Continuous Learning Research Articles

An intensive eye-tracking intervention in children with dyskinetic cerebral palsy: a multiple case study

This study aimed to explore the effects of a four-week intensive eye-tracking intervention on children with dyskinetic cerebral palsy (DCP), focusing on goal attainment, communication competencies, stress levels, subjective workload, and caregivers’ perception of psychosocial impact. A multiple case study design with non-concurrent, staggered multiple baselines was employed, involving three children aged 7, 12, and 13 years. The study included a randomized baseline period of two or three weeks, an intensive eye-tracking intervention, and a six-month follow-up. Two individual eye-tracking goals were identified and assessed using the Goal Attainment Scale, while communication competencies were evaluated with the Augmentative and Alternative Communication Profile: A Continuum of Learning. Stress levels were monitored through Heart Rate Variability measured by the Bittium Faros 360° ECG Holter during eye-tracking tasks. Subjective workload and psychosocial impact were assessed using pictograms and the Psychosocial Impact of Assistive Devices Scale, respectively. Descriptive statistics were applied for analysis. All participants attained and retained their eye-tracking goals, regardless of their initial functional profiles or prior experience with eye-tracking technology. Post-intervention improvements in communication competencies were maintained at the six-month follow-up. Variations in stress levels, subjective workload, and psychosocial impact were observed among participants across different phases of the study, aiding the interpretation of the results. The study concludes that a structured, tailored, four-week intensive eye-tracking intervention can yield successful results in children with DCP, irrespective of their baseline communication abilities or functional profile. Recommendations for future research, including more robust methodologies and reliable computerized tests, are provided.

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning

This study presents an innovative approach to real-time modeling of urban drainage networks, leveraging a highly accurate coupled one- and two-dimensional hydrodynamic model to generate a training dataset for node water levels. By employing global states inferred from monitoring points as model inputs, this study overcomes the limitations imposed by the scarcity of monitoring data and the challenge of capturing all node levels. The Crossformer algorithm, which simultaneously accounts for correlation at both temporal and feature scales, is applied to enhance the precision of simultaneous water level predictions across the network. Comparative analysis of different prediction patterns reveals that extending predictions based on a high-accuracy infrastructure offers more benefits than direct modifications to the algorithm's structure. In addition, this paper pioneered the application of online continuous learning concepts to update the prediction model in real time, achieving a balanced integration of measured and simulated data. Consequently, this paper establishes a complete monitoring-predicting-updating real-time simulation system for urban drainage networks.

An educational intervention study on mandatory reporting of intimate partner violence: changes in knowledge and attitudes among healthcare providers

BackgroundIntimate partner violence (IPV) is a major public health concern. Healthcare providers can play a crucial role in reporting cases of IPV or suspected IPV injuries to the police or the criminal justice system, commonly referred to as mandatory reporting. However, mandatory reporting of intimate partner violence (MR-IPV) is a debated topic that can pose complex challenges for healthcare providers. This underscores the importance of training programs to ensure that healthcare providers can fulfill their MR-IPV obligations.MethodsWe developed an educational intervention on MR-IPV and assessed its impact on healthcare providers’ knowledge and attitudes. The study used a pre- and post-test design with three measurement points: baseline (T0), immediately after the intervention (T1), and six months later (T2). The intervention was conducted at a university college in Norway, with data collected between October 2022 and May 2023. The intervention was delivered to 37 healthcare providers who were currently part-time students in mental healthcare. Changes in knowledge and attitudes between T0 and T1, and T0 and T2 were analyzed through nonparametric tests on related samples using the Marginal homogeneity (Stuart–Maxwell) test. Risk differences (RD), along with their corresponding 95% confidence intervals (CI), were calculated for selected categories.ResultsThe number of participants knowing the MR law increased from 2.9% at baseline to 62.9% at T1 (RD = 0.60, 95% CI: 0.41—0.79) and to 31.4% at T2 (RD = 0.29, 95% CI: 0.11–0.46). The number of participants reporting knowing relevant criteria increased from 0.0% at baseline to 68.6% at T1 (RD = 0.69, 95% CI: 0.50–0.87) and to 34.3% at T2 (RD = 0.34, 95% CI: 0.16—0.53). We observed several persistent changes in healthcare providers' attitudes towards MR, including finding MR to be a useful instrument and generally complying with MR requirements.ConclusionsThe findings suggest that this educational intervention can have a positive impact on healthcare providers' attitudes and compliance with MR-IPV. Before the intervention, few participants reported knowing the MR law and its application criteria, demonstrating the importance of continuous learning and evidence-based training programs.

