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Traditionally, clinician educators are tasked with the responsibility of training future physician workforce. However, there is limited identification of skills required to fulfill this responsibility and a lack of consensus on effective faculty development for career growth as a clinician educator. The newly released Accreditation Council of Graduate Medical Education (ACGME) Clinician Educator (CE) Milestones framework outlines important skills for clinician educators and provides the opportunity to create robust faculty development. In this paper, members of the Society of General Internal Medicine Education Committee discuss the importance of these CE Milestones, outline the novel themes highlighted in the project, and provide recommendations for proper application on both the individual and institutional levels to optimize faculty development. The paper discusses strategies for how to apply the CE Milestones as a tool to create a culture of professional growth and self-directed learning. Using a reflective approach, CE faculty and mentors can identify areas of proficiency and opportunities for growth, thereby creating individualized professional development plans for career success. Institutions should use aggregate CE Milestones data as a needs assessment of their faculty "population" to create targeted faculty development. Most importantly, institutions should not use CE Milestones for high-stakes assessments but rather encourage reflection by CE faculty and create subsequent robust faculty development programs. The ACGME CE Milestones present an exciting opportunity and lay an important foundation for future CE faculty development. [ABSTRACT FROM AUTHOR]

The paper introduces the student‐coined term digital instinct which describes students' disposition to resorting to digital technology for solving problems and doing school assignments. Taking cues from the term digital instinct, the paper describes a student perspective on digital literacy emerging from 100 lived experience descriptions and interviews with 37 Danish upper secondary students. The findings show that students' digital practice is characterised by experience‐based, intuitive and familiar use of technologies. Most notably, students employ digital technologies as cognitive partners that help them carry on with assignments that they initially did not understand, but that they were able to complete with the help of the computer. The study examines the nature of this partnership through the words of the students and identifies how technologies expand student agency but fall short of a reflective use of digital technologies. Recognising the strengths and weaknesses of students' digital practices may inform the concept of digital literacy and encourage teachers to acknowledge the digital instinct as a steppingstone to foster students' reflective use of digital technologies. Practitioner notesWhat is already known about this topic Students have inadequate command of digital literacies as described in curricular terminology.Students have positive as well as negative perceptions of the value and usefulness of digital technologies in school.Students both over‐ and underestimate their own digital literacies.What this paper adds Students have a fundamental utilitarian conception of digital technologies that either make schoolwork easier or more difficult, and they do not articulate that their use of digital technologies provides them with digital literacies.Students' conception of a digital instinct describes an intuitive and familiar, albeit unreflected, use of technologies where students employ experience‐based and self‐taught methods for using digital technologies.The digital instinct accounts for a feeling of agency among students, manifested in a widespread confidence that they can do assignments and solve problems when they make use of their computer.Implications for practice and/or policy Curricular terminologies struggle to capture what students can and cannot do with technology, and how much they understand the underlying technology.Teachers can involve and build on students' experience‐based digital practices as a starting point for developing digital literacy among students—also as an entry to a curricular perspective.Teachers should acknowledge students' conception of a 'digital instinct' as an important disposition in its own right. [ABSTRACT FROM AUTHOR]

Background: Both socially regulated learning and cognitive quality are important factors affecting collaborative knowledge building, but the current research lacks a joint quantified evaluation method that combines these two aspects. Objectives: Based on the existing framework, we proposed a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building, and further extracted the evaluation indicators. Methods: This study involved 74 learners enrolled in a course named 'the Application of Modern Educational Technology' as the subject of study, and made a case analysis with this method. Results and Conclusions: First, the indicators of this joint evaluation method could complement the indicators of traditional frequency statistics methods and facilitate an analysis of regulated learning and cognitive quality in collaborative knowledge building. Second, the group with a higher proportion of high‐level cognitive quality and socially shared regulation in collaborative knowledge building achieved better final scores. Finally, four types of groups were obtained through clustering: excellent, risky, mixed group, and highly collaborative group. This study introduced a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building and provides suggestions for future research. Lay Description: What is currently known about this topic?: Online collaborative knowledge building is increasingly important.Regulated learning and cognitive quality are considered very important for successful collaborative learning.Without quantitative methods, it is difficult to assess regulated learning and cognitive quality during collaborative knowledge building in a timely manner. What does this paper add?: This paper provided a joint evaluation method of regulated learning and cognitive quality (JERC) for online collaborative knowledge building.A case study was conducted to identify JERC's usefulness. Using descriptive statistics and visualization, it was determined that indicators of JERC can not only capture the state of cognitive quality and regulated learning status, but also elucidate the nature of that status (i.e., comprehensive, dynamic, cyclical).Through clustering analysis, four distinct groups were identified, each exhibiting different characteristics and moving patterns of regulated learning and cognitive quality over time. These patterns indicate that the quality of cognitive and regulatory learning cannot be determined solely by behavioural frequency. Moreover, an excessive proportion of individual regulation cannot predict better learning performance. Implications for practice: A computable representation model of regulated learning and cognitive quality was constructed to support automated perception and intervention in regulated learning and cognitive processing.With JERC, it is possible for researchers to capture, process, analyse and predict dynamic data to examine online collaborative knowledge building combined with shared physiological arousal events.The results of JERC can be used by researchers to develop advanced techniques, such as AI deep learning, for designing adaptive supports that can foster students' online collaborative knowledge building. [ABSTRACT FROM AUTHOR]

This paper describes authors' experience with designing and teaching a new course on "Fundamentals of Automation Engineering", based on a hybrid Project-Based Learning approach. The proposed challenge for students was to develop a low-cost Material Handling Machine, equipped with basic control functions. The project was executed in three stages, with different activities/tasks performed during each stage, which finally led to successful completion of the challenge. Detailed course file containing syllabus, learning objectives, assessment rubric, project goals, and deliverables was provided to students at first stage. Handouts and assignments were also provided weekly to students, which required extensive use of library resources (self-learning). Feedback survey was conducted fortnightly and suggestions were considered for course improvement. In this paper, authors discuss course organization, learning and assessment activities, and perception of students. [ABSTRACT FROM AUTHOR]

