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This study compared the effects of support for peer feedback, peer feedforward and their combination on students' peer learning processes, argumentative essay quality and domain‐specific learning. Participants were 86 BSc students who were randomly divided over 43 dyads. These dyads, in a two‐factorial experimental design, were assigned to four conditions including: peer feedback (n = 22), peer feedforward (n = 22), mixed (n = 20) and control group (n = 22) conditions. An online peer feedback environment named EduTech was designed which allowed us to implement various types of support in the form of question prompts. In this online environment, students were asked to write an argumentative essay on a controversial topic, to engage in peer learning processes and to revise their essay. Overall, the results showed that students in the three experimental conditions (peer feedback, peer feedforward and their combination) benefited more than students in the control group condition (without any support) in term of peer learning processes, argumentative essay quality and domain‐specific learning. However, there was no significant difference among the three experimental conditions. This implies that peer feedforward can be as important as peer feedback in collaborative learning environments which is often neglected both in theory and practice. Practitioner NotesWhat is already known about this topic? Writing argumentative essays is a common practice for higher education students in various disciplines which deal with controversial issues.Writing argumentative essay requires solid argumentation strategies which makes it a challenging task for higher education students.Additional instructional support is needed to help students write high‐quality argumentative essays.What this paper adds? Peer learning is a promising instructional strategy for improving students' argumentative essay writing and learning.Online support in the form of question prompts to guide students during peer learning can improve their argumentative essay writing and learning.Next to the peer feedback, peer feedforward is also a promising instructional approach to support students' argumentative essay writing and learning.Implications for practice and/or policy Given the positive effects of peer learning processes, the use of peer feedback and peer feedforward should be given more attention by teachers to support students write high‐quality argumentative essays for controversial issues.Teachers and educational designers should not only provide opportunities for students to engage in peer feedback processes (how I am doing?) but also in peer feedforward processes (where to next?). [ABSTRACT FROM AUTHOR]

At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed "ontology enrichment method" (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology. [ABSTRACT FROM AUTHOR]

The present paper elucidates the issue of developing scientific thinking in contemporary university education systematically, taking into account certain principles. The problem of forming scientific thinking in university education resides in two aspects. The first is related to students, their attitudes, skills, and motivation to develop scientific thinking. The second aspect concerns the ability of highly qualified teaching staff to ensure optimal development of scientific thinking in students. In this context, several internal and external factors contributing to the development of scientific thinking in university education are presented. As a result, three directions of intervention are proposed to shape scientific thinking at the initial and continuous training stage. As a basis, the analysis of the Domain-Specific Learning Model is proposed, which would allow students to assimilate scientific concepts limited to the field of their studies. [ABSTRACT FROM AUTHOR]

Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement measurements to address these issues through employing learning analytics and educational data mining (e.g., Azevedo in Educ Psychol 50(1):84–94, 2015; Henrie in Comput Educ 90:36–53, 2015). This study responded to this call by developing learning analytics to study the multifaceted aspects of engagement (i.e., group behavioral, social, cognitive, and metacognitive engagement) and its impact on collaborative learning. The results show that group behavioral engagement and group cognitive engagement have a significantly positive effect on group problem-solving performance; group social engagement has a significantly negative effect; the impact of group metacognitive engagement is not significant. Furthermore, group problem-solving performance has a significant positive effect on individual cognitive understanding, which partially mediates the impact of group behavioral engagement and fully mediates the impact of group social engagement on individual cognitive understanding. The findings have important implications for developing domain-specific learning analytics to measure students' sub-constructs of engagement in CSCL. [ABSTRACT FROM AUTHOR]

Inquiry-based science instruction has been proposed as an optimal learning environment for language-integrated teaching. While its potential for developing both content knowledge and language skills has been shown for students with limited language proficiency, research focusing on mainstream classrooms has mainly considered domain-specific learning. Despite the effectiveness of inquiry-based science instruction for student outcomes, research on the role of specific aspects of instructional quality is limited. Addressing this research gap, the present study investigates the relationship between teachers' instructional support during inquiry-based science classes and elementary school students' learning gains in science content knowledge and academic language proficiency. Multilevel regression analyses are based on data from 459 German elementary school students from Grades 3 and 4 who participated in a longitudinal intervention study that took place over one school year. Our findings indicate a strong impact of students' prior knowledge on learning outcomes and compositional effects for the language-related measures. Relations between teachers' instructional support, as measured by the Classroom Assessment Scoring System (CLASS; Pianta et al., 2008), and students' learning outcomes did not emerge. In interpreting the results, the overall mediocre quality of teachers' instructional support needs to be considered. [ABSTRACT FROM AUTHOR]

