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1. How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process

   https://doi.org/10.1111/bjet.13372  

   Lawrence, LuEttaMae ; Echeverria, Vanessa ; Yang, Kexin ; Aleven, Vincent ; Rummel, Nikol ( August 2023 , British Journal of Educational Technology)

   Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, calledPair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control withPair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems.

   What is already known about this topic:

   Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.

   Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.

   What this paper adds:

   Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.

   Describes how we adapted six design methods to design AI features.

   Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.

   Implications for practice:

   We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.

   Our work also includes a reflection on our design process and implications for future design processes.

    

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2. A human‐centred learning analytics approach for developing contextually scalable K‐12 teacher dashboards

   https://doi.org/10.1111/bjet.13383  

   Wiley, Korah ; Dimitriadis, Yannis ; Linn, Marcia ( September 2023 , British Journal of Educational Technology)

   This paper describes a Human‐Centred Learning Analytics (HCLA) design approach for developing learning analytics (LA) dashboards for K‐12 classrooms that maintain both contextual relevance and scalability—two goals that are often in competition. Using mixed methods, we collected observational and interview data from teacher partners and assessment data from their students' engagement with the lesson materials. This DBR‐based, human‐centred design process resulted in a dashboard that supported teachers in addressing their students' learning needs. To develop the dashboard features that could support teachers, we found that a design refinement process that drew on the insights of teachers with varying teaching experience, philosophies and teaching contexts strengthened the resulting outcome. The versatile nature of the approach, in terms of student learning outcomes, makes it useful for HCLA design efforts across diverse K‐12 educational contexts.

   What is already known about this topic

   Learning analytics that are aligned to both a learning theory and learning design support student learning.

   LA dashboards that support users to understand the associated learning analytics data provide actionable insight.

   Design‐based research is a promising methodology for Human‐Centred Learning Analytics design, particularly in the K‐12 educational context.

   What this paper adds

   Leveraging a longstanding, yet fluid, research‐practice partnership is an effective design‐based research adaptation for addressing the high variation in instructional practices that characterize K‐12 education.

   Using both quantitative and qualitative data that reflects students' developing knowledge effectively supports teachers' inquiry into student learning.

   Teachers' use of learning analytics dashboards is heavily influenced by their perspectives on teaching and learning.

   Implications for practice and/or policy

   Impact on student learning outcomes, alongside usability and feasibility, should be included as a necessary metric for the effectiveness of LA design.

   LA dashboard developers should both leverage learning data that reflect students' developing knowledge and position teachers to take responsive pedagogical action to support student learning.

   LA researchers and developers should utilize a long‐term, yet fluid, research‐practice partnership to form a multi‐stakeholder, multidisciplinary design team for Human‐Centred Learning Analytics design.

    

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3. Co‐designing teacher support technology for problem‐based learning in middle school science

   https://doi.org/10.1111/bjet.13363  

   Hutchins, Nicole M. ; Biswas, Gautam ( July 2023 , British Journal of Educational Technology)

   This paper provides an experience report on a co‐design approach with teachers to co‐create learning analytics‐based technology to support problem‐based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal roles of the participants in the design process. Our results provide precedent knowledge on co‐designing with experienced and novice teachers and co‐constructing actionable insight that can help teachers engage more effectively with their students' learning and problem‐solving processes during classroom PBL implementations.

   What is already known about this topic

   Success of educational technology depends in large part on the technology's alignment with teachers' goals for their students, teaching strategies and classroom context.

   Teacher and researcher co‐design of educational technology and supporting curricula has proven to be an effective way for integrating teacher insight and supporting their implementation needs.

   Co‐designing learning analytics and support technologies with teachers is difficult due to differences in design and development goals, workplace norms, and AI‐literacy and learning analytics background of teachers.

   What this paper adds

   We provide a co‐design workflow for middle school teachers that centres on co‐designing and developing actionable insights to support problem‐based learning (PBL) by systematic development of responsive teaching practices using AI‐generated learning analytics.

   We adapt established human‐computer interaction (HCI) methods to tackle the complex task of classroom PBL implementation, working with experienced and novice teachers to create a learning analytics dashboard for a PBL curriculum.

   We demonstrate researcher and teacher roles and needs in ensuring co‐design collaboration and the co‐construction of actionable insight to support middle school PBL.

