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Background: The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) has long recognized and championed increasing diversity within the surgical workplace. SAGES initiated the Fundamentals of Leadership Development (FLD) Curriculum to address these needs and to provide surgeon leaders with the necessary tools and skills to promote diversity, equity, and inclusion (DEI) in surgical practice. In 2019, the American College of Surgeons issued a request for anti-racism initiatives which lead to the partnering of the two societies. The primary goal of FLD was to create the first surgeon-focused leadership curriculum dedicated to DEI. The rationale/development of this curriculum and its evaluation/feedback methods are detailed in this White Paper. Methods: The FLD curriculum was developed by a multidisciplinary task force that included surgeons, education experts, and diversity consultants. The curriculum development followed the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional design model and utilized a problem-based learning approach. Competencies were identified, and specific learning objectives and assessments were developed. The implementation of the curriculum was designed to be completed in short intervals (virtual and in-person). Post-course surveys used the Kirkpatrick's model to evaluate the curriculum and provide valuable feedback. Results: The curriculum consisted of interactive online modules, an online discussion forum, and small group interactive sessions focused in three key areas: (1) increasing pipeline of underrepresented individuals in surgical leadership, (2) healthcare equity, and (3) conflict negotiation. By focusing on positive action items and utilizing a problem-solving approach, the curriculum aimed to provide a framework for surgical leaders to make meaningful changes in their institutions and organizations. Conclusion: The FLD curriculum is a novel leadership curriculum that provided surgeon leaders with the knowledge and tools to improve diversity in three areas: pipeline improvement, healthcare equity, and conflict negotiation. Future directions include using pilot course feedback to enhance curricular effectiveness and delivery. [ABSTRACT FROM AUTHOR]

The European Journal of Psychology of Education (EJPE) will now be publishing a new format called "Special Section" to encourage collaboration among researchers in the field of psychology of education. A Special Section consists of 4 to 6 papers focused on a specific area of research proposed by a group of researchers, the editorial team, or the editorial board. The papers go through the same peer review process as regular papers and are published in the journal as a collection. The goal of this format is to promote visibility for European collaborative research projects, foster collaboration, and explore new ideas and approaches in psychology of education. Several Special Sections are already planned for publication between 2024 and 2025. [Extracted from the article]

In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there's a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance. [ABSTRACT FROM AUTHOR]

Play, which is both a context and process for children's learning, has become a pivotal pedagogical component in global early childhood education. The uptake of intentional teaching has contributed to the shift in understandings of play, from viewing play as a means of recreation and entertainment to a more socio-cultural perspective that advocates for educators' proactive and engaged roles in play. The recent update of the Early Years Learning Framework (2023) in Australia has included an emphasis on intentionality in play-based learning. However, there is little academic work on intentionality, and recent studies demonstrate educators' struggles in conceptualising their roles as intentional teachers. Drawing on the findings of a literature review, this discussion paper aims to conceptualise intentional teaching from two aspects: intentionality and decision-making. This paper argues that while educator and child intentionality are the starting point of intentional actions, the decision-making process enables educators to have ongoing intentionality behind their teaching practices. It concludes by advocating for a broader understanding of intentional teaching that focusses on the underlying thought processes behind educators' decisions and actions. [ABSTRACT FROM AUTHOR]

Skeleton-based action recognition methods have become a research hotspot due to their robustness against variations in lighting, complex backgrounds, and viewpoint changes. Addressing the issues of long-distance joint associations and time-varying joint correlations in skeleton data, this paper proposes a Multi-Stream Attention-Enhanced Recursive Graph Convolution method for action recognition. This method extracts four types of features from the skeleton data: joints, bones, joint movements, and bone movements. It models the potential relationships between non-adjacent nodes through adaptive graph convolution and utilizes a long short-term memory network for recursive learning of the graph structure to capture the temporal correlations of joints. Additionally, a spatio-temporal channel attention module is introduced to enable the model to focus more on important joints, frames, and channel features, further improving performance. Finally, the recognition results of the four branches are fused at the decision level to complete the action recognition. Experimental results on public datasets (UTD-MHAD, CZU-MHAD) and a self-constructed dataset (KTH-Skeleton) demonstrate that the proposed method achieves accuracies of 94.65%, 95.01%, and 97.50%, respectively, fully proving the good performance of the method. [ABSTRACT FROM AUTHOR]

The move away from transmission-based lecturing toward a more student-centred active learning approach is well evidenced in STEM higher education. However, the examination of active learning has generally remained confined to formal timetabled contexts, with assumptions made that students independently manage the transition between timetabled and non-timetabled learning. This paper introduces research findings from a mixed methods study that used an ecological approach when investigating student transitions between a formal lecture theatre and adjacent informal breakout space in a UK STEM university. Using quantitative occupancy monitoring data to analyse usage patterns of both spaces, in combination with qualitative ethnographic observations and field interviews, permitted a purposeful exploration of student engagement with transitions within and between the two learning spaces. The ecological approach aided the discovery of spatial, pedagogic and agentic transitions and tensions, which subsequently informed strategic modification of space across the institution to facilitate the adoption of active learning pedagogy. [ABSTRACT FROM AUTHOR]

