Simulation Learning Research Articles

Simulation Use in Respiratory Therapy Programs in Saudi Arabia: Results of a National Survey.

The use of simulation-based methods for teaching and learning in the education of health professions is increasing, but its prevalence in Saudi Arabia among respiratory therapy programs has yet to be investigated. The purpose of this study is to identify the use of simulation-based learning (SBL) in respiratory therapy programs in Saudi Arabia. A cross-sectional study was performed by sending Google forms survey via Email to directors of respiratory therapy programs in Saudi Arabia (N=16) to evaluate how each one used simulations as an educational tool. The survey was returned with a total response of 12 out of all 16 program that were initially contacted (75% response rate). Among the respondents, approximately 75% of the programs are using SBL, while high-fidelity simulation is used by all programs. The present data show that 67% of the respiratory therapy programs has a space for simulation within the department, while 33% utilizes institutional simulation centers. For short simulation scenarios, debriefing is not conducted in 67% of the programs. There is acceptance by program directors that simulation hours should be counted towards clinical hours. About 67% of respondent programs have mandatory simulation learning activities, and 100% agree that simulations should be used more. However, lack of training and shortage of staff are among barriers to increase the use of SBL. SBL is commonly used and relatively varied among respiratory therapy programs. However, it requires some improvements in several aspects, including the use of debriefing and increasing the number of properly trained staff.

Application of machine learning methods in the analysis of interface bonding strength for overmolded hybrid thermoset‐thermoplastic composites

AbstractThe focus of this study is to apply finite element method (FEM) and machine learning methods to investigate the interfacial bonding strength of continuous fiber reinforced thermoset composite (TSC)‐thermoplastic structures manufactured through co‐curing and overmolding processes, with polyamide 6 (PA 6) as the thermoplastic material. A model for interfacial healing degree in TSC‐PA 6 structures was developed. Then, FEM was employed to study the influence of various injection overmolding process parameters on the interfacial bonding strength of TSC‐PA 6 structures. The results show that there is a strong correlation between the degree of interface healing and the bonding strength. Subsequently, six machine learning methods were employed to correlate interfacial healing degree with diverse injection molding process parameters. Simulation data were utilized for training, calibration, and validation of the six machine learning models. Based on the results of simulation and machine learning predictions, a quantitative analysis of the significance of injection molding process parameters on healing degree was conducted. These parameters are ranked in descending order of importance as follows: insert temperature, melt temperature, and injection rate.Highlights The interfacial healing degree model of PA 6 was established and validated through the integration FEM and experiment. Six machine learning models were built to predict the interfacial healing degree. Grad boosting performed best in predicting the interfacial healing degree. Insert temperature had the greatest impact on the interface healing degree.

Rapid Root Cause Analysis: Improving OBGYN Resident Exposure to Quality Improvement and Patient Safety Curricula.

Introduction Each year, millions of patients in the United States experience harm as a result of the healthcare they receive. One mechanism used by health systems to learn how and why errors occur is root cause analysis (RCA). RCA teams develop action plans to create and implement systemic changes in healthcare delivery in order to prevent future harm. The American Council on Graduate Medical Education (ACGME) recognizes the importance of analyzing adverse events, and it requires that all residents participate in real or simulated patient safety activities, such as RCAs.Often, institutional RCAs necessitate the assimilation of participants on short notice and demand considerable time investment, limiting the feasible participation of graduate medical education (GME) trainees. This presents a gap between ACGME expectations and the reality of resident involvement in patient safety activities. We present the first iteration of a quality improvement project encompassing a three-hour resident physician training course with simulated RCA-experiential learning. The purpose of this project was to produce a condensed, educational RCA experience that adequately trains allGME learners to serve as informed healthcare safety advocates while also satisfying ACGME requirements. Methods The course ("rapid RCA") was conducted during protected weekly academic training. All residents of the San Antonio Uniformed Services Health Education Consortium (SAUSHEC) Obstetrics and Gynecology (OBGYN) residency program who had not previously participated in a real or simulated RCA were required to take the "rapid RCA." Pre- and post-course surveys were completed anonymously to assess baseline knowledge, new knowledge gained from the course, and attitudes toward the course and its importance to resident training. Results Fourteen OBGYN residents attended the "rapid RCA," indicating that 64% (14 out of 22) of the program had no previous experience or opportunity to participate in a real or simulated RCA. Participation in the course demonstrated a significant gain of new knowledge with an increase from 0/14 to 10/14 (71%) residents correctly answering all pre- and post-course questions, respectively (p < 0.001). Additionally, on a Likert scale from 1 to 5, with 5 indicating "expert level," residents indicated they felt more comfortable on patient safety topics after taking the course (mean pre-course score 1.85 to post-course score 3.64,p < 0.001). All participants indicated they would prefer to take the "rapid RCA" as opposed to the only available local alternative option for a simulated RCA, currently offered as a full-day intensive course. Conclusion A meaningful increase in patient safety knowledge and attitudes toward topics covered in an RCA was demonstrated through the implementation of a "rapid RCA" in OBGYN residents. We plan to incorporate this into our annual curriculum to satisfy ACMGE requirements. This format could be adapted for other specialties as applicable.

Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework’s performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework’s 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework’s advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field.

Voices From the Industry: How EdTech Leaders Responded to the COVID-19 Pandemic

Purpose Educational technology (“EdTech”) served a pivotal role in keeping schools functioning during the beginning of the COVID-19 pandemic. Little is known about EdTech leaders’ roles in shaping this response. This study explores EdTech leaders’ perspectives and backgrounds, their response to the pandemic, how they envision their roles as educators, and their perspectives about how technology facilitates educational innovation. Design/Approach/Methods This study uses a qualitative, phenomenological approach to understand how 11 EdTech leaders experienced the pandemic. Participants were recruited for interviews in summer 2021 via purposive sampling to include diverse backgrounds and perspectives. Data were analyzed inductively. Findings The findings show that a four-category typology can be used to describe EdTech leaders’ diverse backgrounds and experiences. Leaders emphasized equity and open collaboration in their pandemic responses, by expanding access to their tools and adapting their products as users’ needs evolved. EdTech leaders anticipate streamlined user experiences, improvements in online learning, and increased adoption of artificial intelligence and simulated learning environments. Originality/Value This study addresses a gap in the research concerning EdTech leaders’ perspectives on their efforts to support educators and their experiences during the pandemic. We hope this study sparks additional research on EdTech leaders’ experiences and roles in education.

Transfer learning for metamaterial design and simulation

Abstract We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under open boundary conditions in electromagnetic metamaterials. Our aim is to assess the efficiency of transfer learning across a range of problem domains that vary in their resemblance to the original base problem for which the ResNet model was initially trained. We use a quasi-analytical discrete dipole approximation (DDA) method to simulate electrically large metasurface arrays to obtain ground truth data for training and testing of our deep neural network. Our approach can save significant time for examining novel metasurface designs by harnessing the power of transfer learning, as it effectively mitigates the pervasive data bottleneck issue commonly encountered in deep learning. We demonstrate that for the best case when the transfer task is sufficiently similar to the target task, a new task can be effectively trained using only a few data points yet still achieve a test mean absolute relative error of 3 % with a pre-trained neural network, realizing data reduction by a factor of 1000.

Effect of structural parameters on compression performance of autoclaved aerated concrete: Simulation and machine learning

Autoclaved aerated concrete (AAC) has broad applications in civil engineering due to its lightweight, thermal and sound insulation. However, the relation between the complex random porous structures and the compression performances of AAC is complex, limiting the optimization and application of AAC. In this study, finite element simulation was implemented and validated based on the experimental results. The simulation results showed that the compressive strength of AAC increased by 31.7, 45.8, and 134% with the porosity decreasing from 70% to 40%, the average pore diameter decreasing from 1.5 to 0.5 mm, and the pore connectivity decreasing from 50% to 0, respectively. Then, based on the numerical dataset, integrated machine learning methods were implemented to rapidly predict the compressive properties of AAC and analyze the main factors affecting the compressive properties. The ML results showed that the CatBoost model had the best predictive performance based on the small dataset of simulation results, with an average relative error of 18% and 7% for compressive strength and modulus of AAC, respectively. The component modulus was the most important feature for predicting the compressive strength and modulus of AAC.

Numerical Simulation and Machine Learning Prediction of the Direct Chill Casting Process of Large-Scale Aluminum Ingots.