   Implications for practice and/or policy

   Learning analytics researchers will be able to use the workflow as a tool to support their PBL co‐design processes.

   Learning analytics researchers will be able to apply adapted HCI methods for effective co‐design processes.

   Co‐design teams will be able to pre‐emptively prepare for the difficulties and needs of teachers when integrating middle school teacher feedback during the co‐design process in support of PBL technologies.

    

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4. Leveraging complexity frameworks to refine theories of engagement: Advancing self‐regulated learning in the age of artificial intelligence

   https://doi.org/10.1111/bjet.13340  

   Hilpert, Jonathan C. ; Greene, Jeffrey A. ; Bernacki, Matthew ( May 2023 , British Journal of Educational Technology)

   Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected fromn = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control,n = 48), (2) perform poorly and received intervention (treatment,n = 95) and (3) perform well (not flagged,n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

   What is already known about this topic

   Self‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.

   SRL is a dynamic, temporal process that leads to purposeful student engagement.

   Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.

   What this paper adds

   A Markov process for measuring dynamic SRL processes using log data.

   Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.

   Evidence that SRL processes can be meaningfully impacted through educational intervention.

   Implications for theory and practice

   Complexity approaches inform theory and measurement of dynamic SRL processes.

   Static representations of dynamic SRL processes are promising learning analytics metrics.

   Engineered features of LMS usage are valuable contributions to AI models.

    

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5. Patches as an expressive medium for exploratory multi‐agent modelling

   https://doi.org/10.1111/bjet.13087  

   Brady, Corey ( April 2021 , British Journal of Educational Technology)

   This paper outlines the potential gains for Constructionist research and praxis in modelling that might be obtained by recognising the power of the Patch—a humble computational being in the NetLogo modelling environment that has been overshadowed by its more popular fellow agent, the Turtle. To contextualise this opportunity, I describe how Constructionist modelling has thrived by promoting forms of learning that rely on learners’ identifying with agents. I argue that patches are a neglected agent type in this multi‐agent modelling tradition, and that the possibilities for learners to adopt the patch perspective in support of exploratory forms of modelling and aesthetic expression have been under‐researched. Nevertheless, I show there are a variety of powerful ways for learners––both individually and in groups––to identify with patches. I describe ongoing research showing how taking an aesthetic approach to patches has the potential to support individuals and groups in powerful forms of learning with and about multi‐agent modelling.

   What is already known about this topic

   Turtles (movable agents in Logo and Constructionist environments descended from Logo) can be ‘transitional objects’ that provide learners a way to make powerful ideas their own.

   These agents can be powerful ‘objects‐to‐think‐with’ in large part because they encourage learners to identify with them in a form of learning known as ‘syntonic learning’.

   Expressive activities that draw on learners’aestheticinterests can support their learning with and about computational representations.

   Multi‐agent modelling is a powerful extension of Logo‐based learning environments that provides access to powerful ideas about complex systems and their emergent properties.

   In the multi‐agent setting, individual learners and/or groups of learners can identify syntonically with agents to provide entry points for reasoning about complexity.

   What this paper adds

   Patches (non‐movable agents in the NetLogo modelling environment) are under‐represented in the research on multi‐agent modelling, and the potential for learners to adopt the patches’ perspective has been neglected.

   An aesthetically driven approach to patches can ground students’ understanding of their expressive value.

   Participatory activities in which learners play the role of patches (called ‘Stadium Card’ activities) can ground the patch perspective, so that learners can achieve a form of syntonicity and/or collectively adopt the perspective of patches in the aggregate.

   Participatory activities that blend intrinsic and extrinsic perspectives on the patch grid can further enhance learners’ facility with programming for patches and their understanding of patches’ collective expressive power.

   Implications for practice and/or policy

   Balancing the focus between turtles and patches can enrich the modelling toolbox of learners new to agent‐based modelling.

   Patchesdocapture important aspects of individual and collective experience, and so can be good objects‐to‐think‐with, especially when conceptualising phenomena at a larger scale.

   The expressive potential of the patch grid is an important topic for computer science as well (eg, through 2D cellular automata). This is a rich context for learning in itself, which can be made accessible to groups of learners through physical or virtual participatory role‐play.

   Moreover, physical or virtual grids of people‐patches may exhibit novel aggregate computational properties that could in turn become interesting areas for research in computer science.

    

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