Reducing education costs is a crucial policy element with broad support in the U.S. However, is lowering the cost of learning a panacea for eliminating income inequality? In this paper, I theoretically examine the relationship between income inequality and the cost of education by building a three-stage overlapping generation model with two sectors and two education systems. In contrast to conventional studies treating education as a unified concept or in hierarchical order, I consider two types of education, each targeting the training of workers for different roles in production. Workers who decide to spend time learning and improving creativity skills can work to produce intermediate goods used in current production and illuminate future production technology. Coders who produce industrial robots are one example of workers who receive this type of education. The other type of education only improves workers' efficiency and helps them become experts in positions. For instance, office clerks who receive computer training can become more productive in dealing with their daily tasks. I pick a reasonable set of parameters, and the simulation result implies that reducing the cost of practical training may end up enlarging income inequality. The key is whether the effect resulting from the wage gap between jobs (wageeffect) dominates the effect of changing the share of workers in jobs (compositioneffect). Reducing the cost of learning creativity encourages marginal experts to learn creativity and marginal basic workers to receive training, leading to declining wage gaps and income inequality. [ABSTRACT FROM AUTHOR]

Learning and teaching to program is an arduous task. It requires a lot of commitment, dedication, and passion from everyone involved. Programming courses have high dropout and failure rates. Throughout time, several educational research works have been carried out to study the different learning processes and characteristics of students. With this work, we present and describe our vision and model of teaching and learning of initial programming to minimize the problems. We present a technological tool, called HTProgramming (Help To Programming), which complements the teaching and learning process. This allows students to practice a wide variety of activities with immediate feedback, directly related to content and themes for learning programming. It allows the teacher to follow the whole process and students' results. Using a machine-learning (neural network) predictive model of student failure, it will allow the teacher to anticipate possible student failure and act quickly. In this paper, we apply the Design Scientific Research Methodology to tackle teaching and learning difficulties to initial programming. We also include the results and evaluation of the application. Students consider the application an important tool for their learning process. The student failure prediction model presents very realistic values. [ABSTRACT FROM AUTHOR]

Teaching and learning in higher education is informed by a multitude of conditioning factors, not least the values systems and outlook of academics. Understanding the epistemological positioning taken by academics in relation to teaching and learning is therefore important if we are to make judgments about how we educate now, and could do so in the future. Developments in educational theory and digital technology have opened-up new possibilities for the ways in which students learn, and to a degree this has been accelerated by the responses from universities to the COVID-19 pandemic. How then should we conceive the future? Heutagogy is one of a number of theoretical approaches that has attracted interest from those who wish to see greater student control over the learning journey- but how widespread is this view amongst academics? This paper reports on a qualitative study in which 12 academics in an English Business School were asked to describe their views on teaching and learning, which we can encapsulate through the concept of epistemic cognition. The findings infer that there is little epistemological underpinning for heutagogy and that if academics are going to innovate, then additional resource and professional development should be put in place to support epistemic reflexivity, and a shift in their epistemological positioning. The paper conceptualises academics' positioning through a typology of epistemic views. [ABSTRACT FROM AUTHOR]

Approaches to Learning (AtL) is widely included in many state standards for preK, as a process-oriented disposition that describes how children learn. Play is one pedagogical strategy teachers can employ to support the development of AtL. This paper uses Vygotsky's depiction of two kinds of play, object play and socio-dramatic play, to provide the theoretical framework to articulate the links between play and learning. Exploration, i.e., object play supports children's curiosity and problem-solving. Sociodramatic play, dramatizing, helps children develop symbolic representation necessary for imagination and creativity. Both kinds of play require intentional teacher structuring and interaction. This paper concludes by presenting ways teachers can encourage object and socio-dramatic play in the classroom. [ABSTRACT FROM AUTHOR]

Internationalisation of the higher education system continues to have a strong impact on national education policies. From an international perspective, learning to teach in globally competent and culturally responsive ways is a core element of teacher education. For this purpose, academically and practice-oriented student teacher exchange programmes have been established. It is often taken for granted that corresponding internships abroad offer positive learning experiences, but research shows that these expectations are frequently not met. This paper; therefore, investigates if and how learning takes place in multi-week international internships, what shapes, enables, limits or obstructs this learning. The investigation is based on a qualitative-hermeneutic approach with data collected from group discussions and analysed according to the documentary method. The analyses led to two 'ideal types': the first is based on learning from contrasts and includes sub-types in which the student teachers' perceptions and interpretations are differentiated or distanced to a greater or lesser extent. The second ideal type, in which learning takes place through challenges, is also divided into sub-types which differ in the degree of self-efficacy experienced by the students. The paper concludes with some suggestions on how pre-service teachers can be supported so that the learning potential of their internships can be exploited more fully. [ABSTRACT FROM AUTHOR]