Knowledge diffusion in regional innovation ecosystems is an important factor that influences the regional innovation efficiency. In regional innovation ecosystems under digital empowerment, the knowledge diffusion enables the optimal allocation of innovation resources and promotes the sustainable development and ecological evolution of regional innovation ecosystems. In this paper, a SEIR (Susceptible–Exposed–Infected–Recovered) model is proposed for knowledge diffusion in regional innovation ecosystems under digitization. The basic reproduction number of the proposed model is calculated and its stability is validated. Finally, the expressions for the optimal control system and the optimal control parameters are presented. According to the research conclusions of this paper, the knowledge-diffusion ability of innovation agents in an innovation ecosystem affects the knowledge diffusion in a system; the contact rate between innovation agents affects the efficiency of knowledge diffusion in the system and the structure of the system; the digital transmission ability of innovation agents affects the breadth of knowledge diffusion in the system; and the self-learning ability of innovation agents affects the efficiency of knowledge diffusion in the system.The digital technologies help heterogeneous innovation agents in regional innovation ecosystems to break down the knowledge silos. At the same time, the digital technologies enhance the ability of innovation agents to absorb and learn knowledge in regional innovation ecosystems under digitization, thereby increasing the infection rate of knowledge diffusion in such systems.These conclusions extend the theoretical boundaries of innovation ecosystems and knowledge diffusion and offer management implications for enterprises and governments in decision-making. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the relative translational control for multiple spacecraft formation flying. This paper proposes an engineering-friendly, structurally simple, fast and model-free control algorithm. Design/methodology/approach: This paper proposes a tanh-type self-learning control (SLC) approach with variable learning intensity (VLI) to guarantee global convergence of the tracking error. This control algorithm utilizes the controller's previous control information in addition to the current system state information and avoids complicating the control structure. Findings: The proposed approach is model-free and can obtain the control law without accurate modeling of the spacecraft formation dynamics. The tanh function can tune the magnitude of the learning intensity to reduce the control saturation behavior when the tracking error is large. Practical implications: This algorithm is model-free, robust to perturbations such as disturbances and system uncertainties, and has a simple structure that is very conducive to engineering applications. Originality/value: This paper verified the control performance of the proposed algorithm for spacecraft formation in the presence of disturbances by simulation and achieved high steady-state accuracy and response speed over comparisons. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy sources and power electronic devices accessed in the distribution network, the harmonic pollution problem has become increasingly serious. The traditional centralized harmonic mitigation strategy has difficulty in effectively dealing with these scattered and random harmonics. Therefore, a distributed harmonic mitigation strategy based on the dynamic points incentive of blockchain communities is proposed in this paper. Firstly, a comprehensive voltage sensitivity partitioning method with harmonic weight differentiation is proposed to realize the reasonable partitioning of each control node and controlled node in the distribution network concerning variability in harmonic components and their distribution. Then, a harmonic mitigation strategy based on the dynamic integral excitation of self-learning algorithms is constructed to promote self-organized optimization and active distributed coordinated control of mitigation devices. The strategy ensures that the total harmonic voltage distortion rate of each node meets the requirements by adjusting the partitioned collaboration to realize optimal harmonic mitigation. By setting optimized partitions in different scenarios and conducting simulation verification, the results demonstrate the effectiveness of the strategy in this paper. It stimulates synergy between devices through a dynamic incentive mechanism and significantly reduces the total harmonic voltage distortion rate across various test scenarios, reflecting the adaptability of the harmonic mitigation method presented. [ABSTRACT FROM AUTHOR]

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users' activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users' learning analytics (LAs). The LAs can be processed by the instructors for the students' evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. [ABSTRACT FROM AUTHOR]

Automatic question generation and automatic question answering from text is a fundamental academic tool that serves a wide range of purposes, including self-study, coursework, educational assessment, and many more. Manual construction of questions is a time-consuming and complicated process that requires experience, whereas automating the process diminishes the costs of manual question creation and fulfills the need for a persistent supply of questions for the tutors and self-evaluators. This paper uses an encoder–decoder architecture-based text-to-text transfer transformer (T5) intending to generate several types of question–answer pairs over a given context, including subjective question–answers having short and long answers, fill-in-the-blanks-type question–answers, Boolean answer (yes-or-no)-type questions, and multiple-choice question-answers. The model has been evaluated on benchmark datasets—SQuAD, QuAC, and BoolQ; over automated metrics—BLEU, ROUGE, METEOR, F1, and accuracy. The model outperformed previous baseline models, with 18.87 and 25.24 scores over BLEU-4 and METEOR metrics, respectively. The paper also demonstrates that the proposed system efficiently generates question–answer pairs where the baseline approaches struggled. The evaluation analysis also shows that the generated question–answer pairs are comparable with existing systems and even better in terms of diversity. Also, the generated questions are grammatically and contextually correct, and the answer generated matches the question in the textual context. [ABSTRACT FROM AUTHOR]

Technology-enhanced learning environments provide ample opportunities for learners to self-regulate their learning processes and activities for achieving the intended learning outcomes in various disciplines from soft to hard sciences and from humanities to the natural and social sciences. This special issue discusses the emerging technological advancements and cutting-edge research on self-regulated learning dealing with different cognitive, motivational, emotional, and social processes of learning both at the individual and group levels. Specifically, it discusses how to optimally use advanced technologies to facilitate learners' self-regulated learning for achieving their own individual learning needs and goals. In this special issue, seven researchers/research teams from the fields of collaborative learning, computational thinking, educational psychology, and learning analytics presented contributions to self-regulated learning with the goal of stimulating cross-border discussion in the field. [ABSTRACT FROM AUTHOR]

Winnicott's unique contributions to a psychoanalytic theory of depression are not as familiar as Freud and Klein's writings. I concentrate on six areas: depression as a developmental achievement which denotes unit status; the role of destruction which arises from love (as opposed to hate which for Winnicott is a more mature affect); the importance of contributing‐in and the response of the (m)other in recovery from depression; the startling idea that the patient seeks out the analyst's depression; some thoughts on the differences between Winnicott and Freud and Klein; and lastly, the effect of depression on the development of self, creativity and the capacity to play. Winnicott does not shirk the darker side of being human, but at the same time he offers us a hopeful picture. [ABSTRACT FROM AUTHOR]

