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Objective: Self-regulated learning (SRL) and metacognition are recognized as critical thinking processes essential for effective achievement, problem-solving, and critical thinking, among other aspects. While there is abundant research on these concepts for typical populations, particularly with white ethnic groups from regions such as the United States and Europe, these constructs have not been extensively investigated in other population groups or ethnic minorities. Therefore, the purpose of this study was to examine how these two phenomena are experienced by the Katanzama indigenous culture in Santa Marta, Colombia. Methodology: To achieve this, a structured interview protocol was developed with questions about how this indigenous group experiences SRL, metacognition, and the act of teaching. Results: Iterative thematic analyses revealed six themes emerging from the interview data: 1) Knowledge of context and cultural relevance in learning; 2) Regulation and adaptation in learning processes; 3) Diversification of teaching strategies; 4) Learning from mistakes; 5) Planning and organization of study; and 6) Self-criticism and pursuit of continuous improvement. Conclusions: The implications for research, theory, and practice are discussed. [ABSTRACT FROM AUTHOR]

The present research aimed to determine the relationship between metacognitive strategies and critical thinking in English as a second language teaching, with the purpose of improving critical thinking. With a quantitative approach and through a quasi-experimental design, a correlational study was carried out using two instruments: the Metacognitive Awareness Inventory (mai) and the Cornell Critical Thinking Test Level Z, applied to 50 fourth-level students from the Language Center at Pontificia Universidad Católica, Ambato campus. To this end, an expert validation was conducted, where the instruments were evaluated. After their application, a positive correlation between metacognition and critical thinking was evidenced, using the Wilcoxon test. A p-value (Asymptotic Significance (two-tailed)) of less than 0.01 was obtained, leading to the conclusion that the more metacognitive strategies are used, the more students can enhance critical thinking areas, such as interpretation, analysis, evaluation, self-evaluation, inference, explanation, and, especially, self-regulation and self-learning of a second language. [ABSTRACT FROM AUTHOR]

Physical neural networks (PNN) using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training PNN is difficult and heavily relies on external computing resources. An emerging concept to solve this issue is called physical self-learning that uses intrinsic physical parameters as trainable weights. Under external inputs (i.e., training data), training is achieved by the natural evolution of physical parameters that intrinsically adapt modern learning rules via an autonomous physical process, eliminating the requirements on external computation resources. Here, we demonstrate a real spintronic system that mimics Hopfield neural networks (HNN), and unsupervised learning is intrinsically performed via the evolution of the physical process. Using magnetic texture-defined conductance matrix as trainable weights, we illustrate that under external voltage inputs, the conductance matrix naturally evolves and adapts Oja's learning algorithm in a gradient descent manner. The self-learning HNN is scalable and can achieve associative memories on patterns with high similarities. The fast spin dynamics and reconfigurability of magnetic textures offer an advantageous platform toward efficient autonomous training directly in materials. [ABSTRACT FROM AUTHOR]

Chatbots are emerging technologies with the potential to improve teaching and learning processes. This paper conducts a systematic review of research on chatbots in education, focusing on articles published in Online-Journals.org from 2011 to 2024. The aim is to examine the various aspects addressed by the authors, such as design principles, pedagogical roles, interaction styles, and evaluation methods for chatbots in educational contexts. The tools were classified according to the type of user they targeted, revealing that 42% were aimed at students, 11% at teachers, 29% at both types of users, and 18% at external users. The characteristics of the tools along the above dimensions were analyzed, highlighting trends, good practices, and observed limitations. The key findings, challenges, and implications of using chatbots to improve learning outcomes, and experiences were discussed. It was concluded that chatbots are an emerging technology that offers benefits such as teaching personalization, self-learning, and real-time feedback but also poses challenges, such as evaluation and research into their effectiveness for education. [ABSTRACT FROM AUTHOR]

In the field of aerospace, real-time and accurate monitoring of the milling state is of great significance for improving processing quality and processing efficiency. Relying on milling force signals to monitor tool wear state has problems such as high cost and limited size of machined parts. Aiming at the above problem, a milling cutter wear condition monitoring method based on spindle current is proposed. Firstly, based on the linear correlation between the milling force and the current increment theory, the milling experiment was carried out. The characteristic variation trend and spectral characteristics of the milling force signal and spindle current signal under the change of spindle speed, feed rate, and radial cutting width are analyzed. The cosine similarity demonstrates that the similarity of the root mean square values of the two is 0.9865, 0.9943, and 0.9421, respectively, and the changing trend of the spectral amplitude is consistent. In the iSESOL platform, a self-learning feature boundary model is built from learning data, signal interception, root mean square feature extraction, drawing boundary, and threshold judgment to monitor the wear state of the milling cutter online. Finally, the data in each wear stage are selected to verify the model, and the validity of the model is proved by setting the threshold beyond the boundary and the alarm trigger condition, in real-time, and accurately monitoring the wear state of the milling cutter online. [ABSTRACT FROM AUTHOR]

