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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]

Previous studies have demonstrated that students who are engaged in learning tasks and make errors before receiving instruction on how to complete them, achieve better learning outcomes than students who first receive instruction and then complete the learning activities with the aim of avoiding errors. Although simulation literature often refers to errors as learning opportunities, to date, there is limited understanding of how pedagogical approaches that promote learning from errors can guide the design of simulation-based learning in healthcare education. To (a) present the Learning from Errors conceptual model; and (b) provide an example of how educators can use this model. The Learning from Errors model is drawn from critical elements of two pedagogical approaches, productive failure and error management training and pedagogical features of high-quality healthcare simulations. We describe the Learning from Errors model, which emphasises the need for adopting pedagogical methods that explicitly use errors as learning opportunities and ultimately inform simulation design. We then illustrate the application of this model to a simulation example. The model includes the following elements: i) normalisation of errors, ii) challenging simulation scenarios, iii) self-directed learning, iv) collaborative teamwork and v) comparison with best practice. This discussion paper presents the Learning from Errors conceptual model, an evidence-based approach that can assist educators in the design of simulations that embrace errors as a catalyst for learning. [ABSTRACT FROM AUTHOR]

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To explore a cohort of mental health nurses' views and experiences in developing and applying critical appraisal of research skills to identify helping and hindering factors. In accordance with evidence-based practice principles, there is an expectation that student (mental health) nurses will become competent in understanding research to make judgements about its trustworthiness and applicability in the clinical setting. This process is termed the 'critical appraisal of research' and it is deemed important to healthcare professionals because it facilitates the use of evidence from research to underpin practice. The study incorporated qualitative secondary research using thematic analysis and a poststructural lens. Following undertaking a module including tuition on the critical appraisal of research, a cohort of student mental health nurses' submitted essay reflections focusing on critical appraisal experiences were thematically analysed. The thematic analysis included both deductive and inductive components in adjunct to a poststructuralist analysis to explore influencing factors of language, meaning and subjectivity. The study identified there are both pedagogical factors and wider societal considerations influencing the critical appraisal of research experiences. Psychological and emotional factors, incorporating anxiety components were identified as a key barrier to engagement and understanding which has not previously been identified in comparable research. Enablers to the understanding and application of critical appraisal skills were identified as peer learning, accessing academic supervision, gaining a satisfaction of personal learning and self-directed learning with library skills support. The findings indicate that there are specific pedagogic considerations which can be applied when supporting mental health student nurses in developing and gaining skills in the critical appraisal of research. It is recommended that these are applied universally for nursing students and considered for other populations of healthcare students. • A key barrier to critical appraisal comprehension included anxiety experiences. • Satisfaction with learning promoted growth in knowledge. • Accessing academic supervision was perceived to enable progression immediately. [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]

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]

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]

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]

A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation. In this paper, a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task. Firstly, the physical human-robot cooperation model, including the role factor is built. Then, a reinforcement learning model that can adjust the role factor in real time is established, and a reward and action model is designed. The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot's Jerk during the repeated installation. Finally, the roles adjustment rule established above continuously improves the comprehensive performance. Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified. The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot's Jerk. [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]

As an open network architecture, Wireless Computing Power Networks (WCPN) pose new challenges for achieving efficient and secure resource management in networks, because of issues such as insecure communication channels and untrusted device terminals. Blockchain, as a shared, immutable distributed ledger, provides a secure resource management solution for WCPN. However, integrating blockchain into WCPN faces challenges like device heterogeneity, monitoring communication states, and dynamic network nature. Whereas Digital Twins (DT) can accurately maintain digital models of physical entities through real-time data updates and self-learning, enabling continuous optimization of WCPN, improving synchronization performance, ensuring real-time accuracy, and supporting smooth operation of WCPN services. In this paper, we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models. We adopt an enumeration-based optimal placement algorithm (EOPA) and an improved simulated annealing-based near-optimal placement algorithm (ISAPA) to achieve minimum average DT synchronization latency under the constraint of DT error. Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency. [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]

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]

This paper presents a case study in engineering education, focusing on enhancing conceptual understanding, real-world application, comprehension, teamwork, self-directed learning, and familiarity with design codes and standards. The study involves student analysis of artifact safety and optimization, engaging them in practical problem-solving. The outlined methodology covers artifact selection, analysis, and assessment of the assignment's impact. The findings exhibit improvements in knowledge acquisition, real-world relevance, teamwork, and self-learning. Moreover, the study highlights the assignment's enhanced appeal and challenge compared to conventional assignments. Students express a preference for more such assignments over routine ones. The paper underscores experiential learning's significance in cultivating critical skills for modern engineering and advocates integrating practical applications into curricula for holistic skill development. [ABSTRACT FROM AUTHOR]

