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1. Flexible Work and Personal Digital Infrastructures

   Jarrahi, M ; Newlands, G ; Butler, B ; Savage, Saiph ; Lutz, C ; Dunn, M ; Sawyer, S. ( January 2021 , Communications of the ACM)

   null (Ed.)

   Flexible, contingent, or 'agile,' working arrangements provide workers with greater autonomy over when, where, or how to fulfill their responsibilities. In search of increased productivity and reduced absenteeism, organizations have increasingly turned to flexible work arrangements. Although access to flexible work arrangements is more prevalent among high-skilled workers, in the form of flextime or co-working, the past decade has also witnessed growth of independent contractors, digital nomadism, digitally enabled crowdwork, online freelancing, and on-demand platform labor. Flexible work arrangements reduce commutes and can enable workers with care-responsibilities to stay in the workforce. Younger workers also see flexibility as a top priority when considering career opportunities. Flexible working arrangements can also be mutually beneficial, enabling organizations to scale dynamically. Specific skill sets can be immediately accessed by turning to freelancers to fill organizational gaps. A growing number of organizations and workers rely on short-term and project-based relationships, using online platforms such as Upwork or Fiverr to connect. However, flexible work arrangements often come entwined with precarity cloaked in emancipatory narratives. Fixed salaries and benefits have given way to hourly rates and quantified ratings. Flexible workers often face unpredictability and uncertainty as they carry more risk and responsibility, and are burdened with a great portion of administrative costs (that is, overhead) associated with organizational support systems. Flexible workers at Google, for instance, outnumber full time workers but face far more unpredictability. Current formulations consider organizations as relatively fixed 'containers', which encapsulate the work performed and the information and communications technology (ICT) systems used to perform it.12 However, flexible work arrangements take place outside of organizational containers. In this new sociotechnical dynamic, flexible workers interact with a diversity of digital tools that defy centralized, top-down standardization or governance. We capture this diversity of digital tools through the concept of Personal Digital Infrastructures (PDIs), which denote an individualized assemblage of tools and technologies, such as personal laptops, smartphones, cloud services, and applications brought together by workers to perform their work tasks. Yet, flexible workers constantly reconfigure their PDIs as the technology landscape, client-relationship, and task requirements shift. For flexible work arrangements to be mutually beneficial, PDI integration in ICT systems for work is increasingly necessary, beyond a narrow focus on enterprise systems supporting standard work. Our collective research on flexible work arrangements indicates that PDIs present non-trivial challenges, but a more effective design of ICT systems for work can facilitate the integration of these bottom-up infrastructures. The nuanced understanding of PDIs presented here highlights their interplay with flexible work arrangements across key dimensions (spatial, temporal, organizational, and technological) and suggests key priorities for technology and platform developers. 

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2. 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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3. Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification

   https://doi.org/10.3390/en14051481  

   Roth, Jonathan ; Chadalawada, Jayashree ; Jain, Rishee K. ; Miller, Clayton ( March 2021 , Energies)

   null (Ed.)

   As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use. 

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4. Visualization and ecohydrologic models: Opening the box

   https://doi.org/10.1002/hyp.13991  

   Tague, Christina ; Frew, James ( December 2020 , Hydrological Processes)

   Earth system models synthesize the science of interactions amongst multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field‐based and data‐driven science by combining theory, empirical relationships derived from observation and new data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Their complexity, often encoding 100s of relationships, means that they are effectively “black boxes,” at least for most users, sometimes even to the teams of researchers that contribute to their design. This opacity complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human–computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.

    

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5. 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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