Search

Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and non-convex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms come with a variety of tunable parameters that are notoriously challenging to tune by hand. A growing body of research has demonstrated the power of using a data-driven approach to automatically optimize the parameters of tree search algorithms. These techniques use a training set of integer programs sampled from an application-specific instance distribution to find a parameter setting that has strong average performance over the training set. However, with too few samples, a parameter setting may have strong average performance on the training set but poor expected performance on future integer programs from the same application. Our main contribution is to provide the first sample complexity guarantees for tree search parameter tuning. These guarantees bound the number of samples sufficient to ensure that the average performance of tree search over the samples nearly matches its future expected performance on the unknown instance distribution. In particular, the parameters we analyze weight scoring rules used for variable selection. Proving these guarantees is challenging because tree size is a volatile function of these parameters: we prove that, for any discretization (uniform or not) of the parameter space, there exists a distribution over integer programs such that every parameter setting in the discretization results in a tree with exponential expected size, yet there exist parameter settings between the discretized points that result in trees of constant size. In addition, we provide data-dependent guarantees that depend on the volatility of these tree-size functions: our guarantees improve if the tree-size functions can be well approximated by simpler functions. Finally, via experiments, we illustrate that learning an optimal weighting of scoring rules reduces tree size. [ABSTRACT FROM AUTHOR]

The aims of this study were to first determine the extent of owners’ fundamental knowledge of equine care, second, gain their opinions on the implementation of a mandatory certificate of knowledge and third, to investigate the association of certification on horse owner knowledge. An online survey comprising 23 questions (18 closed, 5 open) was distributed worldwide via snowball sampling, and completed by 1847 horse owners who were all ≥18 years old from 17 different countries. Almost all (96%, n = 1781) completed surveys were usable. Just over half (52%) of participants held a current equine knowledge certification, and 59% believed a mandatory certificate of knowledge should be necessary to buy, own, or sell a horse. Horse owners with an equine certificate of knowledge were more likely to be in favor of a mandatory certificate (Pearson’s Chi-squared = 50.79, df = 1, p < 0.0001). The mean correctly answered knowledge questions was 68%, indicating that approximately 1/3 of horse owners lacked fundamental horse care knowledge. Further examination indicated that participating horse owners showed the least understanding in the behavioral interactions domain (ANOVA F(4, 24) = 4.98, p < 0.01), specifically in relation to pain recognition and learning theory terminology. Arguably, this deficit in knowledge and likely correct application increases the risk of owners using misinformed or inappropriate techniques to manage horse behavior and training, predisposing the horse to experiencing a sub-optimal quality of life. Overall no significant difference was found between the total correct scores of horse owners with or without a certificate (ANOVA F(1, 56) = 0.78, p > 0.05); however, a series of Chi-squared tests revealed that for the more complex questions, horse owners with a certificate scored better than those without (all p < 0.05). Further work is needed to develop an evidence-based assessment framework if mandatory certification of horse owners is to become an effective tool for improving horse welfare and quality of life. [ABSTRACT FROM AUTHOR]

We give investigations on a kernel function approximation problem arising from learning theory and show the convergence rate from the view of classical Fourier analysis. First, we provide the general definition for a modulus of smoothness and a K -functional, and show that they are equivalent. In particular, we give explicit representation for some moduli of smoothness. Second, we establish some Jackson-type inequalities for the approximation error associated with some non-radial kernels. Also we apply these results to some concrete classical kernel function spaces and give Jackson-type inequalities for some concrete RKHS approximation problems. Finally, we apply these discussions to learning theory and describe the learning rates with the moduli of smoothness. The tools we used are Fourier analysis and the semigroup operator. The results show that the Jackson-type inequalities of approximation by some radial kernel functions on compact set with nonempty interiors cannot be expressed with the classical moduli of smoothness. [ABSTRACT FROM AUTHOR]

Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability. [ABSTRACT FROM AUTHOR]

We offer this synthesized framework as a tool to reveal mathematical activity in a non-formal making-space. In particular, we connect research at different grain sizes to illustrate and explain how mathematics plays a crucial, if often implicit, role in making activities. We begin by describing the Approximate Number System and the Ratio-Processing System, and explaining how those systems connect to both embodied cognition and Thompson's (1994) conceptualization of quantities. Then, we examine how prediction and anticipation relate, with a particular emphasis on how social feedback guided the emergent mathematical activity. We offer this framework as a way to perceive, account for, and understand the multi-layered nature of experiencing mathematical activities. [ABSTRACT FROM AUTHOR]

