Training Design Research Articles

Small Scale, High Precision: Robotic Surgery in Neonatal and Pediatric Patients-A Narrative Review.

This narrative review explores the evolution and implications of robotic-assisted surgery in pediatric and neonatal cases, focusing on its advantages, drawbacks, and the specific diseases amenable to this innovative technology. Following PRISMA guidelines, 56 relevant articles from the past five years were selected, emphasizing advancements in precision, reduced trauma, and expedited recovery times for pediatric patients. Despite challenges like cost and training, ongoing research shapes pediatric robotic-assisted surgery, promising improved outcomes. The technology's benefits include enhanced precision, minimized scarring, and faster recovery, addressing the challenges in delicate pediatric procedures. Challenges encompass cost, training, and instrument design, but ongoing refinements aim to overcome these. This review underscores psychological and musculoskeletal considerations for patients and surgeons. While acknowledging limitations and preferred pathologies, this review outlines the transformative potential of robotic-assisted surgery in reshaping pediatric surgical care. This comprehensive assessment concludes that, despite challenges, ongoing advancements promise a future of enhanced precision and tailored care in pediatric surgery.

Sensory manipulation as a countermeasure to robot teleoperation delays: system and evidence

In the realm of robotics and automation, robot teleoperation, which facilitates human–machine interaction in distant or hazardous settings, has surged in significance. A persistent issue in this domain is the delays between command issuance and action execution, causing negative repercussions on operator situational awareness, performance, and cognitive load. These delays, particularly in long-distance operations, are difficult to mitigate even with the most advanced computing advancements. Current solutions mainly revolve around machine-based adjustments to combat these delays. However, a notable lacuna remains in harnessing human perceptions for an enhanced subjective teleoperation experience. This paper introduces a novel approach of sensory manipulation for induced human adaptation in delayed teleoperation. Drawing from motor learning and rehabilitation principles, it is posited that strategic sensory manipulation, via altered sensory stimuli, can mitigate the subjective feeling of these delays. The focus is not on introducing new skills or adapting to novel conditions; rather, it leverages prior motor coordination experience in the context of delays. The objective is to reduce the need for extensive training or sophisticated automation designs. A human-centered experiment involving 41 participants was conducted to examine the effects of modified haptic cues in teleoperations with delays. These cues were generated from high-fidelity physics engines using parameters from robot-end sensors or physics engine simulations. The results underscored several benefits, notably the considerable reduction in task time and enhanced user perceptions about visual delays. Real-time haptic feedback, or the anchoring method, emerged as a significant contributor to these benefits, showcasing reduced cognitive load, bolstered self-confidence, and minimized frustration. Beyond the prevalent methods of automation design and training, this research underscores induced human adaptation as a pivotal avenue in robot teleoperation. It seeks to enhance teleoperation efficacy through rapid human adaptation, offering insights beyond just optimizing robotic systems for delay compensations.

Best practices for implementing a technology-based intervention protocol: Participant and researcher considerations

Technology-based interventions present a promising approach to support health and wellness for older adults with a range of cognitive abilities. Technology can enhance access to interventions and support scaling of programs to reach more people. However, the use of technology for intervention delivery requires particular attention to users’ needs and preferences and ensuring the materials are adaptable and supportive of a diverse range of technology proficiency levels. We share best practices based on lessons learned from the deployment of a randomized controlled trial (RCT) wherein we delivered an 8-week social engagement intervention through a video technology platform called OneClick for older adults with varying cognitive abilities. We developed a set of best practices and guidelines informed by the lessons learned through this RCT implementation. Technology-based interventions require attention to the application (e.g., video calls), system requirements (e.g., system memory, broadband internet), training (e.g., adaptability based on user competency), and support (e.g., handouts, live contact). These best practices relate to user needs; training design; personnel responsibility; structuring delivery and content; and evaluating success. These research-based best practices can guide the design, development, and implementation of technology-based interventions to support older adults with varying cognitive abilities.