The terms Language Disorder, Developmental Language Disorder (DLD), Language-based learning disabilities, Specific Learning Disorder, and Specific Learning Disability are commonly used to describe children struggling to learn at school. In this position paper, the definitions and distinctions between these terms are discussed, and key overlaps and differences described. Although often used interchangeably, Specific Learning Disorder and Specific Learning Disability are not synonymous. Based on current definitions, both children with DLD and children with Specific Learning Disorder could be classified as having a Specific Learning Disability in the educational setting. In educational settings, children with DLD may additionally be identified using terms such as Language Impairment, Speech, Language, Communication Needs (SLCN), and others. Despite the problematic overlap in the names Specific Learning Disorder and Specific Learning Disability, one advantage of the latter term is the acknowledgement that many skills underlying academic learning are language-based thereby capturing the disability experienced by children with language or academic learning disorders. [ABSTRACT FROM AUTHOR]

Wearable sensors are widely used in medical applications and human–computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Current unsupervised domain adaptation methods for intelligent fault diagnosis focus on learning domain-invariant representations under covariate shift assumption. However, the covariate shift assumption is usually unsatisfied when each fault class in different domains consists of multiple modes with skewed proportions, which is common in industrial scenarios. Imbalanced data from multiple modes cause the presence of within-domain class imbalance and between-domain label distributional shift. This paper introduces a novel subpopulation shift that further considers the domain shift from a subpopulation perspective, i.e., that covariate shift and label distributional shift across domains are caused by shifts in the multiple modes. To address this issue, a label propagation-based unsupervised domain adaptation is proposed based on a realistic expansion assumption. We apply the theoretical analysis of the proposed method with a bi-level optimization strategy adapted from meta-learning. Using joint optimization of a teacher model and a student model, the label propagation-based model-agnostic meta-learning (LPMAML) not only propagates supervision information from the source to the target but also adjusts the teacher's strategy throughout the student's learning process. To alleviate the noise caused by label distributional shift, we integrate a sampling-based alignment method that aligns the empirical label distributions across the two domains into LPMAML. Experimental results on three bearing datasets show that the proposed method has impressive generalization ability under covariate, label distributional, and subpopulation shifts. The proposed method offers consistent improvements to unsupervised domain adaptation (UDA) methods. Compared with the vanilla UDA methods, the average diagnosis accuracies of the proposed method on the label distributional shift benchmark and subpopulation shift benchmark are improved by 8.21% and 7.63%, respectively. [ABSTRACT FROM AUTHOR]

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. Computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has gained much attention since sensor technology has become more advanced and cost-effective. HAR is a process of identifying the daily living activities of an individual with the help of an efficient learning algorithm and prospective user-generated datasets. This paper addresses the technical advancement and classification of HAR systems in detail. Design issues, future opportunities, recent state-of-the-art related works, and a generic framework for activity recognition are discussed in a comprehensive manner with analytical discussion. Different publicly available datasets with their features and incorporated sensors are also descr-processing techniques with various performance metrics like - Accuracy, F1-score, Precision, Recall, Computational times and evaluation schemes are discussed for the comprehensive understanding of the Activity Recognition Chain (ARC). Different learning algorithms are exploited and compared for learning-based performance comparison. For each specific module of this paper, a compendious number of references is also cited for easy referencing. The main aim of this study is to give the readers an easy hands-on implementation in the field of HAR with verifiable evidence of different design issues. [ABSTRACT FROM AUTHOR]

There are no conflicts between intentional teaching and play-based learning. However, educators find it challenging to establish the pedagogical relationship between them as they struggle to conceptualise and enact their role in the play-based context. In particular, educators' confidence level is not enough to teach science and engineering in a play-based context. However, there is an increasing demand to integrate teaching practice for STEM learning in early childhood settings. Play is a pleasure and a leading source for children's learning and cultural maturation process as part of their social and cultural experiences, and adults' support can enhance children's STEM learning process in a play-based context. This paper investigates how educators' intentional teaching plan can support children's small science and engineering learning process in culturally valued play. A total of 50 h of video data, representing 64 children aged from 10 months to 5 years, were collected through digital video observation over a period of seven weeks in an early childhood centre in Australia. This paper uses the dialectical interactive approach to analyse a 95-min video clip of children's (3 to 5 years of age) play in a cultural context. It is argued that educators progress their learning and confidence to teach science and engineering in play-based settings when they could choose the activity from their community culture or centre-based practice. The findings of the study provide a pedagogical model for educators, which provides a conceptual framework for STEM-based learning in a culturally valued play. [ABSTRACT FROM AUTHOR]

Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector representations, called uSF, which allows the model to predict not only color and semantic label of each point, but also estimate the corresponding values of uncertainty. We show that with a small number of images available for training, a model that quantifies uncertainty performs better than a model without such functionality. Code of the uSF approach is publicly available at https://github.com/sevashasla/usf/. [ABSTRACT FROM AUTHOR]