The large-scale ingot of the 7xxx-series aluminum alloys fabricated by direct chill (DC) casting often suffers from foundry defects such as cracks and cold shut due to the formidable challenges in the precise controlling of casting parameters. In this manuscript, by using the integrated computational method combining numerical simulations with machine learning, we systematically estimated the evolution of multi-physical fields and grain structures during the solidification processes. The numerical simulation results quantified the influences of key casting parameters including pouring temperature, casting speed, primary cooling intensity, and secondary cooling water flow rate on the shape of the mushy zone, heat transport, residual stress, and grain structure of DC casting ingots. Then, based on the data of numerical simulations, we established a novel model for the relationship between casting parameters and solidification characteristics through machine learning. By comparing it with experimental measurements, the model showed reasonable accuracy in predicting the sump profile, microstructure evolution, and solidification kinetics under the complicated influences of casting parameters. The integrated computational method and predicting model could be used to efficiently and accurately determine the DC casting parameters to decrease the casting defects.

Experiential learning in higher education: Assessing the role of business simulations in shaping student attitudes towards sustainability

Education for sustainable development requires innovative pedagogies that entice students' engagement, ensure effective learning outcomes, and develop students' attitudes towards sustainability. The study aimed to investigate the factors that shape students' learning outcomes of a business simulation that incorporates sustainability-related decisions and to examine the effect of business simulation learning outcomes on their satisfaction, intention to continue using business simulations as a learning tool, and attitudes towards sustainability. A survey was distributed at the end of a business simulation course for undergraduates in a private university in the New Administrative Capital in Cairo, Egypt. A total of 262 students participated in the study. The results supported all research hypotheses except for the relationship between learning outcomes and student satisfaction. The study extends our understanding of the use of experiential learning in shaping student attitudes towards sustainability. The future of implementing more business simulations in curriculums depends on students' willingness to continue using such experiential learning tools in the future along with the power of positive word of mouth associated with their experience. The study offers several important implications for higher education institutions leaders and educators.

Outcomes of the MAMA Training: A Simulation and Experiential Learning Intervention for Labor and Delivery Providers to Improve Respectful Maternity Care for Women Living with HIV in Tanzania.

Respectful maternity care (RMC) for women living with HIV (WLHIV) improves birth outcomes and may influence women's long-term commitment to HIV care. In this study, we evaluated the MAMA training, a team-based simulation training for labor and delivery (L&D) providers to improve RMC and reduce stigma in caring for WLHIV. The study was conducted in six clinical sites in the Kilimanjaro Region of Tanzania. 60 L&D providers participated in the MAMA training, which included a two-and-a-half-day workshop followed by a half-day on-site refresher. We assessed the impact of the MAMA training using a pre-post quasi-experimental design. To assess provider impacts, participants completed assessments at baseline and post-intervention periods, measuring RMC practices, HIV stigma, and self-efficacy to provide care. To evaluate patient impacts, we enrolled birthing women at the study facilities in the pre- (n = 229) and post- (n = 214) intervention periods and assessed self-reported RMC and perceptions of provider HIV stigma. We also collected facility-level data on the proportion of patients who gave birth by cesarean section, disaggregated by HIV status. The intervention had a positive impact on all provider outcomes; providers reported using more RMC practices, lower levels of HIV stigma, and greater self-efficacy to provide care for WLHIV. We did not observe differences in self-reported patient outcomes. In facility-level data, we observed a trend in reduction in cesarean section rates for WLHIV (33.0% vs. 24.1%, p = 0.14). The findings suggest that the MAMA training may improve providers' attitudes and practices in caring for WLHIV giving birth and should be considered for scale-up.

Virtual Reality Simulation in a Health Assessment Laboratory Course: A Mixed-methods Explanatory Study Examining Student Satisfaction and Self-confidence.

The purpose of this mixed-methods study was to examine the relationship between virtual reality simulation (VRS) and student satisfaction and self-confidence in a health assessment laboratory course. Second-year students (n=37) completed a postoperative respiratory distress scenario using Elsevier's Simulation Learning System with Virtual Reality. All participants completed the Satisfaction and Self-Confidence in Learning Scale; a subset participated in 1:1 semistructured interviews. Satisfaction and self-confidence scores were strongly correlated. VRS experiences of fidelity, communication confidence and competence, learning with peers, integrated learning and critical thinking, and a safe space to learn were related to students' satisfaction and self-confidence. VRS experiences are correlated with high student satisfaction and self-confidence.