In this article, we examine citizens' willingness to comply with Covid-19 prevention strategies of mandatory mask-wearing and recommended social distance/contact limitation measures from competing theoretical perspectives. These include self-control, deterrence, learning, social control theories, and fear of the infection. Data for the study come from 508 respondents from St. Petersburg—Russia's second largest city—in May 2020, when the Covid-19 regional legislation that mandated citizens to wear masks in public went into effect. Overall, our findings suggest mixed support for various theoretical perspectives. Among the variables included in the analysis, fear of the infection is positively related to compliance with both mandated and recommended measures. Fear of Covid-19 infection, fear of punishment, and fear of disapproval on behalf of significant others that would follow non-compliance appear to be strong predictors of law-abiding behavior. Learning and self-control theories explain compliance with non-mandatory measures, but not with mask-wearing, which carried a penalty for violating the mandate. [ABSTRACT FROM AUTHOR]

There is no history without theory. But what is the place of a theory of history and other theories in history study programs? Despite the great influence that theory (of history) has on historical thinking and work, it often plays a subordinate role in the study of history itself. There is no consensus onwhat exactlytheoryis, nor onthe consequences of different understandings oftheoryfor universityteaching. Its valuefor historical professionsis aslittle discussed asthe question ofwhether and how the theory of history can be made explicit and accessible to the wider public in schools, museums, and other history-related settings. Understanding historiographical work as a theoretically reflected practice, the article argues for the teaching of theoretical literacy in the study of history in order to learn critical, reflective thinking and multiperspectivity. Finally, the article suggests ways in which »theory as practice« can be integrated into university teaching. [ABSTRACT FROM AUTHOR]

Purpose: The present study aims to evaluate students' perceptions of different types of written assessment within the context of dental materials science and to identify how various learning styles exhibited by dental students, as well as gender and ethnicity, may influence their perceptions of these assessment methods. Methods: The study was conducted among second‐year dental students in a dental materials science class, during which students participated in a mock assessment that included extended matching questions (EMQs) and single correct answer (SCA) questions. An online questionnaire comprising three sections: demographic information, Honey and Mumford's Learning Styles Questionnaire, and 15 closed‐ended questionnaire items employing a 5‐point Likert scale were administered to assess students' perceptions of the assessment methods. Statistical analyses were conducted using the Kruskal‐Wallis test, Mann‐Whitney U test, and a post‐hoc Bonferroni test. Results: All 70 students participated in the study with the majority being female, Chinese, and activist learners. Median total scores among various learning styles, gender, and ethnicity did not exhibit significant differences. Analysis of individual questionnaire items revealed mixed perceptions regarding EMQs. Reflectors generally held more positive perceptions of EMQs, while theorists showed the lowest total median scores toward EMQs. Most students did not prefer EMQs over SCAs, and some students expressed confusion about EMQs, especially those with a theorist learning style. Conclusion: Although students generally showed good perceptions towards these assessment methods, further research is needed to better understand the interplay of learning styles, assessment preferences, and educational outcomes in dental education. [ABSTRACT FROM AUTHOR]

Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, that is, a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of information systems research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent. [ABSTRACT FROM AUTHOR]