Unsupervised hashing for cross-modal retrieval has received much attention in the data mining area. Recent methods rely on image-text paired data to conduct unsupervised cross-modal hashing in batch samples. There are two main limitations for existing models: (1) learning of cross-modal representations is restricted to batches; (2) semantically similar samples may be wrongly treated as negative. In this paper, we propose a novel category-level contrastive learning for unsupervised cross-modal hashing, which alleviates the above problems and improves cross-modal query accuracy. To break the limitation of learning in small batches, a selected memory module is first proposed to take global relations into account. Then, we obtain pseudo labels through clustering and combine the labels with the Hadamard Matrix for category-centered learning. To reduce wrong negatives, we further propose a memory bank to store clusters of samples and construct negatives by selecting samples from different categories for contrastive learning. Extensive experiments show the significant superiority of our approach over the state-of-the-art models on MIRFLICKR-25K and NUS-WIDE datasets. [ABSTRACT FROM AUTHOR]

This paper uses narrative inquiry, ethnographic observations, and philosophical consideration of phenomenology to explore the role of mindfulness training, specifically a yoga-based practice, in supporting resiliency, academic achievement, and holistic well-being. Holistic well-being has become a critical and significant area of focus in education as increasing stress, anxiety and depression are resulting in both mental and physical illness, causing a significant decrease in overall resiliency observed both in students and teachers. This problem has been compounded by COVID-19, increasing use of technology and social isolation (Kush, Badillo-Giocoechea, Musci, & Stuart, 2022). It is critical to seek ontological ways of being and epistemological opportunities that consider relationality and holism to deepen our understanding of the complexity of our human experience, individually and collectively. The purpose of this paper is to offer proactive considerations to achieve well-being in our increasingly complex and overwhelming educational environments by considering the lived experience of the author as a yoga practitioner, academic researcher, and classroom educator. It begins by exploring the concept of interconnectivity and reflection on lived experience, and then it establishes a working definition for both resiliency and yoga. It then connects the key concepts of yoga practice to educational neuroscience, curriculum, teaching, and learning. Finally, it explores practical applications to teaching and learning and future research to be considered regarding resiliency, yoga, and holistic well-being. [ABSTRACT FROM AUTHOR]

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Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes. [ABSTRACT FROM AUTHOR]

The accurate prediction of a lithium-ion battery's State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network's output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application. [ABSTRACT FROM AUTHOR]

Although smartphones are equipped with accessibility functions, they still pose significant problems for visually impaired people. Sometimes these functions cannot fulfil the expectations of users. Early mobile devices had physical buttons and a keypad, and visually impaired users could navigate around the keypad using the tactile markers on the buttons. However, the lack of tactile markers makes it much more difficult to operate a touchscreen device. This paper describes an e-learning platform that is designed to improve the accessibility of smartphone applications for students who are visually impaired but have some useful vision. A User-Centered Design approach was used to develop an effective solution for visually impaired students. A study was conducted during the development of the described platform, and the results showed that our suggested design improves task completion time as compared to the initial version. Participants also expressed higher levels of satisfaction when using the improved design of this platform. The modified design was also assessed using the Mobile Application Rating Scale (MARS), and the results indicate that it is quite reliable and rated well among visually impaired children. Furthermore, developers can use our suggested design guidelines such as clear navigation, color contrast, immediate feedback, icon arrangements, button and text size in the development of new applications. [ABSTRACT FROM AUTHOR]

The primate brain and artificial intelligence (AI) can both be conceptualized as information processing systems, each with its own distinct biological and computational architectures. While there are parallels between them, their respective structural and functional connections show significant differences. In this paper, we examine the central role of feedback processing in both the primate brain and AI systems, which has been shown to be crucial in shaping neural processing. By reviewing the key features of feedback processes in the primate brain, which allows the brain to incorporate prior knowledge, contextual information, and task-demands into early-stage processing, we highlight the divergence in goals and functions between biological and AI systems. Understanding these differences is crucial for elucidating the cognitive capabilities of the primate brain and for addressing computational challenges in AI. In advocating "Cognition-Inspired-Computation", we suggest that integrating insights from feedback processing in the primate brain into AI research will offer potentially significant improvements for the advancement of AI systems. [ABSTRACT FROM AUTHOR]

This paper critically analyzes the potential impact of ChatGPT, a creative artificial intelligence tool, on learning and teaching, focusing on its impact on using writing assignments as a means of assessing knowledge. The paper examines the challenges this tool presents to learners and teachers in various aspects, including writing as a means of constructing knowledge, designing writing assignments, and assessing ChatGPT-assisted writing tasks. The paper examines the challenges posed by ChatGPT for writing from three different aspects: the role of ChatGPT as a learning support tool, the consequences of using ChatGPT for acquiring 21st-century skills, and challenges regarding the evaluation of the learners. The analysis indicates that ChatGPT might serve as a performance support system rather than a learning support tool. It also argued that incorporating ChatGPT into the learning process requires a significant degree of critical thinking from both students and teachers. It is crucial that they utilize this tool with the primary objective of enriching their thinking and writing abilities rather than seeking to circumvent the cognitive information processing mechanisms as a shortcut to produce writing products. [ABSTRACT FROM AUTHOR]