Although the importance of students' argumentative peer feedback for learning is undeniable, there is a need for further empirical evidence on whether and how it is related to various aspects of argumentation-based learning namely argumentative essay writing, domain-specific learning, and attitudinal change while considering their epistemic beliefs which are known to be related to argumentation. In this study, a pre-test–post-test design was conducted with 42 higher education students who were asked to write an argumentative essay on the GMOs, engage in argumentative feedback, and revise their essay. The results showed that argumentative peer feedback improves students' argumentative essay writing and domain-specific learning. Furthermore, argumentative peer feedback caused attitudinal change. However, findings did not prove any impact of students' epistemic beliefs on argumentation-based learning. This is against broadly shared theoretical assumption that argumentation-based learning is related to students' epistemic beliefs. We discuss these results and provide an agenda for future work. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) aims to reduce knowledge gap between domains and improve the prediction ability of models in the target domain. However, the representations learned from feature extraction network often contain redundant information, which is harmful to domain alignment. In addition, many methods only focus on either single-source DA task or multi-source DA task, which limits their real-world applications. In this paper, we propose a simple but effective method called Category and Domain Features Augmentation (CDFA), which consists of two components: Contrastive Classifier Network (CCN) and Domain-specific Learning Network (DSLN). CDFA can remove the specific representation at the feature extraction stage to alleviate transfer difficulty, where CCN is used to increase the probabilistic output of samples and avoid misclassification, and DSLN facilitates the separation of redundant information from all representations by learning domain-specific representations. Empirical evaluations on several cross-domain benchmarks under single-source and multi-source DA scenarios illustrate the competitive performance of CDFA with respect to the state-of-the-art. [Display omitted] • The effects of the classification layer on the sample of decision boundaries is emphasized. • The correlation and independence of all representations with domain-specific representations is investigated. • Removal of redundant information by separating domain-specific representations. • A novel method based on domain separation category and domain feature alignment is proposed. • Successful application in single-source and multi-source domain adaptation scenarios. [ABSTRACT FROM AUTHOR]

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective 'whole-of-enterprise' data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where 'enterprise big tables' are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science. [ABSTRACT FROM AUTHOR]

Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning practices, wherein online platforms are used as resources for both curricular learning activities and students' interest‐driven learning. Methods: The learning practices of 73 students were examined using a mixed‐methods design and a conceptualization of practices accounting for the context and purpose of their enactment. The dataset includes students' self‐reports on domain‐specific learning activities, three‐month‐long web‐browsing history of multiple platforms and stimulated‐recall interviews. The data were analysed through latent class and thematic analyses. Results and Conclusion: Five distinct patterns were found in the use of online platforms. These patterns show that different types of platforms were used purposely and in combination during curricular and interest‐driven activities aimed at learning software development. Moreover, students' purposes were driven by both the need to progress in their learning activities and the development of their interest in software development. The findings highlight the complexity of students' learning with online platforms, which develop quite extensively beyond curricular boundaries. Implications: The findings stress the need to recognize that undergraduate students' learning practices involve multiple online platforms and activities beyond the formal curriculum and that these play a key role in developing students' interests in learning software development. Moreover, our findings indicate the importance of taking into account the way students' learning practices unfold within platforms and how these relate to domain‐specific practices. Lay Description: What is already known about this topic?: Online platforms are mostly used by teaching staff in undergraduate education to support student learning during formal curricular tasks.Research knowledge is limited mostly to specific features of social networking platforms, such as Facebook Groups or instant messaging technologies.Students informally use interest‐driven activities and both social and domain‐specific platforms to support their learning.Research on students' interest in using massive open online courses suggested that students' learning is motivated by academic success, prospective economic opportunities, and personal interests. What this paper adds?: Students combine various types of online platforms purposely (e.g., search engines or Q&A sites), many of which are domain‐specific (e.g., Stack Overflow).Students use online platforms informally during work on curricular tasks to, for example, collaborate, solve problems, and understand concepts or methods.Domain‐specific (e.g., software engineering) platforms are used during other study‐related leisure and entrepreneurial activities.Online platforms play a key role in how students develop their interests in learning. Through them, students connect not only with other students but also with professionals, companies, and information about them. Implications of study findings for practitioners: Curriculum developers and faculty should consider preparing students to use disparate types of online platforms beyond what is included in the curriculum.Curriculum developers should also consider other activities beyond the classroom that are study‐related and meaningful for students and their learning. [ABSTRACT FROM AUTHOR]