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents' abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The adoption of chatbots in education is steadily increasing, offering a myriad of benefits to learners engaging in self-directed study. However, the reliability of information provided by chatbots poses certain obstacles, particularly in subjects with high academic content such as physics. This study proposes a five-step process for constructing chatbots, enabling educators to independently develop chatbots to support self-learning among students. This process focuses on enabling chatbots to autonomously respond to physics-related inquiries, a capability often lacking in prebuilt chatbots. A new chatbot for physics lessons on the conservation of energy law was developed by applying this methodology. To assess the feasibility and effectiveness of the developed chatbot, we conducted a pedagogical experimental study involving 100 tenth grade students divided into control and experimental groups. To ensure comparability between the groups, students were selected based on their physics grades and teacher feedback. Experimental data was collected through online feedback forms and a 45-minute physics test, processed using statistical techniques. The results indicate a high level of readiness among participants to utilize chatbots for self-learning and acknowledge their beneficial impact. Furthermore, students' knowledge quality improved with chatbot usage, affirming the feasibility and effectiveness of incorporating chatbots into physics education. These findings also validate the proposed five-step chatbot development process as rational and applicable. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

In here a novel method is described to improve student success rates in a first-year basic chemistry theoretical/practical hybrid course (n = 31 students) by implementing simple ways of formative assessment. This to reduce student dropout rates following the philosophy of encouraging students' self-control. Essential is to train first-year bachelor students in their self-learning skills and to enhance their evaluative judgment. As a result, students are able to provide better quality of the assessment products at the end of the course. In practice the course is redesigned and intervention tools are integrated at multiple levels throughout the course. The lecturers' role was adapted to a coaching role, thereby introducing low-effort personalized micro-interventions to meet the personalized needs of students. To clarify these learning needs for students, awareness of the quality desired for the final assessment products is important. Awareness was improved by providing examples of varying quality and introducing multiple peer- and self-assessment moments during the course. The final evaluation of the course examination products showed that the quality of the laboratory notebook was substantially higher after following this approach. Additionally students learned other important skills such as self-learning skills, collaborating in practical work and giving and receiving feedback. Unexpectedly, the high perceived lecturers' workload decreased. The work presented here provides a novel approach in the form of a model and a practical blueprint with tools for a practical chemistry course design which develops students' self-learning skills thereby substantially improving student success rates. In our example course, the ultimate student success rate increased form 83 % to 95 % after using this novel approach. • A context-rich environment enables Chemical Engineering students' learning. • Examples help students to get a clear understanding of Intended Learning Outcomes. • Comparison of various examples leads to a set of distinctive criteria for students. • Combining a feedback process with formative assessment enables deep learning. • Continuously keeping track of learning progress provides improved student success. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) is a classic problem of image processing. This paper proposes a self-learning interpolation method (SLIM) based on a single image by combining grid feature mapping with binary decision tree, which is not only transparent as the interpolation-based methods, but also achieves comparable performance as the learning-based methods. Firstly, it downsamples the given image I LR to obtain its low–low-resolution image I LLR , which is used to obtain sample data for the self-learning interpolation algorithm for enlarging I LLR to get I LR . Secondly, it provides a structural feature classification method to divide all of the samples into several groups, such that each class of I LLR is mapped to a matrix of coefficients for calculating the values of the pixels of I LR . The image I LR is approximated by executing the decision tree to refine the corresponding mapping matrix. Finally, the resulting high-resolution image I HR is obtained from the given image I LR by using the mapping matrixes. Experimental results show that SLIM achieves more smooth edges and better details on subjective vision than prevailing SR methods, and it is a transparent one but achieves comparable performances on PSNR and SSIM with the learning-based methods, while it outperforms the interpolation-based methods. It means that SLIM is both transparent and efficient and has much better subjective vision than other SR methods. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) algorithm is an excellent human-inspired optimization technique. This paper proposes an innovative improved version of TLBO—collectivism teaching–learning-based optimization (CTLBO) algorithm. This algorithm imitates group and individual behaviours in the reality of teaching and learning, applies group-individual multi-mode cooperation strategies to form new search mechanism. The CTLBO contains three phases, i.e. preparation phase, teaching and learning phases. In the preparation phase, there are two operators, i.e. teacher self-learning and teacher-learner interaction operators. In the teaching phase, class teaching and performance-based group teaching operators are implied. In the learning phase, neighbour learning, student self-learning and team-learning strategies are mixed together to form three operators. Two sets of experiments are conducted to test the performance of CTLBO. The first set of experiments validates the improvement effect of CTLBO by comparing it with the original TLBO and other authors' improved versions of TLBO. The second set of experiments illustrates the advantage of CTLBO by comparing it with other 17 meta-heuristic algorithms in solving 30 general benchmark functions and 15 CEC2015 test suit functions. The results of experiments show that CTLBO algorithm has significant improvement effect compared with TLBO. It is the most effective one amongst the improved versions of TLBO selected for comparison, and outperforms all other 17 meta-heuristic algorithms. The algorithm can significantly improve the convergence ability and the accuracy in solving different-scale complex optimization models. [ABSTRACT FROM AUTHOR]