Twelve tips for orienting preclinical healthcare students to simulation education

Simulation-based education (SBE) is common in healthcare education and is increasingly being incorporated in preclinical curriculum. Preclinical students typically have had little exposure to the clinical setting (i.e. hospital patient rooms, equipment) and often feel uncomfortable when first placed in the simulated clinical environment. Prebriefing, a standard of best practice in simulation, prepares learners for simulation exercises. To successfully integrate SBE in preclinical education, we recommend expanding the prebriefing to include: multiple activities that orient learners to the learning space and the structure of a simulation activity, the goals of simulation as a learning process, faculty modeling of a simulated patient encounter, and expected learner outcomes. This approach increases student familiarity with the simulation learning environment and performance expectations, which can reduce cognitive load and improve learning outcomes. We describe 12 tips for increasing the scope of the prebriefing to promote effective learner participation and development during preclinical SBE.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

A Multiscale Approach for Assessing Shale Oil Availability: Digital Core, Molecular Simulation, and Machine Learning Analysis

Summary This study presents a novel multiscale approach for assessing the accessibility of shale oil in cores by use of focused ion beam-scanning electron microscopy (FIB-SEM) equipment to build digital core, watershed, and maximum ball methods to extract pore size and shape factor. Then, molecular simulation is used to study the availability of shale oil in individual pores with different shapes and radii. Finally, combining the results of the above two scales, machine learning is used to predict shale oil availability across the entire core. On the core scale, the watershed and maximum ball methods are used to extract the core pore network model, and it is found that square pores occupy the highest proportion among the three pore types, and most of the radii are distributed in the range of 2–3 nm. The molecular-scale dynamic simulation results show that the adsorption forms of shale oil are different in different pores, and the adsorption of shale oil in circular pores is less than that in flat pores. The proportion of shale oil adsorption in square pores is the highest, followed by triangular pores, and the proportion of shale oil adsorption in circular pores is the lowest. The random forest machine learning algorithm is used to predict the availability of shale oil with different pore shapes and obtain the shale oil availability ratio of the whole core. The results show that the pores with a more obvious angular structure show a lower shale oil availability ratio. In general, the impact of pore shapes on shale oil availability is not significant, and the difference between different pore shapes is only 10%. The multiscale evaluation method for shale oil availability proposed in this study is helpful to better understand the availability of shale oil in reservoirs and to optimize recovery strategies.

Simulation in midwifery: the role of motivation in designing an undergraduate experience

Simulated learning is gaining momentum in midwifery education. Evidence shows that it offers multiple benefits for students, including improved decision making, empathy and interdisciplinary teamworking. Motivational theory can be used to design implementation of simulation at a curriculum level, which is an ongoing process of identifying and implementing appropriate design actions. Using motivational theory as a guide enables relevant actions to be identified for the implementation of simulated learning in midwifery, while also providing a theoretical basis for evaluating the impact of simulated learning in a midwifery curriculum. When implementing simulation, it is important to establish an expert working group, responsible for discussing the appropriate application of motivational learning theories when developing the key design actions. The aim of this article is to share how three main design actions were developed by a simulation expert working group, drawing on key motivational learning theories.

Disaster risk reduction efforts through education in Indonesia: A literature review

Indonesia is a country that is prone to natural disasters, such as earthquakes, floods, volcanic eruptions and tsunamis. To reduce the risk of disasters, efforts through education are important. This article aims to conduct a literature review regarding disaster risk reduction through education in Indonesia. Education has an important role in increasing public awareness, increasing knowledge, and developing the skills needed to reduce the impact of disasters and increase responsiveness in dealing with them. This research wants to find out other forms of approaches that have been implemented to reduce disaster risk in education in Indonesia and find out what the results are. A review of existing literature shows that there are 3 efforts to reduce disaster risk through education, namely formal education, informal education and non-formal education. The research results show that the three educational pathways are implemented quite well in Indonesia. Forms of disaster risk reduction through education include integration of learning in schools, local wisdom, training, outreach and disaster mitigation simulations from disaster awareness activist groups and from certain institutions/institutions to the community, as well as disaster mitigation programs from community groups themselves. To reduce disaster risk in Indonesia, sustainable and coordinated efforts are needed between the government, educational institutions and the community. Increasing the role of education in protecting and preparing people for disasters is important to minimize the negative impacts of disasters in the future.