Annette M Ilg,1 Christine P Beltran,2 Jenny A Shih,3,4 Tuyen T Yankama,5 Margaret M Hayes,6,&ast; Ari L Moskowitz6,7,&ast; 1Division of Emergency Critical Care, Department of Emergency Medicine, Mass General Brigham, Boston, MA, USA; 2Carl J, Shapiro Institute for Education and Research, Beth Israel Deaconess Medical Center, Boston, MA, USA; 3Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; 4Department of Pulmonary and Critical Care, Brigham and Women’s Hospital, Boston, MA, USA; 5Department of Pharmacy, Beth Israel Deaconess Medical Center, Boston, MA, USA; 6Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; 7Division of Critical Care Medicine, Department of Medicine, Montefiore Medical Center, Bronx, New York, NY, USA&ast;These authors contributed equally to this workCorrespondence: Annette M Ilg, Mass General Brigham, Department of Emergency Medicine, Division of Critical Care, 75 Francis St, Boston, MA, 02115, USA, Email ailg@bwh.harvard.eduBackground: Incorporating unfamiliar therapies into practice requires effective longitudinal learning and the optimal way to achieve this is debated. Though not a novel therapy, ketamine in critical care has a paucity of data and variable acceptance, with limited research describing intensivist perceptions and utilization. The Coronavirus-19 pandemic presented a particular crisis where providers rapidly adapted analgosedation strategies to achieve prolonged, deep sedation due to a surge of severe acute respiratory distress syndrome (ARDS).Question: How does clinical experience with ketamine impact the perception and attitude of clinicians toward this therapy?Methods: We conducted a mixed-methods study using quantitative ketamine prescription data and qualitative focus group data. We analyzed prescription patterns of ketamine in a tertiary academic ICU during two different time points: pre-COVID-19 (March 1–June 30, 2019) and during the COVID-19 surge (March 1–June 30, 2020). Two focus groups (FG) of critical care attendings were held, and data were analyzed using the Framework Method for content analysis.Results: Four-hundred forty-six medical ICU patients were mechanically ventilated (195 pre-COVID-19 and 251 during COVID-19). The COVID-19 population was more likely to receive ketamine (81[32.3%] vs 4 [2.1%], p < 0.001). Thirteen respondents participated across two FG sessions (Pre-COVID = 8, Post-COVID=5). The most prevalent attitude among our respondents was discomfort, with three key themes identified as follows: 1) lack of evidence regarding ketamine, 2) lack of personal experience, and 3) desire for more education and protocols.Conclusion: Despite a substantial increase in ketamine prescription during COVID-19, intensivists continued to feel discomfort with utilization. Factors contributing to this discomfort include a lack of evidence, a lack of experience, and a desire for more education and protocols. Increase in experience with ketamine alone was not sufficient to minimize provider discomfort. These findings should inform future curricula and call for process improvement to optimize continuing education.Keywords: analgosedation, continuing education, learning theory, ketamine, critical care

Objectives. This paper delves into the intersection of mathematical modelling and learning theory, exploring how mathematical frameworks have been instrumental in advancing our understanding of educational processes and pedagogical practices with the aid of Artificial Intelligence (AI). Material and methods. Advancing learning theories through mathematical modelling offers several significant benefits and opportunities. This is because mathematical models provide a precise and systematic framework for representing complex educational phenomena, allowing researchers to formalize theoretical concepts, relationships, and mechanisms in a clear and rigorous manner. Results and Conclusions. This paper focused on such learning theories as cooperative learning theory, Maslow motivation theory, and social learning theory. By employing mathematical models, researchers have been able to dissect complex educational phenomena, elucidate underlying mechanisms, and predict outcomes with a level of precision that traditional qualitative approaches often struggle to achieve.

Knowing the behavioural responses of horses while planning the training processes is crucial in constituting the appropriate training program. The purpose of the study was to examine some behavioural and physiological responses of Arabian horses participating in flat races against the reactivity to human and fear tests. Within the scope of this study, 15 female Arabian horses aged between 2 and 8 years were subjected to reactivity to passive human test, reactivity to active human test, and handling tests from reactivity to human tests and the novel surface test, the static novel object test, and the startling novel object tests from fear tests. Significant relationships (P

The image of craving as a desire of unimaginable and irresistible force is poised to solve the puzzle of addiction: persistent drug use despite severe negative consequences. But the image is flawed. Drawing on science, philosophy, and first‐person testimony, I argue against irresistibility and develop a more nuanced, heterogeneous account of craving for drugs. Craving comes in three varieties, each corresponding to a kind of answer to the question why people crave drugs: cue‐induced, goal‐focused, and attachment‐based. This in turn grounds an approach to addressing addiction that is humane and heterogeneous, moving beyond strategies that are fundamentally controlling. [ABSTRACT FROM AUTHOR]