The architecture design studio is a complex pedagogical space and constantly evolving practice that aims to train students in a wide range of skills. The complex nature of the studio makes it a challenging topic to approach as a whole and requires a method that can address its multitude of dimensions all at once. This study explores an adaptation of the Analytic Hierarchy Process (AHP) in evaluating the effectiveness of four learning mediums embedded in architecture studio pedagogy: critiquing (crit), jury, peer learning, and self-learning. A set of four main criteria and twelve sub-criteria were extracted from the program's learning outcomes to create the hierarchy table. Each criterion/sub-criterion was compared to each of the other criteria using a pair-wise comparison survey. The survey was conducted among fourth-year students and teachers in a department of architecture in Cyprus. The results indicate a complex set of minor differences between students' and teachers' responses. The students' over-dependency on crit as their medium of choice for learning is among the notable findings of the study. Furthermore, the potential for utilizing AHP to develop more comprehensive models for architectural pedagogy is highlighted. [ABSTRACT FROM AUTHOR]

Introduction: Self-directed Learning (SDL) has been suggested as a principle of adult learning to promote lifelong learning abilities among students. Though SDL is not a new concept, there is a lack of uniform implementation across the institutions in India. The Competency-based Medical Education (CBME) proposed by the National Medical Commission in 2019 has emphasised SDL for medical students. Aim: To evaluate the effectiveness of an online, case-based SDL activity in Biochemistry for the first-year MBBS students. Materials and Methods: This was a quasi-experimental study conducted at Father Muller Medical College, Mangaluru, Karnataka, India from May to July 2021. A purposive sampling technique was used, and 138 first-year MBBS students were enrolled for the research. The topic of lipid metabolism was chosen for SDL. Parallel to the online didactic lectures, a casebased, team-based, online SDL was implemented. Students were provided with case scenarios and were instructed to discuss the case scenarios and find answers to the accompanying questions in allotted groups for 15 days. A three-hour session was held for the presentation of the SDL, followed by a posttest and reflections of students. The data were analysed using Statistical Package for Social Sciences (SPSS) version 25.0. The significance of the difference in pretest and post-test scores was assessed by paired t-test, and the level of significance was set at a p-value <0.05. Qualitative data were subjected to descriptive statistics, and thematic analysis of reflections was conducted. Results: The average post-test score of the participants was significantly higher by 99.3% compared to the pretest score (p-value <0.001). On average, 108 out of 138 (78.2%) agreed (agree/strongly agree) that SDL helped them to achieve the learning objectives,115 (83.3%) agreed that SDL helped them to develop as lifelong learners, 115 (83.3%) agreed that SDL helped them to develop as a health professional, and 83 (60.1%) agreed that enjoyed learning in online mode. The inclination of 110 (79.7%) students to have more SDL sessions in the future shows a behavioural change in favour of SDL. Time constraints and internet connectivity were the main challenges. Conclusion: SDL was effective and was received positively by the majority of the students. It is the need of the hour to develop lifelong learning and critical thinking among medical students and to make learning interesting with the use of case scenarios. Future research should assess the intermediate and long-term outcomes of case-based SDL on learning, behavioural changes, and its impact on patient care and the health of society. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing applications and agent-based implementations are scientifically investigated due to the enormous potential of industrial process optimization. The most widespread data-driven approach is the use of experimental history under test conditions for training, followed by execution of the trained model. Since factors, such as tool wear, affect the process, the experimental history has to be compiled extensively. In addition, individual machine noise implies that the models are not easily transferable to other (theoretically identical) machines. In contrast, a continual learning system should have the capacity to adapt (slightly) to a changing environment, e.g., another machine under different working conditions. Since this adaptation can potentially have a negative impact on process quality, especially in industry, safe optimization methods are required. In this article, we present a significant step towards self-optimizing machines in industry, by introducing a novel method for efficient safe contextual optimization and continuously trading-off between exploration and exploitation. Furthermore, an appropriate data discard strategy and local approximation techniques enable continual optimization. The approach is implemented as generic software module for an industrial edge control device. We apply this module to a steel straightening machine as an example, enabling it to adapt safely to changing environments. [ABSTRACT FROM AUTHOR]