The main thesis of Heyes' book is that all of the domain‐specific learning mechanisms that make the human mind so different from the minds of other animals are culturally created and culturally acquired gadgets. The only innate differences are some motivational tweaks, enhanced capacities for associative learning, and enhanced executive function abilities. But Heyes' argument depends on contrasting cognitive gadgets with cognitive instincts, which are said to be innately specified. This ignores what has for some years been the mainstream nativist/anti‐empiricist view, which commits only to partially specified learning systems that become elaborated and built through domain‐specific learning. [ABSTRACT FROM AUTHOR]

The conceptualization of pre-service teachers' knowledge integration typically involves the distinction of two types: first-order knowledge integration, which includes merging domain-specific knowledge entities into a common knowledge base, and second-order knowledge integration, which refers to the integrated (simultaneous) application of knowledge from diverse domains. This study investigates the effect of instructional prompts in the form of (a) relevance instructions and (b) guiding questions on promoting pre-service teachers' first-order knowledge integration in a reading- and writing-based learning setting with three domain-specific study texts: one text each referring to content knowledge (CK), general pedagogical knowledge (PK), or pedagogical-content knowledge (PCK). Furthermore, the study explores whether pre-service teachers' second-order knowledge integration depends on the degree to which they engaged in first-order knowledge integration when reading and writing about different domain-specific learning contents. The study applied a three parallel group experimental design. An analysis of essays written by N = 83 German language pre-service teachers indicated positive effects of both prompts on first-order knowledge integration. Moreover, a mediation analysis showed that pre-service teachers' second-order knowledge integration is mediated by their first-order knowledge integration. The results are discussed and integrated into the existing body of research, practical implications are presented, and limitations of the study are explained. [ABSTRACT FROM AUTHOR]

This non-experimental study employed three questionnaires adopted from and validated by prior studies. To carry out the study, an electronic survey created through Wenjuanxing, a computer program for conducting an online survey in China, was employed and convenience sampling technique was used. Guided by the control-value theory of educational psychology, as well as the broaden-and-build theory and the well-being theory of Positive Psychology, this questionnaire study investigated 880 non-English major freshmen' foreign language enjoyment (FLE), foreign language class anxiety (FLCA), and foreign language boredom (FLB) in online English courses, as well as their correlations. Also measured were their joint predictive effects on the general and domain-specific learning outcomes of online English classrooms (reading-to-writing group and speaking group). With statistical analysis carried out by SPSS 26.0 and AMOS 23.0, the results showed that: (1) learners had relatively high levels of FLE and FLCA, but a medium level of FLB; (2) a small negative correlation was found between students' FLE and FLCA, a medium to high negative correlation between FLE and FLB, and a small to medium positive correlation between FLCA and FLB; FLE has a significant positive correlation with learners' actual performance and self-perceived performance, while FLCA and FLB have a significant negative correlation with both; and (3) After entering the same regression model, all three emotions have a significant predictive effect on learners' self-perceived performance while only FLE and FLCA had a significant predictive effect on their actual performance in the online context. Domain-specifically, the reading-and-writing group demonstrated similar trends while there were no significant correlations between emotions and actual performance in the speaking group. The findings can provide pedagogical implications for online foreign language teaching and theoretical contribution to foreign language emotion research. [ABSTRACT FROM AUTHOR]

The academic self‐concept (ASC) is a domain‐specific construct, which plays an influential role in academic motivation and choices. Although some ASC models and instruments have been developed for mathematics and science, none have been specifically designed to measure the physics ASC. Therefore, this study focuses on designing and validating a model for measuring the physics ASC. In the proposed model, students' physic academic self‐perception is measured by a novel instrument called Physics Academic Self‐Concept Inventory (PASCI) based on domain‐specific learning targets. The major physics learning targets considered in the PASCI design are physics knowledge, attitudes toward physics, and physics‐related skills. Confirmatory factor analysis and psychometric methods are used to test and validate the proposed model and the PASCI instrument on the data collected from 861 male and female students attending secondary high schools in Iran. The results indicate that the devised model has appropriate fit measures and robust structural validity so that in multiple group analysis, model parameters are invariant. The author suggests that the idea of physics ASC should be modeled as a hierarchical and multidimensional structure based on physics learning targets to be effectively helpful to physics education researchers and teaching physics in classrooms. [ABSTRACT FROM AUTHOR]

There is widespread agreement that student teachers need to construct an integrated knowledge base across multiple domains. This study examined the contributions of intraindividual factors of self-regulated learning to explaining student teachers' (a) integration of knowledge across topics and domains (i.e., integrative learning) and (b) disjointed processing of potentially domain-specific learning content (i.e., separative learning). The factors considered were study approaches; cognitive, metacognitive, and resource-related learning strategy use; epistemological and pedagogical beliefs; and career choice motivation. The study applied a cross-sectional survey design and examined separative and integrative learning in N = 103 student teachers by way of multiple regression analyses with backward eliminations. A key finding is that deep and strategic study approaches and certain cognitive learning strategies contributed significantly to explaining integrative learning in student teachers. Epistemological and pedagogical beliefs were not able to predict integrative learning. Regarding separative learning, the study identified the surface study approach, specific epistemological and pedagogical beliefs, and the "usefulness" motive for career choice as positive predictors and critical thinking as a negative predictor. The study demonstrates differences in how integrative and separative learning are shaped by distinct intraindividual factors. Implications are discussed with regard to student teachers' self-regulated learning and pre-service teacher education. [ABSTRACT FROM AUTHOR]

Featured Application: 1. A systematic review of literature was conducted concerning augmented reality (AR) in architectural and civil engineering education. 2. AR is discovered to possess a wide range of applications in general pedagogies and domain-specific learning. 3. The advantages and challenges of AR in this field are reported. 4. Analysis was performed based on fundamental information, application domains, AR development tools, system types, teaching devices, teaching methods, learning strategies, and research methods. 5. Discussions and suggestions were made regarding the advantages in pedagogies and education, the challenges to systems and curriculums, the selection of suitable systems, and the emerging trends of AR and building information modeling (BIM) combined. 6. Collaboration should occur among different roles. The trends and research have become an integrated and collaboration issue, that should be conducted interactively with pedagogical findings and resources integrated across different parties. Architectural and civil engineering (ACE) education is inextricably connected to real-world practice. The application of augmented reality (AR) technology can help to establish a link between virtual and real-world information for students. Studies of applying AR in ACE education have increased annually, and numerous research have indicated that AR possesses immense application potential. To address and analyze pertinent research issues, published studies in the Scopus database were explored, and revealed that problems persist and are worthy of attention, such as the selection of system types and devices, the application of research methods, and appropriate learning strategies and teaching methods. Courses with objective grading standards should be given priority in AR experimental courses for a meticulous investigation of AR influence on students' learning outcomes and ultimately improvement of classroom quality. Suitable types of AR systems should be selected based on course content, prior to the design and development of the system. It is recommended to develop markerless systems for a larger application range to benefit students with additional convenience. Systems can also be accompanied by functions, such as instant online assessments, synchronized assessments, and exchange capabilities to assist learning what has been taught and develop critical thinking abilities. The combination of AR and building information modeling (BIM) in architectural and civil practice, which has immense application potential, has become an emerging research trend. Collaboration between academics and practice should be enhanced with roles and knowledge of instructors, engineers, designers, and computer experts integrated for an optimal connection between general pedagogy and domain-specific learning. Teaching methods that emphasize "locations", as well as "roles", can be adopted in order to create a superior reality learning environment with diversified learning methods. The trends and research have become an integration and collaboration issue that should be performed interactively with pedagogical findings, and resources integrated across roles, fields, and university departments. [ABSTRACT FROM AUTHOR]

Prior research has investigated global mobility through the lenses of consumer acculturation, identity, and possessions with a focus on consumers' socialization and identity management in the host consumer culture. It has neglected, however, the ways that globally mobile consumers simultaneously navigate the multiple, cross-border markets in which they are embedded. We adopt the social network perspective to investigate the transnational consumer lifestyles of people who live and consume simultaneously in two or more countries and sustain multiple relationships of a diverse nature (e.g. market, social, financial, professional) across borders. Through a qualitative study, we dimensionalize the transnational social space inhabited by transnational consumers and demonstrate how it shapes their consumption. We introduce the concept of transnational market navigation, defined as the process of strategically and pragmatically selecting and leveraging social networks to engage simultaneously with multiple cross-border markets. We identify three transnational market navigation strategies: clustering consumption, embracing commercial lock-ins, and developing cluster-based competency. By mobilizing a network perspective to examine consumption in global mobility, we show that globally mobile consumers are also motivated by ways of being (the actual social and commercial relationships and consumption practices with which consumers engage), in addition to the identities associated with their consumption. [ABSTRACT FROM AUTHOR]

ABSTRACT The present study fills a gap in existing visitor research by focusing on the preschool age group. The study explores relationships between parent explanation, children's exploratory behavior, and their domain-specific learning in a shadow exhibition. In addition, the effect of a preceding theater show on child and parent behaviors is examined. In the study, parent-child pairs were observed while playing in a science center exhibition illustrating physical principles of shadows. Parent explanation was scored on a number of domain-general categories, such as causal explanations, evidence descriptions, and content-related directions. Preschoolers' exploratory behavior and learning were quantified by using nonverbal measures. The Exploratory Behavior Scale was used to assess children's exploration of the physical environment. Children's learning was assessed in the knowledge domain of shadow size. Results showed a positive relationship between one type of parent explanation, evidence descriptions, and preschoolers' exploratory behavior. No positive relationships between children's exploration and their domain-specific learning or between parent explanation and children's domain-specific learning were found. Last, theater attendance was found to affect children's learning on shadow size. [ABSTRACT FROM AUTHOR]

Learning using the Internet has become a vital factor for academic success in higher education. Students increasingly rely on the Internet as their main information source. However, related research is still an emerging and highly fragmented field. Therefore, this study aims to provide a comprehensive and integrative review of research literature on student learning vis-à-vis using the Internet. The review encompasses research on students' strategies for searching, evaluating, and selecting online information, the use of this information for domain-specific learning, and the influence of the characteristics of both online information sources and learners on searching, evaluating, and selecting online information. Moreover, research on how this information-related behavior develops and affects learning outcomes is considered. The literature research, conducted in 2020, employed the database Education Resources Information Center (ERIC) and the search engine Google Scholar. Based on relevant search phrases and keywords, over 500 publications were found. After a second search, 55 fitting publications from 2009 onwards were identified. The review reveals a lack of research on the characteristics of online information, how these characteristics affect student learning in higher education, and how student learning can be fostered by improving students' information-related strategies. Research integrating these different perspectives is even scarcer. [ABSTRACT FROM AUTHOR]

A key question in cognition is whether animals that are proficient in a specific cognitive domain (domain specific hypothesis), such as spatial learning, are also proficient in other domains (domain general hypothesis) or whether there is a trade-off. Studies testing among these hypotheses are biased towards mammals and birds. To understand constraints on the evolution of cognition more generally, we need broader taxonomic and phylogenetic coverage. We used Australian eastern water skinks (Eulamprus quoyii) with known spatial learning ability in three additional tasks: an instrumental and two discrimination tasks. Under domain specific learning we predicted that lizards that were good at spatial learning would perform less well in the discrimination tasks. Conversely, we predicted that lizards that did not meet our criterion for spatial learning would likewise perform better in discrimination tasks. Lizards with domain general learning should perform approximately equally well (or poorly) in these tasks. Lizards classified as spatial learners performed no differently to non-spatial learners in both the instrumental and discrimination learning tasks. Nevertheless, lizards were proficient in all tasks. Our results reveal two patterns: domain general learning in spatial learners and domain specific learning in non-spatial learners. We suggest that delineating learning into domain general and domain specific may be overly simplistic and we need to instead focus on individual variation in learning ability, which ultimately, is likely to play a key role in fitness. These results, in combination with previously published work on this species, suggests that this species has behavioral flexibility because they are competent across multiple cognitive domains and are capable of reversal learning. [ABSTRACT FROM AUTHOR]

