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1. Multimodal analytics for collaborative teacher reflection of human-ai hybrid teaching: design opportunities and constraints

   https://doi.org/10.1007/978-3-031-42682-7_45  

   Karumbaiah, S.; Liu, P.; Maksimova, A.; De Vylder, L.; Rummel, N.; Aleven, V. (August 2023, Springer)

   Viberg, O.; Jivet, I.; Muñoz-Merino, P.; Perifanou, M.; Papathoma, T. (Ed.)

   Past research shows that teachers benefit immensely from reflecting on their classroom practices. At the same time, adaptive and artificially intelligent (AI) tutors are shown to be highly effective for students, especially when teachers are involved in supporting students’ learning. Yet, there is little research on how to support teachers to reflect on their practices around AI tutors. We posit that analytics built on multimodal data from the classroom (e.g., teacher position, student-AI interaction) would be beneficial in providing effective scaffolding and evidence for teachers’ collaborative reflection on human-AI hybrid teaching. To better understand the design opportunities and constraints of a future tool for teacher reflection, we conducted storyboarding sessions with seven in-service teachers. Our analysis revealed that certain modalities (e.g., position v. video) might be more beneficial and less constrained than others in identifying reflection-worthy moments and trends. We discuss teachers’ needs for reflection in classrooms with AI tutors and their boundaries in using multimodal analytics. 

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2. Supporting Upper Elementary Students’ Engineering Practices in an Integrated Science and Engineering Unit.

   https://doi.org/10.18260/1-2--35258  

   Lilly, S.; McAlister, A.; Fick, S.; Chiu, J. L.; McElhaney, K. (June 2020, Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line.)

   To support teachers in providing all students with opportunities to engage in engineering learning activities, research must examine the ways that elementary teachers support how diverse learners engage with engineering ideas and practices. This study focuses on two teachers' verbal supports in classroom discussions across two class sections of a four-week, NGSS-aligned unit that challenged students to redesign their school to reduce water runoff. We examine the research question: How and to what extent do upper-elementary teachers verbally support students' engagement with engineering practices across diverse classroom contexts in an NGSS-aligned integrated science unit? Classroom audio data was collected daily and coded to analyze support through different purposes of teacher talk. Results reveal the purpose of teachers’ talk often varied between the class sections depending on the instructional activity and indicate that teachers utilized a variety of supports toward students' engagement in different engineering practices. In one class, with a large percentage of students with individualized educational plans, teachers provided more epistemic talk about the engineering practices to contextualize the particular activities. For the other class, with a large percentage of students in advanced mathematics, teachers provided more opportunities for students to engage in discussion and support for students to do engineering. This difference in supports may decrease the opportunities for some students to rigorously engage in engineering ideas and practices. This study can inform future research on the kinds of educative supports needed to guide teaching of integrated engineering activities for diverse students. 

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3. Theorizing and Measuring Collective Productive Disciplinary Engagement

   Cheng, B.; Gresalfi, M.; Bell, A.; Brady, C.; Vogelstein, L.; Damsa, C.; Palonen, T.; Rogat, T.; Traynor, A.; Adeoye, T.; et al (June 2019, Computer-supported collaborative learning)

   This symposium aims to explore current research working toward conceptualizing and measuring productive disciplinary engagement (PDE) contextualized in diverse learning and project contexts. Disciplinary engagement is critical for fostering students’ deep, integrated understanding of STEM content and disciplinary practices. However, there are significant challenges to reaching this engagement quality, with CSCL environments providing opportunities and supports for engagement, but also posing challenges. This symposium aims to account for recent developments, as presenters showcase rich range in exploring application of PDE in diverse domains, grade bands, and learning contexts. The presentations also showcase a range of methods to analyze PDE as collective, situated, cross-contextual, dynamic, and generative. 

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4. Integrating Youth Perspectives into the Design of AI-Supported Collaborative Learning Environments

   https://doi.org/10.3390/educsci14111197  

   Humburg, Megan; Dragnić-Cindrić, Dalila; Hmelo-Silver, Cindy E; Glazewski, Krista; Lester, James C; Danish, Joshua A (November 2024, Education Sciences)

   This study highlights how middle schoolers discuss the benefits and drawbacks of AI-driven conversational agents in learning. Using thematic analysis of focus groups, we identified five themes in students’ views of AI applications in education. Students recognized the benefits of AI in making learning more engaging and providing personalized, adaptable scaffolding. They emphasized that AI use in education needs to be safe and equitable. Students identified the potential of AI in supporting teachers and noted that AI educational agents fall short when compared to emotionally and intellectually complex humans. Overall, we argue that even without technical expertise, middle schoolers can articulate deep, multifaceted understandings of the possibilities and pitfalls of AI in education. Centering student voices in AI design can also provide learners with much-desired agency over their future learning experiences. 

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5. The impact of middle school students’ writing quality on the accuracy of the automated assessment of science content

   https://doi.org/10.22318/icls2024.136951  

   Gnesdilow, D; Dey, I; Gengler, D; Malkin, L; Puntambekar, S; Passonneau, RJ; Kim, C (June 2024, Proceedings of the 4th Annual Meeting of the International Society of the Learning Sciences 2024)

   Hoadley, C; Wang, XC (Ed.)

   Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback. 

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