The research analyzes the relationship between autonomous learning and academic self-efficacy of students in the technical-professional training of the Technological Institute of a Chilean university. Two instruments were applied. The first one, the Self-Directed Learning Scale. The second, the Scale of Perceived Self-Efficacy Specific to Academic Situations. First year students of professional technical careers participated in both applications. The research is ascribed to the positivist, hypothetical-deductive paradigm. With a quantitative, descriptive-correlational approach. The results show that these students feel motivated in their learning process and consider themselves capable of selfmanaging the process. However, it is necessary to delve into the actions that the teacher may take during the teaching-learning process, which may be negatively affecting in relation to self-efficacy factors. [ABSTRACT FROM AUTHOR]

In the engineering education system, the student's learning styles and abilities to acquire new skill sets have been transformed drastically due to the rising use of technology. The study provides a novel approach to self-learning through the use of technology-enhanced techniques aimed to improve student skills. The tool leverages digital platforms and interactive resources to create an engaging and personalized learning environment. This paper presents an examination of the effectiveness of a technology-enhanced self-study learning tool in enhancing student abilities such as knowledge, use of IT tools, communication, and information sharing across several disciplines. The tool's architecture integrates adaptive learning, multimedia content, and assessment mechanisms that adapt to individual learning styles and pace. This study intends to evaluate the tool's influence on improving students' knowledge, retention, and application of subject matter by examining user interactions and performance data. The methodology used is focused on a particular topic, data collection through the use of all multimedia resources, presentation, posting of videos, peer review, and comments on each task to improve abilities. With the deployment of technology-enhanced techniques, students' skills were observed to improve; around 8% of students' skills were improved in the internal evaluation. [ABSTRACT FROM AUTHOR]

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

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In the education, the use of advanced natural language processing techniques has gained prominence for their potential to revolutionize the teaching and learning experience. Self-learning in English teaching has become increasingly popular due to its flexibility and accessibility. With the advent of digital resources and language learning apps, students can now engage in language acquisition on their own terms. This paper presented a novel framework that combines the power of the Bidirectional Encoder Representations from the Transformers (BERT) model with the Hidden Condition Random Model (HCRM) for the enhancement of English teaching. The primary goal is to provide educators and institutions with a robust tool for evaluating the relevance and quality of teaching materials. The HCRM architecture incorporates sentiment analysis, feature extraction, and classification, making it a comprehensive solution for assessing the suitability of documents in the context of English teaching. The model takes into account the opinions of both students and teachers, ensuring a holistic perspective on the teaching materials' effectiveness. By effectively analyzing sentiments and extracting pertinent features, the HCRM facilitates a nuanced understanding of the potential impact of educational content. This paper's findings suggest that the integration of BERT with HCRM has the potential to greatly enhance English teaching by providing a more accurate, holistic, and data-driven approach to material assessment. The innovative framework presented in this research holds promise for improving the quality and relevance of teaching materials in the field of English instruction. [ABSTRACT FROM AUTHOR]

This study investigates how virtual and augmented reality role games impact self-learning in higher education settings. A qualitative research–action approach that involved creating augmented reality micro-stories to encourage creativity and critical thinking was used. Through role-playing, students collaborated and gained a deeper understanding of the course, improving their self-learning abilities. The findings indicate that incorporating virtual and augmented reality into higher education positively affects self-learning, promoting active student engagement and meaningful learning experiences. Additionally, students perceive these immersive educational methods as bridging the gap between virtual and in-person learning environments, ultimately leading to enhanced educational results. [ABSTRACT FROM AUTHOR]

Personal learning environments or their acronym PLEs are understood as a set of tools, resources, connections, and activities that each person regularly uses for learning. This study examines the PLE approach in a university setting, evaluating its impact on student learning in a university subject within an education master's program. The effect is assessed from two perspectives: a quantitative one based on the 'PLE test'—administered at the beginning and end of the course—and a qualitative one based on the assessment provided by the students themselves regarding the impact of training in digital tools and competencies on their PLE, at the beginning and end of the course. The 'PLE test' measures four factors: time organization, creation–editing, searching–investigating, and collaborating–contact networks. In all four factors, by the end of the course, there is a significant increase in usage scores, especially in the last two. The students' assessments of the evolution of their PLEs reflect the evident impact of this approach as a strategy for more effective and self-managed learning. [ABSTRACT FROM AUTHOR]