Let X be a set and H a collection of functions from X to { 0 , 1 } . We say that H shatters a finite set C ⊂ X if the restriction of H yields every possible function from C to { 0 , 1 } . The VC-dimension of H is the largest number d such that there exists a set of size d shattered by H , and no set of size d + 1 is shattered by H . Vapnik and Chervonenkis introduced this idea in the early 70s in the context of learning theory, and this idea has also had a significant impact on other areas of mathematics. In this paper we study the VC-dimension of a class of functions H defined on F q d , the d-dimensional vector space over the finite field with q elements. Define H t d = { h y (x) : y ∈ F q d } , where for x ∈ F q d , h y (x) = 1 if ‖ x - y ‖ = t , and 0 otherwise, where here, and throughout, ‖ x ‖ = x 1 2 + x 2 2 + ⋯ + x d 2 . Here t ∈ F q , t ≠ 0 . Define H t d (E) the same way with respect to E ⊂ F q d . The learning task here is to find a sphere of radius t centered at some point y ∈ E unknown to the learner. The learning process consists of taking random samples of elements of E of sufficiently large size. We are going to prove that when d = 2 , and | E | ⩾ C q 15 / 8 , the VC-dimension of H t 2 (E) is equal to 3. This leads to an intricate configuration problem which is interesting in its own right and requires a new approach. [ABSTRACT FROM AUTHOR]

The aim of the research project was to consider whether theoretical underpinnings of the concept of differentiation would help a group of modern languages student teachers develop their understanding and practice of differentiation in the classroom. This was a small-scale inquiry with qualitative data collected from three sources: student teachers, mentors and the course tutor of a one-year initial teacher education programme. The focus of this article is on the student teacher data only, collected via their reflections and responses to specific set tasks. Key findings to date have revealed that: a Postgraduate Certificate in Education course structure based on a theoretical framework with a focus on student learning enabled student teachers to engage in a wide range of effective differentiation strategies; they adopted approaches to differentiation mainly involving different levels of ability; they focused on the individual needs of their learners and adopted an inclusive approach to differentiation. Recommendations include the use of a wide range of theoretical perspectives to support student teachers in making the links between theory and practice explicit; the use of directed reading of theoretical texts; and the adoption of a reimagined definition of differentiation. [ABSTRACT FROM AUTHOR]

This article provides a systematic review of mobile learning frameworks that address issues of pedagogy published between 2011 and 2022. The objective of the review was to gain a clear picture of recent developments in mobile learning frameworks to provide an understanding of current directions in mobile learning pedagogy. Eighteen peer‐reviewed journal articles that presented new mobile learning pedagogical frameworks were examined and evaluated based on the characteristics of each framework. The two main areas of analysis were the pedagogical approaches integrated into the frameworks and their definitions of mobile learning. We conclude that mobile learning frameworks have become more diverse over time, in many cases tending to focus on specific aspects of mobile learning rather than attempting to address overarching concepts. With respect to pedagogies and their underlying theories of learning, social constructivism, heutagogy, collaborative learning, experiential learning, inquiry‐based learning, and student‐centred learning were mentioned most frequently. However, although many frameworks make reference to pedagogy, there is limited analysis of how mobile learning pedagogy might be defined as distinct from other contexts of learning. The key characteristics of mobile learning, consistent through most of the reviewed frameworks, comprise the portability of mobile devices across multiple contexts, connectivity, and accessibility, as well as learner‐centredness (including personalisation and self‐regulation). One key aspect of identifying the uniqueness and future potential of mobile learning is the special affordances addressed by mobile learning theory. We conclude that this is an area where further research is required. [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]

Pedagogical agents (PAs), animated or not, have received increased research attention during the last two decades. They constitute a modern and intelligent educational technology that has gained momentum due to the recent pandemic that has greatly affected education. In the framework of e-learning, PAs assume the role of mentors, guides, or facilitators who support learners through their online learning tasks. The current research focuses on the educational and pedagogical aspects of the role and uses of PAs within e-learning environments. Major concerns are the learning theories underlying PA-related educational interventions; the outcomes sought in the cognitive, social/emotional, and metacognitive domains; the evaluation tools employed; and the results measured. In a systematic literature review that covers the last fourteen years, recent published research is analyzed to answer research questions such as the popularity of PAs in teaching and learning; the frequency of PA use across education grades, contexts, and taught subjects; the design (type, form, and communication modalities) of Pas; the learning outcomes sought through the PAs across the various education domains (cognitive, social, metacognitive, affective); the evaluation tools; and the results reported. Although the results reported are predominantly positive, they are not balanced. While the cognitive (knowledge) domain is adequately researched (89% of the works), other educational/pedagogical domains of interest are under-researched: social skills (metacognitive skills) are researched by only 26 percent (18%) of the studies reviewed. Along with a surprising dominance of behaviorism, these are the most striking findings; they point toward new directions for PA research, development, and use. [ABSTRACT FROM AUTHOR]