Volume 6, Issue 1 of Relay Journal features three articles on learner autonomy, as well as reflections on mentoring and a review of a previous paper. The first article discusses the trialing of a self-access social learning space at a Japanese university, highlighting the results and potential next steps. The second article explores self-directed learning activities and strategies used by adult Japanese learners, identifying areas for improvement. The third article presents a research synthesis of publications from the Research Institute for Learner Autonomy Education, providing an overview of the institution's research. The journal also includes reflective practice papers on professional mentoring. The editorial team encourages readers to engage with the articles and share their thoughts. [Extracted from the article]

This concept paper explores the need to apply a more flexible and personalized curriculum approach in art and design education. This approach aims to foster student creativity, empower learning freedom, and focus on the uniqueness of individual students. Literature highlights reveal several weaknesses in the conventional curriculum such as flexibility, emphasis on technical aspects, and a lack of interdisciplinary. Reforms in art education need to emphasize the integration of other disciplines, technology, and creative elements in the learning approach. A rigid curriculum structure can hinder the development of creativity and innovation. Therefore, students need more space to explore alternative and creative approaches in learning art and design. Self-directed learning has a huge positive impact on creative thinking and problemsolving skills, gives students the freedom to pursue their interests and inclinations, improves resilience, and encourages innovative thinking. A balance between student autonomy and educational goals is important. Students need to understand the learning objectives and be given the opportunity to choose a project that interests them. Formative assessment, a supportive learning environment, and collaboration between students are essential in achieving this balance. Taking into account the above recommendations, art and design education can become more dynamic, relevant, and can foster creativity and innovation among students. This is important in facing the era of advancing globalization and rapid digitalization. Therefore, a more flexible and student-centered curriculum approach is a positive step in advancing art and design education. [ABSTRACT FROM AUTHOR]

This paper analyzes the lateral control technology of autonomous semi-trailer trucks. Existing researches on the lateral control algorithm of semi-trailer trucks focus on making the head-truck or trailer follow a track well while ignoring the motion characteristics during the turning process, leading to specific security issues. Meanwhile, it is difficult to cope with the complex and uncertain factors influencing lateral control effect, such as the curvature of the desired trajectory, the load, and the velocity of the semi-trailer truck. This paper proposes a parametric self-learning model predictive control (MPC) based on the Proximal Policy Optimization of One Step (OSPPO) method to solve these problems. After modeling the kinematics of the semi-trailer truck, a lateral motion controller for the relationship between the head-truck and trailer based on the MPC method is established. The traditional MPC method has difficulty in adapting to the changeable influencing factors. Thus, a deep reinforcement learning algorithm named OSPPO is introduced to improve the flexibility of the MPC method. OSPPO establishes the nonlinear mapping relationship between the critical parameter of the MPC method and the factors influencing control effect by self-learning, avoiding a large amount of labeled data for training. In simulations, Trucksim and Matlab were used to conduct co-simulation to verify the usefulness of the control method. The method was implemented on an autonomous semi-trailer truck for many outdoor scenes. The actual experimental results showed the validity and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Despite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not adequately develop such skills, thus making them inaccessible in practice to many. Related to this is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning. This paper presents preliminary findings from an investigation into MOOC learners' SRL skills and the relationship to how participants learn. Following a design science methodology, we have developed a novel MOOC platform to support learner choice and to assist participants in defining learning goals and developing individual study paths. This paper describes the architecture of the system and presents findings from a pilot MOOC developed on the platform. Our results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL skills including help seeking and task strategies. The contextualised nature of SRL skills means that even learners with a strong background of formal education may not deploy the best strategies for MOOC learning. This work is of significance to MOOC development in general as it highlights the need for targeted strategies to encourage SRL in MOOC platforms and innovation. [ABSTRACT FROM AUTHOR]

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [ABSTRACT FROM AUTHOR]

The inclusion of data analytics in the accounting curriculum is an important task that has been recommended by professional and academic accounting bodies as well as accrediting agencies. This research paper is part of asurvey which investigated how data analytics is being included in the accounting curriculum in general and in different courses in particular. This paper examines whether there are differences in the degree of inclusion of data analytics in different courses in the accounting curriculum and differences in the knowledge and preparation of faculty teaching these courses. The study reveals that the majority of accounting faculty perceive themselves as having limited expertise in data analytics. Only 19% of respondents rate themselves as extremely or very knowledgeable in this area. Conversely, a significant 74% consider their knowledge to be moderate or slight, with 6% admitting to having no knowledge whatsoever. The findings from this study indicate that approximately half of the faculty have acquired their data analytics skills through self-teaching. Merely 24% have undergone formal instruction in this field, while around 17% have received no preparation at all. The study highlights notable disparities in knowledge levels among instructors teaching various courses. Within our surveyed cohort, 48.4% of Analytics in Accounting faculty and 33.3% of AIS faculty perceive themselves as extremely knowledgeable or very knowledgeable about data analytics, contrasting with only 5.2% of Tax faculty and 7.5% of Intermediate faculty. The findings from this study illustrate substantial variations in the level of preparation among faculty members teaching different courses. [ABSTRACT FROM AUTHOR]

To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation. [ABSTRACT FROM AUTHOR]

The article discusses the importance for graduate students to use their self-efficacy and self-regulation to improve their writing performance. Discussion of the concept of self-efficacy and the factors that influence it which rely on the individual's interpretation of success or failure is provided. Emphasis on how self-efficacy depends on an individual's confidence about the quality of the content they are producing is offered.

In this article an internal-family-systems-informed view is proposed on the present development of western civilization, applying it to inter-cultural relationships on a broader level in order to render possible a proper unblending from our current culture, which can in time lead to the emergence of a self-culture, one that can encompass not a global perspective, but an empathetic one that takes into account and respects the specific needs behind the roles of previous and current cultures. It is the present paper's proposal that firefighter cultures are imperialistic dominant civilizations, characterized by self-harm and a politics of fear and a continual trial-and-error to make life more beautiful because unnatural. In contrast, manager agricultural cultures are focused on daily living tasks and, given their greatest resemblance to self, there is now a regressive movement of interest taken to agricultural or rural communities, perceived as maintaining their connection to nature and naturalness. This presents the most needed occasion to integrate and recuperate the humanity in our current culture, whose anti-humanism and dysfunctionality can be sensed ever more acutely. In an effort to update the parts about the self, this paper aims to view our current potentially falling empire culture as a protecting firefighter of an ultimate extreme (given that it most probably includes socially assessed self-harm and suicidal tendencies beyond any other previous civilization), that could highly use this information regarding its positive intention and the potential healing of the most painful wounds that it tried to protect from. In finding out what the current cultural part wants the self to know about, we might discover the desperation in suffering or the darkness of the unknown, and thus our role as firefighters in mankind's cultural struggle with legacy burdens such as fragility and unknowingness. Therefore, the present paper aims to propose a type of cultural psychological work that can redefine our ideal culture from one on endless profit and pleasure for everyone to one of continual awareness of unfolding systems models. We do not need to take our current culture to dissolution beyond hope if we acknowledge its firefighter tendencies to be honored in their attempt to protect. [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]

Problem Based Learning and Computational Thinking together have been applied in several studies, proven to be effective to bring out the best of problem solving. While both of them share several common features, computational thinking is a way of solving problems in problem based learning. One of the major components that direct a student towards critical thinking and self-directed learning are reflections. The components of computational thinking can be used to intervene reflections. This paper proposes to use decomposition for reflections. A multi method study was conducted on 37 students who were asked to write reflections for software architectural problems. The data was qualitatively analyzed using structural, in vivo and focused coding. Further, we used ttest to quantitatively analyze the data after receiving evaluation parameters from the qualitative analysis. The results are presented in this paper along with the themes generated and how reflections are effective when decomposition is explicitly stated. The work positively contributes towards design of effective PBL reflections. [ABSTRACT FROM AUTHOR]

Demand for English language proficiency among the engineering graduates with adept digital communication skills is growing in the contemporary global context. Industry 4.0 places English in the center of digital communication. It demands young engineering graduates with digital communication skills in English. This not only places importance of graduates learning English communication skills but also on the teachers to initiate novel pedagogical approaches. It is imperative to initiate these transitions during the students' graduation stage to ensure their readiness for the industry. The digital teaching and learning in English underscores the need for a fresh pedagogical approach that aligns with the preferences of millennials, facilitating learning across virtual and traditional platforms. Smart classrooms bridge the divides between the teacher and the taught. The evolving landscape of English education calls for a pedagogy infused with technology. Post-COVID, digital pedagogy is supplanting conventional one-on-one teaching methods, integrating techniques like blended learning through Learning Management Systems LMS), flipped classrooms, self-directed learning, and collaborative approaches. This paper argues for the integration of digital pedagogy in regular classroom teaching and learning in English. The paper suggests that the use of digital media should not stop with the resumption of offline teaching, after COVID, rather should be an integral part of pedagogy in teaching English to engineering graduates. It is believed that digital pedagogy augments learner engagement, fostering active learning. The study underscores the significance and scope of digital pedagogy in the computer-centric era. The present paper represents the findings of the empirical research which sought to compare and contrast the conventional learning mode with blended learning. [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]

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]

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]