This paper reviews the evidence base for an in-hospital 12-month training program in neurodiagnostic technology utilizing two educational tracks: Electroencephalography (EEG) or Polysomnography (PSG), employing standardized didactic courses via the ASET – The Neurodiagnostic Society EEGCore Curriculum EEG 200–211 and the A-STEP online sleep self-study modules by the American Academy of Sleep Medicine (AASM). Specifically, we examine the purpose, strategy, and outcomes for the training program that was established in 2016 at Ann & Robert H. Lurie Children's Hospital of Chicago to support mission sustaining service lines. In addition, we report the results from a series of student course evaluations and an independent assessment of the program by ABRET Neurodiagnostic Credentialing and Accreditation through the application for programmatic recognition for EEG. Finally, we present a set of recommendations for organizations looking to develop a neurodiagnostic technology training program. [ABSTRACT FROM AUTHOR]

Criminal forensics in an Internet-of-Things (IoT) environment often requires complex investigations because IoT devices usually generate a large amount of electronic data, especially image data, which brings great difficulties to traditional digital forensic methods. Therefore, designing a secure management solution for image data and making it to be image evidence available in court is a new challenge. In this paper, we present a new secure image evidence management framework based on multi-bits digital watermarking and blockchain. In this framework, we first propose a flexible and robust self-learning based watermark embedding algorithm, which can embed both image marks and binary messages into the latent space of the input images and also improve the resistance of a broad range of attacks (geometric transform, JPEG, noise, etc.) to the watermarked images. And then, we design a smart contract resided in blockchain, which can achieve safe storage and automatically authentication of embedded watermarks. The experimental results on both watermarking algorithm and smart contracts have demonstrated the feasibility and efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

Background: Some studies have researched the correlation between flipped learning and cognitive learning outcomes; however, there is a paucity of research elaborating on the effects of flipped language learning on cognitive load (CL). Objectives: This study investigates the effects of using flipped learning designs (student‐led, teacher‐led and collaboration‐led) on learning outcomes and CL management during out‐of‐class activities versus during‐class activities. Methods: A mixed methods research was used to concurrently triangulate different strands of data on the effects of flipped learning designs on English as foreign language (EFL) learners' learning outcomes and CL. A total of 122 EFL learners were randomly assigned to either the control or experimental group and participated in the study. The interpretations were made based on running one‐way analysis of variance between four groups. Results and Conclusion: This study evidenced the efficacy of the interactive use of collaboration‐led flipped learning design over student‐led and teacher‐led designs for improving learning outcomes and lowering CL during computer‐assisted language learning (CALL) course. Pedagogically, CALL practitioners need to develop and integrate a well‐organized combination of student‐led and teacher‐led flipped learning activities for during‐class and out‐of‐class phases with respect to the CL and learning material complexity, while also considering the specific contexts and goals of their instructional settings. Theoretically, the results indicate the necessity of merging active learning and sociocultural theories into an instructional design to take advantage of the identified affordances of flipped language learning approach in terms of psychological effects (CL management) and learning effects (CALL outcomes). Lay Description: What is already known about this topic: The flipped language learning approach involves receiving online instruction outside the classroom and is a learner‐centered pedagogy. What this paper adds: This study contributes by triangulating different strands of data on the effects of flipped learning designs on learning outcomes and cognitive load.The findings highlight the effectiveness of collaboration‐led design for flipped language learning, facilitating engagement in both out‐of‐class and during‐class activities. Implications for practice: The findings emphasize the importance of designing and implementing instructional designs that align with emerging educational technologies.Teachers should consider the flexibility, fluency and novelty of the learning material when designing flipped learning activities. [ABSTRACT FROM AUTHOR]

The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional Neural Networks with Homomorphic Encryption (CNN-HE) by Self-Learning Activation Functions (SLAF). SLAFs are polynomials with trainable coefficients updated during training, together with synaptic weights, for each polynomial independently to learn task-specific and CNN-specific features. We theoretically prove its feasibility to approximate any continuous activation function to the desired error as a function of the SLAF degree. Two CNN-HE models are proposed: CNN-HE-SLAF and CNN-HE-SLAF-R. In the first model, all activation functions are replaced by SLAFs, and CNN is trained to find weights and coefficients. In the second one, CNN is trained with the original activation, then weights are fixed, activation is substituted by SLAF, and CNN is shortly re-trained to adapt SLAF coefficients. We show that such self-learning can achieve the same accuracy 99.38% as a non-polynomial ReLU over non-homomorphic CNNs and lead to an increase in accuracy (99.21%) and higher performance (6.26 times faster) than the state-of-the-art CNN-HE CryptoNets on the MNIST optical character recognition benchmark dataset. [ABSTRACT FROM AUTHOR]

This paper describes (1) the reasoning behind a self-directed learning assignment in an advanced qualitative research methods course, and (2) doctoral students' experiences with that assignment. Drawing on instructor and student perspectives, a professor and four doctoral students collaborated to reflect and report on the process and product of one assignment within a course. The doctoral students approached the assignment in unique ways: delving into one specific research methodology, comparing multiple research designs, and exploring current uses of qualitative research at a research conference. The piece contributes to the body of literature about students' and instructors' experiences in graduate qualitative research methods courses and argues for providing more opportunities for students to direct their own learning in graduate coursework. [ABSTRACT FROM AUTHOR]

In this paper, a discrete model of delays at oversaturated intersections was established by using the graphic analysis method and a reward function. The problem of optimizing signal control at an oversaturated intersection was transformed into a Markov decision process by utilizing queue length as the state variable and choice of green ratio as the action. Following this, a self-learning method for signal optimization (SLSO) at oversaturated intersections based on deep learning (dueling Q-leaming, Dueling DQN) with priority experience replay (PER) was proposed. Moreover, dynamic learning rate and dynamic e-greedy strategy were used to accelerate convergence and achieve better model optimization. Constraints and penalties on queue length were set to avoid traffic overflow. The results of comparative simulations showed that the traditional method yielded the longest delay while the two-stage bang-bang control method led to the shortest delay, with a reduction in the delay of about 13.3%. The proposed SLSO could reduce the delay by about 12.3% and the maximum queue length by 1.7% without constraints on queuing, and by 5.1% and 13%, respectively, with constraints on queuing in place. Finally, a real-world 4-phase intersection was selected to prove that the proposed SLSO method is widely applicable. [ABSTRACT FROM AUTHOR]