O AVANÇO DAS INOVAÇÕES DIGITAIS NA EDUCAÇÃO CORPORATIVA

This study aims to present the advancement of digital innovations in corporate education, observing how new technologies and business models influence teaching and learning at work. Digitalization has generated new opportunities for professional development, allowing a more effective integration between educational practices and the work environment, and promoting continuous learning in a competitive market. Corporate education must be a fundamental pillar for change in future organizations, transforming into digital learning ecosystems. These ecosystems will face challenges to improve usability and the educational experience, promoting technological, social and cultural changes within companies. The proposal is to conduct exploratory research with a qualitative approach and inductive logic, which focuses on the quality and depth of the information collected, rather than being based on pre-existing theoretical concepts. Inductive logic, which is based on observation of facts, allows the validation or refutation of existing concepts. The chosen method is a qualitative literature review, ideal for exploring the complexity of innovative corporate education and its role in organizational transformation. This method is suitable for investigating ideas and models about educational practices and technologies in a context of digital transformation, as it allows a practical analysis of the phenomenon in its real environment.

Co-designing an interprofessional digital education resource on delirium: a student-led approach

BackgroundInterprofessional education is crucial for healthcare students to develop collaborative skills and provide effective patient care. However, opportunities for interprofessional learning are often limited in healthcare curricula. The present study aimed to engage students from different health professions in co-designing an educational resource on delirium recognition and management through an interprofessional lens and explore their experiences of this process.MethodsTwo co-design workshops were conducted with students from medicine, nursing, pharmacy, and occupational therapy programmes at two universities across the island of Ireland. Focus groups were held following these workshops to explore students’ experiences of the co-design process. The workshops involved a range of activities, including theme generation, scenario development, resource creation (podcasts, simulations), and focus group discussions. Data from focus groups were analysed thematically.ResultsA total of 19 students participated across the two workshops. Three themes were identified: (1) Relationship development, where students identified the benefits of co-creating the resource and valued the flexibility, collaboration, and social aspects of the co-design approach; (2) Interprofessional collaboration, which challenged students’ assumptions about other disciplines, fostered teamwork and communication, and highlighted the need for early and continuous interprofessional learning; (3) Professional growth, with students reporting increased confidence in managing delirium, working with other professions, and engaging in novel experiences like podcasting and simulation.ConclusionsThe co-design process facilitated interprofessional collaboration, peer learning, and personal growth among healthcare students. Students appreciated the opportunity to co-create an educational resource while developing interprofessional skills. The study demonstrates the potential of co-design as a methodology for enhancing interprofessional education and promoting effective teamwork in healthcare.

Leadership essential to steer success within the retail sector

PurposeThe purpose of this investigation was to determine the leadership necessary for the retail sector which is having to embrace complexity induced through accelerating changes in consumer behaviour and expanding information accessibility.Design/methodology/approachSemi-structured interviews were used for across 10 disparate retail organisations deployed as case studies safeguarding all quality considerations were fully integrated.FindingsThe findings reveal brands are coping with greater complexity than before. Loyalty is eroding whilst social media increases the complexity for brands to uphold control of their image. Developing the market without sacrificing exclusivity or alienating core consumers necessitates effective leadership. The deductions revealed that despite some leadership styles seemingly more conducive, that specific qualities are more important for the retail sector.Research limitations/implicationsBy selecting ten organisations every effort was made to ensure a greater degree of credibility in the findings. It is acknowledged that this investigation is sector specific. Nonetheless, there exists considerable transferability value as the role of leadership within the retail sector can mirror expectations within other environments.Originality/valueIt was validated that leaders need to foster an organisational culture promoting continuous learning whilst introducing a consumer-first perspective across the business. The findings reinforce how specific traits, behaviours and styles are imperative amongst leaders preserving the success of the retail organisation with less focus on specific leadership styles.

Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes

Abstract Manufacturing processes undergo continuous changes in order to meet various requirements, such as process/product changes and variations in tool/workpiece conditions, leading to mixed, heterogenous, or anomalous data. As a result, a quality prediction model trained from previous data may not perform well when new tasks emerge. In order to achieve in-time and accurate product quality prediction, it is crucial to develop a predictive method that adapt to variations in the manufacturing system, capable of learning from new tasks without forgetting previous ones and detecting unknown tasks. This study proposes a deep learning method integrated with continual learning for in-situ quality prediction that is capable of learning from new tasks without forgetting previous ones. To demonstrate this idea, deep CNNs are designed to analyze in-process sensor data, which consist of shared layers to capture the common underlying features across all tasks, and task-specific layers that capture specific characteristics of each individual task. In order to identify the task to which incoming product belongs, a task prediction approach based on task relevancy using filter subspace distance is proposed. When new data come in, the model first identifies the task, followed by predicting the quality of the current product. The proposed method is demonstrated on two case studies, including quality prediction of the workpiece using acoustic emissions during the laser-induced plasma micro-machining process, and quality prediction of the product through thermal images during the hot stamping process.

A new perspective on optimizers: leveraging moreau-yosida approximation in gradient-based learning

Machine Learning (ML) heavily relies on optimization techniques built upon gradient descent. Numerous gradient-based update methods have been proposed in the scientific literature, particularly in the context of neural networks, and have gained widespread adoption as optimizers in ML software libraries. This paper introduces a novel perspective by framing gradient-based update strategies using the Moreau-Yosida (MY) approximation of the loss function. Leveraging a first-order Taylor expansion, we demonstrate the concrete exploitability of the MY approximation to generalize the model update process. This enables the evaluation and comparison of regularization properties underlying popular optimizers like gradient descent with momentum, ADAGRAD, RMSprop, and ADAM. The MY-based unifying view opens up possibilities for designing new update schemes with customizable regularization properties. To illustrate this potential, we propose a case study that redefines the concept of closeness in the parameter space using network outputs. We present a proof-of-concept experimental procedure, demonstrating the effectiveness of this approach in continual learning scenarios. Specifically, we employ the well-known permuted MNIST dataset, a progressively-permuted MNIST and CIFAR-10 benchmarks, and a non i.i.d. stream. Additionally, we validate the update scheme’s efficacy in an offline-learning scenario. By embracing the MY-based unifying view, we pave the way for advancements in optimization techniques for machine learning.

A Study on Work-life Balance Initiatives and Training Programs Towards Employee Productivity and Career Development

This study explores the impact of work-life balance initiatives and training programs on employee productivity and career development, particularly in the IT sector. It highlights the importance of flexible work arrangements, wellness programs, and continuous learning opportunities in enhancing employee engagement, reducing stress, and fostering career growth. The research is based on data collected from 104 IT employees and uses statistical methods such as correlation, regression analysis, chi-square, and ANOVA to assess the relationship between these initiatives and key outcomes like productivity, well-being, and retention. The findings suggest that while flexible work options and training programs moderately support career advancement, challenges such as time management and insufficient resources still exist. The study recommends better aligning training programs with employee career goals, offering improved time management workshops, and allocating adequate resources to create a work environment that balances personal well-being with professional growth.

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology.

Interpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, postoperative changes, medical devices, image artifacts, lack of integration of prior and concurrent imaging examinations and clinical information, and the satisfaction-of-search effect. Model training and development should account for such pitfalls to minimize errors and optimize interpretation accuracy. More broadly, AI algorithms should be exposed to diverse and complex training datasets to yield a holistic interpretation that considers all relevant information beyond the individual examination. Successful clinical deployment of AI tools will require that radiologist end users recognize these pitfalls and other limitations of the available models. Furthermore, developers should incorporate explainable AI techniques (e.g., heat maps) into their tools, to improve radiologists' understanding of model outputs and to enable radiologists to provide feedback for guiding continuous learning and iterative refinement. In this article, we provide an overview of common pitfalls that radiologists may encounter when using interpretive AI products in daily practice. We present how such pitfalls lead to AI errors and offer potential strategies that AI developers may use for their mitigation.

Linking personal initiative and family help as well as social support: a case study of learning challenges and solutions for older adults in rural China during the COVID-19 pandemic.

The sudden outbreak of the COVID-19 Pandemic has caused serious damage to the continuous learning of older adults around the world. While the existing literature focused more on older adults' learning in developed countries, few studies explored older adults' learning in developing countries with low social and cultural capital. This study took family-school cooperation in China as the platform and explored learning challenges and solutions through unstructured interviews with 12 older adults. The study found that due to policies of working and studying from home, older adults face obstacles in accessing physical learning institutions and digital learning knowledge, skills, and psychology. I further found that the older adults were not forced to accept the challenge passively, but created a lifelong learning model with Chinese characteristics by building self-learning based on personal initiative, developing sustainable intergenerational learning rooted in Chinese family culture, and participating in online learning in schools and enterprises under the government guidance. This study provided new knowledge for understanding the learning challenges and solutions of older adults in rural China. It is emphasized that policy value and practice enlightenment were highlighted and discussed in conversations with active aging, intergenerational learning, and harmonious societies.