This paper explores the adoption of an entangled eclecticism perspective in Learning Experience Design (LXD), integrating a sociotechnical-pedagogical systems approach. It emphasizes the significance of considering the sociocultural, technological, and pedagogical dimensions of learning as a cohesive, interconnected ecology to design effective learning experiences. The authors discuss the implications of this perspective on learning outcomes, methodologies, and the selection of relevant theories for design. The aim is to guide the creation of learning experiences that are not only effective, efficient, and appealing but also deeply empathetic, culturally sensitive, and responsive to the evolving dynamics of learning environments. Concluding that LXD is a complementary approach to traditional learning design, the paper underscores its evolution, drawing from multiple traditions to offer a holistic framework for educational design and technology. This approach strives for learning experiences that are not just effective and efficient, but also culturally sensitive and empathetic, adapting to the dynamic nature of learning environments. Broader implications of this approach and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Educational outcomes for children are of the utmost importance since they form the foundation of education systems worldwide. Children's learning outcomes have been adversely affected by natural and human disasters since their inception. This paper employs a scoping review approach to investigate the influence of environmental shocks and child labour on children's educational outcomes. Numerous empirical studies have been screened in this study to determine how environmental shocks and child labour influence children's educational outcomes. This present study is the most recent research to examine the influence of environmental shocks and child labour on children's educational outcomes. This study found that environmental shocks (death, climate variability, flooding, and hurricanes) are positively associated with child labour and negatively related to children's learning outcomes, while child labour (farming, domestic chores, fishing, and hawking) has a negative influence on children's educational outcomes. This study also found that the interaction effect of environmental shocks and child labour has detrimental impacts on children's learning outcomes. Available literature has shown that research on the influence of environmental shocks and child labour on children's education and environmental shocks on child labour is still growing. More studies are needed on the influence of other kinds of environmental shocks and child labour on children's educational outcomes as well as the influence of environmental shocks on child labour, particularly on the African continent. This is because there is a dearth of literature despite the fact that they are susceptible to environmental shocks since they are located in the tropics and other areas habitually unprotected from life-threatening weather events, which reduce their per capita income and increase child labour. [ABSTRACT FROM AUTHOR]

Copyright of Unterrichtswissenschaft (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the limited exploration of the impact of virtual and augmented reality on the learning process, particularly within the context of foreign language learning among young learners. Motivated by this research gap, the EULALIA, conducted from 2019 to 2023 (2019-1-IT02-KA203-063228), sought to evaluate the efficacy of integrating technology in language education. Specifically, the study investigates the influence of virtual and augmented reality on foreign language learning, examining its effects on subjectively assessed cultural knowledge, and workflow during classes, as perceived by educators. Involving 239 learners, the study employed a pre/post survey methodology, wherein participants provided feedback before and after working with the EULALIA application or traditional learning methods. The quantitative analysis of survey data was complemented by semi-structured interviews with participating teachers, providing additional insights. The results indicate a statistically significant increase in cultural knowledge among the group utilizing augmented reality. The findings suggest a positive impact on students' learning progression, and workflow during classes. This research contributes valuable insights into the potential of integrating virtual and augmented reality in language education, offering a foundation for future pedagogical advancements. [ABSTRACT FROM AUTHOR]

In this paper, we study an agent-based model for multi-round, pay as bid, sealed bid reverse auctions using techniques from partial differential equations and statistical mechanics tools. We assume that in each round a fixed fraction of bidders is awarded, and bidders learn from round to round using simple microscopic rules, adjusting myopically their bid according to their performance. Agent-based simulations show that bidders coordinate in the sense that they tend to bid the same value in the long-time limit. Moreover, this common value is the true cost or the ceiling price of the auction, depending on the level of competition. A discontinuous phase transition occurs when half of the bidders win. We establish the corresponding rate equations, and we obtain a system of ordinary differential equations describing the dynamics. Finally, we derive formally the kinetic equations modeling the dynamics, and we study the asymptotic behavior of solutions of the corresponding first-order, nonlinear partial differential equation satisfied by the distribution of agents. [ABSTRACT FROM AUTHOR]

Human-centric content-based video retrieval has emerged as a prominent area of research due to its diverse applications. However, this task presents several inherent challenges, including end-to-end image classification and data sampling. Despite the significant progress made by self-supervised learning methods in addressing these challenges, there are still some issues that need to be addressed. Among those, one major concern is the generation of randomly sampled inverse-complementary pairs. The process of generating such pairs requires careful handling to avoid false positives. Moreover, a common assumption that the similarity between video clips is solely temporal neglects the role of other factors, such as motion. To address this issue, a clustering-based multi-featured self-supervised learning model called CMS2L is proposed in this paper. Our model introduces a fundamental improvement by fixing intra-class positive sampling to avoid false labeling during stage training due to looping clusters. Additionally, it employs a second stream with an expanded range of features to achieve a more comprehensive representation of actions. Experimental results on benchmark datasets demonstrate the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Despite the large quantity of research projects about online learning, studies on students' language learning motivation, self-efficacy belief, and metacognitive strategy use in the online learning setting are limited. The present paper aims to fill this gap through assessing learners' metacognitive strategies, language learning motivation, self-efficacy belief, and their perceived progress in English learning. Responses to surveys were administered two times. The collected data were subject to longitudinal mediation analysis. The participants were a total of 627 university students in China. Results showed a positive and significant relationship among the four variables. The findings highlighted four significant longitudinal mediation patterns. Overall, self-efficacy belief predicted the use of metacognitive strategies, which in turn predicted their language learning motivation and perceived online English learning progress. The findings supported the mediating role of language learning motivation and metacognitive strategies. The findings showed the potential to enhance online English learning by facilitating learners' self-efficacy belief, language learning motivation, and metacognitive strategies. [ABSTRACT FROM AUTHOR]

