Improving the functional properties of (K0.5Na0.5)NbO3 piezoceramics by acceptor doping

ZrO2 and TiO2 modified lead-free (K0.5Na0.5)NbO3 (KNN) piezoelectric ceramics are prepared by a conventional solid-state reaction. The effect of acceptor doping on structural and functional properties is investigated. A decrease in the Curie temperature and an increase in the dielectric constant values are observed when doping. More interestingly, an increase in the coercive field E-c and remanent polarization P-r is observed. The piezoelectric properties are greatly increased when doping with small concentrations dopants. ZrO2 doped ceramic exhibits good piezoelectric properties with piezoelectric coefficient d(33) = 134 pC/N and electromechanical coupling factor k(p) = 35%. It is verified that nonlinearity is significantly reduced. Thus, the creation of complex defects capable of pinning the domain wall motion is enhanced with doping, probably due to the formation of oxygen vacancies. These results strongly suggest that compositional engineering using low concentrations of acceptor doping is a good means of improving the functional properties of KNN lead-free piezoceramic system. (C) 2014 Elsevier Ltd. All rights reserved.Postprint (published version

Current and future multimodal learning analytics data challenges

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, highfrequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings

What Makes Learning Analytics Research Matter

The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.</jats:p

Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility

Our 2019 editorial opened a dialogue about what is needed to foster an impactful field of learning analytics (Knight, Wise, &amp; Ochoa, 2019). As we head toward the close of a tumultuous year that has raised profound questions about the structure and processes of formal education and its role in society, this conversation is more relevant than ever. That editorial, and a recent online community event, focused on one component of the impact: standards for scientific rigour and the criteria by which knowledge claims in an interdisciplinary, multi-methodology field should be judged. These initial conversations revealed important commonalities across statistical, computational, and qualitative approaches in terms of a need for greater explanation and justification of choices in using appropriate data, models, or other methodological approaches, as well as the many micro-decisions made in applying specific methodologies to specific studies. The conversations also emphasize the need to perform different checks (for overfitting, for bias, for replicability, for the contextual bounds of applicability, for disconfirming cases) and the importance of learning analytics research being relevant by situating itself within a set of educational values, making tighter connections to theory, and considering its practical mobilization to affect learning. These ideas will serve as the starting point for a series of detailed follow-up conversations across the community, with the goal of generating updated standards and guidance for JLA articles.</jats:p

When Are Learning Analytics Ready and What Are They Ready For

Learning Analytics as a field of inquiry and community is distinct in the way that it brings together in shared pursuit, the research and practice of a particular kind of educational technology. At times this relationship approaches symbiosis: the annual LAK conference offers opportunities to learn both about the latest theoretical, methodological, and technological innovations as well as challenges and effective strategies for using such innovations to support learning in real world contexts. At other times, we feel pulled in multiple directions by the different priorities of each endeavor. Research is first and foremost concerned with advancing the state of the field by building knowledge, theories, techniques and tools with generalizable implications. Practice is primarily focused on action and implementation to have a positive impact on real world learning contexts. While these aims are not unrelated, they often offer quite different answers to the question of when a learning analytics application is ready to be used in an authentic educational setting with actual learners (and real consequences). At the extreme pole of a research perspective, there is always the temptation to try one more way to optimize an analytic (have we considered all possible features, tried all appropriate algorithms, tweaked all available hyper-parameters, explored all possible visualizations etc.). But from the perspective of practice with pressing problems to address, a tool that is available and sufficiently optimised is better than an unavailable perfect one. The ultimate goal is increased impact on learning not simply improved model accuracy. Of course, as a scholarly pursuit, the field of learning analytics does not seek to only develop and implement innovative data-based technologies, but also develop a knowledge base around them. Thus the key question is how can we make a difference in the world while also engaging in a rigorous knowledge producing process?</jats:p

New insights from zinc and copper isotopic compositions of atmospheric particulate matter from two major European cities

This study reports spatial and temporal variability of Zn and Cu isotopes in atmospheric particulate matter (PM) collected in two major European cities with contrasting atmospheric pollution, Barcelona and London. We demonstrate that non-traditional stable isotopes identify source contributions of Zn and Cu and can play a major role in future air quality studies. In Barcelona, fine PM were collected at street level at sites with variable traffic density. The isotopic signatures ranged between −0.13±0.09 and −0.55±0.09‰ for d66ZnIRMM and between +0.04±0.20 and +0.33±0.15‰ for d65CuAE633. Copper isotope signatures similar to Cu sulphides and Cu/Sb ratios within the range typically found in brake wear suggest that non-exhaust emissions from vehicles are dominant. Negative Zn isotopic signatures characteristic for gaseous emissions from smelting and combustion and large enrichments of Zn and Cd suggest contribution from metallurgical industries. In London, coarse PM collected on the top of a building over 18 months display isotope signatures ranging between +0.03±0.04 and +0.49±0.02‰ for d66ZnIRMM and between +0.37±0.17 and +0.97±0.21‰ for d65CuAE633. Heavy Cu isotope signatures (up to +0.97±0.21‰) and higher enrichments and Cu/Sb ratios during winter time suggest important contribution from fossil fuel combustion. The positive d66ZnIRMM signatures are in good agreement with signatures characteristic for ore concentrates used for the production of tires and galvanised materials, suggesting non-exhaust emissions from vehicles as the main source of Zn

CROSSMMLA Futures: Collecting and analysing multimodal data across the physical and the virtual

Workshop proposal for CrossMMLA focused on collecting and analysing multimodal data across the physical and the virtual. Under the current global pandemic, cross physical and virtual spaces play a substantial factor and challenge for MMLA, which is focused on collaborative learning in physical spaces. The workshop proposes an asynchronous format that includes pre-recorded video demonstrations and position papers for discussion, followed by a half-day virtual meeting at LAK'2021

Towards a Convergent Development of Learning Analytics

In the last 7 years, since the first LAK conference, Learning Analytics has grown rapidly as a field from a small group of interested scholars and practitioners to one of the most scientifically successful and institutionally accepted areas of Learning and Educational Technologies. Learning Analytics is often referred as a "Middle-Space" where experts from diverse fields (from the Learning Sciences, Computer Science, Human-Computer Interaction, Psychology and Behavioural Sciences, just to name a few) share their perspectives on how to better understand and optimize learning processes and environments using this new instrument called Data Science