Program Edukasi untuk Peningkatan Keterampilan Administrasi Logistik Unit Food di PT Sekar Bumi Tbk

This service outlines the implementation of an educational program aimed at improving the logistics administration skills of the food unit at PT Sekar Bumi Tbk. Through training needs analysis, comprehensive counseling, and training methods that include theory and practice, this program has succeeded in significantly improving employees' technical and soft skills. The evaluation results show a reduction in errors in managing logistics administration, goods release and goods entry transactions, receiving finished products from the packing team, and following the loading process for local and export shipments. In addition, establishing a culture of collaboration and continuous learning among employees creates an innovative work environment. Thus, this educational program not only strengthens human resource capacity, but also increases the competitiveness of PT Sekar Bumi Tbk in the increasingly competitive frozen food industry, preparing employees to face future challenges more prepared and responsive.

<b>From Imagination to Practice: Unravelling the Professional Identity of a Pre-service EFL Teacher</b>

This study uses a narrative inquiry method, focusing on the transformation journey of a pre-service EFL teacher named Brandon (pseudonym), to explore the complex process of professional identity construction and reconstruction for pre-service teachers to teach EFL from a teaching practicum. The study gathered information about critical events that shaped Brandon's professional identity through semi-structured interviews and diaries before and during his teaching practice. Using an innovative "story constellation" approach, the research creates a three-dimensional narrative, weaving together temporal, personal, social, and contextual aspects. This exploration of Brandon's journey illuminates the core tenets of effective language teaching, including adaptability, empathy, continuous learning, and a student-centred approach, thereby gaining a deeper understanding of the (re)construction of EFL teachers' professional identities. This study widens knowledge of EFL teachers' pre-teaching professional experiences and offers important implications for teacher education programs.

Establishing an upper extremity transplantation program: lessons learnt over 12 years.

As one of the early hand transplant programs in the world, we are often asked to educate other programs on the lessons we have learned and on how to set up a successful hand transplantation program. Two decades of global experience with vascularized composite allotransplantation (VCA) of the upper limb have demonstrated improved functional outcomes and quality of life for carefully screened patients. Despite initial reticence about VCA, over 30 patients in the USA have benefited from hand transplantation, with around 80% long-term successes. We have experience with four bilateral upper extremity patients who have undergone bilateral upper extremity allotransplants. This review article highlights our personal experience and lessons with respect to VCA team assembly, logistics, patient selection, perioperative planning, the operative procedure, and postoperative management. Continual learning and critical evaluation are crucial to maintaining a successful upper extremity VCA program. This will ensure that patients who stand to benefit most from this life-enhancing procedure are identified and are others optimized to achieve the best possible outcomes.

Challenges of Adult Education in China: A Review

This literature review examined the situation of adult education in China, using Knowles' Andragogy Theory and an assessment of legislation, policies, and challenges to adult learning. Knowles' approach emphasizes the value of self-directed, experiential learning, which is critical for adult learners. In China, the government has implemented laws such as the "Lifelong Education Law" to encourage continual learning, although considerable problems remain. These include insufficient finance, out-of-date curricula, restricted access to educational resources, and social attitudes that devalue adult education. Therefore, this review emphasized the need for policy refinement, creative teaching methods, and more investment in infrastructure and educator training. Addressing these difficulties is essential in developing a strong adult education system in China that encourages lifelong learning.

Brain-body-task co-adaptation can improve autonomous learning and speed of bipedal walking.

Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e., n joint, n+1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control, and (ii) uses a simple 3-layer neural network to learn to walk after only 2 minutes of 'natural' motor babbling (i.e., an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain-body collaboration first learns to produce feet cyclical movements 'in air' and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 minutes of 'naive' motor babbling (i.e., an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements 'in air', and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naive babbling presented almost equally persistent trends, and locomotion emerged with naive babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired co-design and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors.