This article discusses whether it is possible to use technology to train democratic values. The response presented is developed through a story detailing institutional experiences in educational innovation. After a literature review on the subject, to identify proposals within various instructional design models, a suitable model was selected and the elements within it that facilitate the design of an educational platform aimed at training children in the values of democracy, participation and peaceful coexistence were identified. It concludes with the adoption of Jonassen's model, which proposes a critical use of technology in educational tasks. Finally, a reflection on the relationship between education, technology and society is offered. [ABSTRACT FROM AUTHOR]

This paper proposes a novel prediction approach for a semi-functional linear model comprising a functional and a nonparametric component. The study establishes the minimax optimal rates of convergence for this model, revealing that the functional component can be learned with the same minimax rate as if the nonparametric component were known and vice versa. This result can be achieved by using a double-penalized least squares method to estimate both the functional and nonparametric components within the framework of reproducing kernel Hilbert spaces. Thanks to the representer theorem, the approach also offers other desirable features, including the algorithm efficiency requiring no iterations. We also provide numerical studies to demonstrate the effectiveness of the method and validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Knowing the behavioural responses of horses while planning the training processes is crucial in constituting the appropriate training program. The purpose of the study was to examine some behavioural and physiological responses of Arabian horses participating in flat races against the reactivity to human and fear tests. Within the scope of this study, 15 female Arabian horses aged between 2 and 8 years were subjected to reactivity to passive human test, reactivity to active human test, and handling tests from reactivity to human tests and the novel surface test, the static novel object test, and the startling novel object tests from fear tests. Significant relationships (P< .05) were detected between the agonistic behaviours of horses and maximum heart rate values in the passive human test, active human test, static novel object test, and startling novel object test. Active human test, it was determined that as the agonistic behaviours of horses increased, the distance to approach humans increased (P < .05). The novel surface test observed that as the horses' agonistic behaviours increased, the number of attempts to cross the surface successfully also increased (P < .01). In the handling test, increased agonistic behaviours caused the test time to prolong (P < .01). As a result, it was determined that various processes in training were prolonged in horses with high agonistic behaviours during the tests. It has also been determined that the trainability of horses may differ within the same breed and sex. For this reason, it is recommended to determine behavioural responses and to plan horse training individually. By understanding the trainability of horses, it is possible to achieve maximum efficiency for their benefit. [ABSTRACT FROM AUTHOR]

This article employs Kolb's Experiential Learning Theory (ELT) to address weaknesses in South Africa's Mathematics education, focusing on the use of concrete mathematical manipulatives for enhancing conceptual understanding in grade 12 learners. A Mathematical manipulative is defined as any material or object from the real world that children can move around to show a mathematical concept. Kolb's Experiential Theory links learning style to academic achievement. Kolb's ELT involves four learning modes: concrete experience, reflective observation, abstract conceptualization, and active experimentation. The use of manipulatives completes Kolb's learning cycle by providing a concrete experience, interaction and reflection with peers, abstract conceptualisation of triangles in the various planes and finally active experimentation. The study aims at improving the application of the sine rule, cosine rule, and area rule in solving 3-dimensional trigonometric problems. This qualitative study investigated the influence of mathematical models on teaching 3D trigonometric problems based on a sample of sixteen Grade 12 learners. Data was collected through observation, interviews, and written responses. The findings in this paper show that learners using manipulatives displayed heightened interests and improved understanding. [ABSTRACT FROM AUTHOR]

The diversity of technologies and tools in the contemporary digital world is ever rising. They enable teachers to plan and develop their own learning materials with ease – from web pages, wikis and blogs to photos and videos. This becomes the challenge of developing ways in which technology can support learners of this course to effectively create their own learning contexts. The article analyzes modern researches in some theories of learning, with the aim of seeking an answer to the question: how teachers can support the learning of students through teaching materials. As a result, a system of science-based recommendations for teachers in the context of digital resource design to use in teaching is proposed. Analysis of empirical research results shows that a template for a quality distance learning e-course that creates a familiar learning environment is more effective for learning than courses without a template. [ABSTRACT FROM AUTHOR]

We offer this synthesized framework as a tool to reveal mathematical activity in a non-formal making-space. In particular, we connect research at different grain sizes to illustrate and explain how mathematics plays a crucial, if often implicit, role in making activities. We begin by describing the Approximate Number System and the Ratio-Processing System, and explaining how those systems connect to both embodied cognition and Thompson’s (1994) conceptualization of quantities. Then, we examine how prediction and anticipation relate, with a particular emphasis on how social feedback guided the emergent mathematical activity. We offer this framework as a way to perceive, account for, and understand the multi-layered nature of experiencing mathematical activities.