In this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC. [ABSTRACT FROM AUTHOR]

This study explores the use of open educational resources (OERs) in studio-based learning and their influence on learning experiences. The research team conducted action research with 30 bachelor of arts students who were completing a video production subject. Students were required to learn from a website containing open online learning resources under a flipped classroom approach. A teaching schedule and website were designed according to several criteria. Research data were collected through observation, reflective journals, and interviews and were analyzed via thematic analysis. Participating students expressed their perceptions of benefits and hesitation in utilizing OERs in learning. They agreed that the use of OERs as flipped classroom learning materials could positively affect their learning, primarily through competence and learning autonomy as indicated in self-determination theory. This investigation provides teachers with valuable experience and suggestions for teaching and learning approaches that incorporate OERs into studio-based education. Students learn from OERs in which they can gain the most up-to-date technical knowledge in an autonomous environment. This experience indicates that this pedagogy greatly and positively influences students' subject-learning experiences, learning outcomes, and self-learning skills. [ABSTRACT FROM AUTHOR]

Educating is not transmitting advances in science and technology to students, but developing in them a scientific conception of the world, about society, nature, human beings, thought and about themselves, which becomes convictions and core of his training. One of the objectives of the research is to recognize the pedagogical category training as a process and result, currently the assimilation of scientific knowledge of contemporary times in the formation of a conception and scientific attitude towards the phenomena of reality, by students. from a scientific thought with a responsible attitude towards knowledge and employment based on social good, which is achieved through the unity between the scientific character and the intentionality of education. The teaching practices are designed to train our teachers as future competent professionals who contribute to raising the quality of an education with responsibility. For this, the use of practical theoretical methods is proposed that allows us to verify the importance of implementing the study-work principle in our young people in training. [ABSTRACT FROM AUTHOR]

Pan-sharpening is a very productive technique to enhance the spatial details of multispectral images with the aid of panchromatic images. Nowadays, deep learning-based pan-sharpening has scored tremendous achievements. However, strict requirement for training image pairs and low generalization hamper the development of supervised pan-sharpening with limited samples. Unsupervised image fusion is an effective technique to fuse images without adequate ground truth as training samples. Existing unsupervised pan-sharpening methods are commonly based on the image generator model, suffering from unsatisfactory spatial details. In this study, we proposed a self-supervised pan-sharpening method with the aid of spectral super-resolution named sSRPNet. Coarsening-scale self-learning exploits the internal information in multispectral images at a coarsening scale and trains the initial fusion model without other labels. Spectral super-resolution injection explores the missing spatial details in the initial fused images and recovers it. Degradation self-learning introduces strong spectral constraints with original multispectral images. Both reduced-resolution and full-resolution experiments on three datasets have proved the superiority of sSRPNet. Furthermore, the proposed spectral super-resolution injection can be implemented on any existing pan-sharpening algorithms, improving their performance. [ABSTRACT FROM AUTHOR]

Copyright of University of Sharjah Journal for Humanities & Social Sciences is the property of University of Sharjah - Scientific Publishing Unit and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, different investigations reveal self-learning as a continuous process, factors such as sleep, reading, music and physical activities that also include leisure and recreation, favor and stimulate our attention levels to correctly capture the different information that the world offers us. medium where we develop. Neuroscience as a subject is essential for the study of the human being since education and psychology are integrated in order to improve the didactic resources for the training of students. The objective of this research is to reflect on the application of this science in education as a perfect combination for academic success. A methodology with a qualitative approach was applied and scientific research methods such as theoretical and empirical were used, which allowed to inquire about neuroscience and neuroeducation in the various fields of education. The results demonstrate the interesting field of neurosciences since each brain is unique and that learning is more effective when it is emotionally significant. In conclusion, this science allows us to know how the nervous system works to produce and regulate emotions, thoughts, behaviors: understand how emotions influence the teaching-learning process, which currently and in the future allows improving teaching methodologies. [ABSTRACT FROM AUTHOR]

This paper evaluates the consumption motivation regarding online learning. The research aims to gather information regarding learning engagement, motivation, satisfaction, and barriers to accessing and completing online courses. Grasping the patterns of digital course participants is vital for academic bodies and decision-makers to create and advocate for engaging online educational journeys. Collecting data concerning various factors aids in pinpointing consumer categories according to their necessities. [ABSTRACT FROM AUTHOR]

