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1. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

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2. Exploring Policies for Dynamically Teaming Up Students through Log Data Simulation

   Yang, K ; Echeverria, V. ; Wang, X. ; Lawrence, L. ; Holstein, K. ; Rummel, K. ; Aleven, V. ( January 2021 , Fourteenth International Conference on Educational Data Mining (EDM 2021))

   Hsiao, I. ; Sahebi, S. ; Bouchet, F. ; Vie, J. J. (Ed.)

   Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners’ behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define feasibility as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as SimPairing. As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies. 

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3. The Mobile Fact and Concept Textbook System (MoFaCTS)

   Pavlik Jr., P. I. ; Olney, A. M. ; Banker, A. ; Eglington, L. ; Yarbro, J. ( July 2020 , CEUR workshop proceedings)

   Sosnovsky, S. ; Brusilovsky, P. ; Baraniuk, R. ; Lan, A. (Ed.)

   An intelligent textbook may be considered to be an interaction layer that lies between the text and the student, helping the student to master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook instruction and so is being transformed into the Mobile Fact and Concept Textbook System (MoFaCTS). In this project, MoFaCTS is being completely retooled to accept texts from a textbook and to automatically create cloze sentence practice content to help the student learn the material in the text. Additional features in the prototype stage include automatically generated refutational feedback for incorrect cloze responses and a dialog system, which will trigger a short conversation by a tutor to correct conceptual misunderstandings. MoFaCTS administers this content via a web browser, providing the teacher with score reports and class management tools. Because the "optimal practice" module is interchangeable and the cloze content can come from any text, the system is highly configurable for different grade levels, populations, and academic subjects. To foster faster research progress, data export supports the DataShop transaction format, which allows quick analysis of data using the DataShop tools. 

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4. Orchestrating the Multidisciplinary Implementation of a Narrative-Centered Learning Environment in Upper Elementary Classrooms.

   Vandenberg, J. ; Boulden, D. ; Fox, K. ; Elsayed, R. ; Smith, A. ; Cheuoua, A.H. ; Minogue, J. ; Oliver, K. ; Ringstaff, C. ; Mott, B. ( January 2022 , Proceedings of the International Conference of the Learning Sciences)

   Integration of computational thinking (CT) within STEM subjects is common, although not often at the elementary school level where teachers have minimal experience with CT. We have designed and are refining INFUSECS, a narrative-centered digital learning environment to support upper elementary students’ CT and science knowledge construction as they create digital stories. We used orchestration as our theoretical framework, to examine how elementary teachers planned to approach this multidisciplinary implementation. Through a series of three focus groups, we learned that teachers planned for their students to take notes or utilize other graphic organizers to align the science content with the narrative planning, to engage in collaborative sense-making, and to observe the teacher modeling use of the INFUSECS system. Ultimately, the results have informed the next phase of our research design as we collect teacher and student level data as INFUSECS is utilized in authentic classroom settings. 

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5. 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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