This article discusses whether it is possible to use technology to train democratic values. The response presented is developed through a story detailing institutional experiences in educational innovation. After a literature review on the subject, to identify proposals within various instructional design models, a suitable model was selected and the elements within it that facilitate the design of an educational platform aimed at training children in the values of democracy, participation and peaceful coexistence were identified. It concludes with the adoption of Jonassen’s model, which proposes a critical use of technology in educational tasks. Finally, a reflection on the relationship between education, technology and society is offered.

This study reports on a newly developed framework of inclusive education, which sets out from three distinct models of disability (medical, relational, and social) and incorporates matching learning theory (behaviourist, cognitivist, and constructivist) and the placement of children with disabilities (exclusive, functional inclusive, full inclusive). Central to the framework is the contradiction between functional and full inclusive concepts, which are traced back to the conflict between a deterministic (medical and relational) and social model of disability. Applied to a complementary review of the literature published between 2006 and 2021, the framework sets the basis for structuring content analysis. An analysis of N = 685 reports shows the coherence of the framework as well as an increase in total publications, especially with full inclusive concepts. Additionally, analysis of the applied methodology shows a predominant role of qualitative methods, which is coherent with the constructivist perception of full inclusion. Implications are discussed regarding inconsistencies and barriers to inclusive education.

The cognitive theory of multimedia learning (Mayer, 2021, 2022), which seeks to explain how people learn academic material from words and graphics, has developed over the past four decades. Although the name and graphical representation of the theory have evolved over the years, the core ideas have been constant—dual channels (i.e., humans have separate information processing channels for verbal and visual information), limited capacity (i.e., processing capacity is severely limited), and active processing (i.e., meaningful learning involves selecting relevant material to be processed in working memory, mentally organizing the material into coherent verbal and visual structures, and integrating them with each other and with relevant knowledge activated from long-term memory). This review describes how the theory has developed (i.e., the past), the current state of the theory (i.e., the present), and new directions for future development (i.e., the future). In addition, the review includes examples of the events and findings that led to changes in the theory. Implications for educational psychology are discussed, including 15 evidence-based principles of multimedia design. [ABSTRACT FROM AUTHOR]

In the realm of large-scale machine learning, it is crucial to explore methods for reducing computational complexity and memory demands while maintaining generalization performance. Additionally, since the collected data may contain some sensitive information, it is also of great significance to study privacy-preserving machine learning algorithms. This paper focuses on the performance of the differentially private stochastic gradient descent (SGD) algorithm based on random features. To begin, the algorithm maps the original data into a low-dimensional space, thereby avoiding the traditional kernel method for large-scale data storage requirement. Subsequently, the algorithm iteratively optimizes parameters using the stochastic gradient descent approach. Lastly, the output perturbation mechanism is employed to introduce random noise, ensuring algorithmic privacy. We prove that the proposed algorithm satisfies the differential privacy while achieving fast convergence rates under some mild conditions. [ABSTRACT FROM AUTHOR]

The rejection of research results is sometimes thought to be justified in cases of individuals embracing fringe ideas that depart significantly from prevailing orthodoxy, or in cases of individuals who lack appropriate expertise or credentials. The case of John Garcia exhibits both of these dimensions, and illustrates that such rejection can delay scientific advancements. Garcia's work decisively challenged what was the orthodoxy in psychology in the midcentury: behaviorism. Behaviorist learning theorists suffered from theory-entrenchment insofar as they failed to acknowledge Garcia's anomalous research findings that ran counter to their theoretical expectations. The case study also illustrates that theories on the margins can become embraced as a result of advancements in adjacent research fields. Studying how Garcia's work moved from fringe to mainstream results in lessons for the philosophy of science and epistemology more generally. Only when we see the mechanisms of exclusion at work can we understand how science and other knowledge production systems can inadvertently act counterproductively via gatekeeping practices that filter out unorthodox points of view. [ABSTRACT FROM AUTHOR]