The original vibration signals of the fault gear under different working conditions have a large distribution difference, and there will be insufficient feature extraction during fault diagnosis, which leads to the problem of low diagnostic accuracy. Therefore, a self-learning model based on residual shrinkage network (SLRSN) is proposed. The model constructs a deep residual shrinkage network as the main network for feature extraction of the original vibration signal to enhance the robustness of the model. Then self-believing loss and self-doubting loss are proposed to achieve self-confidence and suspicion of health status prediction. The first is self-confidence loss, which adopts sub-domain distribution adaptation to actively align learned cross-domain features. The second is self-doubt loss, which provides SLRSN with the ability to extricate from wrong experience. Finally, to mitigate the effects of negative transfer, a novel adaptative weight allocation mechanism is designed to recalibrate the weighting of each source domain sample. Through the experiment of two gearboxes, it is verified that the proposed SLRSN method has good diagnostic reliability under the condition of gear speed and load change. [ABSTRACT FROM AUTHOR]

The present study introduces a new self-learning framework based on a recurrent neural network to compute the nonlinear viscoplastic response of plate structures undergoing isotropic damage. The neural network comprising Legendre Memory Unit (LMU) cells and dense transformations is pretrained with a combination of data-driven and physics-based loss functions. Once pretrained, the model is deployed in the explicit integration scheme to predict the strain increment in the thickness direction satisfying plane stress condition. This results in elimination/reduction in the iterations required to fulfill the plane stress condition and leads to faster convergence since the neural network replaces the root-finding methods. The proposed self-learning method differentiates itself from the well-known Physics-Informed Neural Networks (PINNs) since the neural network model self-learns online through the proposed physics-based loss term without the need for data. We compare the results of the Neural Network-enhanced integration scheme to validate the proposed method with those of the classical explicit integration scheme. • The LMU-based model acts as a nonlinear self-learning solver to satisfy the plane stress condition σ 33 = 0. • The iterations required to achieve the plane stress condition are reduced/eliminated. • The NN model receives constant feedback from the physics-based loss function ensuring convergence. • The internal variables of the LMU model trace the evolution of σ 33 = 0 in the nonlinear domain for each Gaussian point. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study aimed to reveal the relationship between indiscriminate use of smartphones and academic achievement among high school adolescents aged between 15 and 19, for a sample of 52 girls and boys. We employed a correlational descriptive approach to explore the association between two variables, using both the Pearson correlation coefficient and the T-test for analysis of the Internet Addiction Test (IAT) of Youg and the forward digit span test to measure the phonological loop, the reverse digit span test to measure the central administrator, and the Corsi block test to provide a framework for the theoretical and methodological analysis of blind uses of the digital tool among adolescents, to verify the existence of a statistically significant correlation between the uncontrolled use of smartphones and academic success. [ABSTRACT FROM AUTHOR]

This research has the purpose of analyzing the opportunities offered by self-learning and digital skills to coordinate relevant teaching methods in the current social contexts; since they are epistemic models that seek to overcome contemporary cultural crises in favor of enabling democratic coexistence. It is a case study, where inferences are made from the data obtained through observation and the application of questionnaires. The correct education, serving plural and emancipated collective efforts, is far from circumscribing itself to the space of the classroom as a limit that often affirms the hegemony of the State. It highlights that the development of training processes through virtual platforms requires two basic skills: the ability to self-direct learning processes and the efficient management of information and communication technologies. Pedagogical structures are promoted that use technical skills against the appropriation of forms of instruction with the possibility of coordinating knowledge societies capable of weaving dialogical strategies that allow overcoming common problems for the benefit of democratic and fair administrations. [ABSTRACT FROM AUTHOR]

A self-learning approach for optimal feedback gains for finite-horizon nonlinear continuous time control systems is proposed and analysed. It relies on parameter dependent approximations to the optimal value function obtained from a family of universal approximators. The cost functional for the training of an approximate optimal feedback law incorporates two main features. First, it contains the average over the objective functional values of the parametrized feedback control for an ensemble of initial values. Second, it is adapted to exploit the relationship between the maximum principle and dynamic programming. Based on universal approximation properties, existence, convergence and first order optimality conditions for optimal neural network feedback controllers are proved. [ABSTRACT FROM AUTHOR]