Recent advances in smart connected vehicles and intelligent transportation systems are based on the collection and processing of massive amounts of sensor data. There are various internal sensors integrated into modern vehicles that are useful to monitor multiple mechanical and electrical systems, and the shift to semi-autonomous vehicles adds outward-facing sensors such as cameras, lidar, and radar. Low-cost, dependable sensing, connectivity, computational capacity, and powerful analytics are ushering in a new era of vehicle context sensing and vehicular network (VANET). However, due to latency, bandwidth, cost, security, and privacy issues, as well as the growing capabilities of edge computing devices, it is necessary to examine both edge and cloud computing in order to make informed judgments based on their contexts and performances. So, in this paper, we have proposed an architecture that focuses on an intelligent transportation system based on both Cloud and Edge computing in order to monitor and analyze vehicle sensor data and its environment; known as monitoring and analyzing as a service. Our proposed architecture is able to react in real-time to any vehicle context changes in a dynamic, adaptive, and autonomous way. It generates many plans adapted to the vehicle context that will be useful for future research to achieve a self-learning and self-adaptive system. This suggested architecture helped us improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Also, we have reduced latency and increased bandwidth. Finally, we have approved the performance of our architecture based on different evaluation metrics. [ABSTRACT FROM AUTHOR]

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 paper examines, through a survey, the level of education and training in data verification among directors and department heads in the Spanish media (n=190). Findings reveal a limited level of training in this area, mainly acquired through in-house courses or self-directed learning, resulting in a limited knowledge and use of specific digital tools for information verification. Despite this, respondents generally hold a positive self-assessment of their verification skills, with (almost) all expressing a willingness to participate in training initiatives. [ABSTRACT FROM AUTHOR]

Microscopic emission estimation based on driving states plays a crucial role in controlling the pollution of on-road vehicles. Existing research has evolved from fitting nonlinear models of driving cycles and emission factors to utilizing neural networks to exploit driving patterns and construct a nonlinear mapping between driving states and emission values. However, due to the small percentage of driving-cycle related to high emissions, it is still challenging to capture the vehicle emission peaks, which lead to the most noteworthy high-emission characteristics being regarded as abnormal disturbances instead. To address the issue, this paper proposes a peak-sensitive microscopic emission estimation framework characterized by sequence-to-sequence learning for on-road vehicles. Sequence-to-sequence learning emphasizes serial pattern mapping from driving sequences to emission sequences to provide more statistical constraints and reduce the estimation uncertainty brought by unstable driving behaviors. Specifically, the framework aims at capturing context features related to high emissions from sequences dynamically in an adaptive and self-learning way and is composed of a driving states embedding module and a dynamic aggregation module. Particularly, an incremental tracking loss (ITL) is proposed to adjust the incremental emissions at adjacent time steps by supervising the differences of the generated sequences, enabling the model to track sudden changes in emissions. Extensive experiments are conducted on the on-board diagnostics (OBD) dataset with 12628 sampling records collected from a heavy-duty diesel vehicle. The results show that the estimation accuracy of our proposed method is significantly better than state-of-the-art methods, and it can effectively capture high-emission peaks. [ABSTRACT FROM AUTHOR]

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the "teaching" and "learning" stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm's superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Self‐direction has become an important skill in the 21st century. To cultivate learners with a high level of self‐direction, it is necessary to diagnose their self‐directed learning (SDL) ability. This study diagnosed and predicted learners' SDL ability based on their actual SDL behaviours. The study was performed in a self‐directed 3D design class lasting 90 minutes. A total of 193 middle school students participated in the study. The results of the Pearson correlation analysis (p < 0.001) showed that the reported perception of SDL ability was significantly correlated with SDL behaviours. The results of the hierarchical multiple linear regression analysis showed that the SDL behaviours explained 84.9% of the variance in SDL ability (adjusted R2 = 0.849, p < 0.001). Therefore, SDL behaviours had significant predictive effects on the reported perception of SDL ability. Moreover, based on the random forest algorithm, the study built an SDL ability prediction model with high performance (accuracy = 0.83, precision = 0.82, recall = 0.84) using SDL behaviours as features. The study provides evidence for the design of effective strategies to enhance SDL ability and promote SDL behaviours.Practitioner notesWhat is already known about this topicTo cultivate learners with a high level of self‐direction, it is necessary to diagnose their self‐directed learning (SDL) ability.SDL is a combination of internal personal attributes and external autonomous behaviours.Few studies have focused on diagnosing SDL ability based on learners' external SDL behaviours occurring during the learning process.What this paper addsThe reported perception of SDL ability was significantly correlated with SDL behaviours.SDL behaviours had significant predictive effects on the reported perception of SDL ability.Based on the random forest algorithm, the study built an SDL ability prediction model with high performance using SDL behaviours as features.Implications for practice and/or policyThe findings indicate that instructors could design effective strategies to promote SDL behaviours for the purpose of enhancing learners' SDL ability.The method and process of building an SDL ability prediction model might provide a reference for related research on ability prediction with behaviours. [ABSTRACT FROM AUTHOR]

Summary: Content interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly improve data retrieval latency, origin server load, network congestion, and overhead. The state‐of‐the‐art work is either driven by flooding approach trying to minimize the adverse effect of Interest flooding or path‐driven approach trying to minimize the additional cost of maintaining routing information. These approaches are less efficient due to storm issues and excessive overhead. Proposed protocol aims to forward interest to the nearest cache without worrying about FIB construction and with significant improvement in latency and overhead. This paper presents the feasibility of integrating reinforcement learning based Q‐learning strategy for forwarding in ICN. By revising Q‐learning to address the inherent challenges, we introduce Q‐learning based interest packets and data packets forwarding mechanisms, namely, IPQ‐learning and DPQ‐learning. It aims to gain self‐learning through historical events and selects best next node to forward interest. Each node in the network acts as an agent with aim of forwarding packet to best next hop according to the Q value such that content can be fetched within fastest possible route and every action returns to be a learning process, which improves the accuracy of the Q value. The performance investigation of protocol in ndnSIM‐2.0 shows the improvement in a range of 10%–35% for metrics such as data retrieval delay, server hit rate, network overhead, network throughput, and network load. Outcomes are compared by integrating proposed protocol with state‐of‐the‐art caching protocols and also against recent forwarding mechanisms. [ABSTRACT FROM AUTHOR]

