Rethinking Research Designs in Mathematics Education: Qualitative and Mixed-Methods Approaches

In the context of the WCQR 2026 (10th World Conference on Qualitative Research, Madrid), this panel discussion explores current methodological frameworks in mathematics education research, emphasizing qualitative and mixed-methods designs and their innovative potential. It analyzes and debates principal research designs, presents empirical classroom applications, and examines intersections between artificial intelligence (AI), metacognition, and qualitative inquiry. Bringing diverse perspectives, it fosters methodological reflection and international collaboration among researchers, educators, and doctoral students, promoting interdisciplinary and ethically grounded approaches to studying mathematics teaching and learning. The panel aims to review methodological design’s state of the art and inspire dialogue on how methodological plurality can advance theory and practice.

The first theme, Exploratory Review of Research Designs in Mathematics Education: Emerging Trends and Methodological Gaps, surveys methodological patterns and shifts from 1980–2021. Using an exploratory review, it identifies prevalent designs—qualitative, mixed-methods, and design-based—and examines their use in addressing the complexities of mathematics teaching and learning (Onwuegbuzie et al., 2023). It situates developments within epistemological paradigms, showing the field’s move from positivist to interpretive and constructivist orientations. Though quantitative and quasi-experimental designs remain relevant, there is a strong trend toward interpretive and mixed-methods approaches that privilege context, meaning, and participant voices (Creswell & Plano Clark, 2017; Tashakkori & Teddlie, 2010). This shift recognizes that mathematical learning involves social meaning-making, not mere procedural acquisition. Research in Latin America and Europe increasingly employs design-based research (DBR) and case studies to explore classroom interaction, teacher development, and mathematical reasoning (Jáuregui & Rodríguez, 2023; Kelle & Buchholtz, 2015). These approaches reveal how teachers design, implement, and refine learning environments. However, gaps persist—such as weak integration between qualitative and quantitative data and limited longitudinal and participatory designs (Bangi, 2018). Bridging these requires developing methodological literacy that values pluralism and aligns methods with the studied phenomena’s complexity.

The second theme, Exploring Qualitative Research Designs in Mathematics Education: Case Studies and Classroom Narratives, examines interpretive approaches revealing social and cognitive dimensions of learning mathematics. Case studies and ethnographies provide detailed descriptions of reasoning processes, concept development, and pedagogical decisions (Stake, 1995; Merriam & Tisdell, 2016). They reconstruct meanings and trace understanding through authentic interaction. Classroom narratives and life histories capture how learners negotiate meaning, confront abstraction, and build confidence and agency. Narrative and phenomenological approaches analyze learners’ experiences of problem solving, resilience, and meaning-making through collaboration (Creswell & Poth, 2018; Sfard, 2008). These approaches, privileging interpretation, context, and reflexivity, deepen understanding beyond numerical measures. They emphasize ethics and give voice to participants as co-constructors of meaning. By fostering dialogue and reflection, qualitative research illuminates how mathematical thinking evolves in diverse, real-world classrooms.

The third topic, Teachers as Researchers: Action Research for Transforming Mathematics Teaching Practices, considers educators as active agents of inquiry. Action research empowers teachers to investigate their practice, reflect on pedagogical challenges, and co-create knowledge with students (Kemmis et al., 2014). It promotes a participatory model valuing professional experience as legitimate knowledge. In mathematics education, action research tackles issues such as enhancing mathematical communication, integrating technology, and developing inclusive strategies for diverse learners (Badaraco & Carrera, 2024; Carr & Kemmis, 1986). Through iterative cycles of planning, acting, observing, and reflecting, teachers refine strategies and deepen their understanding of student engagement with mathematical ideas. This design embodies qualitative research’s emancipatory dimension, emphasizing agency and collaboration. It bridges research and practice, aligning with WCQR’s mission of linking inquiry to social transformation. By empowering teachers as researchers, this approach promotes critical reflection on institutional structures, curricula, and equitable access to mathematical knowledge.

The fourth presentation, Mixed-Methods Approaches in Mathematics Education: Integrating Quantitative Insights and Qualitative Depth, demonstrates how combining data types enriches understanding of educational phenomena. Mixed-methods research has grown markedly, representing about one-third of mathematics education studies published in major journals (ERIC, 2012; García & Moreno, 2019). This rise reflects awareness that complex educational problems demand multiple evidence forms. Explanatory sequential (QUAN → QUAL) and exploratory sequential (QUAL → QUAN) designs capture both outcomes and underlying processes (Creswell & Plano Clark, 2017). For instance, researchers have paired standardized assessments with qualitative interviews to reveal how beliefs and attitudes influence problem solving (Kelle & Buchholtz, 2015). Mixed-methods approaches connect the “what” of results with the “why” of learner experiences. Although challenges persist regarding paradigm coherence and interpretation, mixed-methods research supports triangulation and actionable insights for curriculum design, teacher training, and policy (Johnson & Onwuegbuzie, 2004). Well-designed studies unite evidence-based decisions with contextual richness and participant voice.

The fifth topic, Artificial Intelligence and Metacognition in Mathematics Learning, explores a frontier of educational research. Emerging evidence shows that AI tools—adaptive platforms, intelligent tutors, generative systems—enhance metacognitive awareness and self-regulated learning (Kouzalis et al., 2024). Through qualitative and mixed-method lenses, researchers analyze how learners reflect, set goals, and monitor progress (Popenici & Kerr, 2017). Metacognition—the ability to plan, evaluate, and regulate one’s thinking—is a strong predictor of mathematical success (Flavell, 1979; Veenman et al., 2006). AI environments provide immediate feedback and reflective opportunities, supporting self-assessment and adaptive reasoning. Integrating AI-mediated learning with reflective methodologies reveals new human–machine interactions. This intersection challenges epistemological assumptions and demands innovative methods capable of analyzing qualitative dimensions of digital interaction while recognizing algorithmic mediation’s impact on learning.

Together, these perspectives converge on a central argument: methodological innovation is vital for advancing mathematics education research. The integration of qualitative, mixed, and technology-enhanced approaches expands understanding of learning as multifaceted and context-bound. Yet realizing this potential requires integration across paradigms, epistemological coherence, and cross-disciplinary methodological competence (Creswell & Plano Clark, 2017; Tashakkori & Teddlie, 2010). Sustained dialogue between theory and practice ensures research remains responsive to classroom realities and technological change.

In conclusion, this panel supports WCQR 2026’s mission by promoting debate and discussion about methodological diversity in mathematics education research. It positions qualitative inquiry at the center of educational innovation, connecting interpretive traditions with new evidence and technology-enhanced learning. By merging methodological rigor and contextual relevance, the panel seeks to inspire future studies that integrate depth, reflection, and collaboration in understanding mathematics learning as a human, social, and technological phenomenon.