Project-based learning (PjBL) is one of the most predominantly practiced forms of active learning pedagogy in higher education. PjBL framework provides an authenticated platform/forum for 21st-century learners to acquire skills for solving real-life complex engineering problems through collaborative and iterative learning. The work presented in this paper describes a PjBL approach implemented as an instructional pedagogy and for a summative assessment of an undergraduate (UG) course on Digital Signal Processing (DSP). Students from Electronics and Telecommunication Department study this course in Third Year Engineering as a core course. The projects are chosen to make students identify and resolve real-life signal processing problems utilizing their subject knowledge and programming skills. PjBL implementation framework and rubrics-based evaluation devised for their summative assessment are discussed along with a study case. Student feedback analysis on the attainment of course outcomes and skills enhanced after completion of the course project is discussed. Through this analysis, it has been found that the effectiveness of the PjBL pedagogy employed has led to increasing students' engagement and interest in learning signal processing topics. It has also grown their self-learning ability and created awareness about the practical applications of the Digital Signal Processing course. [ABSTRACT FROM AUTHOR]

This study aims to reveal the relationship between primary school students' perceptions of self-learning and decision-making skills and whether gender, the education level of parents, and the number of siblings cause a difference. Within the framework of the stated purposes, the correlational survey model, which is among the quantitative research approaches, was used as a method in the research. The population consists of public primary schools' students studying in the city centre of Diyarbakır in the 2021-2022 academic year. The sample consists of 420 students determined by the "Simple Random Sampling" method, which is one of the probability sampling types, among the participants in the population. Descriptive and inferential statistical methods were employed to analyse the data. In the study, it was determined that there was a moderate, positive, and significant relationship between primary school students' self-learning and decision-making skills. In the study, it was determined that the self-learning skills of primary school students differed significantly according to gender and the number of siblings, but did not differ significantly according to the education level of their parents. In addition, the decisionmaking skills of primary school students were found to differ significantly according to gender and did not differ significantly according to the education level of their parents and the number of siblings. [ABSTRACT FROM AUTHOR]

This study examines the influence of instructional environments on students' academic enjoyment, motivational support, and learning strategies within the framework of control-value theory. Addressing a gap in empirical research on rubric-guided self-learning (SL), the study explores how peer and teacher motivation interact with achievement emotions to shape learning outcomes. A sample of 1410 students in grades 7 and 8 participated, with SL environments incorporating competency rubrics compared to traditional teacher-centered settings. Multigroup structural equation modeling was used to test latent interactions of motivational support on students' enjoyment, effort, and control strategies. Results indicate that SL environments foster greater enjoyment than traditional learning, though the impact of peer motivation lessens as enjoyment increases in SL contexts. Notably, peer motivation played a more complex role in traditional classrooms, at times moderating the relationship between enjoyment and effort negatively. These findings suggest that SL environments may better support autonomous motivation by reducing reliance on peer influence, highlighting the benefits of self-regulated learning models that prioritize intrinsic motivation. This study provides practical insights for designing learning environments that enhance student enjoyment and engagement. • Study employs control-value theory to assess a rubric-based self-learning in school. • Analysis involves 1410 students, enhancing validity. • Rubrics in self-learning foster enjoyment. • Peer motivation paradoxically lessens effort in traditional learning. • Second paradox: peer motivation diminishes control strategy use. [ABSTRACT FROM AUTHOR]

Training of university teachers is a fundamental component of professional teacher development. Within the different learning modalities, self-learning or self-training is particularly important today, as the boundaries between different learning contexts are increasingly blurred thanks to the increase in ICT. In order to understand the configuration of learning processes of university teachers in the field of social and legal sciences, a qualitative study was carried out with eight of the best teachers at the University of A Coruña, evaluated according to the ANECA DOCENTIA program. Two semi-structured interviews were used for data collection. The results are organized around three main elements: perceptions regarding the need for self-training, from which assessments and difficulties regarding training are extracted; past and present influences from people and experiences that lead us to want to self-train; and the resources used for self-learning. The data reveal that a fundamental component of teacher updating is based on continuous self-learning, especially in nonformal and informal contexts. The family, other teachers and the students themselves are key in learning processes, together with technological resources. [ABSTRACT FROM AUTHOR]

Copyright of Revista Docencia Universitaria is the property of Universidad Industrial de Santander and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present text makes you a reflective description about the strategies implemented by the teachers of Gabriela Mistral both gender Urban School, of the municipality San Sebastián de Buenavista, department of Magdalena, during the development of the methodology "Academic Work at Home" due to the suspension of academic activities in on - site by students and teachers by the risk of infection of COVID-19. Uis reflection pretends to explain the way it was possible to continue the teaching process in emerging educational in spaces and the importance of factors such as collaborative work, the rethinking of classroom practices, the implementation of TIC and self-learning, to construct a new paradigm of the concept of teaching in a new reality educational and in a context of crisis. Emerging, resilience, self-learning, learning, innovation. [ABSTRACT FROM AUTHOR]

