systematically interpreting the internal representations of deep neural networks. This study introduces a multi-dimensional quantification and visualization approach which can capture two temporal dimensions of a model learning experience: the "information processing trajectory" and the "developmental trajectory." The former represents the influence of incoming signals on an agents decision-making, while the latter conceptualizes the gradual improvement in an agents performance throughout its lifespan. Tracking the learning curves of a DNN enables researchers to explicitly identify the model appropriateness of a given task, examine the properties of the underlying input signals, and assess the models alignment (or lack thereof) with human learning experiences. To illustrate the method, we conducted 750 runs of simulations on two temporal tasks: gesture detection and natural language processing (NLP) classification, showcasing its applicability across a spectrum of deep learning tasks. Based on the quantitative analysis of the learning curves across two distinct datasets, we have identified three insights gained from mapping these curves: nonlinearity, pairwise comparisons, and domain distinctions. We reflect on the theoretical implications of this method for cognitive processing, language models and multimodal representation. Author summaryDeep learning networks, specifically recurrent neural networks (RNNs), are designed for processing incoming signals sequentially, making them intuitive computational systems for studying cognitive processing that involves dynamic contexts. There has been a tradition in the fields of machine learning and neuro-cognitive science to examine how a system (either humans or models) represents information through various computational and statistical techniques. Our study takes this one step further by devising a technique for examining the "learning curves" of deep learning networks utilizing the sequential representations as part of RNNs architectures. Just as humans develop learning curves when solving problems, the introduced method captures both how incoming signals help improve decision-making and how a systems problem-solving abilities enhance when encountering the same situation multiple times throughout its lifespan. Our study selected two distinct tasks: gesture detection and emotion tweet classification, to illustrate the insights researchers can draw from mapping models learning curves. The proposed method hinted that gesture learning experiences are smoother, while language learning relies on sudden knowledge gains during processing, corroborating the findings from previous literature.

scientific communication and education↗