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1. Instructional practice learning through Instructional Incubator engagement

   Woodcock, Cassandra S.E.; Antoine, Haley; Tarnowski, Madison; Huang-Saad, Aileen (January 2019, Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections)

   While student-centered learning has been shown to improve learning experiences in the engineering classroom, adoption of these evidence-based strategies has been slow. Research has shown that faculty beliefs about teaching and limited exposure to formal training influence effective implementation of evidenced-based instructional practices. Thus, in an effort to explore ways to implement long-term instructional change in engineering higher education, a graduate-level course, the Instructional Incubator (I2), was developed to expose future educators to instructional design and evidence-based practices. In the I2, student participants developed new biomedical engineering short-courses in an active learning classroom. For the first two iterations of the I2, we examined how this immersive experience influenced participants’ perceived teaching abilities and understanding before and after enrolling in the I2. Both I2 cohorts reported an increase in knowledge of engineering education related terms and showed a shift away from behaviorist and cognitive beliefs about teaching and learning. Introduction 

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2. Automated feedback on discourse moves: Teachers’ perceived utility of a big data tool.

   https://doi.org/10.3102/1887987  

   Scornavacco, K.; Jacobs, J.; Clevenger, C. (March 2022, Annual meeting of the American Educational Research Association)

   Using new technology to provide automated feedback on classroom discourse offers a unique opportunity for educators to engage in self-reflection on their teaching, in particular to ensure that the instructional environment is equitable and productive for all students. More information is needed about how teachers experience automated data tools, including what they perceive as relevant and helpful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers over two years with a big data tool that analyzes classroom recordings and generates information about their discourse practices in near real-time. Findings revealed that teachers perceived the tool as having utility, yet the specific feedback that teachers perceived as most useful changed over time. In addition, teachers who used the tool throughout both years increased their use of talk moves over time, suggesting that they were making changes due to their review of the personalized feedback. These findings speak to promising directions for the development of AI-based, big data tools that help shape teacher learning and instruction, particularly tools that have strong perceived utility. 

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3. Different instructors—different symmetry: variation in instructional approaches and content emphasis in inorganic chemistry

   https://doi.org/10.1039/D5RP00275C  

   Shi, Lu; Hilborn, Shanna; Pazicni, Samuel; Popova, Maia (January 2025, Chemistry Education Research and Practice)

   Symmetry is a foundational concept in inorganic chemistry, essential for understanding molecular properties and interactions. Yet, little is known about how instructors teach symmetry or what shapes their instructional and curricular choices. To investigate this, we analyzed classroom observations from fourteen inorganic chemistry instructors from various institutions, focusing on their use of student-centered practices and emphasis on symmetry content. We then conducted semi-structured interviews to explore the reasoning behind their decisions, using the Teacher-Centered Systemic Reform (TCSR) model to interpret influences from personal factors (e.g., teaching experience), teacher thinking (e.g., beliefs about teaching and learning), and contextual factors (e.g., classroom layout). Minute-by-minute analyses of teaching revealed four instructional profiles (student-centered, high-interactive, low-interactive, and instructor-centered) and four content profiles, ranging from an emphasis on symmetry fundamentals (e.g., symmetry elements and operations, point group assignment) to symmetry applications (e.g., spectroscopy, molecular orbitals, character tables). Three themes emerged: (1) instructional approaches and content emphasis vary substantially across instructors; (2) more student-centered instructors tend to focus on foundational symmetry concepts and skills, whereas more instructor-centered instructors tend to prioritize advanced applications; and (3) instructors’ beliefs and prior experiences, more than personal and contextual factors, drive instructional decisions for teaching symmetry. 

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4. Formal Verification of Interoperability Between Future Network Architectures Using Alloy

   https://doi.org/https://doi.org/10.1007/978-3-030-48077-6_4  

   Jahanian, Mohammad; Chen, Jiachen; Ramakrishnan, K. K. (May 2020, Rigorous State-Based Methods. ABZ 2020. Lecture Notes in Computer Science)

   The Internet is composed of many interconnected, interoperating networks. With the recent advances in Future Internet design, multiple new network architectures, especially Information-Centric Networks (ICN) have emerged. Given the ubiquity of networks based on the Internet Protocol (IP), it is likely that we will have a number of different interconnecting network domains with different architectures, including ICNs. Their interoperability is important, but at the same time difficult to prove. A formal tool can be helpful for such analysis. ICNs have a number of unique characteristics, warranting formal analysis, establishing properties that go beyond, and are different from, what have been used in the state-of-the-art because ICN operates at the level of content names rather than node addresses. We need to focus on node-to-content reachability, rather than node-to-node reachability. In this paper, we present a formal approach to model and analyze information-centric interoperability (ICI). We use Alloy Analyzer’s model finding approach to verify properties expressed as invariants for information-centric services (both pull and push-based models) including content reachability and returnability. We extend our use of Alloy to model counting, to quantitatively analyze failure and mobility properties. We present a formally-verified ICI framework that allows for seamless interoperation among a multitude of network architectures. We also report on the impact of domain types, routing policies, and binding techniques on the probability of content reachability and returnability, under failures and mobility. 

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5. 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)

   Abstract 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. Practitioner notesWhat is already known about this topicSuccess 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 addsWe 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 policyLearning 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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