Category Archives: Educator professional learning

Engaging Educators in Emerging Technology Research

AI in Education, Computer Science, Educator professional learning, EducatorCIRCLS, Teachers & Researchers
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Image by Tung Lam from Pixabay

by Cassandra Kelley, Sarina Saran, Deniz Sonmez Unal, and Erin Walker

This blog post discusses the outcomes of an Educator CIRCLS workshop that disseminated computer science education research findings to practitioners while prompting broader discussions of AI in classrooms

This past summer and fall of 2024, Educator CIRCLS hosted a series of webinars, workshops, and convenings between researchers and practitioners focused on artificial intelligence (AI) literacy. Specifically, they were designed to engage participants in reflective conversations about ethics, equity, and other problems or possibilities of practice concerning the integration of AI (especially genAI) in PreK-12 education.

As part of this series, our team from the University of Pittsburgh piloted a novel strategy for research dissemination, in which we developed supplemental curricular resources or guided activities and shared them with educators in a workshop format. The goals behind these activities were twofold:

  • To facilitate discussion among educators about current research on the integration of emerging technologies that incorporate AI (e.g., robots and intelligent tutoring systems) and how they might impact the future of learning in education settings, and

  • To provide a mechanism for educators to think critically about ways to introduce elements of AI literacy to students via real world exercises that can simulate the work that researchers are doing (see Translating Research on Emerging Technologies for Educators for further background context about the design of this workshop).

During the planning stage of the workshop, we felt it was pertinent to get a better understanding of PreK-12 teachers’ experiences with professional learning for computer science (CS) education. We wanted to speak directly with them about the impact of these experiences on their practice and seek their recommendations for how these professional development programs are designed.

We interviewed 20 educators from 16 states, who taught across different grade levels and/or content areas. Most interviewees felt a disconnect with research dissemination as a form of professional learning and expressed their desire to better understand how emerging technologies connect with research-based practices and learning theories. They discussed how previous workshops they have attended either focus directly on the technology tools or on a mandated “turnkey curriculum” based on rote memorization and knowledge transfer (e.g., Advanced Placement CS course materials). Teachers expressed how they appreciated receiving curricular resources because such resources help them to stay current in this ever-evolving field. They would like to see less “direct instruction” lessons and more real-world approaches with project-based or problem-based learning (PBL) that promote inquiry—similar to what is expected in the industry. They also emphasized the need for further collaborative opportunities to ideate on promoting digital/AI literacy through their instruction.

Following our conversations with teachers, we intentionally designed a workshop with guided activities, based on research projects on emerging technologies, that could expose practitioners to existing literature and findings while potentially seeding new ideas for curricula. Our workshop design incorporated the following structure: (1) outline the theoretical framework and CS concepts, (2) have participants experience different roles (e.g., student, educator, and researcher) within inquiry-based activities, (3) share project research findings, (4) discuss implications for practice and ways to address AI literacy, and (5) reflect on the overall format of the workshop and considering how to improve the design of future workshops.

We featured two research projects:

Our first session on teachable robots presented a research project that examined middle school students’ interactions with Nao robots in mathematics instruction. Participants were asked to think about the design and implementation challenges in building a robotic dialogue system for learning from the perspective of a student, educator, and a researcher. They explored CS concepts related to Natural Language Processing (NLP) by: (1) determining keywords used in solving a math problem, (2) reviewing sample dialogue scripts and Artificial Intelligence Mark-up Language (AIML) that researchers used to program the Nao robot, and (3) interacting with prototype simulations created in Pandorabots that represented social and nonsocial versions of a chatbot. We also shared further extensions that could potentially be remixed or adapted for use with students, such as revising the dialogue by adding more social elements, writing a new script for solving a different math problem in AIML, developing a chatbot to test the code, or experimenting with a program such as Scratch to create a dialogue between two sprites.

Our second session on neuroimaging and educational data-mining presented a research project that examined how students process information while interacting with intelligent tutoring systems. A major component of this study focused on the analysis of data collected by these systems to uncover patterns or trends that can inform and potentially improve teaching and learning practices. Additionally, neuroimaging brain data was collected as a proof of concept to explore how it might be analyzed to better understand how cognition, attention, and emotion affect learning (for further background on how this equipment works, see Neuroscience in Education). Similar to the first workshop, we presented guided activities to help participants think about the design of intelligent tutoring systems and the types of data collected ; participants created their own data visualizations from sample datasets for analysis using the free educational software, Common Online Data Analysis Platform (CODAP) and categorized example brain activation images based on the corresponding levels of task difficulty. Further extension activities were shared, such as outlining specific actions that an intelligent tutoring system might take to provide feedback (e.g., hints, prompting questions, or praise) in response to student behavior and debunking “neuro-myths” in education.

At the conclusion of each workshop, we asked educators their thoughts about the potential benefits and challenges of integrating these emerging technologies in PreK-12 classrooms and what they would like future research to explore. Our goal was to hear practitioner voices and gather input for researchers and developers to consider. This led to a focused discussion on the need to promote AI literacy in education, especially to address ethics and transparency.

Key takeaways from the experience are:

  • Teachers appreciate the opportunity to learn more about innovative research projects, but they especially like the idea of being in dialogue with researchers and potentially playing a role in the work that’s being done. Many volunteered to pilot future projects exploring the implementation of curricula and/or emerging technologies with their students if invited.

  • Teachers expressed that the content in our guided activities, while rigorous, enabled them to be more reflective. They were engaged with the hands-on simulations of the research and discussed how “active learning helped to promote deeper thinking.” As one participant mentioned, the activities allowed her to “think outside of the normal pedagogy box.”

  • Teachers had mixed feelings on the relevance of the workshop content and how to bring it into their schools or classrooms. Some thought it would be challenging to implement the activities with students due to external factors and other curricular mandates. As one participant stated, “one tension with cutting-edge research is that it’s difficult to be practical in the moment. I think you’re on the right track with scaling down the technology or bringing the insights to the classroom level…this [workshop] is way more effective than most formats, but I think you would have a difficult time getting educators to opt in.” Meanwhile another participant said, “in both workshops, the concepts and practice of the teachable bot and neuroimaging was beyond the ‘here and now’ of teaching and learning, but the examination of how our current concepts of pedagogy may change as we catch up to the technology.” Additionally, several teachers discussed how the workshop offered new ways for them to think about bringing in real-world data and student-led projects to promote further inquiry and AI literacy.

  • Teachers valued the opportunity to collaborate with other educators and researchers. They liked exploring different lenses (e.g., student, teacher, and researcher) while engaging in reflective discussions about the impact of research on their practice. One teacher highlighted how it felt like a “safe space to troubleshoot uses of AI and educational data mining” and another expressed appreciation for “garnering others’ experiences to get further ideas for their own classroom.”

Based on overall positive feedback from our teacher participants, we believe this research dissemination workshop model is worth exploring with other projects, especially since educators felt they were able to take something meaningful away from the experience. As one participant stated, “I feel very fortunate to be involved in this work. I’m very happy that your team is working to push the boundaries of how we learn and teach.” This gives us hope that researchers will consider the importance of collaborating and co-designing with educators. Additionally, this work validates the need for further mediation between research and practice, which potentially can include creating new roles for “knowledge brokers” (Levin, 2013) to promote further dialogue across these boundaries in order to truly make a broader impact.

Thank you to Sarah Hampton and Dr. Judi Fusco for their thinking and feedback on this post.

References:

Levin, B. (2013, February). To know is not enough: Research knowledge and its use. Review of education, 1(1), 2-31. DOI: 10.1002/rev3.3001

About the Authors

Cassandra Kelley, Ed.D. has over fifteen years of experience in K-12 and teacher education. She earned her doctorate degree in Learning Technologies from Pepperdine University and is passionate about exploring new tools that can improve teaching and learning. She currently serves as a Broader Impacts Project Coordinator at the University of Pittsburgh and supports CIRCLS with Expertise Exchanges in the AI CIRCLS and Educator CIRCLS sub communities. Cassandra also teaches graduate courses for National University in the Master of Science in Designing Instructional and Educational Technology (MSDIET) Program.

Sarina Saran is a third-year undergraduate student at the University of Pittsburgh pursuing a B.S. in Computer Science and a B.A. in Media and Professional Communications. She is curious about the intersection of technology and communication, and she has been able to develop a greater understanding of the challenges in this area as an Undergraduate Research Assistant in the Office of Broader Impacts.

Deniz Sonmez Unal is a Ph.D. candidate in Intelligent Systems at the University of Pittsburgh. Her research focuses on modeling student cognitive states using multimodal data, including interaction logs, verbal protocol data, and neural signals to enhance the diagnostic capabilities of intelligent tutoring systems.

Erin Walker, Ph.D. is a co-PI of CIRCLS and a tenured Associate Professor at the University of Pittsburgh, with joint appointments in Computer Science and the Learning Research and Development Center. She uses interdisciplinary methods to improve the design and implementation of educational technology, and then to understand when and why it is effective. Her current focus is to examine how artificial intelligence techniques can be applied to support social human- human and human agent learning interactions.

Translating Research on Emerging Technologies for Educators

AI in Education, Computer Science, Educator professional learning, EducatorCIRCLS, Teachers & Researchers

Image by mcmurryjulie from Pixabay
by Cassandra Kelley

This blog post discusses the development of an Educator CIRCLS workshop aimed to “translate” or disseminate computer science education research findings to practitioners while promoting AI literacy.

Have you ever played the telephone game, where a sentence is whispered into someone’s ear and passed from person to person, until the final person reveals the message aloud to see how closely it aligns with what was originally said? I am frequently reminded of this childhood game in my role as the Broader Impacts Project Coordinator at the University of Pittsburgh and CIRCLS, where I think about how we can “translate” research into practice for practitioners; however, the game has become much more challenging due to the technical terminology, academic jargon (e.g., research methodologies), and other contextual phrases that are often included within the message being communicated. Moreover, all of the players have individual “language barriers” (e.g., prior knowledge, experience, expertise, etc.) that add another layer of difficulty to ensure the mediated message is comprehensive for all.

My broader impacts position, inspired by the National Science Foundation (NSF) merit review criteria, was created as an avenue for broadly disseminating research on emerging technologies for teaching and learning—similar to programs such as Research Practice Partnerships (RPP) or Research Experiences for Teachers (RET). I was drawn to this opportunity because I feel it is critical not only for educators to learn about and understand education research, but also for researchers to consider the direct impact of their work on practice. I firmly believe in the importance of bridging the gap that currently exists between research and practice by promoting partnerships among all stakeholders, which can include further engagement in participatory research and involvement in co-design models.

As a former PreK-12 educator and higher education faculty member supporting pre-service teachers, my initial concern about this translation process was thinking about how students and teachers will benefit. From my own experience working in school systems, I have observed an institutional culture where research and policy are “thrown” at teachers through mandates and other recommendations. Educators’ voices are often missing from the conversation and there is not an immediate focus on how to best support their practices, which truly should center on the needs of students. I have also witnessed researchers temporarily engaging with educators for the purpose of conducting a study and then disappearing, which I personally know can feel like a one-sided transaction.

These factors led me to consider novel strategies for research dissemination that could potentially build stronger connections between researchers and practitioners. Specifically, I wanted to explore the development of supplemental curricular resources to be shared with teachers during a workshop so they could have opportunities to: (1) interact with computer science (CS) education concepts and understand their relationship within research findings, (2) experience the role of a learner and researcher, (3) engage in discussion with other educators and researchers about the impact of specific research projects on practice—especially with regard to the integration of emerging technologies, and ultimately to (4) bring elements of their professional learning back into the classroom via guided activities that could be adapted for implementation with students.

It is important to note that these goals were shaped through many discussions with practitioners, especially after having the opportunity to speak directly with 20 educators about their experiences as participants in professional development programs for CS education. I sought their recommendations for how we might design and structure a workshop to disseminate research findings via our supplemental curricular resources (see Engaging Educators in Emerging Technology Research for further details about the facilitation of this workshop). Throughout these reflective conversations, it was frequently mentioned how most programs tend to be “technocentric” and focus more on “new shiny technology tools” rather than pedagogy for classroom integration or research-based practices and learning theories. Educators advocated for further rigor and inquiry-based activities that immerse them into the research literature, paired with opportunities for collaboration and the exchange of ideas or curricular resources; each of these elements would be intentionally incorporated into our workshop design.

Additionally, I connected with members of interdisciplinary research teams to better understand their different areas of expertise and the methodologies used across projects. I had to consider the application of CS terminology and concepts within each project (many of which were new to me) and pinpoint the key areas to focus on in the translation. Fortunately, I was introduced to a new undergraduate student in the lab who was double majoring in both CS and communication. She was eager to help and became a translator for me when I wore my “learner hat,” similar to how a teacher’s assistant or tutor might provide direct instruction to dive deeper into the content. Likewise, I would then put on my “teacher hat” and explain pedagogical concepts (e.g., scaffolding, asking different levels of questions, Universal Design for Learning- UDL strategies, etc.) or learning theories (e.g., constructivism, sociocultural learning, project-based learning, etc.) while we discussed how we could take research findings and use them to develop supplemental curricula or guided activities for dissemination to educators.

A final consideration in the development of these guided activities was how to simulate the research procedures in an immersive way without the technology equipment. This was necessary because we wanted to acknowledge potential constraints of implementation in schools, such as access issues and the need for further technical support or training—not to mention how expensive these emerging technologies are. Therefore, we engaged in further conversations with the research teams about how we might develop user-friendly prototypes of simulations that educators could interact with on their own devices. Our discussions reminded us that there may be further barriers to research dissemination in the traditional schooling environment including challenges with existing curricula requirements and/or scheduling constraints. For this reason, we decided it might make more sense to frame our activities as supplemental or enrichment materials that can be adapted/remixed across a variety of settings (e.g., after school programs or summer camps).

In summary, these convenings with researchers and practitioners across what Wenger-Trayner and colleagues (2014) refer to as the “boundaries in landscapes of practice” helped us consider the institutional culture bounding each landscape. We found it extremely valuable learning from multiple perspectives and using these insights to help us identify existing boundaries and ways to collectively navigate them.

Key takeaways from the experience are:

  • Acknowledge the systemic barriers with regard to education policy and practices in different community settings.

  • Engage in learning partnerships by collaboratively negotiating and exploring the existing boundaries. This includes actively listening to all voices (e.g., researchers and practitioners) from different landscapes (e.g., PreK-12 education, higher education, and industry) to create a two-way dialogue of mutual reflection.

  • Focus less on the technology and more on the diffusion of innovative ideas as well as the AI literacy needed by ALL stakeholders for advancement of these ideas.

  • Develop immersive guided activities that promote further conversations about AI literacy while being grounded in research and learning theories. Be sure to clearly communicate these connections when translating back-and-forth and offer opportunities for reflective discussion.

  • Seek feedback at every stage of the iterative process and prioritize the community partnerships across the landscapes of practice above all.

  • Remember that the ultimate shared goal or vision is to positively impact the future of learning for students.

Since I personally identify as both a researcher and practitioner, I have learned firsthand the importance of negotiating my own experiences to build a bridge between my understanding of the teaching practice and students’ needs, while also thinking critically about advancing the field of education research. In order to bring these landscapes together, researchers must consider ways to make their work more accessible so they can get the necessary buy-in from teachers that will propel institutional change and innovation in the future of schooling and education. Likewise, educators need to keep seeking opportunities to stay abreast of current research findings, especially to help lead this exploration of new pedagogical practices or emerging technologies that can support teaching and learning. One avenue to achieve this is establishing sustained partnerships between researchers and practitioners through co-design or participatory research. Moreover, the incorporation of “intermediaries” or “knowledge brokers,” which Levin (2013) defines as “people or organizations that translate or transmit research,” similar to my position as a Broader Impacts Project Coordinator, can “play a critical role in the process of diffusing ideas and practices in education” (p. 21). It is my hope that researchers will take into account how emerging scholars, such as school administrators, academic coaches, a subgroup of PreK-12 teachers, post-doctoral students, or graduate students, might be leveraged to help spearhead this essential translation of research into practice.

Thank you to Sarina Saran, Deniz Sonmez Unal, Sarah Hampton, Dr. Erin Walker, and Dr. Judi Fusco for their thinking and feedback on this post.

References:

Levin, B. (2013, February). To know is not enough: Research knowledge and its use. Review of education, 1(1), 2-31. DOI: 10.1002/rev3.3001

Wenger-Trayner, E., Fenton-O’Creevy, M., Hutchinson, S., Kubiak, C., & Wenger-Trayner, B. (Eds.). (2014). Learning in landscapes of practice: Boundaries, identity, and knowledgeability in practice-based learning. Routledge.

About the Author

Cassandra Kelley, Ed.D. has over fifteen years of experience in PreK-12 and teacher education. She earned her doctorate degree in Learning Technologies from Pepperdine University and is passionate about exploring new tools that can improve teaching and learning. She currently serves as a Broader Impacts Project Coordinator at the University of Pittsburgh and supports CIRCLS with Expertise Exchanges in the AI CIRCLS and Educator CIRCLS sub communities. Cassandra also teaches graduate courses for National University in the Master of Science in Designing Instructional and Educational Technology (MSDIET) Program.