The article considers the possibility of translating literary texts from a natural language into a target language using an automatic machine translation system. The author considers the possibilities of biological intelligence in comparison with machine "intelligence", which is a prototype of artificial intelligence. The paper gives an example (based on the game in GO) of the emergence of neural networks that appear as a result of self-learning of automated systems. New types of neural networks create an algorithm that always wins (the game of GO) against a person, even with the title of “world champion”. Studies show that automated translation systems must be taught to recognize lexico-semantic groups that would be based on the linguistic categories of natural language. Another task is to teach the machine to “think” and operate with images, since natural language is closely related to images and, at present, cannot be fully algorithmized. The practical application of automatic translation of poetic works shows that automatic machine translation systems, not possessing a psyche, are not able to translate texts of this type. [ABSTRACT FROM AUTHOR]

Copyright of Educação is the property of EDIPUCRS - Editora Universitaria da PUCRS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study aimed to explore the role of English songs in Saudi students' self-learning of English as a foreign language. The study employed one data collection tool which was an online questionnaire. The questionnaire was completed by 38 Saudi students at the Community College of King Saud University in the second semester of the academic year 2020. The examination and analysis of the data showed that listening to English songs motivated the Saudi students to learn English in a self-study mode, augmented their English vocabulary, improved their speaking skill and developed their spelling or writing skill to a considerable extent. It is, therefore, recommended that more large-scale quantitative or qualitative studies on the role of English songs in Saudi students' self-learning of English as a foreign language be conducted to confirm the findings of the present study, challenge them or yield new ones. [ABSTRACT FROM AUTHOR]

Recent significant changes in technology such as Artificial Intelligence (AI) and big data analysis have a wide impact in many areas of human societies, not least in education. Advances in efficiency of new technologies, with impacts in renewable energy and transport affected by environmental concerns and climate change mitigation, demand a reassessment and adjustment of the industrial structure of the economy. Technological advances enhancing creativity and analytical skills mean people must learn and adapt to a fast-changing economic environment. This paper present some key changes in Australian educational areas in recent years that are important and relevant to the subject of education innovation and philosophy to achieve outcomes in training and sustainable economic development, as anticipated by the community and government. Education courses and curricula in many countries have been transformed by technological advance. It is the swift incorporation of new technologies that best prepares students for the dynamics of economic and social change. Even though technological responsiveness is not a new phenomenon, it is established that the teaching methods found to be most successful include complementary and supplemental courses producing multi-skilled and creative graduates. These courses are delivered with significant emphasis upon self-development skills and are less focused upon traditional content. A move toward horizontal focus and a student-centric balance is the current trend, displacing vertical over-specialisation of learning. Yet, the central role of educator is seen as still appropriate and necessary where the aim is to equip graduates with creative and adaptive skills. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) is being integrated into many different areas of our lives, from industrial engineering to video-games, and also including teaching and education. We have several examples where VR has been used to engage students and facilitate their 3D spatial understanding, but can VR help also teachers? What are the benefits teachers can obtain on using VR applications? In this paper we present an application (VR4Health) designed to allow students to directly inspect 3D models of several human organs by using Virtual Reality systems. The application is designed to be used in an HMD device autonomously as a self-learning tool and also reports information to teachers in order that he/she becomes aware of what the students do and can redirect his/her work to the concrete necessities of the student. We evaluate both the students' and the teachers' perception by doing an experiment and asking them to fill-in a questionnaire at the end of the experiment. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent's population generated by adaptive mutation and hybrid crossover operator, ensuring the results' continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms. [ABSTRACT FROM AUTHOR]

This study examines the use of a reflective sketchbook journal (RSJ). The research focuses on developing self-learning habits by using journals for academic observation and applying theory to practice. Data were collected in mixed-methods from 12 third-year undergraduate art education students in Thailand. Results found that students were effective in observation, comparison and analysis of philosophies of different schools – from art activities, to the physical layout and scheduling of those places visited. The sketchbook aided reflective thinking and utilized many art techniques, highlighting the relationships between content, application of theories, diagramming and mind mapping. Approximately 75 per cent of the experimental group described the content and applied theories in text format and sketches. In addition, students reflected that jotting down content with personal words enhanced their ability for remembering, understanding and applying knowledge. However, some students believed that the RSJ is an important and useful tool that they intended to use again in other disciplines. [ABSTRACT FROM AUTHOR]