Rethinking Student Engagement: Why Activity Is Not the Same as Learning

David Burns

What Is Engagement?

Engagement is one of those broad terms in education that can mean different things to different people. It is often used to describe students who appear interested, curious, motivated, or actively participating in classroom activities. In many schools, these visible behaviors are treated as evidence that learning is taking place. The underlying assumption sounds something like this: If students are actively talking, moving, collaborating, or “discovering,” they must also be learning.

The difficulty is that these observable behaviors are only indirect indicators of learning. They tell us what students are doing, but not necessarily what or how they are thinking. A classroom can be filled with enthusiastic discussion, movement, and participation while relatively little learning occurs. Conversely, students may appear quiet and attentive during a carefully modeled lesson while engaging in the very cognitive processes that produce durable learning. The critical question, therefore, is not simply whether students appear engaged, but what kind of engagement is taking place.

This distinction is important because learning is ultimately a cognitive process, not a behavioral one. Learning occurs when students attend to relevant information, process it in working memory, connect it to existing knowledge stored in long-term memory, and ultimately construct new knowledge. In other words, cognitive engagement—not behavioral engagement—is what matters most.

This idea aligns closely with Richard Mayer's (2009) thinking. