Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 19th Aug 2026, 21:29:11 EET
|
Daily Overview |
| Session | ||
24 SES 10 A: Digital Tools & Formative Assessment
Paper Session | ||
    | ||
24. Mathematics Education Research
Paper Shared Ownership of Mathematics Teaching: Collaborative Development of Teachers’ Professional Digital Competence Norwegian Centre for Mathematics Education, NTNU, Norway Presenting Author:This proposal presents findings from the ongoing project Leadership and Learning for the Development of Teachers’ Professional Digital Competence (LeadDig), which involves five schools over a three-year period. In a review of research on digital technology integration in mathematics education, Dockendorff and Gomez Zaccarelli (2025) demonstrate that successful use of digital tools depends less on access to technology and more on teachers’ collective professional learning, pedagogical coordination, and systemic support. The overall aim of the project is to strengthen teachers’ professional digital and assessment competence in order to improve students’ learning opportunities when digital tools are used in teaching. Cultural-Historical Activity Theory (CHAT) (Engeström, 2001) is employed as a developmental and analytical framework to support the establishment and analysis of a shared object related to the use of digital tools across subjects, with particular emphasis on mathematics. This sub-study follows one lower secondary school participating in the project. At this school, a shared object was collaboratively formulated: How can leadership-supported teacher collaboration contribute to learning through the use of digital tools? While leadership support constitutes an important condition for the collaborative work, the analytical focus of this sub-study is on the mathematics teachers’ collective activity and how the shared object is translated into concrete instructional practices involving digital tools. During the first year of the project, two development questions were identified specifically for the mathematics department: (1) How can teachers facilitate productive student collaboration when digital tools are used? (2) How can teachers ensure equal access for students to engage in substantive mathematical activity using digital tools? Methodology, Methods, Research Instruments or Sources Used The study employs a mixed-method, design-based approach informed by CHAT and practice-based professional learning. During autumn 2024, unstructured classroom observations (Given, 2008) were conducted across subjects with a focus on mathematics and digital tool use. Field notes were written in a narrative style, and the researchers acted as observer-as-participant. To gain insight into students’ reasoning about the use of digital tools in mathematics, a web-based questionnaire was administered. Students responded to open- and closed-ended questions about their experiences with digital tools, and their responses were structured in tables. Some closed-ended items used a four-point Likert scale (Albaum, 1997). In line with methodological recommendations (Cohen et al., 2018), equal distances between categories were used (“disagree”, “partially disagree”, “partially agree”, “agree”), representing ordinal data (Stevens, 1946). As a result of the experience from the first year, the collaboration model was redesigned for year two. First the project in the last year focuses mainly on mathematic teachers. Secondly, the joint observations and analyses of lessons is expanded to included co-planning of the lesson that is being observed, drawing on cycles of enactment and investigation (Lampert et al., 2013) and Norwegian adaptations (Kazemi & Wæge, 2015; Mosvold, Fauskanger & Wæge, 2018). The mathematics team (n=6) now engages in joint planning, enactment by one teacher, structured observation using predefined foci, and joint reflection. Teacher time-outs (Mosvold, Wæge & Fauskanger, 2023) are incorporated to support real-time professional noticing. Three full cycles will be conducted in spring 2026. Data sources include observation notes, planning artefacts, audio recordings, and reflective discussions . The final phase of the study draws on CHAT to analyze how the involved teachers collaboration develops over time. The analysis focuses on how the shared object of improving mathematics teaching with digital tools is negotiated and transformed through joint planning, classroom enactment, and collective reflection. We will look at tensions and contradictions in the collaboration, as well as toward how organizational arrangements, roles, and mediating artefacts shape the teachers’ collective activity. These analyses provide insight into how shared ownership of mathematics teaching and collective professional digital competence are developed. Conclusions, Expected Outcomes or Findings In the first project year, joint observations and analyses of lessons across subjects were conducted, but several challenges emerged: teachers felt positioned in a justificatory role and not in a learning situation, colleagues lacked insight into planning of the lesson observed, and not all participants taught the subject the lesson focused on. Classroom observations also revealed that students mainly worked individually with digital tools (also confirmed by the web-based questionnaire), teachers conceptualized adaptation as giving different tasks to different students, and collaboration among mathematics teachers was limited. With the new organization of the project, two categories of expected outcomes are anticipated: 1. Professional learning and shared pedagogical understanding The collaboration cycles are expected to lead to a more coherent and collectively developed understanding of what constitutes high quality use of digital tools in mathematics teaching at the school. Through joint planning, shared enactment, structured observation and collective reflection, teachers are likely to develop a stronger shared language for instruction, a common set of principles for equitable digital tool use, and a deeper, collectively negotiated view of what meaningful mathematical activity looks like for all students. 2. Sustainable organizational structures for mathematics teacher collaboration A further outcome concerns the establishment of long term, leadership supported structures for ongoing collaboration among the mathematics teachers. By involving school leaders directly in analyzing lessons, framing priorities, and supporting the collaboration model, the project aims to institutionalize routines for joint planning, shared enactment, professional dialogue, and coordinated development work. These structures are expected to extend beyond the project period and contribute to lasting capacity building within the mathematics department. Findings from the three planned cycles conducted in spring 2026 will be analyzed before summer 2026 and presented at ECER 2026. References Albaum, G. (1997). The Likert scale revisited. Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education. Chrzanowska, J. (2002). Interviewing Groups and Individuals in Qualitative Market Research. Sage. Dockendorff, M., & Zaccarelli, F. G. (2025). Successfully preparing future mathematics teachers for digital technology integration: a literature review. International Journal of Mathematical Education in Science and Technology, 56(5), 948-979. Engeström, Y. (2001). Expansive Learning at Work: Toward an Activity-Theoretical Reconceptualization. Journal of Education and Work, 14(1), 133–156. Given, L. M. (2008). The Sage Encyclopedia of Qualitative Research Methods. Sage. Kazemi, E., & Wæge, K. (2015). Learning to Teach within Practice-Based Methods Courses. Journal of Mathematics Teacher Education, 18, 243–268. Lampert, M. et al. (2013). Using Designed Instructional Activities to Enable Novices to Manage Ambitious Mathematics Teaching. In Z. Brantlinger (Ed.), International Handbook of Mathematics Teacher Education. Mosvold, R., Fauskanger, J., & Wæge, K. (2018). Teachers’ Collective Professional Knowledge: Using Video to Support Noticing. Nordic Studies in Mathematics Education. Stevens, S. S. (1946). On the theory of scales of measurement. 24. Mathematics Education Research
Paper ***WITHDRAWN*** Exploring Teachers’ AI-TPACK Through Project-Based Professional Development: A Mixed-Methods Study 1: Gaziantep University, Turkey (Türkiye); 2: Middle East Technical University, Türkiye; 3: Manisa Celal Bayar University, Türkiye Presenting Author:The increasing role of technology in education creates a continuous need for in-service teachers to develop new knowledge and skills regarding the pedagogical integration of digital tools. In the current era of digital transformation, the emergence of Artificial Intelligence (AI) has become a critical frontier, offering teachers the ability to provide personalized learning experiences tailored to individual student needs, analyze performance data to refine instructional processes, and deliver more effective feedback (Holmes et al., 2019). Within this context, the Technological Pedagogical Content Knowledge (TPACK) framework, which enables teachers to combine pedagogical and content knowledge with technology, plays a vital role in professional development. However, given AI’s distinctive algorithmic and adaptive nature, there is an urgent need to understand teachers' approaches within an evolved framework: AI-TPACK. Understanding teachers’ self-efficacy through the sub-dimensions of AI-TPACK is essential for evaluating their potential to transform classroom practices. This study aims to provide theoretical and practical contributions to teacher education by determining the relationships within the AI-TPACK framework and examining how these competencies vary across demographic variables. The theoretical foundation of this research is rooted in Shulman’s (1986) Pedagogical Content Knowledge (PCK), which posits that effective teaching requires an intersection of pedagogical strategies and subject matter expertise. Shulman (1987) argued that teachers must not only possess knowledge but also understand how to transform that knowledge into forms that are accessible to students. Over time, this concept was expanded into the TPACK model by Mishra and Koehler (2006), incorporating Technology Knowledge (TK) as a third primary