A preliminary design of a hybrid train’s on-board batteries for a 25 kV-50 Hz high speed railway line

The paper reports a technical-economic comparison for a Turkey high-speed railway line, between 25 kV AC electrification and the use of hybrid trains with on-board storage systems. On-board battery storage systems have been designed to operating in specific parts of the line. Different types of technologies have been taken into account, evaluating the use of high specific power/energy lithium-ion electrochemical cells. The models of the battery pack have been designed in the MATLAB/Simulink environment, in order to carry out the complete operation check, also evaluating the time and power required for recharging. An economic evaluation and comparison have been carried out in order to evaluate the convenience of using a hybrid train on specific part of a real line in order to not totally electrified the line.

Using data clustering to reveal trainees’ behavior in cybersecurity education

AbstractIn cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students’ cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the designers in performing such analytic and evaluation tasks is ad-hoc and insufficient. With unsupervised machine learning methods, we designed a tool for clustering the trainee actions that can exhibit their strategies or help pinpoint flaws in the training design. By using a k-means++ algorithm, we explore clusters of trainees that unveil their specific behavior within the training sessions. The final visualization tool consists of views with scatter plots and radar charts. The former provides a two-dimensional correlation of selected trainee actions and displays their clusters. In contrast, the radar chart displays distinct clusters of trainees based on their more specific strategies or approaches when solving tasks. Through iterative training redesign, the tool can help designers identify improper training parameters and improve the quality of the courses accordingly. To evaluate the tool, we performed a qualitative evaluation of its outcomes with cybersecurity experts. The results confirm the usability of the selected methods in discovering significant trainee behavior. Our insights and recommendations can be beneficial for the design of tools for educators, even beyond cyber security.

Defining the Educational Needs for a Community-Based Hematology/Oncology Career: A National Needs Assessment.

Little is known about the specific needs during training for hematology/oncology providers practicing in community-based settings. We conducted a national survey of hematologists/oncologists employed in community or academic-community hybrid settings to delineate their educational needs. An electronic questionnaire was developed and distributed nationally through professional organizations. We primarily assessed whether survey participants received any specific training during fellowship for community-based practice. Participants were also surveyed regarding training experiences that might have affected their preparation. Relative risk (RR) and 95% CI were calculated using modified Poisson regression to identify factors associated with receiving training specifically for community-based settings. Of 125 participants from across 25 states, 63% were male and 58% identified as White. Less than half (41.6%, binomial 95% CI, 32.8 to 50.7) received any training in a community-based setting. Participants identified rotations in community settings (47%), direct mentorship from community-based physicians (40%), and longitudinal clinic in a community setting (36%) as experiences that would have been valuable. Specific curricula of interest included medical operations and administration (63%), health policy (35%), and quality improvement (27%). Respondents in clinical practice for <10 years were more likely to have received any training specifically for a community-based career (RR, 2.13 [95% CI, 1.18 to 3.86]). Our study demonstrates substantial unmet needs as they relate to deliberately training fellows destined for community-based careers. Prospective design of clinical training and curricula emphasizing longitudinal exposures to and key aspects of health care delivery in the community setting are paramount to achieving optimal goal-concordant hematology/oncology training during fellowship.

Research on the use of interactive technologies in the process of teaching design and their role in the formation of aesthetic consciousness of future designers in the process of professional training

The purpose of the study is to analyze the possibilities of using interactive technologies in the process of teaching students majoring in design and gaining the necessary level of aesthetic consciousness. To realize the purpose of the article, the methods of analysis with the use of statistical software, content analysis, survey, triangulation method were used. The results are based on the empirical data of the survey. The survey involved 250 people and was conducted in accordance with the principles of safety, anonymity, gender equality, and objectivity. The most popular integrative technologies used to train design professionals are project, game, information and communication, and case technologies. They contribute to the development of creative skills and creative thinking among students. However, given the growing importance of digital competence, it is also necessary to use information and communication technologies. The professional competence of future designers should include digital literacy and information competence, which will contribute to the development of their professional skills and aesthetic ideas. It is indicated that the most important aspect of the development of professional competence is the formation of their aesthetic culture, which is a key element of creative activity. The scientific novelty of the study is to analyze the factor of psychological consciousness through the prism of acquiring the necessary knowledge and skills in the field of design through the use of information and communication technologies. The conclusions show that interactivity also contributes to the development of communication skills and the formation of a collective aesthetic consciousness.

CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus’s diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.

Pharmacy residency training program increases residents' confidence in inpatient medical emergency response.

The benefits of pharmacists' involvement in medical emergencies are well established, but optimal methods of training pharmacists for emergency response are unknown. The primary objective of this report is to describe the design and evaluation of a pharmacy resident medical emergency response training (PR-MERT) program for preparing trainees to respond to hospital medical emergencies, including cardiac arrest and rapid sequence intubation (RSI). The PR-MERT program was a year-long longitudinal experience designed to prepare postgraduate year 1 pharmacy residents for medical emergency response. During the first month, the residents completed an orientation session that encompassed several lectures, certification by the American Heart Association in basic life support and advanced cardiovascular life support, standardized simulation scenarios, and mock medical emergencies. The trainees continued to utilize these skills and clinical knowledge through a longitudinal didactic lecture series, resident case conferences, and practice-based application by responding to real-life medical emergencies. Residents were assessed and coached throughout the program by clinical pharmacy preceptors and a "code coach" with extensive medical emergency response experience. After the year-long training, residents completed an anonymous survey assessing self-confidence and the structure of the program. The results showed improved confidence in medication selection and dosing, as well as anticipating the needs of the team and speaking up in cardiac arrest and RSI situations. Residents were satisfied with the training offered and structure of the program. The development of a PR-MERT program at an academic medical center was successful in achieving longitudinal learning objectives and improving residents' confidence in responding to medical emergencies. The implementation of a similar medical emergency training curriculum in inpatient pharmacy residency programs may be beneficial.

How virtual reality, augmented reality and mixed reality facilitate teacher education: A systematic review

AbstractBackgroundVirtual reality (VR), augmented reality (AR) and mixed reality (MR) have sparked recently in improving the effectiveness of teacher education. However, there is a lack of review regarding the utilisation of these technologies in this field. These three technologies, namely VR, AR and MR, can be collectively referred to as extended reality (XR) (as mentioned in reference Tang et al., 2022).ObjectivesRemarkably, the utilisation of XR‐based technologies in teacher education needs to be explored. Moreover, research questions related to the training objectives, methodological features, and the effects of XR‐based teacher education remain unanswered.MethodsTo this end, the present study conducted a systematic review to analyse 52 articles from six databases (including Web of Science, Scopus, IEEE Xplore, ERIC, ScienceDirect, and ACM Digital Library).ResultsThe results indicate that XR technologies have been primarily used to train teachers' procedural knowledge, for instance, classroom management. Furthermore, most studies have primarily focused on pre‐service teachers (PSTs) rather than in‐service teachers and utilised small sample sizes, with VR emerging as the most frequently employed tool. Finally, the majority of the studies reported that XR‐based training affected teachers positively.ConclusionsIt urges researchers and developers to consider theory‐driven training design, which increases the potential to better understand what features of XR promote in‐service teachers' and PSTs' learning and how they do so. This article additionally conducts a SWOT (strengths, weaknesses, opportunities, and threats) analysis of XR‐based teacher education to offer more insightful recommendations and foster further discussion.

Inverse design of reflective metasurface antennas using deep learning from small‐scale statistically random pico‐cells

AbstractA small‐scale data‐selection method is proposed for deep‐learning‐based inverse design of reflective metasurface antennas (MAs). The overall design process involves discretization, coding, data selection, simulation, feature representation, training, prediction, and metasurface design. Unlike conventional method that selects the training data empirically after simulation from a large quantity of unit cells, the proposed statistically random data filtering (SRDF) method selects the training data set randomly before simulation by leveraging the geometric‐coding statistics of the unit cells. The advantages of the proposed data‐selection method include lower simulation cost (due to fewer simulations needed), improved generality, and higher design accuracy simultaneously. The design process starts from the discretization of a unit cell into a grid array of binaurally‐coded pico‐cells. Afterwards, the proposed SRDF method selects 0.01% of all the possible unit cells for simulation. With the simulated results, a deep‐convolutional‐variational‐autoencoder network is trained, leading to an accurate and bidirectional prediction of both the geometry and reflection phase of a unit cell in seconds. The proposed data‐selection method is validated by designing two reflective MAs with the predicted unit cells, each pointing a pencil beam off the boresight by 30° and 60°, respectively. Experiments are in line with full‐wave simulations.

KPI SYSTEM DEVELOPMENT TO IMPROVE PERSONNEL TRAINING SYSTEM AT A MACHINE-BUILDING ENTERPRISE

In modern conditions, there is an extremely rapid obsolescence of professional knowledge, so the issues of improving the skills of employees, mastering new competencies is one of the key factors for the success of the enterprise. In other words, the personnel training system has a direct impact on the achievement of the strategic goals of the organization, therefore, it is necessary to manage this system accordingly. In this article, using the example of a machine-building enterprise, the personnel training process underlying the improvement of the entire personnel training system is considered. The article suggests a method for evaluating the effectiveness of personnel training based on KPIs using the example of training design bureau employees. The authors have identified indicators of the effectiveness of the learning process that affect the quality of products, developed a sequence of building stages of the KPI system, and also established its relationship with the strategic management system of the organization.

Development of claroline based training design for elementary school teachers in composing learning media

Development of claroline based training design for elementary school teachers in composing learning media

An ecological approach for skill development and performance in soccer goalkeeper training: Empirical evidence and coaching applications

ABSTRACT Ecological approaches in sport consider that athletes adapt to properties of the task and the surrounding environment. Thus, task and environment are key constraints of performance. Yet, the influence of task and environmental constraints on athletes’ performance needs empirical examination, especially in sport-specific contexts such as soccer goalkeeping. This study aimed to examine if and how task and environmental constraints influenced goalkeepers (GKs’) performances. We monitored performance coefficients of two professional female GKs across 13 training tasks that varied based on 9 constraints, referring to both interactions among athletes and properties of the surrounding landscape. Results showed that constraints explain ~ 47% of the observed variability in GKs’ performances. Numerical complexity (i.e., the potential interactions between athletes) showed a major influence on performance, which indicates that number of interactions among athletes may constrain GKs’ perceived opportunities for action. Field dimensions and landscape representativity (including elements such as penalty area(s), target goal(s) and constraints for shooting) showed positive relationships with performance, supporting that training designs retaining closer proximity to the game may benefit GKs’ performances. Overall, results supported that athlete-environment couplings could be understood as a multifactorial model and hence, a combination of task constraints are necessary for designing effective learning environments.

Comparative reliability analysis of electric aircraft versions for NASA’s X-57 based on Lz-transform method

As the goals of air transport shift towards more-electric or all-electric airplanes, different drive train configurations have been explored recently. A major goal on the way towards the inclusion in commercial air traffic is high reliability. One of the experimental electric airplanes is NASA’s X-57 “Maxwell”, which consists of fourteen electric motors powered from a battery pack. The aim of this paper is to assess the reliability of the proposed design of the X-57 by using the Lz-transform approach, as well as to propose several alternative designs to its electric drive train, in order to use less vehicle mass on the motors and more on the battery pack, without sacrificing the original availability and expected performance, with a final goal to increase the flight range. The reliability analyses show that the replacement of X-57’s three-phase motors with six-phase ones greatly improves availability of the electric drive train due to the use of fault-tolerant electric machines. Additionally, all of the further proposed alternative designs have higher availability than the X-57. The alternatives with CHB inverter topologies generally achieved higher availability values and higher expected performance than the B6 variants. Finally, the use of a distributed propulsion system with smaller take-off motors leads to a motor-mass advantage compared to more conventional drive train designs.

Optimalisasi Tata Kelola dan Peningkatan Komitmen Organisasi: Studi Pada Pelatihan Kepemimpinan di PCIM Malaysia

Muhammadiyah, as an Islamic movement with a moderate and cosmopolitan vision, faces unique challenges in managing ethnic, cultural and religious diversity in various countries. The leadership of the Special Branch of Muhammadiyah (PCIM) in Malaysia, which oversees 10 Special Branch Leaders of Muhammadiyah (PRIM), faces obstacles in organizational commitment and governance. To overcome this, PKM-KI UMS proposed a solution through three training programs: Achievement Motivation Training, PDCA Training, and Islamic Leadership Training, with a focus on developing leadership modules. This program aims to increase organizational commitment and governance in PCIM and PRIM Malaysia, as well as strengthen the Muhammadiyah community in Malaysia in facing organizational governance challenges and promote Muhammadiyah's moderate and cosmopolitan vision amidst socio-cultural dynamics across nations and countries. Program methods include initial surveys, needs analysis, training design, evaluation, and program sustainability. The program results show that leadership training can significantly increase commitment and effectiveness of organizational governance. This program provides new insights into the importance of leadership training that focuses on aspects of Islamic motivation, management and leadership in the context of cross-cultural organizations.

Centering the Learner Within Instructional Design: The Evolution of Learning Design and the Emergence of Learning Experience Design (LXD) in Workforce Training and Development

Evolving from its instructional design origins and directly related to contemporary learning experience design (LXD), learning design has emerged as a movement within the field of workplace learning and development that seeks to shift the focus of training and education from the content to the learner. The origins of this learner focus can be traced to the development of instructional design theories and the emergence of learning sciences in the late 20th century, along with the expansion of competencies required of the modern instructional designer. Advances in our understanding of how people learn and changes in sociocultural aspects related to learning have led designers to structure experiences for learners that increase their autonomy and seek to meet each learner's unique needs.

Supporting fluid teams: a research agenda.

Fluid teams are teams that are rapidly assembled from across disciplines or areas of expertise to address a near-term problem. They are typically composed of individuals who have no prior familiarity with one another, who as a team must begin work immediately, and who disband at the completion of the task. Prior research has noted the challenges posed by this unique type of team context. To date, fluid teams have been understudied, yet their relevance and application in the modern workplace is expanding. This Perspective article presents a concise overview of critical research gaps and opportunities to support selection, training, and workplace design for fluid teams.

Wearable Stretch Sensors for Ergonomics-Related Human Movement Monitoring

Physical ergonomics has shown to be an effective method of keeping an eye on any illnesses associated with, like, job-related tasks. Wearable sensors and artificial intelligence have been used in conjunction to enhance experimental ergonomic research techniques in the field of physical ergonomics, according to a number of recent studies. This study proposes the Smart Work wear System, a module-based ambulatory system that addresses work-related physical and psychosocial stresses that might impact health and performance. It supports risk assessment, technique training, and workplace design while reducing the requirement for expert trainers and ergonomists. Substituting measurements for observations improves measurement accuracy and repeatability. A modular platform allows for the connection of a variety of sensor types based on the individual requirements of each case.

An adaptive sampling algorithm for reduced‐order models using Isomap

AbstractThe use of reduced‐order models (ROMs) in physics‐based modeling and simulation is a popular tool for drastically lowering the computational cost associated with high‐fidelity simulations. ROMs use training data from a set of computed high‐fidelity simulations with different design parameters that control physical and geometric properties of the full‐order model. The quality of the training data dictates the performance of the ROM, making the choice of training design parameters important. A widely used method for generating training parameters for ROMs is Latin hypercube sampling (LHS), a statistical method that aims to maximize the distance and minimize the correlation amongst produced samples. However, LHS fails to account for the physics of the full‐order model and can lead to an over‐representation of some physical regimes in the training data while neglecting others. In this work, we present a computationally efficient adaptive sampling method for ROMs using Isomap, a versatile algorithm for nonlinear dimensionality reduction. Using an initial number of samples, the adaptive sampling algorithm iteratively generates samples based on a low‐dimensional manifold of the training data. When applied to two external aerodynamics problems, the proposed adaptive sampling algorithm offers significantly improved performance in predicting physical fields and coefficients of lift and drag for a given computational budget for both non‐intrusive and projection‐based ROMs.