Enhance Training Effectiveness Research Articles

Training transfer in MSMEs of India: examining the role of performance coaching, HRD climate and employee agility

Purpose The relatively low emphasis on training is often attributed to concerns about the uncertain return on investment in training initiatives. However, there has been limited research conducted to understand the critical success factors for training effectiveness in these enterprises. The current research aims to address this gap by examining the role of training transfer factors in enhancing training effectiveness within micro, small and medium enterprises (MSMEs) in India. Influenced by the Learning Transfer System framework proposed by Holton et al. (2007), this study aims to focus on investigating the impact of performance coaching, human resource development (HRD) climate and employee agility on training transfer in MSMEs. Design/methodology/approach Data has been collected from 411 respondents working in MSMEs of India through validated survey instruments. MSMEs have been chosen owing to the increasing contribution of the sector to the Indian gross domestic product and the lack of sufficient management research in the sector. The statistical software SPSS-AMOS has been used to create structural equational modeling for testing the hypotheses. Findings The result shows that performance coaching is the most impactful factor for training transfer, followed by HRD climate and employee agility. Practical implications The result suggests MSME practitioners activate a positive coaching environment in the organization by following the best practices in performance coaching. Further, the research suggests practitioners to realize the significance of HRD climate and employee agility. Appropriate management practices have been suggested to groom and develop HRD climate and employee agility for higher training transfer. Originality/value To the best of the authors’ knowledge, the research is first of its kind to study the impact of performance coaching, employee agility and HRD climate on training transfer in MSMEs.

Assessing the Effectiveness of Audio-Visual vs. Visual Neurofeedback for Attention Enhancement: A Pilot Study with Neurological, Behavioural, and Neuropsychological Measures.

Electroencephalogram (EEG) based Neurofeedback training has gained traction as a practical method for enhancing executive functions, particularly attention, among healthy individuals. The neurofeedback protocols based on EEG channel locations, frequency bands, or EEG features has been tested. However, the improvement in attention was not measured by comparing different feedback stimulus types. We believe that multisensory nature feedback even with few training sessions may induce strong effect. Therefore, this study compares the effect of audio-visual and visual feedback stimuli for attention enhancement utilizing neurophysiological, behavioural and neuropsychological measures. Total 21 subjects were recruited, undergoing six alternate days of neurofeedback training sessions to upregulate EEG beta power of frontocentral (FC5). Dwell time, fractional occupancy and transition probability were also estimated from the EEG beta power. Audiovisual group (G1) as compared to visual group (G2) demonstrate a significant increase of global EEG beta activity alongside improved dwell time (t = 2.76, p = 0.003), fractional occupancy (t = 1.73, p = 0.042) and transition probability (t = 2.46, p = 0.008) over the course of six neurofeedback training sessions. Similarly, the group (G1) shows higher scores (t = 2.13, p = 0.032) and faster reaction times (t = 2.22, p = 0.028) in Stroop task, along with increased score in Mindfulness Attention Awareness Scale (MAAS-15) questionnaire (t = 2.306, p = 0.012). Audiovisual neurofeedback may enhance training effectiveness, potentially achieving the same outcomes in fewer sessions compared to visual-only feedback. However, sufficient training days are essential for effect consolidation. This highlights the feasibility of completing neurofeedback training, a significant challenge in practice.

AONet: Attention network with optional activation for unsupervised video anomaly detection

AbstractAnomaly detection in video surveillance is crucial but challenging due to the rarity of irregular events and ambiguity of defining anomalies. We propose a method called AONet that utilizes a spatiotemporal module to extract spatiotemporal features efficiently, as well as a residual autoencoder equipped with an attention network for effective future frame prediction in video anomaly detection. AONet utilizes a novel activation function called OptAF that combines the strengths of the ReLU, leaky ReLU, and sigmoid functions. Furthermore, the proposed method employs a combination of robust loss functions to address various aspects of prediction errors and enhance training effectiveness. The performance of the proposed method is evaluated on three widely used benchmark datasets. The results indicate that the proposed method outperforms existing state‐of‐the‐art methods and demonstrates comparable performance, achieving area under the curve values of 97.0%, 86.9%, and 73.8% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech Campus datasets, respectively. Additionally, the high speed of the proposed method enables its application to real‐time tasks.