domain. The TPACK framework emphasizes that technology integration is not merely about technical proficiency but about the harmonious alignment of technology with content and pedagogy. While this framework has served the educational community for nearly two decades, the rise of advanced AI necessitates a re-evaluation of its components. As noted by Ning et al. (2024), technology is the most dynamic element in the TPACK triad. As educators’ understanding of AI deepens, traditional domains undergo a transformation: Technological Pedagogical Knowledge (TPK) evolves into AI-TPK, and Technological Content Knowledge (TCK) into AI-TCK, culminating in an integrated AI-TPACK that incorporates AI literacy into pedagogical reasoning. This study utilizes the AI-TPACK framework to investigate the competencies of 218 mathematics teachers who participated in a project-based professional development program. The research is guided by the following comprehensive research questions:
By addressing these questions, the study seeks to highlight the "demographic invariance" in AI adoption, exploring whether gender, experience, or school level acts as a barrier or a facilitator in the digital transition. This international dimension is crucial, as global educational policies—such as the European Digital Education Action Plan (2020)—emphasize equitable access to high-quality digital training for all educators. Furthermore, the qualitative component of the study, involving 23 teachers, provides a deep-dive into the "how" of AI integration, examining the role of AI as a pedagogical partner in addressing subject-specific challenges like mathematical abstraction. Ultimately, this research aims to contribute to a more conscious and strategic design of teacher training programs that align with the requirements of the AI era. Methodology, Methods, Research Instruments or Sources Used This research employs an explanatory sequential mixed-methods design (Creswell & Plano Clark, 2017), a robust framework that allows for a comprehensive understanding of complex educational phenomena by first establishing broad quantitative trends and then exploring them through detailed qualitative insights. The study was situated within a project-based professional development (PBPD) program designed to empower mathematics educators to transition from traditional ICT users to AI-competent practitioners. The methodology is specifically aligned with the European Digital Education Action Plan (2021-2027), emphasizing the equitable development of digital competencies across diverse educator demographics. The quantitative phase involved a sample of 218 in-service mathematics teachers selected through convenience sampling from various middle and high schools. This phase aimed to determine the current state of AI-TPACK self-efficacy and its relationship with demographic variables. Following this, the qualitative phase engaged 23 purposively selected teachers who had successfully completed the PBPD program. These participants provided deep-dive reflections, allowing the researchers to understand how the statistical "demographic invariance" observed in the large-scale data translated into specific pedagogical strategies in the classroom. Data collection was facilitated through two primary instruments. Quantitative data were gathered using the 39-item AI-TPACK scale, originally developed by Ning et al. (2024) and adapted into Turkish by Bilici et al. (2024). The scale measures seven sub-dimensions: Content Knowledge (CK), Pedagogical Knowledge (PK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Content Knowledge (AI-TCK), AI-Technological Pedagogical Knowledge (AI-TPK), and the integrated AI-TPACK. For the qualitative phase, an interview form was developed based on expert opinions to evaluate how teachers operationalize AI to remediate student misconceptions and teach abstract concepts. Statistical analysis was conducted to address the research questions regarding gender, school level, and experience. Independent Samples T-tests were utilized to compare groups based on gender and school level (middle vs. high school), while a One-Way ANOVA was performed to examine differences across teaching experience levels. Pearson correlation coefficients were calculated to explore the interplay between the seven knowledge domains. In the second stage, the qualitative data were subjected to thematic analysis, where responses were coded and categorized into themes such as "conceptual scaffolding," "visualization of abstraction," and "AI as a pedagogical partner." This methodological synergy ensures that the study provides both a generalizable overview of teacher readiness and a nuanced understanding of classroom application in the AI era. Conclusions, Expected Outcomes or Findings The findings demonstrate a significant descriptive gap between teachers’ traditional pedagogical foundations and their AI-integrated competencies. While high scores in Content Knowledge (M=4.25) and Pedagogical Knowledge (M=4.34) reflect strong professional baselines, the notably lower AI-TPACK scores (M=3.55) align with Ning et al. (2024), who suggest that AI-specific knowledge requires a distinct developmental trajectory beyond general ICT literacy. Correlation analysis confirmed that AI-technological literacy is a significant predictor of pedagogical content integration (p<.01), echoing Bilici et al. (2024) regarding the synergistic nature of the AI-TPACK sub-dimensions in the Turkish context. A pivotal contribution of this study is the observed demographic invariance. Statistical analyses revealed no significant differences based on gender (t(216) = 0.063, p = .950), school level (Middle vs. High School) (t(216) = 0.063, p = .950), or teaching experience (F(4, 213) = 0.454, p = .770). This supports the emerging consensus that generative AI acts as a "new common ground," where prior experience does not necessarily dictate readiness (Aheto, 2024; Ng et al., 2025). Qualitative insights from 23 teachers further explain this by illustrating how AI serves as a "pedagogical scaffold" for abstract mathematical concepts: Case Study - T#8 (High School): Focuses on using AI to visualize abstract concepts: "AI allows students not just to reach the correct answer, but to discover the logic and different perspectives, leading to deeper learning". Case Study - T#9 (High School): Utilizes AI as a conceptual scaffold: "I use AI-driven simulations to help students understand difficult math concepts truly, moving beyond rote memorization". The study concludes that although teachers possess strong pedagogical foundations, they require domain-specific, project-based support to operationalize AI-TPACK. The results advocate for professional development models that move beyond general AI literacy, focusing instead on how AI can serve as a "pedagogical partner" in addressing specific mathematical challenges like abstract thinking and misconceptions. References Aheto, S. P. K. (2024). Artificial intelligence in education: The new frontier for teacher professional development. Journal of Digital Learning in Teacher Education, 40(2), 85-102. https://doi.org/10.1080/21532974.2024.2323241 Bilici, S. C., Guzey, S. S., & Yamak, H. (2016). Assessing pre-service science teachers’ technological pedagogical content knowledge (TPACK) through observations and lesson plans. Research in Science & Technological Education, 34(2), 237–251. https://doi.org/10.1080/02635143.2016.1144050 Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications. European Commission. (2020). Digital Education Action Plan 2021-2027: Resetting education and training for the digital age. https://education.ec.europa.eu/focus-topics/digital-education/action-plan Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. NNing, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978 Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14. 24. Mathematics Education Research
Paper The Role of Digital Tools in Mathematics Classrooms in Norwegian Primary Schools. Norwegian University of Science and Technology, Norway Presenting Author:In Norway digital competence is defined as one of five basic skills in the National curriculum (The Ministry of Education and research, 2006; The Norwegian Directorate for Education and Training, 2019). The basic skills are defined as necessary tools for learning and development across all subjects, as stated in the Framework for basic skills (The Norwegian Directorate for Education and Training, 2017). In the subject mathematics digital competence involves using graph plotter, spreadsheets, CAS, dynamic geometry programs and programming to explore and solve mathematical problems. It also involves finding, analyzing, processing and presenting information using digital tools. The background for this study is our work in the project Leadership and Learning for the Development of Teachers Professional Digital Competence (LeadDig). This was a three-year project (2023-2026). The underlying idea for the research project was to develop schools (and teachers) that learn, and we wanted to focus, generally, on the use of iPad and PC in school subjects and, specifically, in mathematics. Digital technologies have the potential to support students’ learning in mathematics by providing multiple representations of mathematical objects and enhancing opportunities for active learning (Hedegus et al., 2017). Despite its potential, technology use in mathematics education remains limited, as its integration into classroom practice is a complex and demanding process (Drijvers, 2019). Based on these findings we wanted to explore how Norwegian teachers have implemented digital tools in the teaching and learning of mathematics. The research question of this study is: How do teachers use iPad and other digital tools in mathematics classrooms in three primary schools in Norway? Theoretical framework To look at the role of digital tools in the mathematics classroom we chose to connect the role to mathematics teaching practices (National Council of Teachers of Mathematics, 2014) and the pedagogical opportunities map (Pierce & Stacey, 2010). The mathematics teaching practices (National Council of Teachers of Mathematics, 2014) provide a research-based framework for enhancing teaching and learning of mathematics, and they have the potential to support and promote a deep learning of mathematics. Using digital tools in mathematics teaching might also contribute to better learning, and we wanted to see how digital tools could influence the practices. In total there are eight mathematical teaching practices:
The Pedagogical Opportunities Map (POM) is originally a theoretical framework associated with a specific type of technology (Pierce and Stacey, 2010), but we feel that many of the opportunities highlighted by the framework are suitable for all kinds of technology. POM illustrates that technology can enhance learning in ten different ways, and these ways are organized in three levels: tasks, classroom and subject. For example, technology might support the social dynamics in the classroom by encouraging student participation. It is important to note that POM focuses on the pedagogical opportunities with technology. The framework also illustrates that the functional opportunities of technology can change curriculum and assessment. We can summarize by saying that technology can change what mathematics is taught, how mathematics is assed and how it is taught and learned. In our study the focus is on pedagogical opportunities and how it is taught and learned. Methodology, Methods, Research Instruments or Sources Used In the Leaddig project, mathematics instruction was observed across multiple classrooms in the five participating schools. The present study focuses on mathematics lessons from two of these schools. Data was collected through unstructured classroom observations (Given, 2008), with attention directed both to mathematics teaching in general and, more specifically, to the use of digital tools in instruction. During the observations, detailed field notes were taken, aiming to document classroom activities and interactions as comprehensively as possible through narrative-style descriptions. The researchers’ role during the observations was neither that of a complete observer nor that of a full participant. Rather, it is best characterized as observer as participant (Given, 2008), as the researchers’ presence in the classroom had some influence on students. Data collection commenced in August 2023 and continued until the time of writing. Our objective was to examine mathematics instruction across multiple educational levels while also observing a diverse set of teachers. This approach was intended to provide a more comprehensive understanding of the practices and conditions present in Norwegian classrooms. The data material was analyzed based on the two frameworks described earlier. First, we tried to connect our observations and field notes to the mathematics teaching practices. For example, we tried to find observations that could help us say something about how the digital tools influenced the practice use and connect representations. Afterwards we analyzed our data material based on the pedagogical opportunities map. What could our observations tell us about how digital tools changed the way mathematics was taught and learned? Conclusions, Expected Outcomes or Findings Our preliminary findings: The digital technology was used for tasks instead of the students’ workbooks. It was up to the students to translate and understand the multiple representations provided by the technology. It seemed like the teachers held an optimistic view of digital technology, which seemed to reflect an assumption that technology use alone could lead to students’ learning of the content . The use of digital tools did not utilize the opportunities described by the Pedagogical Opportunities Map. References Drijvers, P. (2019). Head in the clouds, feet on the ground—A realistic view on using digital tools in mathematics education. In A. Buchter, M. Glade, R. Herold-Blasius, M. Klinger, F. Schacht, & P. Scherer (Eds.), Vielfaltige Zugange zum Mathematikunterricht (pp. 163–176). Springer. Given, L. M. (Ed.) (2008). The SAGE encyclopedia of qualitative research methods. (Vols. 1-0). SAGE Publications, Inc., https://doi.org/10.4135/9781412963909 Hegedus, S., Laborde, C., Brady, C., Dalton, S., Siller, H. S., Tabach, M., Trgalova, J., & Moreno-Armella, L. (2017). Uses of technology in upper secondary mathematics education. Springer. National Council of Teachers of Mathematics (2014) Principles to Actions: Ensuring Mathematical Success for All. National Council of Teachers of Mathematics, Reston. Pierce, R., & Stacey, K. (2010). Mapping pedagogical opportunities provided by mathematics analysis software. International Journal of Computers for Mathematical Learning, 15(1), 1–20. The Ministry of Education and research. (2006). Læreplanverket for Kunnskapsløftet (Midlertidig utg. juni 2006. ed.). The Ministry of Education and research; The Norwegian Directorate for Education and Training. The Norwegian Directorate for Education and Training. (2017). Framework for basics skills. www.udir.no Retrieved from https://www.udir.no/laring-og-trivsel/rammeverk/rammeverk-for-grunnleggende-ferdigheter/ The Norwegian Directorate for Education and Training. (2019). Læreplanverket Kunnskapsløftet 2020. Retrieved from https://www.udir.no/laring-og-trivsel/lareplanverket/ | ||