More Like this

1. Supporting collaborative classroom networks through technology: An actor network theory approach to understanding social behaviours and design

   https://doi.org/10.1111/bjet.13274  

   Kumar, Vishesh ; Tissenbaum, Mike ( September 2022 , British Journal of Educational Technology)

   This paper presents an implementation of Connected Spaces (CxS)—an ambient help seeking interface designed and developed for a project‐based computing classroom. We use actor network theory (ANT) to provide an underutilized posthumanist lens to understand the creation of collaborative connections in this Computational Action‐based implementation. Posthumanism offers an emerging and critical extension to sociocultural perspectives on understanding learning, by pushing us to decenter the human, and consider the active roles that human and non‐human entities play in learning environments by actively shaping each other. We analyse how students in this class adjusted their help‐seeking and collaborative habits following the introduction of CxS, a tool designed to foster (more inter‐group) collaboration. ANT proposes generalized symmetry—a principle of considering human, non‐human and more than human entities with equivalent and comparable agency, leading to describing phenomena as networks of actors in different evolving relationships with each other. Analysing collaborative interactions as fostered by CxS using an ANT approach supports design‐based research—an iterative design revision process highlighting understandings about design as well as learning—by providing a temporal and informative lens into the relationship between actors and tools within the environment. Our key findings include a framing of technologies in classrooms as bridgingagentic gapsbetween students and becoming actors engaging in different behaviours; learners enacting new agencies through technologies (for instance a more comfortable non‐intrusive help seeker), and the need for voicing and teachers to connect help networks in CxS equipped classrooms.

   What is already known about this topic

   Collaborative learning is a valuable skill and practice; opportunities to mentor others are critical in empowering minoritized learners, especially in STEM and computing disciplines.

   School norms solidify a power and expertise hierarchy between teachers and learners and fail to productively support learners in learning from each other.

   Additionally, lack of awareness about peers' knowledge is a common hindrance in students knowing who to ask for help and how.

   What this paper adds

   An example of a designed interface called Connected Spaces with potential to foster more inter‐student collaboration, especially outside of mandated within‐group collaboration—in the form of cross‐group help seeking and help giving.

   A design based research study using actor network theory highlighting the limitations of Connected Spaces in sparking notable behaviour change among students by itself but being retooled as a teacher support tool in enabling cross‐group collaborations.

   Presenting conceptions of collaboration through technologies as bridging agentic gaps and acting with new agencies in performing help‐seeking related actions.

   Provoking the idea of testing emerging technologies in classrooms along with sharing our analyses and reflections with the classroom as a key idea in computing education—surfacing the gap between designed intentions and the different kinds of extra social work needed in the on‐ground success of different technologies.

   Implications for practice and/or policy

   Designers and researchers should create and test more interfaces alongside teachers across different classrooms and contexts aimed at supporting different kinds of voluntary collaborative interactions.

   Curricula, standards and school practices should further center providing students with opportunities to engage as mentors and build communities of learning across disciplines to empower minoritized students.

   Researchers engaging in design based research should consider using more posthumanist lenses to examine educational technologies and how they affect change in learning environments.

    

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2. Embracing equity and excellence while constructing science teacher identities in urban schools: Voices of new Teachers of Color

   https://doi.org/10.1002/tea.21795  

   Varelas, Maria ; Segura, David ; Bernal‐Munera, Marcela ; Mitchener, Carole ( July 2022 , Journal of Research in Science Teaching)

   We explored how new Teachers of Color grappled with equity and excellence as they were constructing science teacher identities while learning to teach in a teacher education program committed to equity, justice, and excellence, and eventually teaching in urban schools where inequities and injustices persist. The theoretical framing, compiled from various bodies of literature, weaved together what we consider as essential parts of teacher identity construction and provided a lens with which to examine how conceptions of equity and excellence that the study participants were constructing meshed with their multiple identities, considerations on legitimate knowledge production, and dialectical relationships with which they grappled. Using transcendental phenomenology, we learned from and with three Black and Latinx teachers and their narratives. The teachers intertwined similarly and differently their evolving conceptions of equity and excellence into their evolving science teacher identities as they engaged in forms of contentious local practice and reflected on their experiences as science Teachers of Color teaching predominately Students of Color. Their multiple identities were meshed with histories of larger institutions—science, schooling, and society—and together these were shaping their conceptions of equity and excellence. The intermingling of equity and excellence, which was guiding the curricular and instructional decisions they were making in their classrooms, was also linked to what they considered as legitimate knowledge production in science classes and what counted as knowledge that their students needed to know at different times. The various dilemmas defined by opposing poles with which they were grappling also functioned as scales on which their coordinated equity‐excellence unit of meaning was forming. Based on the study, we offer insights into practices that science teacher educators may consider as they prepare new teachers, and work with practicing teachers, to embrace and coordinate equity and excellence in their ever‐developing science teacher identities.

    

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3. Simulations as practice‐based spaces to support elementary teachers in learning how to facilitate argumentation‐focused science discussions

   https://doi.org/10.1002/tea.21659  

   Mikeska, Jamie N. ; Howell, Heather ( August 2020 , Journal of Research in Science Teaching)

   In order to deepen students' understanding of natural phenomenon and how scientific knowledge is constructed, it is critical that science teachers learn how to engage students in productive scientific argumentation. Simulations for teachers are one possible solution to providing practice‐based spaces where novices can approximate the work of facilitating argumentation‐focused science discussions. This study's purpose is to examine how preservice elementary teachers (PSETs) engage in this ambitious teaching practice within an online simulated classroom composed of five upper elementary student avatars. In this study, which is part of a larger research project, we developed and used four performance tasks to provide opportunities for PSETs to practice facilitating argumentation‐focused science discussions within a simulated classroom. The student avatars were controlled on the backend by a human‐in‐the‐loop who was trained to respond to the teachers' prompts in real time using predesigned student thinking profiles and specific technology, such as voice modulation software. We used analysis of transcripts from the PSETs' video‐recorded discussions to examine how the PSETs engaged the student avatars in scientific argumentation, with particular attention to the teaching moves that supported argument construction and argument critique. We also used survey and interview data to examine how the PSETs viewed the usefulness of these simulation‐based tools to support their learning. Findings show that there was variability in the extent to which the PSETs engaged the student avatars in argument construction and argument critique and the teaching moves that the PSETs used to do so. Results also indicated that PSETs strongly perceive the value of using such tools within teacher education. Implications for the potential of simulations to provide insights into novices' ability to engage students in scientific argumentation and to support them in learning in and from their practice, including how to productively integrate these tools in teacher education, are discussed.

    

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4. The information won't just sink in: Helping teachers provide technology‐assisted data literacy instruction in social studies

   https://doi.org/10.1111/bjet.13255  

   Shreiner, Tamara L. ; Guzdial, Mark ( July 2022 , British Journal of Educational Technology)

   In this study, support for teaching data literacy in social studies is provided through the design of a pedagogical support system informed by participatory design sessions with both pre‐service and in‐service social studies teachers. It provides instruction on teaching and learning data literacy in social studies, examples of standards‐based lesson plans, made‐to‐purpose data visualization tools and minimal manuals that put existing online tools in a social studies context. Based on case studies of eleven practicing teachers, this study provides insight into features of technology resources that social studies teachers find usable and useful for using data visualizations as part of standards‐ and inquiry‐based social studies instruction, teaching critical analysis of data visualizations and helping students create data visualizations with online computing tools. The final result, though, is that few of our participating teachers have yet adopted the provided resources into their own classrooms, which highlights weaknesses of the technology acceptance model for describing teacher adoption.

   What is already known about this topic

   Data literacy is an important part of social studies education in the United States.

   Most teachers do not teach data literacy as a part of social studies.

   Teachers may adopt technology to help them teach data literacy if they think it is useful and usable.

   What this paper adds

   Educational technology can help teachers learn about data literacy in social studies.

   Social studies teachers want simple tools that fit with their existing curricula, give them new project ideas and help students learn difficult concepts.

   Making tools useful and usable does not predict adoption; context plays a large role in a social studies teachers' adoption.

   Implications for practice and/or policy

   Designing purpose‐built tools for social studies teachers will encourage them to teach data literacy in their classes.

   Professional learning opportunities for teachers around data literacy should include opportunities for experimentation with tools.

   Teachers are not likely to use tools if they are not accompanied by lesson and project ideas.

    

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5. Machine learning for the educational sciences

   https://doi.org/10.1002/rev3.3310  

   Hilbert, Sven ; Coors, Stefan ; Kraus, Elisabeth ; Bischl, Bernd ; Lindl, Alfred ; Frei, Mario ; Wild, Johannes ; Krauss, Stefan ; Goretzko, David ; Stachl, Clemens ( November 2021 , Review of Education)

   Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.

   In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.

   The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.

   The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.

   Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

    

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