Integrated energy cluster hierarchical regulation technology considering demand response

Integrated Energy Cluster (IEC), the regional aggregation of integrated energy systems (IES), has accumulated plenty of dispatchable resources with the development of energy market. This, while significantly providing system Demand Response (DR) potential, also complicates the interaction of the IEC with the main grid and increases the difficulty of system scheduling. To address this issue, this paper proposes a Reinforcement Learning-driven multi-agent hierarchical regulation framework that makes full use of DR to maximize the benefits of both IEC and main grid. Firstly, in the context of the DR market, a mechanism for IECs to bid in the real-time DR market is proposed. Furthermore, an "IEC-main network" hierarchical regulation model taking account of DR is established to minimize the IEC operation cost and maximize the societal benefit. Moreover, an optimization algorithm utilizing Deep Deterministic Policy Gradient (DDPG) with Multi-process (MP) and Priority Experience Replay (PER) mechanism is proposed to allow adaptability to high-latitude and large-scale applications. In case study, the proposed model and algorithm is tested on an 8-node system and a 24-node system. The result indicates that the hierarchical regulation model considering DR can improve the system economy by 2.59 % than that without DR and the improved DDPG algorithm can enhance training effectiveness in comparison with DDPG and PER-DDPG.

Enhancing Education and Training Effectiveness for Microtrans Public Transportation Crews in DKI Jakarta

This research examines the effectiveness of education and training programs for Microtrans public transportation crews in DKI Jakarta, focusing on improving operational efficiency and service quality. Through a mixed-methods approach combining qualitative interviews and surveys, key challenges were identified across six critical indicators: punctuality of training sessions, achievement of training targets, implementation of training outcomes, utilization of training benefits, post-training work performance, and behavioral changes. Findings reveal significant gaps, including delays in training sessions, missed training targets, and difficulties in applying acquired skills on the job. Recommendations include enhancing scheduling systems, aligning curricula with operational needs, and integrating practical training methods to bridge these gaps. Furthermore, fostering a culture of continuous learning and professional development among crews is essential to sustain improvements over time. By addressing these challenges, stakeholders can enhance crew competency, service reliability, and overall public transportation quality in DKI Jakarta

Analysis of the Role of Basketball in Improving Physical Fitness Levels

This research aims to delve into the role of basketball in improving physical fitness through a literature review method. Literature analysis was conducted to investigate the impact of basketball's physical activity on aspects of physical health, cardiovascular health, as well as social and psychological aspects of players. Findings from scientific literature indicate that basketball contributes positively to the comprehensive development of strength, speed, and agility. Moreover, the intensity of the game has proven to influence increased heart rate and lung capacity, supporting optimal cardiovascular health. Social and psychological aspects also became a focal point, revealing that team interaction, game strategies, and collective responsibility can enrich players' experiences and potentially enhance their psychological well-being. Meanwhile, the integration of technology in basketball training offers innovative and effective methods to enhance training effectiveness and optimize skill development. The conclusion of this research indicates that basketball is not just a sport but a holistic means to improve physical fitness and overall well-being. As a recommendation, it is suggested that educational institutions and communities consider integrating basketball into their fitness programs and enhance the use of technology to support effectiveness. These conclusions and recommendations are expected to serve as a foundation for the development of more relevant and results-oriented fitness programs.