Deep learning (DL) techniques have recently attracted considerable attention in the field of seismic data denoising. However, most DL-based seismic denoising models require a considerable number of paired noisy–clean samples for training, which limits their application in practice. In this paper, a novel self-supervised DL scheme for noise attenuation in reflection seismic data is proposed based on the Neighbor2Neighbor strategy. The proposed method adopts a U-shaped convolutional network as the main network and incorporates regularization loss during training to improve the stability of network training, thereby constructing an end-to-end self-learning process for seismic data denoising. Specifically, a neighborhood subsampling workflow is built on single noisy seismic data to generate paired noisy images for training. Next, an unsupervised evaluation metric, grounded solely in noisy data, is adopted to quantitatively evaluate the performance of the denoising algorithm without labeled data. The application to synthetic datasets suggests that the proposed method achieves better denoising results than traditional (e.g., f−x deconvolution and wavelet analysis) and self-supervised methods, and rivals a widely used supervised DL model, the denoising convolution neural network. In real application to seismic data denoising without noise-free labels, the proposed method more effectively suppresses migration artifacts and random noise while better preserving the signal integrity compared to other methods. These improvements demonstrate that the proposed method is an independent, novel, and powerful data-driven DL scheme suitable for real seismic data denoising. [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]

The study introduces a novel approach to identify potential citation manipulation within academic papers. This method utilizes perturbations of a deep embedding model, integrating Graph-Masked Autoencoders to merge textual information with evidence of graph connectivity. Consequently, it yields a more intricate model of citation distribution. By training a deep network with partial data and reconstructing masked connections, the approach capitalizes on the inherent characteristics of central connections amidst network perturbations. It demonstrates its ability to pinpoint trustworthy citations within the analyzed dataset through comprehensive quantitative evaluations. Additionally, it raises concerns regarding the reliability of specific references, which may be subject to manipulation. [ABSTRACT FROM AUTHOR]

The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a common goal of improving education drive this transformative journey. In a time when resources and information are more readily available than ever, traditional teaching strategies are changing to include digital tools that meet the various needs of students. For a teaching services support system (TSS), this paper proposed a novel machine learning-based model that utilizes the powerful backpropagation (BP) neural network, well known for its machine learning and data analysis capabilities. Increasing the effectiveness of online learning is the primary objective of the TSS, which focuses on contributing to a complete learning environment and promoting self-directed learning. This work closely examines the construction and functionality of the BP neural network within the TSS, contribution visions into input–output mechanisms, activation functions, and weight coefficients. This novel approach can bring concerning a digital age educational revolution by increasing the effectiveness and caliber of online learning, rekindling students' enthusiasm for learning, and making the best use of teaching resources. In addition, the research explores the field of data-mining-based online teaching support services in Open Universities. It clarifies the system's architecture, which includes virtual teaching modules, resource management, and user authentication. Machine learning methods such as adaptive genetic algorithms and BP neural networks optimize the system's architecture. Experimental results achieved remarkable BP neural network accuracy and stability, significantly enhancing instructional quality and student engagement. The results show an impressive 83.4% accuracy, outperforming traditional methods such as SVM, KNN, DT, RF, and XGBoost. This demonstrates how learning outcomes and productivity can be enhanced by incorporating machine learning into education. [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]

Background: This paper analyzes a remembered shared experience of cocurricular designing and coteaching an experiential learning pilot project in a university study abroad program (SAP) that emphasized social justice. Purpose: We look back because the pilot program is a significant demonstration of what complexities can arise when feminist educators bring their own politics and pedagogies to experiential learning and study abroad education contexts; we understand this as valuable because "experience" broadly is complex. Memory is crucial to feminist educators because it indicates the development of care, ethics, pedagogy, politics, and priorities, and so, we ask: what pedagogical insights can be gained through revisiting our enmeshed personal/professional memories of cocurricular designing and coteaching an experiential learning series for study abroad students? Methodology: We use self-study and memory work to analyze memory objects (curriculum materials) and narratives (of teaching) to explore this pedagogical work and highlight generative tensions that emerged while employing a critical feminist approach to work not initially conceptualized with this framing. Findings: We uncovered three key tensions: personal, community, and racial that troubled emergent issues related to difference, discourse, and positionality. Implications: This project offers pedagogical insights into how oppression and inequality may enter experiential learning and study abroad spaces. [ABSTRACT FROM AUTHOR]

This paper aims to offer a new perspective on role modelling by examining adolescents' own efforts to lead a morally virtuous life. While traditional approaches to moral education emphasize the importance of teachers as role models, this study proposes a shift in focus towards adolescents' own role models. Drawing on the philosophical concept of moral self-cultivation and psychological insights on identity development and social cognitive learning, it is argued that adolescents have the ability to cultivate their moral character by emulating others. However, empirical evidence suggests that adolescents often do not perceive teachers as their role models. Consequently, moral educational theories are advised to demand less of teachers as role models. Teachers could invest more time in understanding students' personal role models and encourage them to compare these role models with their own behavior. By acknowledging and addressing students' role models, teachers can facilitate the moral self-cultivation of adolescents. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the self-study methods used by pilots while grounded and their perception of competence decline and confidence upon their return to flying. Background: Previously, long absences from flying were managed on a case-by-case basis. Thousands of pilots returning to flying as the pandemic eases have burdened airline training systems. Limited research has been conducted on the decline in skills of airline pilots while operationally absent from the cockpit. Few studies have considered this topic in the context of a pandemic. Method: A questionnaire study was conducted with 234 airline pilots who were grounded during the COVID-19 pandemic. Results: Uncertainty regarding sudden and indefinite periods of grounding made it challenging to maintain motivation to self-study. This matter was aggravated by the additional financial and personal stress caused by the state of the airline industry and the outcomes of the pandemic. The participants anticipated a decline in manual flying skills as the worst outcome after being absent from the flight deck. However, these pilots proved quick to recover these skills when they resumed flying. It took significantly more time for pilots to regain proficiency in applying knowledge, procedures and compliance with regulations, situation awareness and workload management. Conclusion: The study proposes recommendations for pilots and airlines to harness essential self-study practices in competency areas identified to have significantly declined. Application: The outcome of this paper guides airlines, pilots and regulators in better understanding how grounded pilots observe skill decline in a broader range of competencies. [ABSTRACT FROM AUTHOR]

