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1. Surveying Teachers’ Preferences and Boundaries regarding Human-AI Control in Dynamic Pairing of Students for Collaborative Learning

   Yang, K. ; Lawrence, L. ; Echeverria, V. ; Guo, B. ; Holstein, K. ; Rummel, N. ; Aleven, V. ( January 2021 , European Conference on Technology Enhanced Learning, EC-TEL 2021)

   null (Ed.)

   Orchestration tools may support K-12 teachers in facilitating student learning, especially when designed to address classroom stakeholders’ needs. Our previous work revealed a need for human-AI shared control when dynamically pairing students for collaborative learning in the classroom, but offered limited guidance on the role each agent should take. In this study, we designed storyboards for scenarios where teachers, students and AI co-orchestrate dynamic pairing when using AI-based adaptive math software for individual and collaborative learning. We surveyed 54 math teachers on their co-orchestration preferences. We found that teachers would like to share control with the AI to lessen their orchestration load. As well, they would like to have the AI propose student pairs with explanations, and identify risky proposed pairings. However, teachers are hesitant to let the AI auto-pair students even if they are busy, and are less inclined to let AI override teacher-proposed pairing. Our study contributes to teachers’ needs, preference, and boundaries for how they want to share the task and control of student pairing with the AI and students, and design implications in human-AI co-orchestration tools. 

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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. Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning

   https://doi.org/10.1007/978-3-030-57717-9_17  

   Echeverria, V. ( January 2020 , Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020)

   Dynamically transitioning between individual and collaborative learning activities during a class session (i.e., in an un-planned way, as-the-need-arises) has advantages for students, but existing orchestration tools are not designed to support such transitions. This work reports findings from a technology probe study that explored alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning, focused on how control over the transitions should be divided or shared among teachers, students, and orchestration system. This study involved 1) a pilot in an authentic classroom scenario with AI support for individual and collaborative learning; and 2) design workshops and interviews with students and teachers. Findings from the study suggest the need for hybrid control between students, teachers, and AI systems over transitions as well as for adaptivity and/or adaptability for different classrooms, teachers, and students’ prior knowledge. This study is the first to explore human–AI control over dynamic transitions between individual and collaborative learning in actual classrooms. 

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5. A multimodal approach to support teacher, researcher and AI collaboration in STEM +C learning environments

   https://doi.org/10.1111/bjet.13518  

   Cohn, Clayton ; Snyder, Caitlin ; Fonteles, Joyce_Horn ; T S, Ashwin ; Montenegro, Justin ; Biswas, Gautam ( September 2024 , British Journal of Educational Technology)

   Recent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross adifficulty threshold,and (2) thepoint of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback.

   What is already known about this topic

   Collaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.

   Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.

   Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI.

   What this paper adds

   We extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration.

   We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom.

   From our discussions with the teacher, we define two types ofinflection pointsto address students' STEM+C difficulties—thedifficulty thresholdand theintervention point—and discuss how thefeedback latency intervalseparating them can inform educator interventions.

   We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline.

   Implications for practice and/or policy

   Our case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback.

   Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment.

   Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula.

    

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