The Influence of Pre-Training Factors on Training Effectiveness Mediated by Motivation to Learn, Motivation to Transfer, and Self-Efficacy – Case Study on Non-Ministerial Government Institutions

While acknowledging the importance of training, the challenge lies in understanding and optimizing its effectiveness, especially in the context of the government institution’s ever-changing landscape and the associated budgetary considerations. It examines whether pre-training factors—organizational support, training environment, trainer quality, and training need analysis— influence training effectiveness directly or are mediated by motivation to learn, motivation to transfer, and self-efficacy in non-ministerial government institutions in Indonesia. Data were collected from 202 respondents across 19 institutions using purposive sampling and analyzed with Covariance Based-Structural Equation Modeling (CB-SEM) using Lisrel 8.80. The findings reveal that trainer quality significantly affects motivation to learn, motivation to transfer, and self-efficacy but does not directly impact training effectiveness. Instead, its influence is mediated by motivation to transfer and self-efficacy. This underscores the crucial role of trainers in enhancing training effectiveness by boosting participants' motivation and self-efficacy. The study highlights the need for organizations to invest in high-quality trainers through ongoing professional development, robust evaluation systems, and incentives to improve training outcomes and achieve organizational goals more efficiently.

A Strong Core for a Strong Recovery: A Scoping Review of Methods to Improve Trunk Control and Core Stability of People with Different Neurological Conditions

Objective: The purpose of this scoping review is to provide valuable insights for clinicians and researchers for designing rehabilitative interventions targeting the trunk and core for individuals who have experienced traumatic events, such as stroke or spinal cord injury, or are grappling with neurological diseases such as multiple sclerosis and Parkinson’s disease. We investigated training methods used to enhance balance, trunk control, and core stability. Methods: We conducted an extensive literature search across several electronic databases, including Web of Science, PubMed, SCOPUS, Google Scholar, and IEEE Xplore. Results: A total of 109 articles met the inclusion criteria and were included in this review. The results shed light on the diversity of rehabilitation methods that target the trunk and core. These methods have demonstrated effectiveness in improving various outcomes, including balance, trunk control, gait, the management of trunk muscles, overall independence, and individuals’ quality of life. Conclusions: Our scoping review provides an overview on the methods and technologies employed in trunk rehabilitation and core strengthening, offering insights into the added value of core training and specific robotic training, focusing on the importance of different types of feedback to enhance training effectiveness.

The Validity and Reliability of a Real-Time Biofeedback System for Lumbopelvic Control Training in Baseball Players.

This study validates real-time biofeedback for lumbopelvic control training in baseball. The lumbopelvic region is crucial for generating kinetic energy in pitching. Real-time biofeedback enhances training effectiveness and reduces injury risk. The validity and reliability of this system were examined. This study was to investigate the validity and reliability of the real-time biofeedback system for lumbopelvic control training. Twelve baseball players participated in this study, with data collected in two sessions separated by a week. All participants needed to do the lateral slide exercise and single-leg squat exercise in each session. Pelvic angles detected by the real-time biofeedback system were compared to the three-dimensional motion capture system (VICON) during training sessions. Additionally, pelvic angles measured by the biofeedback system were compared between the two training sessions. The real-time biofeedback system exhibited moderate to strong correlations with VICON in both exercises: lateral slide exercise (r = 0.66-0.88, p < 0.05) and single-leg squat exercise (r = 0.70-0.85, p < 0.05). Good to excellent reliability was observed between the first and second sessions for both exercises: lateral slide exercise (ICC = 0.76-0.97) and single-leg squat exercise (ICC = 0.79-0.90). The real-time biofeedback system for lumbopelvic control training, accurately providing the correct pelvic angle during training, could enhance training effectiveness.

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.

Comparison of ground reaction force among stride types in baseball pitching.

This study aims to investigate the different ground reaction force (GRF) characteristics from different stride types. This study included 30 Taiwanese elite college and professional baseball pitchers. Each pitcher was classified into one of the following stride types: tall and fall (TF), dip and drive (DD), or mixed (MX). Our findings indicated that DD pitchers pressed their bodies forward earlier than TF pitchers. In contrast, the GRF of the pivot leg of TF pitchers increased slowly in the first half of the stride phase and continued to increase even after the stride leg touched the ground. This type of continuous pressing in TF was different from that in DD and MX pitchers, which decreased rapidly into the arm-cocking & acceleration phases. We conclude that the lower extremities were used differently by TF, DD and MX pitchers. This information could serve as a training guideline for coaches and pitchers to enhance training effectiveness, while simultaneously lowering injury risks.

