Stop Pretending Novices Are Little Experts

One of the most robust findings to emerge from cognitive science is that experts and novices do not think in the same way. This difference is not simply a matter of experts knowing more facts or possessing greater intelligence. Rather, experts have developed extensive, well-organized networks of domain-specific knowledge that fundamentally alter how they process information, solve problems, and make decisions. Because so much relevant knowledge has been stored and organized in long-term memory, experts are able to recognize patterns, identify important information, anticipate misconceptions, and devote their limited working memory resources to higher-order thinking. Novices, by contrast, lack these knowledge structures and must devote far more mental effort simply to understanding the basics of a concept or task. What appears effortless for an expert is often extraordinarily demanding for a novice.

This distinction between novice and expert cognition is one of the foundational insights of the learning sciences, yet it is frequently overlooked when educators plan instruction. In fact, many of the most celebrated student-centered approaches seem to rest on assumptions that are difficult to reconcile with what cognitive science tells us about how expertise develops. While these approaches are often praised for increasing activity, engagement, collaboration, and student ownership, they frequently place students in situations that require forms of thinking that are characteristic of experts rather than novices.

When We Ask Novices to Perform Expert Tasks

A common feature of many student-centered instructional models is the expectation that students will learn by doing the kinds of things experts do. Students are asked to teach one another, facilitate discussions, learn concepts independently, solve complex problems, evaluate competing claims, and generate explanations for ideas they have only recently encountered. These activities are often presented as opportunities for deeper learning, but they deserve closer scrutiny.

The issue is not that these are unworthy goals. The ability to explain, evaluate, synthesize, and apply knowledge is undoubtedly valuable. The issue is that these are outcomes of expertise, not starting points for developing it. A historian can evaluate the reliability of sources because they possess extensive historical knowledge. A scientist can generate hypotheses because they understand the underlying concepts and principles of their field. A mathematician can solve novel problems because they have mastered foundational knowledge and procedures. In each case, sophisticated thinking emerges from knowledge; it does not emerge independently of it.

Yet many student-centered instructional approaches invert this relationship. Rather than helping students acquire the knowledge that makes expert thinking possible, they often assume that placing students in expert-like roles will somehow produce expert-like knowledge and thinking. Cognitive science suggests the opposite. The ability to think critically, solve problems, and reason effectively is heavily dependent upon what one already knows.

Jigsaw as a Case Study

Jigsaw may be one of the clearest examples of this broader problem. The strategy is often celebrated because it increases participation and creates opportunities for collaboration. Students are divided into groups, assigned a section of content, and asked to become the “expert” on that topic before returning to teach their peers. On the surface, the approach appears sensible. Every student has a role. Every student contributes. Every student participates.

The question, however, is whether participation alone is sufficient justification for the instructional tradeoffs involved.

When a student spends fifteen or twenty minutes learning about a topic and is then designated as the group’s expert, we are asking that student to engage in a remarkably complex cognitive task. Expert-level teaching requires determining what information is most important, organizing ideas coherently, communicating them clearly, monitoring the understanding of others, responding to questions, and identifying misconceptions when they arise. These are not novice-level skills. They are the kinds of skills that become possible only after substantial knowledge has been acquired and organized.

The student in a jigsaw activity is not an expert. The student is a novice who has had a brief encounter with new information. Yet the structure of the activity assumes that this novice is capable of performing tasks that are normally associated with expertise. The result is that students often find themselves struggling not only to learn the material but also to teach it simultaneously.

The Cognitive Load Problem

From the perspective of cognitive load theory, this difficulty should not be surprising. Working memory is extremely limited, and learning new information places significant demands on that system. When students are expected to learn content, determine what matters most, organize their understanding, prepare an explanation, deliver that explanation, and respond to peer questions all at the same time, the cognitive demands of the task can quickly become overwhelming.

Advocates of student-centered learning often point to the level of activity taking place during these lessons as evidence of their effectiveness. Students are talking. Students are moving. Students are collaborating. The classroom appears intellectually vibrant. Yet visible activity is not the same thing as learning. Cognitive science has shown that learning occurs within the mind, and a strategy that appears engaging on the surface may actually impose unnecessary cognitive demands that interfere with learning.

This is one of the reasons expert guidance remains so important. An expert teacher can reduce unnecessary cognitive load by chunking new information, highlighting critical concepts, modeling thinking processes, and correcting misconceptions before they become entrenched. When that guidance is replaced by novice-to-novice instruction, many of those supports disappear.

The Misconception Multiplier

Another challenge is that novices are often poor judges of their own understanding. One of the defining characteristics of expertise is the ability to recognize errors and misconceptions. Experts possess rich knowledge structures that allow them to evaluate information and detect inaccuracies. Novices do not.

As a result, when a misconception emerges during direct instruction, an expert teacher is often able to identify and correct it immediately. When a misconception emerges within a student-led learning structure like jigsaw, however, it may go unnoticed. Worse, it may be shared with multiple peers who accept it as accurate. In this way, the very mechanism that makes strategies like jigsaw attractive, students teaching students, can also become a mechanism for spreading misunderstanding.

This is not merely a hypothetical concern. If students are still in the process of learning the content themselves, we should expect inaccuracies, omissions, and misunderstandings to occur. The question is not whether students will make mistakes. The question is whether the instructional design is structured in a way that minimizes those mistakes or amplifies them.

Participation Is Not the Same as Learning

Supporters of student-centered strategies such as jigsaw often point to increased participation as evidence of success. Certainly, active participation matters. Students should be continually prompted to think, discuss, write, retrieve, explain, and engage with content. But participation is a means, not an end of learning. 

This distinction is essential because many of the problems that student-centered learning strategies attempt to solve can be addressed through other approaches that carry fewer cognitive costs. Turn-and-talk, retrieval practice, mini whiteboards, call and respond, and strategic cold calling can all increase attention, participation, and accountability while ensuring that expert instruction remains at the center of the learning process. Students remain cognitively engaged without being required to assume responsibilities for which they are not yet prepared.

The key question should never be whether students are active. The key question should be whether their activity supports learning or competes with it.

Expertise Cannot Be Simulated

Perhaps the most fundamental issue with many student-centered approaches is the assumption that expertise can be approximated through what basically results in expert role-playing. If students act like historians, they will think like historians. If students act like scientists, they will think like scientists. If students act like teachers, they will think like teachers.

The evidence suggests otherwise. Historians, scientists, and teachers think differently because they possess extensive domain-specific knowledge acquired over years of study and practice. Their sophisticated thinking is not the cause of their expertise; it is the result of it.

If cognitive science has taught me anything, it is that prior knowledge matters. Expertise develops through the gradual accumulation and organization of knowledge, supported by expert guidance, practice, feedback, and repeated exposure over time. It does not emerge because we assign students expert roles before they possess expert knowledge.

This is why I remain skeptical of instructional approaches that routinely place novices in positions that require expert performance. Students absolutely should discuss, apply, retrieve, and elaborate on what they are learning. But these activities are most effective when they build upon expert explicit instruction rather than replace it. The goal of teaching should not be to pretend novices are experts. The goal should be to help novices become experts, one carefully guided step at a time.

References

Kirschner, Paul A., and Carl Hendrick. How Learning Happens: Seminal Works in Educational Psychology and What They Mean in Practice. Routledge, 2020.

Lovell, Oliver. Cognitive Load Theory in Action. John Catt Educational, 2020.

Ruiz Martín, H. (2024). How Do We Learn? A scientific approach to learning and teaching. Jossey-Bass.

Sherrington, Tom. Rosenshine’s Principles in Action. John Catt Educational, 2019.