Being directed against the matters that teaching-learning-based optimization algorithm (TLBO) was prone to plunge into local optimum and slow optimization speed, the paper raised a dynamic adaptive teaching-learning-based optimization algorithm (ITLBO). Firstly, the paper came up with an inertia weight related to the sigmoid function and introduced it into the teacher phase and the student phase to dynamically adjust the population's movement step size, which could suppress the decay of population diversity in the early stage of the algorithm to expand the optimization area, and enhanced the local search ability in the later stage of the algorithm to increase the solutions accuracy. Secondly, in the teacher phase, this paper developed a mechanism of dynamically updating teachers to enhance teachers teaching level, ensuring that students learn from the global optimal solution in a timely manner and improving the convergence speed of the algorithm. Thirdly, in the student phase, this paper put forward a learning approach combining self-study and learning from the most outstanding student to ensure the students comprehensive development, thereby improving the search ability and convergence speed of the algorithm. To validate the effectiveness of ITLBO, adopting 16 standard test functions tested the effectiveness of the inertia weight and the contribution of each operator to the overall algorithm. It conducted the simulation comparative experiment on TLBO, RTLBO, EPGTOA, and ITLBO. A series of test results reveal that ITLBO has more fantastic convergence capability and stability. [ABSTRACT FROM AUTHOR]

In this paper, a teacher educator discusses her experience of professional learning through self-study. The author found the transition between being a classroom teacher and teacher educator challenging, as has been noted in recent literature. Thus, she decided to embark on a self-study, which was focused on the enhancement of her teaching so that she was not merely transmitting knowledge to her students about the teaching of literacy. Data was gathered using a range of qualitative methods including reflective journals, peer observations, and student focus groups. Themes emerged from the data relating to the importance of activating prior knowledge in learning, the necessity of creating dynamic lecturing spaces and the need to share learning while learning in a university lecture hall. As a self-study, this paper is highly contextual but is intended to provide valuable insights into teaching and learning in higher education. [ABSTRACT FROM AUTHOR]

The paper considers real-time cyberattack detection methods based on an algebraic approach. The author has applied the algebraic matching method, which relies on solving behavioral equations and algebraic modeling. The paper proposes to improve the efficiency of attack detection and prevention by combining a neural network that classifies attacks with an algebraic matching method. This scheme is called a cognitive network, the concept of which was first conceived in 2005. The cognitive network properties, such as double and self-learning, are discussed. The technological line for cyberattack detection using cognitive networks is presented. [ABSTRACT FROM AUTHOR]

Historical investigation of novel changes over the ages. It demands observations from different dimensions, confronting one another and sometimes presenting opposite views provided by additional studies. Considering literary analysis about the emergence, structure, component, or features of novels somewhat has diffused the discussion. This paper reviewed the comprehensive theory of Mikhail Mikhailovich Bakhtin regarding the novel, the most celebrated discourse of books. The world merges into an open-ended, multi-voiced, dialogic reality as a novel gives way to distributing entirely incompatible parts among different perspectives of equal importance. Bakhtin opposes monologic speech and acknowledges dialogic speech, which determines social relations where the speaker is embedded. The dialogic discourse offers a radical liberalization of both the self and the concept of culture. The present paper traced the implied dialogism or the social relations within the framework of culture and subculture. Thus language which functions in a novel is not "symptomatic" of "persons," but persons are the bearer of the language, with the "specific set of social and ideological valuations" that entails in a novel. [ABSTRACT FROM AUTHOR]

Background: The precise effect and the quality of different cases used in dermatology problem-based learning (PBL) curricula are yet unclear. Aim: To prospectively compare the impact of real patients, digital, paper PBL (PPBL) and traditional lecture-based learning (LBL) on academic results and student perceptions. Methods: A total of 120 students were randomly allocated into either real-patients PBL (RPBL) group studied via real-patient cases, digital PBL (DPBL) group studied via digital-form cases, PPBL group studied via paper-form cases, or conventional group who received didactic lectures. Academic results were assessed through review of written examination, objective structured clinical examination and student performance scores. A five-point Likert scale questionnaire was used to evaluate student perceptions. Results: Compared to those receiving lectures only, all PBL participants had better results for written examination, clinical examination and overall performance. Students in RPBL group exhibited better overall performance than those in the other two PBL groups. Real-patient cases were more effective in helping develop students' self-directed learning skills, improving their confidence in future patient encounters and encouraging them to learn more about the discussed condition, compared to digital and paper cases. Conclusion: Both real patient and digital triggers are helpful in improving students' clinical problem-handling skills. However, real patients provide greater benefits to students. [ABSTRACT FROM AUTHOR]