Supply chain strategies in a global context: a customer value-based perspective

ABSTRACT Customer value is influenced by several factors, which impose major challenges to global Supply Chains (SCs) and their management. This study aims to understand how companies tackle these challenges by focusing their global SC management on major strategies and supporting practices. Based on customer value theory, and recognising major trends affecting what end consumers value, we identify four global SC strategies: customer-driven, service-driven, resource-efficient, and closed-loop. A multiple case study carried out in eleven companies in the consumer goods industry explores the practices adopted per each SC strategy in managing global sourcing, production, and distribution networks. Results show the key requirement of selecting tailored practices for SC management that align with the context and the value expected by customers. Operational SC practices entail managing collaborative actions both up and downstream and competing with other SCs and can benefit from the implementation of appropriate digital technologies for customer value creation and delivery, as well as for continuous learning about customer needs.

Reinforcement Learning in AI-Driven Assessments : Enhancing Continuous Learning and Accessibility

This article explores the transformative potential of Reinforcement Learning (RL) in AI-driven assessments, examining its impact on personalized learning experiences and continuous skill development. We investigate how RL algorithms enable adaptive learning systems to create dynamic, individualized learning paths, provide real-time feedback, and optimize long-term educational outcomes. The article delves into key aspects such as personalized learning trajectories, adaptive difficulty adjustments, and the integration of gamification elements to enhance engagement. We discuss the application of these technologies in various contexts, from traditional educational settings to professional development and certification preparation. The article also addresses the challenges and limitations of implementing RL in educational technology, including concerns about algorithmic bias and the complexity of modeling human learning processes. By analyzing current research and potential future directions, this paper provides a comprehensive overview of how RL is reshaping the landscape of education and assessment, offering insights into the future of adaptive learning systems and their role in fostering lifelong learning in an increasingly digital world.

Autonomous AI Systems for Continuous Learning and Real-Time Decision Making

Autonomous AI systems are upsetting navigation by constantly learning and pursuing ongoing choices without human oversight. These frameworks have applications across businesses like medical services, money, and assembling, where ongoing information is fundamental for functional achievement. This paper investigates the parts of independent simulated intelligence frameworks, including constant learning models, continuous information handling structures, and dynamic procedures. We likewise look at the difficulties such frameworks face, including information security, reasonableness, and taking care of edge cases. Through contextual analyses in various enterprises, we show the capability of independent computer-based intelligence frameworks to reshape the fate of savvy navigation. At last, we propose future bearings for research, zeroing in on moral structures and half and half learning models to further develop flexibility and straightforwardness in these frameworks.

Mediating Disputes in the Barangays: The Work World of Lupon Chiefs

Conflicts are resolved when destructive behavior is reduced, and hostile attitudes are reduced. Accordingly, dispute management can be directed towards dispute resolution. Disputes among communities are necessarily part of human conditions. The court's role was to settle disputes concerning the two opposing parties' rights and interests, suitability, and judgment. However, in most countries worldwide, the court system has become overpopulated in the twentieth century, especially in civil litigation or civil law issues. This correctional system occurs within a setting known as the barangay justice system. It reflects a formal recognition of historical dispute resolution and socialization practices. In the Philippines, minor disputes and criminal offenses are required to be "amicably settled" by the barangay leader or captain without the presence of lawyers (Metillo et al., 2022). This study explored the experiences of the Chiefs of Lupong Tagapamayapa in barangay justice administration in the coastal areas of Northern Iloilo, Philippines, through a qualitative research method. The informants of this study were the selected ten (10) chiefs of the Lupong Tagapamayapa. All participants were subjected to an individual in-depth interview. The findings of this study are described in emergent themes. Six (6) emergent themes described the positive and negative experiences of the participants in barangay justice administration. For the positive experiences, three (3) emergent themes were developed, namely Personal Satisfaction from Resolving Conflicts, Influence on Community Peace, and Resolution and Avoidance of Legal Proceedings. For the negative experiences, three (3) emergent themes were also developed, to wit: Frustration with Unresolved Conflicts, Legal Impediments and Personal Limitations, and Non-compliance and Lack of Cooperation. Meanwhile, three (3) emergent themes were formulated to explain how the participants address the challenges encountered by the informants in the administration of the barangay justice, namely: Further Assessment and Understanding of Issues, Seeking Legal Guidance with the Higher Authorities, and Psychological Strategies and Personal Involvement. Finally, three (3) emergent themes were created to express the aspirations of the participants to enhance the quality of services they provided in the barangay justice system, to wit: Continuous Learning and Personal Development, Embodying Integrity and Leading by Example, and Provision of Compensation and Incentives. Based on the emergent themes identified, it is recommended that the Katarungang Pambarangay system be rendered more effective, equitable, and efficient by implementing the requisite training and development programs for its chiefs and members.