Hazardous and Toxic Material Decontamination Facility Training in Virtual Reality

Patient decontamination is the act of removing or neutralising hazardous substances from an affected individual. To ensure adequate emergency preparedness, regulations require hospitals to train personnel in decontamination procedures regularly. To supplement in-person training, a virtual reality (VR) system is being developed for the training of hospital staff members in the mass decontamination of hazardous and toxic materials (HAZMAT) and/or radioactively contaminated casualties. As a demonstration of the concept, a primary VR prototype is designed to help users familiarize themselves with a chemical scanner tool, intended for examining victims for residual chemical hazards. This initial prototype showcases the benefits of using VR to create training simulations, complementing existing decontamination training methods in a secure and cost-effective manner. The proposed approach features a modularized user-centric design, a novel scanning simulation, and a high-realism virtual environment and workflow to enhance training effectiveness. A pilot user study and assessment suggest that new users were able to achieve a significant level of competency with VR, compared to users who underwent traditional training.

Brain–Computer Interface Using Functional Near-Infrared Spectroscopy for Post-Stroke Motor Rehabilitation: Case Series

Introduction. Non-invasive brain–computer interfaces (BCIs) enable feedback motor imagery [MI] training in neurological patients to support their motor rehabilitation. Nowadays, the use of BCIs based on functional near-infrared spectroscopy (fNIRS) for motor rehabilitation is yet to be investigated.&#x0D; Objective: To evaluate the potential fNIRS BCI use in hand MI training for comprehensive post-stroke rehabilitation.&#x0D; Materials and methods. This pilot study included clinically stable patients with mild-to-moderate post-stroke hand paresis. In addition to the standard rehabilitation, the patients underwent 10 nine-minute MI fNIRS BCI training sessions. To evaluate the quality of fNIRS BCI control, we assessed the percentage of time during which the classifier accurately detected patient's mental state. We scored the hand function using the Action Research Arm Test (ARAT) and the Fugl-Meyer Assessment (FMA).&#x0D; Results. The study included 5 patients at 1 day to 12 months of stroke. All the participants completed the study. All study participants achieved BCI control rates higher than random (41–68%). While three patients demonstrated the clinically significant improvements in their ARAT scores, one of them also showed an improvement in the FMA score. All the participants reported experiencing drowsiness during training.&#x0D; Conclusions. Post-stroke patients can operate the fNIRS BCI system under investigation. We suggest adjusting the feedback system, extending the duration of training, and incorporating functional electromyostimulation to enhance training effectiveness.

Enhanced Soccer Training Simulation Using Progressive Wasserstein GAN and Termite Life Cycle Optimization in Virtual Reality

Virtual Reality (VR) offers the possibility of creating engaging and intensive training programs to enhance sports performance. Football, a widely popular sport worldwide, attracts millions of spectators. However, the physical demands and risks inherent in the game necessitate the creation of a training environment immune to external influences, minimizing sports-related injuries while enhancing player immersion. Existing training methods are affected by external factors like, weather, injuries, and space limitations, hindering effectiveness and skill development. To overcome these issues, in this research, enhanced soccer training simulation using Progressive Wasserstein GAN and Termite Life Cycle Optimization in Virtual Reality (PWGAN-TLCOA-ST-VRT) are proposed. This study begins by analyzing the fundamental features of computer-generated soccer training techniques and introduces the implementation of VRT in football training via PWGAN technology. The goal is to develop virtual systems resilient to external factors and provide theoretical support for the expansion of virtual football sports software. By overcoming constraints like, weather, player injuries, space limitations, and financial constraints, the proposed approach aims to enhance training effectiveness, teaching techniques, and football skill development. Software tools like Poser 8.0, 3Ds MAX and EON Studio are introduced for system development. Evaluation of the proposed PWGAN-TLCOA-ST-VRT method is examined using performance metrics including, precision, accuracy, F1-score, recall, specificity, error rate, computation time, and Receiver Operating Characteristic (ROC) analysis. The proposed PWGAN-TLCOA-ST-VRT technique achieves significant improvements compared to existing approaches like, Analysis of the application of virtual reality technology in football training (AI-ST-VRT), Cortex VR: Immersive analysis and training of cognitive executive functions of soccer players using VR and machine learning, Predict the value of football players using FIFA video game data and machine learning techniques (MLR-ST-VRT)

Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systems

Dynamic pricing is a widely applied strategy by ride-hailing companies, such as Uber and Lyft, to match the trip demand with the availability of drivers. Deciding proper pricing policies is challenging and existing reinforcement learning (RL)-based solutions are restricted in solving small-scale problems. In this study, we contribute to RL-based approaches that can address the dynamic pricing problem in real-world-scale ride-hailing systems. We first characterize the dynamic pricing problem with a clear distinction between historical prices and current prices. We then translate our dynamic pricing problem into Markov Decision Process (MDP) and prove the existence of a deterministic stationary optimal policy. Our solutions are based on an off-policy reinforcement learning algorithm called twin-delayed deep determinant policy gradient (TD3) that performs offline learning of the optimal pricing policy using historical data and applies the learned policy to the next time slot, e.g., one week. We enhance TD3 by creating three mechanisms to reduce our model complexity and enhance training effectiveness. Extensive numerical experiments are conducted on both small grid networks (16 zones) and the NYC network (242 zones) to demonstrate the performance of the proposed algorithm. The results show our algorithm can efficiently find the optimal pricing policy for both the small and large networks, and can significantly enhance the platform profit and service efficiency.

Does augmented reality help in industrial training? A comprehensive evaluation based on natural human behavior and knowledge retention

The complexity and diversity of products significantly increases the difficulty of training workers in modern industries. AR training, as a combination of industrial digital development and new interactive means, has attracted increasingly more attention. However, in human factor and education, research regarding AR based training is still in its infancy. To understand how AR could help industrial training from a human perspective, we evaluate a HoloLens-based AR training method for an assembly task on natural human behavior and the training effectiveness. Forty-four participants were equally divided into AR and ordinary training groups with the equivalent instructional materials, which included text information and 3D models. Results show that although AR did not significantly affect the overall workload and system usability assessment (SUS) recorded, several experimental values increased significantly compared with the ordinary training method, including task completion time, time required to interpret text-based information, amount of attention switching, and training effectiveness. Consequently, the use of AR did not result in a completion time and workload reduction; however, we found that training with the AR system was more effective than ordinary training in increasing the knowledge retention of participants without increasing their overall workload, especially for the month-term memory. Nonetheless, a longer time was required by the users to familiarize themselves with the AR environment and read the text information. The findings of this work could contribute to the design of future AR training systems in industry considering natural human behavior and enhance training effectiveness.

Social Support for Training Effectiveness: Mediating Training Transfer Motivation and Moderating Transfer Design

Organizations invest in training and development to enhance their productivity. But, evidence proved the low effectiveness of the training because of the poor training transfer. Thus, training effectiveness is a permanent issue in developing human competencies and organizational performance. This study examined the effect of social support, that is, supervisor and peer support on training effectiveness, that is, training transfer, training maintenance, and training generalization with the moderated mediation effect of training transfer design and training transfer motivation. This study followed positivist epistemology on cross-section survey data collected from banking employees using multi-stage sampling. Hayes’s approach was used to test hypotheses. The findings of the study revealed a significant positive effect of social support on training effectiveness. Results revealed that peer support is vital in comparison to supervisory support to enhance training effectiveness. Training transfer motivation mediated the effect of peer support and transfer motivation predicting training effectiveness. The study concluded that optimal positive support from peers and supervisors is essential to enhance training effectiveness; the higher training transfer motivation of trainees further supports the relationship. This study emphasizes the significance of workplace factors to increase training transfer effectiveness, providing practical and theoretical implications.