A model of population genetics of the Lotka–Volterra type with mutations on a statistical manifold is introduced. Mutations in the model are described by diffusion on a statistical manifold with a generator in the form of a Laplace–Beltrami operator with a Fisher–Rao metric, that is, the model combines population genetics and information geometry. This model describes a generalization of the model of machine learning theory, the model of generative adversarial network (GAN), to the case of populations of generative adversarial networks. The introduced model describes the control of overfitting for generating adversarial networks. [ABSTRACT FROM AUTHOR]

The structure of a transformer (an artificial intelligence model based on attention models) is considered. Analogies with physical models are discussed ("transformer as an evolution operator for a system of attention models as a Hamiltonian"), with the approaches by Yu.I. Manin ("renormalization and computations") and M. Marcolli ("generative linguistics as algebraic and physical model"), and with the concept of a genome as a "gas of interacting genes" (E.V. Koonin). [ABSTRACT FROM AUTHOR]

• What is currently known about this topic. • Various instructional approaches and impact on learning. • What the paper adds to the existing knowledge. • Conceptual framework of online interactive instruction and its impact on learning. • How the information in your manuscript can be applied to practice. • Shows trends in instructional approaches for undergraduate and graduate curricula. This paper discusses the underlying conceptual theories that support the use of technology-based educational tools and their implications on student learning outcomes. The use of technology-based educational tools has expanded where virtual classrooms, digital simulations, real-time feedback, interactive quizzes, podcasts, and other multimedia tools are integrated into instruction, especially throughout higher education. These educational tools are hypothesized to augment both student learning and attitudes towards learning. The instructional components in technology-based learning are engrained in theory and supported by empirical research. The relationship between active learning and student success, how technology-based educational tools can be designed to facilitate active learning, and how they positively impact student self-efficacy and self-regulation, including metacognitive awareness will be explicated. The theoretical underpinnings of online, interactive learning will also be applied to trends in nursing curricula. [ABSTRACT FROM AUTHOR]

Despite growing numbers of corporate venture capital (CVC) deals and alliances, their effectiveness is not guaranteed. This paper investigates the positive and negative impacts of CVC and alliance activity on product safety under different levels of market turbulence. Using a resource-based learning perspective and panel data from large U.S. firms, we find that both CVC and alliance activity have inverted U-shaped relationships with product recall likelihood. Market turbulence moderates both relationships, but differently. We discuss how learning theory complements the resource-based view to understand why no or rather bold external venturing are less harmful than small-scale "stuck-in-the-middle" initiatives. [ABSTRACT FROM AUTHOR]

Education seeks to build the best quality human resources for the future.One way to make this happen is by knowing the various forms of learning theories that function so that the process of errors in learning can be minimized. Apart from that, to increase students' enthusiasm for learning, teachers must innovate, including byusing a constructivist approach to learning. So this research aims to describe the Constructivist Learning Theory in Education. The method used is a literature study with a qualitative descriptive approach, collecting data from primary and secondary sources, then analyzing the content of relevant discussions and drawing conclusions. The results of the research show that constructivist learning theory is an approach to the individual learning process in which it has principles and characteristics to answer various relevant problems, adapts to the current curriculum, focuses on student activity, and participates in assessing students' opinions when they express their opinions. The steps must include apperception, exploration, consolidation, attitude formation, and assessment using a format. Meanwhile, several learning models that are currently running are based on constructivism, including Problem-Based Learning, Authentic Learning, Inquiry-Based Learning, and Cooperative Learning.

Recent research has shown the need to present knowledge to students in a manner consistent with the characteristics of the human brain, especially during learning mathematics, which contributes to the development of achievement and inductive reasoning. The current study aimed to develop achievement and inductive reasoning by employing brain-compatible learning theory in teaching and learning mathematics for sixth-grade students in Oman. The sample included (74) students, who were divided into two equal groups: experimental and control. Study data were collected using mathematical achievement tests and inductive reasoning. There were significant differences between the two groups in the post-application of the achievement and inductive reasoning tests in favor of the experimental group, which indicates that teaching mathematics according to the theory of learning compatible with the brain had a significant impact on the development of achievement and inductive reasoning among the participants. The study recommended the need to develop and teach mathematics curricula in a way that allows for the practice of many activities and exercises that contribute to the development of students' achievement and inductive reasoning skills. [ABSTRACT FROM AUTHOR]

