Exploring Conjecture Mapping in Educational Design

March 21, 2026

Conjecture mapping has become a powerful framework for helping me think more intentionally about the relationship between design, practice, and outcomes in educational settings. Rather than simply creating a tool or professional learning experience and hoping it works, conjecture mapping pushes me to explicitly articulate how and why specific design elements should lead to improved learning. As Sandoval (2013) explains, conjecture mapping “reifies” design assumptions, making them visible, concrete, and testable, so that both the design itself and the underlying theory can be refined over time. This emphasis on making thinking visible has shifted how I approach instructional design, moving me from intuition-based decisions to a more systematic and research-informed process.

High-Level Conjecture created using Canva(2026)

High-Level Conjecture

At the core of my current work is a high-level conjecture: structured professional learning that intentionally integrates dyslexia knowledge, scaffolded data analysis, and evidence-based literacy strategies will improve teacher decision-making and ultimately lead to better literacy outcomes for students with dyslexia (Sandoval, 2013). This conjecture reflects not only a problem of practice I see in schools with teachers overwhelmed by data and unsure how to translate it into instruction, but also a commitment to bridging the gap between knowledge and application. In this way, the conjecture map becomes a way to clarify both the problem and the proposed solution, while also acknowledging the “varying degrees of uncertainty” inherent in design work (Sandoval, 2013).

Planned Design Features

To embody this conjecture, I am planning a set of design features that work together as an integrated system rather than isolated tools. These include narrative data prompts that help teachers frame student data as meaningful stories, a dyslexia knowledge module to build foundational understanding, stepwise protocols for structured data analysis, and a select bank of evidence-based literacy strategies aligned with the Science of Reading. In addition, I plan to include an MTSS (multi-tiered system of supports) aligned action-planning template and embedded educational technology supports to guide implementation. At this stage, these features remain conceptual and have not yet been piloted, but articulating them in advance aligns with Sandoval’s (2013) assertion that design research requires specificity about what features are expected to do and how they will function together.

Intended Mediating Processes

A critical component of conjecture mapping is identifying mediating processes that bridge design features and outcomes. In my design, these processes include teachers reframing data into student-centered narratives, triangulating patterns across multiple data sources, linking those patterns to instructional decisions, testing instructional adjustments, and developing a stronger sense of instructional agency. These processes are intentionally specified so they can be observed, measured, and refined during implementation (Sandoval, 2013). This aligns with the idea that each “arrow” in a conjecture map represents a relationship that is open to empirical testing and revision. Additionally, these mediating processes reflect a broader understanding of instructional design as a reflective and iterative practice, rather than a linear one (Wilson, 2005).

Expected Outcomes

The expected outcomes of this work are organized across short, intermediate, and long-term goals. In the short term, I anticipate increases in teachers’ dyslexia knowledge and data literacy. Intermediate outcomes include more aligned instructional decisions and more consistent progress monitoring practices. Ultimately, the long-term goal is to reduce literacy gaps and demonstrate measurable reading growth for students with dyslexia. Importantly, these outcomes are not guarantees but hypotheses that will need to be tested through implementation and data collection (Sandoval, 2013). This reinforces the idea that conjecture mapping is not just about design, but about building “trustworthy, usable theories of learning” through systematic inquiry (Sandoval, 2013).

Theoretical and Practice Grounding

The theoretical grounding of this work further strengthens the design. Cognitive Load Theory supports the inclusion of scaffolded tools and stepwise protocols, ensuring that teachers are not overwhelmed by complex data tasks. Situated Learning emphasizes the importance of embedding these activities in authentic classroom contexts, making the learning immediately relevant and applicable. Formative Assessment provides a framework for ongoing progress monitoring and instructional adjustment, while the Science of Reading ensures that the strategies included are evidence-based and aligned with how students learn to read. Together, these frameworks provide a strong foundation that connects theory to practice.

Equally important is the influence of Wilson’s (2005) four pillars of practice, which shape how I think about the design process itself. First, design is a reflective practice, requiring ongoing adjustment and professional judgment rather than rigid adherence to a model. Second, design is inherently collaborative, meaning that teachers, specialists, and stakeholders must engage in dialogue to co-construct meaningful solutions. Third, design is situated in context, reminding me that no tool or intervention exists in isolation from the realities of classrooms, schools, and communities. Finally, design is values-driven, which prompts me to consider issues of equity, access, and inclusion, especially when working with students with dyslexia. These pillars reinforce that effective instructional design is not purely technical, but deeply human and context-dependent (Wilson, 2005).

Next Steps

As I move forward, my next steps involve developing prototype artifacts based on this conjecture map and piloting them with teachers in authentic settings. This will allow me to collect data on both the mediating processes and the outcomes, providing evidence to refine the design and test the underlying conjectures. Consistent with Sandoval’s (2013) framework, this iterative process will help strengthen both the practical tool and the theoretical understanding of how teachers learn to use data and instructional strategies effectively.

Overall, engaging in conjecture mapping has pushed me to be more intentional, transparent, and systematic in my design work. It has transformed my thinking from simply creating professional development to designing a research-informed learning environment with clearly articulated relationships between features, processes, and outcomes. In doing so, it not only improves the design's quality but also contributes to a deeper understanding of teaching and learning.

References

Canva. (2026). Canva [Graphic design platform]. https://www.canva.com

Sandoval, W. (2013). Conjecture Mapping: An Approach to Systematic Educational

Design Research. The Journal of the Learning Sciences, 1-19.

Wilson, B. G. (2005). Broadening our foundation for instructional design: Four pillars

of practice. Educational Technology, 45 (2), 10-15