Background: This research is inspired by the challenges encountered in interpreter training, changes in learning needs in the technological era, the educational paradigm shift caused by the COVID‐19 pandemic, the lack of studies on combining virtual reality (VR)_and mobile technologies, and literature gaps in the field of the incorporation of technology in interpreter education. Objectives: This research aims to develop a mobile‐based VR application for bi‐directional English‐Chinese and Chinese‐English interpreting learning named 'Virtual Interpreting Practice' (VIP) based on an interpreting competence model and to investigate its impact on students' self‐rated language and interpreting proficiency levels as well as their declarative knowledge and operative skills as reflected in different interpreting modes (sight and consecutive) and language directions (English‐Chinese and Chinese‐English). The VIP app contains learning resources, including 13 learning modules covering both interpreting modes, practical topics and contexts, video lectures and demonstrations, fully immersive VR and non‐VR practice, glossary lists, source texts, translated texts and an online feedback platform. Methods: Thirty‐one native Chinese‐speaking English as a second/foreign (ESL/EFL) undergraduate students used the app for self‐directed learning. The data were collected by the pre‐study and post‐study questionnaires as well as the pre‐test and post‐test. Results and Conclusions: The results indicate that the use of VIP significantly raised the students' self‐rated language and interpreting proficiency levels as well as their interpreting performance in both modes and language directions. While the students performed better in sight interpreting and Chinese‐English interpreting, they achieved a significantly higher improvement rate in consecutive interpreting and English‐Chinese interpreting. Lay Description: What is already known about this topic: A number of studies have demonstrated the use of virtual reality (VR) provides users with senses of multiple presence, enhances learner engagement and motivation, and facilitates collaborative, situated, immersive and experiential learning that is not often available in traditional learning environments.The increasing availability of VR has created new opportunities in the area of mobile‐assisted language learning (MALL), paving the way for a new research area called VR‐assisted language learning (VRALL).Using VRALL, numerous benefits in various language skills have been identified, such as listening, speaking, writing and vocabulary acquisition. What this paper adds: While blending VR and mobile technologies may play a more significant role in education settings and create more advantages, the impact of this combination for interpreting learning has remained an unexplored area in higher education. This paper has addressed the research gaps in which there is a lack of VR language learning apps that are bilingual, mobile‐based and specifically designed for interpreting practice in highly immersive learning environments.The results indicate that the use of VIP significantly raised the students' self‐rated language and interpreting proficiency levels as well as their interpreting performance in both sight and consecutive interpreting and language directions (English‐Chinese and Chinese‐English).While the students performed better in sight interpreting and Chinese‐English interpreting, they achieved a significantly higher improvement rate in consecutive interpreting and English‐Chinese interpreting. Implications for practice and/or policy: The results have implications for the roles of students and teachers. The app developed in this study shifts the teacher‐led approach to student‐centred learning. While it allows more learning autonomy, its effectiveness may largely depend on students' ability to self‐regulate and engage.It appears that the effectiveness of VRALL depends on a number of individual and contextual factors, e.g. the design and functionality of the apps, availability of the internet connection and smartphones as well as learning needs and motivation. [ABSTRACT FROM AUTHOR]

Background: Many learners struggle to productively self‐regulate their learning. To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL researchers have largely utilized think aloud or trace data, however, not without challenges. Objectives: In this paper, we present the findings of a study that investigated how concurrent analysis and integration of think aloud and trace data could advance the measurement of SRL and assist in better understanding the mechanisms of SRL processes, especially those details that remain obscured by observing each data channel individually. Methods: We concurrently collected think aloud and trace data generated by 44 university students in a laboratory setting and analysed those data relative to the same timeline. Results: We found that the two data channels could be interchangeably used to measure SRL processes for only 17.18% of all the time segments identified in a learning task. Moreover, SRL processes for around 45% of all the time segments could be detected via either trace data or think aloud data. For another 27.17% of all the time segments, different SRL processes were detected in both data channels. Conclusions: Our results largely suggest that the two data collection methods can be used to complement each other in measuring SRL. In particular, we found that think aloud and trace data could provide different perspectives on SRL. The integration of the two methods further allowed us to reveal a more complex and more comprehensive temporal associations among SRL processes compared to using a single data collection method. In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL. Lay Description: What is already known about this topic: To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL.To measure SRL processes, contemporary researchers have largely utilized think aloud or trace data, however, not without challenges.Think aloud and trace data should complement each other and, when analysed concurrently, can provide a more valid and fuller picture of SRL than when analysed separately, which is typically the case in the SRL research to date. What this paper adds: We proposed a novel alignment approach to triangulate the measurement of self‐regulated learning (SRL) based on trace and think aloud data.We defined integration rules to integrate the measurement results from trace data and think aloud, which helped to provide a fuller picture of SRL.The integrated results revealed a more complex, complete and comprehensive SRL process map compared to using a single method. Implications for practice and/or policy: First, our results suggest that using a single measurement method can often reveal SRL processes only partially.Second, our findings indicated that the integration of the two measurement methods could not address all their methodological shortcomings and more research is needed towards new integrative approaches that can further reduce the number of misaligned results.In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL.More specifically, the integrated measurement of SRL can be used in the future to better (1) test the effects of instructional SRL interventions, for example, scaffolding; and (2) evaluate how learners use specific learning tools. [ABSTRACT FROM AUTHOR]

It is important for university students studying elementary music education to master basic conducting gestures. We have developed a VR training system for self‐study of Saito's music conducting method. The system uses a depth camera and magnetic motion capture system to measure hand movements, and an HMD to superimpose the teacher's hand on the trainee's own hand. With the system, trainee can self‐study while visually and three‐dimensionally checking the motion and timing of the teacher's and the trainee's own hand gestures. In this paper, we describe an evaluation experiment with our system. The results showed that the VR environment is effective for learning three‐dimensional movements, and that the addition of a visual line display function for trajectories, a function to display trajectories from different viewpoints, and a replay function improves the learning effect. Specifically, the results of a short training session of 5 min revealed that the developed system was effective in bringing 75% of the trainees closer to the teacher's gesture. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Dual subtitles show the spoken words of the audio track simultaneously in the original and as a translation when viewing audiovisual media. Since DVD players and TV sets usually do not have a function that allows dual subtitles to be switched on, the paper first describes how this can nonetheless be done by using a computer and special software. Then 36 empirical studies are reviewed that have investigated the effects of dual subtitles. The article comes to the conclusion that they are only of limited use for the classroom but have great value for self-directed learning outside the classroom. [ABSTRACT FROM AUTHOR]