Copula‐based Bayesian reliability assessment for series systems

AbstractBayesian reliability assessment plays an integral role in determining the success of product development and deployment into the market, especially when limited information is available to estimate the reliability of products. Nevertheless, the Bayesian reliability approach has untapped research potential when it is used to evaluate systems consisting of dependent components, that is, a situation that reflects real‐world conditions. This study addresses the research gap by applying a copula to encode the dependency structure between the failure rates of the components, particularly for the simple series systems. Unfortunately, this study found that Bayesian aggregation error still occurs when information is used at different levels. By employing Monte Carlo simulation and classification tree learning, this study also investigates the key factors affecting such error over a wide range of possible scenarios and derives useful guidelines for reliability practitioners so that they can choose a more suitable analysis under certain circumstances.

A Viewpoint on Simulation Debriefing to Optimize Student Learning

Purpose: The purpose of this article is to promote the viewpoint that simulation debriefing is a critical element of the simulation experience, which serves to facilitate students' ability to synthesize information and construct new mental models as they prepare for their future work as speech-language pathologists. The use of simulations in communication sciences and disorders (CSD) has grown significantly over the last decade. This article reviews key concepts in clinical simulation pedagogy and highlights the importance of debriefing as part of the simulation learning experience. Current guidelines and standards for simulations and debriefing in CSD are discussed, and debriefing models commonly used in nursing and medicine are described. Suggestions are provided to enhance current debriefing practices and guide future research in CSD. Conclusions: Clinical simulation, particularly the debriefing component, has the power to close the gaps in students' clinical knowledge and skills and to prepare students for robust and diverse careers. In a review of debriefing models originating from nursing and medicine, several crucial debriefing components are noted. Students need time to (a) react to and defuse emotions related to the simulation experience, (b) gather and describe key simulation details, (c) analyze simulation outcomes, and (d) summarize new knowledge and consider how it applies to future contexts. Additional research is needed to continue developing and refining simulation and debriefing best practices in CSD. Debriefing models from nursing and medicine ought to be explored and replicated in future CSD research.

The influence of local culture-based simulation learning methods on the interest and learning outcomes of social studies

&#x0D; The results of this research show the influence of the interests and learning outcomes of students who were given treatment in the form of the Simulation Based on local culture method compared to students who were not given treatment. Based on the results of the independent sample t-test on hypothesis I, a sig value was obtained. equal to 0.000 &lt; 0.05 then HO is rejected and H1 is accepted, in hypothesis II the sig value is obtained. equal to 0.000 &lt; 0.05, then HO is rejected and H1 is accepted, and the results of the Monova test, obtain a Sig value. 0.000 &lt; 0.05, then HO is rejected and H1 is accepted, meaning that there is an influence of local culture-based simulation learning methods on the interest and learning outcomes of social studies for fifth grade students at SD Negeri Kaluku Bodoa, Tallo District, Makassar City.&#x0D; &#x0D;

Evaluation of The Usability of Virtual Reality as An Educational Tool for Nursing Student Simulators

Technology-based simulation learning methods have great potential to support education for students in the health sector. One of the innovations that can be done is virtual reality. In using virtual reality, paying attention to the quality of virtual reality services to provide learning satisfaction and motivate students to be active in the learning process is essential. VNursLab designed VR as an additional learning media for Padjadjaran University nursing students. Therefore, this study aims to identify nursing students' satisfaction with using VR as a learning media simulator.The method used is pre-experimental quantitative research and a one-shot case study approach. The sampling used in the study was purposive. Eighty-six respondents completed the research by following the VR simulation for 20 minutes and continued filling out the PSSUQ (Post-Study System Usability Questionnaire) questionnaire. The data were analyzed using descriptive analytics by finding the mean value of each aspect of the assessment obtained from the mean of each element of the respondent. The results show that the overall quality of VR VNursLab is good (2.2), with a mean value of system quality (2.3), mean InfoQual (2.21), and IntQual (2.28). Based on these results, VR VNursLab as a learning media with virtual simulation methods has a good quality of use assessment and provides satisfaction with nursing students' use.