Background: The relationships between obesity and cognitive decline in aging are mixed and understudied among Hispanics/Latinos.Objective: To understand associations between central obesity, cognitive aging, and the role of concomitant cardiometabolic abnormalities among Hispanics/Latinos.Methods: Participants included 6,377 diverse Hispanics/Latinos enrolled in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and SOL-Investigation for Neurocognitive Aging (SOL-INCA). Participants were 45 years and older at the first cognitive testing session (Visit 1). Cognitive outcomes (z-score units) included global composite and domain specific (learning, memory, executive functioning, processing speed) measures at a second visit (SOL-INCA, on average, 7 years later), and 7-year change. We used survey linear regression to examine associations between central obesity (waist circumference≥88 cm and≥102 cm for women and men, respectively) and cognition. We also tested whether the relationships between obesity and cognition differed by cardiometabolic status (indication of/treatment for 2 + of the following: high triglycerides, hypertension, hyperglycemia, low high-density lipoprotein cholesterol).Results: Central obesity was largely unassociated with cognitive outcomes, adjusting for covariates. However, among individuals with central obesity, cardiometabolic abnormality was linked to poorer cognitive function at SOL-INCA (ΔGlobalCognition =-0.165, p < 0.001) and to more pronounced cognitive declines over the average 7 years (ΔGlobalCognition = -0.109, p < 0.05); this was consistent across cognitive domains.Conclusion: Central obesity alone was not associated with cognitive function. However, presence of both central obesity and cardiometabolic abnormalities was robustly predictive of cognition and 7-year cognitive declines, suggesting that in combination these factors may alter the cognitive trajectories of middle-aged and older Hispanics/Latinos. [ABSTRACT FROM AUTHOR]

Malaria is an infectious disease which is caused by plasmodium parasite. Several image processing and machine learning based techniques have been employed to diagnose malaria, using its spatial features extracted from microscopic images. In this work, a novel deep neural network model is introduced for identifying infected falciparum malaria parasite using transfer learning approach. This proposed transfer learning approach can be achieved by unifying existing Visual Geometry Group (VGG) network and Support Vector Machine (SVM). Implementation of this unification is carried out by using "Train top layers and freeze out rest of the layers" strategy. Here, the pre-trained VGG facilitates the role of expert learning model and SVM as domain specific learning model. Initial 'k' layers of pre-trained VGG are retained and (n-k) layers are replaced with SVM. To evaluate the proposed VGG-SVM model, a malaria digital corpus has been generated by acquiring blood smear images of infected and non-infected malaria patients and compared with state-of-the-art Convolutional Neural Network (CNN) models. Malaria digital corpus images were used to analyse the performance of VGG19-SVM, resulting in classification accuracy of 93.1% in identification of infected falciparum malaria. Unification of VGG19-SVM shows superiority over the existing CNN models in all performance indicators such as accuracy, sensitivity, specificity, precision and F-Score. The obtained result shows the potential of transfer learning in the field of medical image analysis, especially malaria diagnosis. [ABSTRACT FROM AUTHOR]

This article draws from a multi‐disciplinary, multi‐institutional research project on digital work practices and graduate work readiness. Utilising the concept of technology affordances, we focus on the potential for domain‐specific learning experiences within design education. For the purpose of this project, we have articulated digital capabilities in design by adapting affordance categories in terms of three levels of complexity for scaffolded learning: functional, perceptual and adaptive. In order to further develop the relationship between technology affordances and design education, we analyse data from an industry roundtable in relation to our developed digital capability descriptor. The findings suggest that employers need designers with highly adaptive capabilities to work in increasingly complex interdisciplinary work environments. We also found that the role of designers has changed significantly in recent years that require higher educational institutions to involve industry when developing curricula. [ABSTRACT FROM AUTHOR]