Purpose: This paper aims to determine the impact of mentorship on the development of self-directedness among beginner teachers in their initial years of teaching. Design/methodology/approach: The researcher adopted a positivist paradigm to explore the situation of concern. Quantitative research was conducted, involving the collection and analysis of numerical data. Two closed-ended structured questionnaires were utilised, derived from the 40-item self-directed learning readiness scale (SDLRS) developed by Fisher and King, and a pre-determined questionnaire by Glazerman focused on the first-year teaching experience, induction and mentoring of beginner teachers. Findings: Beginner teachers merely relying on the knowledge obtained from their studies is insufficient to achieve a satisfactory level of self-directedness when starting a teaching career. Most beginner teachers faced significant challenges in their early years of teaching due to the absence of mentoring support. In addition, most indicated that they resume their teaching duties and rely on their district for general support, guidance and orientation. Finally, the results have shown that mentoring positively impacts beginner teachers' self-directedness. Research limitations/implications: The first limitation was that this study was narrowed to one South Africa University part-time B.Ed honours students working as beginner teachers in different provinces at primary and secondary schools. As a result, the findings of this research might be interpreted by some critics as one-sided and not representative of the views of most beginner teachers in South Africa who are working. The second limitation of this study is the sample size. In this study, 222 responses were received. As a result, the findings of this research might be considered not representative of the target sample size. Practical implications: The presence and effective implementation of mentoring programmes in schools can positively impact beginner teachers' professional development and retention during their first years of teaching. Social implications: We contend that our research holds significance for international readership as it aims to garner attention towards potential research endeavours in diverse settings concerning mentorship programs for beginner teachers, specifically promoting self-directed learning. Our research offers opportunities to compare our findings with studies conducted in more comprehensive, comparative contexts and foster research possibilities in broader, contrasting contexts. Originality/value: Based on the findings of this research, the availability and effective use of mentoring programmes would significantly affect beginner teachers' self-directedness, improve their retention rate and alleviate their teaching challenges. This study was the first research on the perceptions of the influence of mentoring on the self-directedness of beginner teachers. [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 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]

This paper examines the integration of interactive 3D applications into the teaching process in mechanical engineering education. An innovative interactive 3D application has been developed as a teaching aid for engineering students. The main advantage is its easy availability through a web browser on mobile devices or desktop computers. It includes four explorable 3D gearbox models with assembly animations, linked technical information, and immersive virtual and augmented reality (AR) experiences. The benefits of using this application in the teaching process were monitored on a group of students at the end of the semester. Assessments conducted before and after the use of the interactive 3D application measured learning outcomes. Qualitative feedback from students was also collected. The results demonstrated significant improvements in engagement, spatial awareness, and understanding of gearbox principles compared to traditional methods. The versatility and accessibility of the application also facilitated self-directed learning, reducing the need for external resources. These findings indicate that interactive 3D tools have the potential to enhance student learning and engagement and to promote sustainable practices in engineering education. Future research could explore the scalability and applicability of these tools across different engineering disciplines and educational contexts. [ABSTRACT FROM AUTHOR]

The continual expansion in the number of images poses a great challenge for the annotation of the data. Therefore, improving the model performance for image classification with limited labeled data has become an important problem to solve. To address the problem, we propose in this paper a simple and effective dual-training-based semi-supervised learning method for image classification. To enable the model to acquire more valuable information, we propose a dual training approach to enhance model training. Specifically, the model is trained with different augmented data at the same time with soft labels and hard labels, respectively. In addition, we propose a simple and effective weight generation method for generating the weight of samples during training to guide the model training. To further improve the model performance, we employ a projection layer at the end of the network to guide the self-learning of the model by minimizing the distance of features extracted from different layers. Finally, we evaluate the proposed approach on three benchmark image classification datasets. The experimental results demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Discussions of involution – a culture of overwork in China – have grown especially rife on the Chinese Internet, reinforced by a dismal outlook on employment. Translated from an academic term 'involution,' 'neijuan' references a contemporary structure of alienation experienced primarily by college students: a sense of being entrapped in a society without opportunities, yet having to endure an endless series of stressful competitions to carve a respectable life. Drawing from fieldwork and interviews, this paper examines the culture of involution through self-study rooms, places where people pay to study. Rapidly expanding in number since 2019, self-study rooms have become a popular tool used by youths to cope with involution. Modulating attentional capacities through design, these rooms assure users that they could study better, and excel in competitive exams and certification processes to find progression in life trajectories. However, in doing so, self-study rooms also normalize an alienated learning and work culture in China, framing the attentional discomfort of forced learning as gain, and the privacy of self-study rooms as forms of individualistic therapy. Users learn not only to push their bodies within these rooms, they also use it to weep privately, expelling pressures to return to 'gainful' behavior. [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]