Smart home technology is expected to be widespread in the future and to accommodate a green transition to reduce and time-shift energy consumption. However, smart technologies also have social consequences, which are important to understand. At a basic level, we need to know more about learning to live with these technologies and how they influence our everyday practices and routines. Providing in-depth longitudinal insights into these processes, this paper presents an auto-ethnography of living with smart home technology: a 20-month diary kept by one of the authors. The paper uses theories of practice to investigate details of learning processes when interacting with three selected technologies: smart alarm and lighting management, smart control of heating, and a smart electric vehicle (EV). Theories of learning have a well-established tradition within theories of practice, and the concept of "knowing how to go on" and the concept of practical intelligibility are central in this work. This paper investigates the adoption of new smart technologies and how they interact with learning processes in different material and social contexts. Such an approach can lay the groundwork for further empirical research with a broader set of materials. It can also provide knowledge to assist in the design of better technologies and in developing policies and regulations to promote this. [ABSTRACT FROM AUTHOR]

In this paper, we study the learning performance of regularized large-margin unified machines (LUMs) for classification problem. The hypothesis space is taken to be a reproducing kernel Hilbert space $ {\mathcal H}_K $, and the penalty term is denoted by the norm of the function in $ {\mathcal H}_K $. Since the LUM loss functions are differentiable and convex, so the data piling phenomena can be avoided when dealing with the high-dimension low-sample size data. The error analysis of this classification learning machine mainly lies upon the comparison theorem [3] which ensures that the excess classification error can be bounded by the excess generalization error. Under a mild source condition which shows that the minimizer $ f_V $ of the generalization error can be approximated by the hypothesis space $ {\mathcal H}_K $, and by a leave one out variant technique proposed in [13], satisfying error bound and learning rate about the mean of excess classification error are deduced. [ABSTRACT FROM AUTHOR]

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks. [ABSTRACT FROM AUTHOR]

We introduce character into organizational learning by building theory about how strength of individual character enhances organizational learning and how unbalanced or weak character undermine organizational learning. Bringing character into organizational learning theory helps to elucidate the type of judgment (i.e. character-based judgment anchored in all dimensions of character) that is missing but required in organizational learning to resolve organizational learning dilemmas that have persisted in the field. In connecting character to organizational learning, we rely on the multi-level processes of the 4I framework of organizational learning as scaffolding to theoretically introduce the processes of character activation, character contagion, and character embeddedness and discuss how the different character configurations and processes enhance organizational learning across levels in an organization. [ABSTRACT FROM AUTHOR]

Purpose – The constructivist learning theories of Vygotsky and Bruner highlight the effectiveness of learning through interaction, collaboration, and cognitive apprenticeship. Both theorists design instructional strategies that prioritize students' cognitive development, fostering creativity, deep understanding, and meaningful learning. While contributing significantly to education, the application of constructivism has its advantages and disadvantages. This research will discuss the principles and concepts of constructivism according to Vygotsky and Bruner, as well as analyze its strengths and weaknesses in classroom learning. Methods – This research employs the literature review method, commonly known as a literature study. In this literature review, the researcher will analyze theories related to the existing materials. The data analysis technique used is thematic analysis, examining key ideas and organizing them into themes and the focus of the research. Findings – The results of this research found two important concepts in Vygotsky's theory, namely the Zone of Proximal Development (ZPD) and scaffolding. Then the discovery learning theory discovered by Bruner is understanding concepts, meanings, and relationships through an intuitive process to finally arrive at a conclusion called discovery learning. Bruner considers that discovery learning corresponds to the active search for knowledge by the learner and provides the best results. Trying to find solutions to problems produces knowledge that is truly meaningful as learning, especially for students. Research Implications – The results of this study show that implementing constructivism requires sufficient time and resources while evaluating student progress can be a difficult task. There have also been criticisms of the ZPD concept that it may have limitations in encompassing the individual diversity of students. In implementation, some teachers may experience difficulties in adopting constructivist principles without adequate training or support.

A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., an , [Formula: see text], and [Formula: see text]), context-free (e.g., [Formula: see text], and [Formula: see text]), and context-sensitive (e.g., [Formula: see text], and xx) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized.