A decade or so ago, reading instruction underwent a public reassessment. Whole language approaches had emphasized immersion in texts and contextual, “cue”-based guessing strategies. The goals were admirable; the results, however, were uneven. Over time, research in cognitive science had clarified something essential: Many children were not being taught in ways that reflected how the brain acquires foundational skills.
The response, at least in its ideal form, was not to narrow literacy instruction but to strengthen it. Schools restored systematic phonics and explicit skill development as the base on which comprehension could grow. The “science of reading” did not eliminate discussion of rich texts. Instead, it reestablished science-based sequence and structure.
Mathematics now finds itself in a similar place. In many classrooms, discovery-first instruction has become common practice. Students are encouraged to generate strategies, explore patterns, and construct algorithms before core procedures are secure. The intention is deeper understanding. But teachers frequently report student frustration, uneven mastery, and widening gaps between those who enter with strong background knowledge and those who do not. In my work with school districts as an educational psychologist, I often hear this from teachers. They want to do better for all their students and they worry particularly about those who don’t take readily to math.
Cognitive load theory explains why students without a strong background struggle. Research in cognitive science has long demonstrated that working memory is limited. When learners encounter too many unfamiliar elements at once, retention declines. Experts can manage complexity because they have an organized knowledge structure that was built over time. Beginners do not. Without such structures, tasks that might appear to be engaging can become cognitively overwhelming. In mathematics, asking students to derive procedures while interpreting new representations and comparing multiple solution paths increases mental demand and cognitive load significantly. What is often described as “productive struggle,” in practice exceeds students’ processing capacity.
Research comparing minimally guided instruction with explicit approaches consistently shows that novice learners benefit from clear modeling and guided practice. When teachers demonstrate procedures before expecting independent generation, students build understanding more efficiently. As foundational skills become fluent, students are better able to reason flexibly. Procedural fluency and conceptual understanding should not be viewed as competing goals. Fluency reduces the mental effort required to execute basic steps, paving the way for deeper thinking.
The lesson from reading reform is straightforward: Engagement alone, especially the kind shown by participation and visible involvement, does not guarantee learning. Students can be active and on task without actually processing the material in a way that leads to learning. Instruction must align with how knowledge is built and stored.
Research … consistently shows that novice learners benefit from clear modeling and guided practice.
The question schools should be asking is this: What does this alignment look like in mathematics classrooms? It would begin with greater clarity at the start of instruction. When new procedures are introduced, students should see them modeled explicitly before being asked to generate their own methods. A clear demonstration, followed by guided practice, reduces unnecessary confusion and gives students a stable reference point. Exploration can still occur but is more productive when students are not trying to invent core steps from scratch.
Curriculum materials also deserve closer scrutiny. In some classrooms, students are presented with several strategies for solving the same problem simultaneously. While the ability to work problems flexibly is an important goal, presenting too many variations at once can overwhelm learners who are still building foundational understanding. A more deliberate progression—introducing one reliable method, securing it, and then expanding options—builds both confidence and competence.
Professional learning should reflect what we know about memory and attention. When teachers understand how cognitive overload manifests, with students losing track of steps, misapplying procedures, or appearing disengaged, they can adjust instruction accordingly. The point is, not all struggle signals growth. Some of it signals overload.
Practice, too, must be reframed. Structured rehearsal of foundational skills is often dismissed as rote. The reality is that fluency reduces cognitive strain. When students no longer need to devote working memory to basic steps, they have greater capacity for reasoning and application. Protecting time for consolidation is not a departure from rigor, it is what makes rigor sustainable.
Assessment systems should reinforce alignment between instruction, practice, and expectations for students. Students should be asked to explain their thinking and apply concepts. They should also be expected to perform procedures both accurately and efficiently. This recommendation treats fluency and reasoning as complementary, rather than competing goals, and sends a clear message of what mastery entails.
These recommendations are not a call to abandon inquiry. If students are struggling, adding more variation, such as introducing multiple approaches at the same time or increasing open-ended exploration, is unlikely to reverse the trend. At early stages of learning, this kind of variation can place additional demands on working memory and interfere with the development of foundational understanding. A steadier approach that secures core knowledge and procedures first and then introduces additional strategies once that foundation is in place is more consistent with how learning develops.
Inquiry has an important place in the teaching of mathematics. It works best, however, when it rests on a foundation of fluency. If we want durable improvement in math achievement, we should design instruction that reflects how students actually learn new material. Knowledge is achieved through clear modeling, careful sequencing, and sufficient practice to make understanding last.