This article maps considerations of inclusiveness and support for students with disabilities by reviewing articles within the field of learning analytics. The study involved a PRISMA-informed systematic review of two popular digital libraries, namely Clarivate's Web of Science, and Elsevier's Scopus for peer-reviewed journal articles and conference proceedings. A final corpus of 26 articles was analysed. Findings show that although the field of learning analytics emerged in 2011, none of the studies identified here covered topics of inclusiveness in education before the year of 2016. Screening also shows that learning analytics provides great potential to promote inclusiveness in terms of reducing discrimination, increasing retention among disadvantaged students, and validating particular learning designs for marginalised groups. Gaps in this potential are also identified. The article aims to provide valuable insight into what is known about learning analytics and inclusiveness and contribute knowledge to this particular nascent area for researchers and institutional stakeholders. [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (re-ID) aims to identify the same person in unsupervised settings, which is a realistic and challenging problem due to getting rid of the dependence on annotations. Existing pseudo-label-based methods have been proved to be effective by discriminative representation learning and high-quality pseudo labels. However, there are some undesirable similarity structures in images affected by different states (e.g., backgrounds, occlusions, or other target confusions) which may generate noisy labels and lead to model degradation. To tackle the above issues, this paper analyzes the key factors for unsupervised re-ID performance from two perspectives: network structure and loss function. Specifically, we propose a multi-confidence contrastive learning with feature refinement (MCFR) model which can enhance the feature discriminative ability and meanwhile alleviate the impact of noisy data. For network architecture, a feature refined block is embedded into a multi-branch network, constructing FR-net. For loss function, a multi-confidence contrastive loss (MCCL) based on similarity and confidence relationship is developed. MCCL can flexibly update the list of hard negative samples in clusters and outliers, promoting the model by learning from confident samples. Extensive experiments including visualizations and ablation studies validate the effectiveness of each component in the MCFR model. Additionally, compared with the state-of-the-art unsupervised re-ID methods, our method achieves considerable performance on three benchmark datasets. [ABSTRACT FROM AUTHOR]

Information systems are increasingly using artificial intelligence (AI). However, AI can be tricked into misbehaving, showing bias, or committing abuse. The root causes of these errors and uncertainties can be hidden away while parallelizing AI algorithms on high-performance computing (HPC) infrastructure. The project outlined in this paper aims to use artificial intelligence from the ground up to generate teaching materials and curricula for student-teachers. Students embark on a journey of discovery, taking calculated risks in a learning environment. The main purpose of this document is to present the primary research results of the two-year pilot project. A secondary purpose of this paper is to disseminate information about this exciting endeavor to encourage like-minded educators and researchers to participate in this project. [ABSTRACT FROM AUTHOR]

In this paper we report on a study of the impact of digital technology (DT) innovations on students' learning in a Western Australian University. The innovations were implemented by 42 course coordinators (CC) following two days of learning design workshops. In collaboration with nine CCs, we conducted an evaluation of their innovations. Data were collected through two structured interviews with each CC and an online questionnaire for 1500 students. Elements of students' course feedback were incorporated into the final analysis of the data. A model for improving students learning in a digital environment was used as a guide to the evaluation process. The findings indicated that DT interventions helped students prepare for laboratory activities and class participation, increased their levels of interaction and collaboration, and provided effective and timely management of feedback from lecturers. Students also reported greater access to learning resources, plus more motivation and engagement. CCs were generally pleased with their innovations; however, in their second interview, at the end of the semester, CCs identified several aspects of their course design that warranted improvement, including the need for more professional support in making those improvements [ABSTRACT FROM AUTHOR]

This special issue aims to sketch the present state of maker learning research, reveal possible tensions, and present future possibilities to articulate principles for learning through design in the era of maker education. The special issue was announced in 2022 in ETR&D, a leading academic journal in educational technology. Of the 50 submissions to the special issue, eighteen (18) were accepted for publication. The editors favored a robust inclusion of papers to help define the contours of the field at present. Four clusters of topics are identified in this collection of papers: (i) STEM+ disciplinary and transdisciplinary learning spaces; (ii) Digital technologies in making, opportunities and challenges; (iii) Assessment practices and frameworks; (iv) Representation, inclusion, and tensions around maker-centered initiatives and reforms. The editors of the special issue believe that these clusters reflect the current state-of-the-art in the field as well as significant questions to guide near future research. Reflecting on these papers but also the overall editorial process, the editors identified several opportunities and provide suggestions on how the field might expand moving forward. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification aims to match pedestrian images between visible and infrared modalities, and its two main challenges are intra-modality differences and cross-modality differences between visible and infrared images. To address these issues, many advanced methods attempt to design new network structures to extract modality-sharing features, mitigate modality differences, or learn part-level features to overcome background interference. However, they ignore the parameter sharing of the convolutional layers to obtain more modality-sharing features. At the same time, only using part-level features lack discriminative pedestrian representations such as body structure and contours. To handle these problems, a parameter sharing and feature learning network is proposed in this paper to mitigate modality differences and further enhance feature discrimination. Firstly, a new two-stream parameter sharing network is proposed, by sharing the convolutional layers parameters to obtain more modality-sharing features. Secondly, a multi-granularity feature learning module is designed to reduce modality differences at both coarse and fine-grained levels while further enhancing feature discriminability. In addition, a center alignment loss is proposed to learn relationships between identities and to reduce modality differences by clustering features into their centers. For the part-level feature learning, the hetero-center triplet loss is adopted to alleviate the strict constraints of triplet loss. Finally, extensive experiments are conducted to validate our method outperforms state-of-the-art methods on two challenging datasets. In the SYSU-MM01 dataset, the Rank-1 and mAP reach 74.0 % and 70.51 % in the all-search mode, which is an increase of 3.4 % and 3.61 % to baseline, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a learning algorithm for tuning the parameters of a family of stabilizing nonlinear controllers for orbital tracking, in order to minimize a cost function which combines convergence time and fuel consumption. The main feature of the proposed approach is that it achieves performance optimization while guaranteeing closed-loop stability of the resulting controller. This property is exploited also to restrict the class of admissible controllers and hence to expedite the training process. The learning algorithm is tested on three case studies: two different orbital transfers and a rendezvous mission. Numerical simulations show that the learned control parameters lead to a significant improvement of the considered performance measure. [ABSTRACT FROM AUTHOR]

Professional StarCraft game players are likely to focus on the management of the most important group of units (called the main force) during gameplay. Although macro-level skills have been observed in human game replays, there has been little study of the high-level knowledge used for tactical decision-making, nor exploitation thereof to create AI modules. In this paper, we propose a novel tactical decision-making model that makes decisions to control the main force. We categorized the future movement direction of the main force into six classes (e.g., toward the enemy's main base). The model learned to predict the next destination of the main force based on the large amount of experience represented in replays of human games. To obtain training data, we extracted information from 12,057 replay files produced by human players and obtained the position and movement direction of the main forces through a novel detection algorithm. We applied convolutional neural networks and a Vision Transformer to deal with the high-dimensional state representation and large state spaces. Furthermore, we analyzed human tactics relating to the main force. Model learning success rates of 88.5%, 76.8%, and 56.9% were achieved for the top-3, -2, and -1 accuracies, respectively. The results show that our method is capable of learning human macro-level intentions in real-time strategy games. [ABSTRACT FROM AUTHOR]

Assessing student engagement in educational settings is critical for monitoring and improving the learning process. Traditional methods that classify video-based student engagement datasets often assign a single engagement label to the entire video, resulting in inaccurate classification outcomes. However, student engagement varies over time, with fluctuations in concentration and interest levels. To overcome this limitation, this paper introduces a frame-level student engagement detection approach. By analyzing each frame individually, instructors gain more detailed insights into students' understanding of the course. The proposed method focuses on identifying student engagement at a granular level, enabling instructors to pinpoint specific moments of disengagement or high engagement for targeted interventions. Nevertheless, the lack of labeled frame-level engagement data presents a significant challenge. To address this challenge, we propose a novel approach for frame-level student engagement classification by leveraging the concept of knowledge transfer. Our method involves pretraining a deep learning model on a labeled image-based student engagement dataset, WACV, which serves as the base dataset for identifying frame-level engagement in our target video-based DAiSEE dataset. We then fine-tune the model on the unlabeled video dataset, utilizing the transferred knowledge to enhance engagement classification performance. Experimental results demonstrate the effectiveness of our frame-level approach, providing valuable insights for instructors to optimize instructional strategies and enhance the learning experience. This research contributes to the advancement of student engagement assessment, offering educators a more nuanced understanding of student behaviors during instructional videos. [ABSTRACT FROM AUTHOR]

With rapid advancements in depth sensors and deep learning, skeleton-based person re-identification (re-ID) models have recently achieved remarkable progress with many advantages. Most existing solutions learn single-level skeleton features from body joints with the assumption of equal skeleton importance, while they typically lack the ability to exploit more informative skeleton features from various levels such as limb level with more global body patterns. The label dependency of these methods also limits their flexibility in learning more general skeleton representations. This paper proposes a generic unsupervised Hierarchical skeleton Meta-Prototype Contrastive learning (Hi-MPC) approach with Hard Skeleton Mining (HSM) for person re-ID with unlabeled 3D skeletons. Firstly, we construct hierarchical representations of skeletons to model coarse-to-fine body and motion features from the levels of body joints, components, and limbs. Then a hierarchical meta-prototype contrastive learning model is proposed to cluster and contrast the most typical skeleton features ("prototypes") from different-level skeletons. By converting original prototypes into meta-prototypes with multiple homogeneous transformations, we induce the model to learn the inherent consistency of prototypes to capture more effective skeleton features for person re-ID. Furthermore, we devise a hard skeleton mining mechanism to adaptively infer the informative importance of each skeleton, so as to focus on harder skeletons to learn more discriminative skeleton representations. Extensive evaluations on five datasets demonstrate that our approach outperforms a wide variety of state-of-the-art skeleton-based methods. We further show the general applicability of our method to cross-view person re-ID and RGB-based scenarios with estimated skeletons. [ABSTRACT FROM AUTHOR]