Background: Online learning has become more prevalent over the past three decades, especially during the COVID‐19 pandemic. Educators and scholars have increasingly emphasized the significance of self‐directed learning (SDL) on successful learning outcomes in online learning environments. Objectives: The purpose of this study was to synthesize the research on the effects of SDL on learning achievement in online learning environments. Methods: This study synthesized the research findings on the effects of SDL on learning achievement in online learning environments using a meta‐analysis method. We analysed 152 samples from 43 studies that have reported the effects of SDL on learning achievement or correlations in online learning. Results and Conclusions: The overall effect size of SDL on learning achievement in online learning showed a medium effect size (g = 0.508). The dimensions of SDL moderated the effects of SDL on learning achievement, and the effects of self‐management were statistically smaller than motivation and self‐monitoring. The types of learning achievement, learning domains, and participant levels did not moderate the effects of SDL on learning achievement. However, the effects of SDL on learning achievement in the cognitive domain were significantly smaller than the effects of SDL in the affective domain. What Are the 1 or 2 Major Takeaways from the Study?: This study confirmed the importance of SDL in online learning environments. SDL can be effective for any learner group, not just for adult learners, in online learning environments. Online instructors should facilitate students' self‐management to obtain successful learning achievement. Lay Description: What is already known about this topic: Self‐directed learning (SDL) has been widely researched in education.Online learners are expected to be self‐directed learners to obtain successful learning achievement.SDL and self‐regulated learning (SRL) are interrelated. What this paper adds: The effects of SDL on learning achievement in online learning environments showed a medium effect size.The effects of self‐management on learning achievement were smaller than motivation and self‐monitoring.The effects of SDL on the cognitive learning domain were smaller than the affective domain.The participants did not moderate the effects of SDL on learning achievement. Implications for practice and/or policy: Online instructors should help develop learners' SDL skills.Online instructors should facilitate students' self‐management to obtain successful learning achievement.SDL can be effective for any learner group, not just for adult learners, in online learning environments. [ABSTRACT FROM AUTHOR]

The evolving linguistic landscape in 21st century classrooms necessitates a re-evaluation of pedagogical approaches, exploring the potential of multilingual writing techniques within TESOL settings. This article draws on my self-study as a TESOL educator navigating contexts and shifting from an English-only approach in the classroom to an openness of language(s) approach (Ortega, 2019). Following Hamilton's (2018) case study approach, I investigate the feasibility of implementing a multilingual pedagogy in an international school in Toronto and explore its influence on students, teachers, and the learning process across the domains of (CMLA) (Prasad & Lory, 2020). For this paper, I focus on data that highlight and reflect the impact of multilingual pedagogy on students, teachers, and the teaching/learning process. I performed a qualitative thematic analysis and found that multilingual pedagogies benefited students on many levels. I conclude with a personal reflection on both the affordances and challenges of implementing multilingual pedagogies. [ABSTRACT FROM AUTHOR]

Individual interest and self‐regulation are two pillars of self‐directed learning. Despite empirical evidence on the interaction between the two with respect to academic achievement, few studies have explored how individual interest and self‐regulation might interact and relate to self‐directed informal learning. This study surveyed 322 university students on self‐regulation and two interest constructs (interest in learning English and interest in pursuing personal interest in English), and tested how these factors related to their engagement in self‐directed use of technology for English learning beyond the classroom. Structural equation modelling analysis revealed that despite both being significant determinants, interest factors, and self‐regulation were associated differently with various types of self‐directed technological activities (instruction‐, information‐, entertainment‐ and socialization‐oriented activities). Self‐regulation was predictive of instruction‐, information‐ and socialization‐oriented activities, which have high cognitive load and require effort management for second‐language learners. Interest in English learning was consistently a significant predictor of all four types of technological activities. Interest in pursuing personal interest in English played a significant role in information‐ and entertainment‐oriented technological activities. The findings recommend greater attention to individual interest when promoting self‐directed informal learning. The findings further suggest adopting a differentiated approach to bolstering self‐directed learning for different purposes. Practitioner notesWhat is already known about this topic Self‐regulation is important to self‐directed learning.Subject‐matter interest and self‐regulation interplay to influence academic performance in formal learning contexts.What this paper adds Interest and self‐regulation interplayed to shape self‐direction in informal learning contexts.Self‐regulation was predictive of instruction‐, information‐ and socialization‐oriented activities.Interest factors added additional explanation power on self‐directed technological activities.Both subject‐matter interest and the integration of personal interest with subject learning were significant determinants.The integration of personal interest with subject learning mediated the influence of subject‐matter interest.Implications for practice and/or policy Educational interventions need to work simultaneously on both interest and self‐regulation to bolster self‐directed learning.Educators need to address different factors when boosting different types of technological activities.A relative stance needs to be taken since self‐regulation might play a less significant role in less taxing technological activities.It is important to deliberately integrate students' personal interests into English learning and help students perceive and act on the integration. [ABSTRACT FROM AUTHOR]

Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both "intraexpert" and "interexpert" variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

After 70 years of intricate development, machine learning, represented by deep learning, is based on the multilevel structure of the human brain and the layer-by-layer analysis and processing mechanism of neuron connection and interaction information. The powerful parallel information processing ability of self-adaptation and self-learning has allowed for breakthroughs in many fields, among which the most representative is image recognition. Therefore, this paper proposed optimizing the RBF algorithm with machine learning (ML) to improve the recognition rate of spectrum sensing. The results showed that the average detection success rates of the RBF algorithm were 93.62%, 95.07%, 96.91%, 98.78% and 99.37% when the SNRs were − 8 dB, − 4 dB, 0 dB, 4 dB and 8 dB, respectively, and the other conditions were kept the same. The average detection success rates of the SVM/RBF algorithm were 97.65%, 99.63%, 99.76%, 99.91% and 99.88%, respectively. The average detection success rate of the SVM/RBF algorithm was significantly higher than that of the RBF algorithm. This indicates that analyzing the RBF neural network algorithm through ML can improve the success rate of spectrum sensing, which highlights a new direction for the application of ML and neural networks. [ABSTRACT FROM AUTHOR]

This study examines through a systematic review, the reference services rendered in academic libraries using artificial intelligence (AI) and by collecting data through environmental scanning. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring the application of artificial intelligence for reference services in academic libraries. Data were collected from Web of Science, Scopus, and LISA databases. Following the rigorous/established selection process, a total of thirty five articles were finally selected, reviewed and analyzed. Thirty five papers were identified, analyzed and summarized on the subject relating to the application of AI and the methods which are most often used. The findings demonstrate that university libraries in Canada and China are leading in the deployment of AI for reference services. The AI techniques used mostly in the scanned university libraries are self-directed learning and natural language processing techniques; while the challenges of using AI for reference services are the problem of quality intelligence, linguistic style, privacy, a threat to intellectual freedom, bias, and cost; inadequate experts, poor network, poor training and lack of innovation, and limited knowledge about the technology. The study indicates university libraries take into account implementing AI for reference services. [ABSTRACT FROM AUTHOR]