Typically, most research and academic institutions own and archive a great amount of objects and research related resources that have been produced, used and maintained over long periods of time by different types of domain experts (e.g. lecturers and researchers). Although the potential educational value of these resources is very high, this potential may largely be underused due to severe accessibility and manipulability constraints. The virtualization of these resources, i.e. their representation as reusable digital learning objects that can be integrated in an e-learning environment, would allow the full exploitation of all their educational potential. In this paper we describe the process model that we have followed during the virtualization of the objects and research resources owned by two academic museums at the Complutense University of Madrid (Spain). In the context of this model we also summarize the main aspects of these experiences in virtualization. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to bring together the available scattered knowledge about teaching and learning in Living Labs within higher education, and to explore their potential for supporting students' sustainability-oriented transformative learning. Design/methodology/approach: A literature review was conducted, applying a realist approach. A sample of 35 articles was analyzed qualitatively, mapping the data according to the realist constructs "context," "intervention," "mechanism" and "outcome" and using the constant comparison method for data analysis. Findings: This study identified multiple characteristics of teaching and learning in sustainability-oriented Living Labs, namely, two socio-physical teaching and learning contexts, two pedagogical approaches as interventions therein, four learning processes as (potential) mechanisms and six sustainability-related learning outcomes. Two main challenges were also identified. Originality/value: To the best of the authors' knowledge, this is the first study that brings together the scattered results from previous studies into a comprehensive description of characteristics and challenges of teaching and learning in Living Labs as sustainability-oriented learning spaces in higher education. The findings can support educators in making scientifically grounded informed choices for teaching and learning in Living Labs and inform future empirical studies to examine when, how and why certain characteristics of teaching and learning in Living Labs, as identified in this study, can support sustainability-oriented transformative learning in higher education. [ABSTRACT FROM AUTHOR]

Adaptive learning support provided by kindergarten teachers before and after (macro-adaptive learning support) as well as during mathematical learning activities (micro-adaptive learning support) is a cross-cutting concept of teaching quality. Effective adaptive learning support enhances children's learning. However, providing it is challenging and teachers need professional development (PD) to improve the quality of their support. This study investigates the mediating role of teaching quality between PD programs for kindergarten teachers and the development of children's mathematical competency. 122 kindergarten teachers and their 825 pupils participated in the study. The teachers were randomly assigned to three groups. Two groups attended PD sessions designed to foster either macro- or micro-adaptive learning support. The third was the materials-only control group. The data was analyzed using a self-developed rating instrument focusing on generic and domain-specific elements of teaching quality related to macro- and micro-adaptive learning support. The multilevel latent change model analysis revealed that the PD programs had positive and significant effects on the teaching quality of kindergarten teachers. A significant positive relationship was also found between micro-adaptive learning support and changes in children's mathematical competency. However, an indirect effect of teaching quality could not be detected, and the PD programs did not have a total effect on children's mathematical competency. The study reinforces the importance of PD that specifically targets macro- and micro-adaptive learning support for kindergarten teachers. The mediation between PD and mathematical competency development requires further investigation. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is an essential task in ensuring reliability in intelligent manufacturing. Recent advances in deep learning-based data-driven methods have shown promising results. However, one non-ignorable challenge is that distribution shift across various machine individuals often results in a performance decline. Domain adaptation approaches appear to be effective in tackling this issue, whereas they often require sufficient unlabeled target data, which is causally infeasible in prognostic tasks in practical scenarios. In this paper, we discuss the significance of prognostic generalization for RUL prediction, and a domain generalization-based scheme is proposed. A domain conditional invariance and specificity disentanglement network (DCISD) is proposed to learn domain conditional-invariant and domain-specific information simultaneously in a unified network. Domain conditional-invariant features are extracted through conditional domain adversarial learning and samples are conditioned by multiple RUL fuzzy sets. Domain-specific features correlated to individual degradation patterns are disentangled to promote sufficiency of degradation information. Moreover, a degradation dynamics-based augmentation method is proposed to mitigate domain imbalance following the degradation dynamics in the latent space. Two bearing run-to-failure datasets are utilized to evaluate the proposed method. Comparative and ablation studies validate the method effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This study contributes to the call for influencing practice by increasing attention to how learning environments can be designed to support learning in statistical inference. We report on a design experiment in secondary school (students 14–16 years old), that resulted in a set of lessons with the learning goal of teaching students how to apply concepts and principles of hypothesis testing for making an inference as to whether or not students in secondary school can taste the difference between two brands of cola soda. The design experiment resulted in four design principles for a simulation-based approach for learning hypothesis testing in secondary school. The design principles highlight the combination of practical and digital simulations of samplings. They stress the need for using random generators that allow for high reliability in collecting sample data and introduce a simulation-based method for determining p-values, i.e. to quantify how likely or surprising a sample result, or a result more extreme, is under a null hypothesis. [ABSTRACT FROM AUTHOR]