A GAN–SVR Prediction Method of the Metal Tube‐Bending Rebound with Small Samples

This paper investigates a predictive algorithm for the angle of the metal tube‐bending rebound with small samples. First, the generative adversarial network (GAN) approach is introduced to address the issues of insufficient sample data. The proposed method can realize data augmentation through a generator, enhancing training effectiveness compared to conventional model‐based and experimental prediction methods. To further reduce the problems caused by the small samples, the Wasserstein distance is utilized as the optimization objective for the GAN approach. Second, after obtaining the augmented dataset, Support Vector Regression (SVR) is employed to predict the rebound model of the metal tube‐bending. A novel predictive algorithm for the angle of the metal tube‐bending rebound based on GAN–SVR is proposed. It exhibits that the GAN–SVR owns more positive prediction ability and error when dealing with small samples than conventional GAN‐radial basis function methd (GAN–BP) and GAN–convolutional neural networks. Finally, the effectiveness of the proposed method is validated through experimental results.

Development and Evaluation of E-Learning Tool for Humanitarian Public Health Training

Background/Introduction:The humanitarian health field has grown rapidly in the last decades. However, education and training programs are still limited in number, especially where it is needed most. Additionally, the cost of the training is too expensive for local humanitarian health respondents. If developed appropriately, E-Learning can potentially address these gaps.Objectives:Develop an effective E-Learning tool for humanitarian public health training.Method/Description:The tool was developed using an institutional-based, action research method. Rapid prototyping - a modified analysis, design, development, implementation, and evaluation (ADDIE) model - was used to identify the content and instructional design. This iterative process involved consultations and feedback from public health and disaster medicine instructors and students at different levels and training programs. Different methods were used to collect and analyze data, such as literature and courses reviews, observation, interviews, and questionnaires.Results/Outcomes:Four scenario-based E-Simulations - targeting entry-level humanitarian health professionals - were developed and piloted based on identified needs. Formative evaluation clarified training objectives and structure. Trainees were satisfied by the simulated scenario, interactivity, questions, and timely feedback. Poor internet was identified as a potential barrier to delivering the training in the humanitarian context.Conclusion:Clear objectives and quality content are the prerequisites for training effectiveness. Yet, instructional design is a critical element that can enhance training effectiveness. The tool’s offline mode is being adapted to improve access in poor internet connection settings. A summative evaluation will be conducted to measure the training’s effectiveness. Furthermore, successful factors in instructional design will be used to inform the development of advanced training in the field.

A measurement scale developed to investigate the effect of leaders' perceptions regarding attitudes towards and commitment to quality management of training

AbstractIt is broadly acknowledged that, for quality management to be effective, it needs to be viewed and implemented as a dynamic and active process by people in an organisation, specifically its leaders. This study aimed to determine the level of leaders' commitment with regard to their perceptions and attitudes concerning quality management of training in corps training units within the South African Army. The Leader Perception and Attitude Scale was developed for this purpose. A cross‐sectional survey was conducted with a sample of 229 leaders at six of the eight South African Army's corps training units. Correlation coefficients were determined, and structural equation modelling was performed. The results indicated that leaders' attitudes had a statistically significant influence on their commitment (p &lt; 0.001), whereas perceptions had no statistically significant influence (p = 0.488). The results contrast with previous studies, which have mostly indicated that perceptions influence employee commitment. By targeting leaders' attitudes, therefore, the South African Army may be able to design and implement appropriate strategies to improve leaders' commitment and thereby enhance training effectiveness. The potential usefulness of a scale that measures the level of commitment of leaders and is able to indicate the level of effect that perceptions and attitudes can have, may be of interest to other military settings and organisations that conduct in‐house training.