Although engagement in the language learning process is important for students' language outcomes, factors that contribute to it are underexamined. Therefore, the aim of this study was to explore the roles of individual differences (growth language mindset and academic emotion) in shaping foreign language (FL) engagement. A total of 1,738 Chinese undergraduates (790 males and 948 females) studying English as a foreign language (EFL) completed questionnaires measuring their growth language mindsets, positive emotions (enjoyment, pride and hope), negative emotions (anger, anxiety, shame, hopelessness and boredom) and engagement during the English learning process. Correlational analyses revealed positive relationships between growth language mindsets, positive emotions, and engagement; and negative relationships between negative emotions and the three other variables. Structural equation modelling analyses showed that both positive and negative emotions were partial mediators between growth language mindset and engagement in FL learning, with positive emotions demonstrating larger effects. The findings are indicative of a motivation–emotion–behavior path in the EFL context. The theoretical and practical implications for language learning and teaching are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper introduces a new perspective on the constitutive role of material culture for memory using the Knossian Kamares pottery style as a case study. It challenges prevalent approaches in mainstream memory studies, which confine memory to individuals' brains or minds, suggesting a deeper relationship between material culture and memory. Presenting a novel methodology rooted in cognitive archaeology to study the long-term making of Knossian Kamares decorations, I suggest that the Knossian Kamares pottery style is a transgenerational memory that enabled generations of artisans to remember, learn, and update technological skills and knowledge. I also claim that, in assuming this distributed, enactive, and non-representational stance on style as memory, it becomes evident that remembering is something we do: an active engagement that emerges with and through material culture in specific sociomaterial settings. [ABSTRACT FROM AUTHOR]

Tracing a student's knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing historical records on online education platforms. Most studies have focused on a student's skill with interactions sequence to predict the probability of correctly answering the latest question. However, they still suffer from the challenge of information sparsity and student forgetting. Specifically, the relationship between question and skill, and the features related to question texts have not been integrated to enrich information exploration. Besides, modeling forgetting behavior remains a challenge in assessing a student's learning gains. In this paper, we present a novel model, namely Informative Representations for Forgetting-Robust Knowledge Tracing (IFKT). IFKT utilizes a light graph convolutional network to capture various relational structures via embedding propagation. Then, the embeddings are assembled with rich interaction features separately as the powerful representation. Furthermore, attention weights assignments are individualized using the relative positions, in addition to the relevance between the current question with historical interaction representations. Finally, we compare IFKT against seven knowledge tracing baselines on three real-world benchmark datasets, demonstrating the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Imagination-to-learn is a specific learning strategy that has been studied in many academic fields. The present study investigated whether imagination is beneficial overall for learning compared to conventional study strategies, while also identifying moderator factors affecting the global effect. A meta-analysis was conducted by scientifically rigorous experiments comparing the learning outcomes of students who were asked to form a mental image of the events described in learning material while reading (imagination condition) or were given no imagination instructions (control condition). A total of 21 experimental studies published on the PsycINFO, Web of Science, ProQuest Dissertations and Theses, ERIC and Google Scholar databases were included, yielding 70 pair-wise comparisons with N = 2625 participants. An overall positive effect of imagination-to-learn was found for both retention (g+ = 0.40, 95% CI [0.23, 0.58], z = 4.63, p <.001) and transfer (g+ = 0.51, 95% CI [0.22, 0.43, z = 3.43, p <.001]) performance. However, analysis of the funnel plots showed that publication bias was present in the reporting of learning outcomes. Analysis of the moderators indicated that the effect sizes differed significantly only with respect to learners' prior knowledge for transfer performance and their educational level for retention scores. Based on these findings, the present study provides important directions for future research and practices. [ABSTRACT FROM AUTHOR]

This paper examines the utility of virtual simulation as an inclusive learning activity. Using a framework derived from literature, the Virtual Simulated International Placements (VSIP) were developed, and we explored the experiences of learners and facilitators to identify the pedagogical practices that they adopted during the learning activities with an approach inspired by phenomenography. Data from focus groups with 6 learners and 6 facilitators who participated in the VSIP were analysed along with 64 survey responses from learner participants. The findings of the study indicate there were three key concepts of the virtual simulation which supported inclusive learning environments. Overall, the participants' reflections indicated that the VSIP offered an accessible and usable way to engage in international placements that supported a personalised learning journey. Finally, the findings suggested VSIP should incorporate a diverse range of perspectives, backgrounds, and experiences to create and promote an inclusive learning environment. Further research can explore if these activities create a greater acceptance of different peoples' perspectives and cultures as well as impact stereotypes and biases. In addition, key concepts that made this teaching and learning activity inclusive could aid future investigation into how and why virtual simulations can be inclusive. [ABSTRACT FROM AUTHOR]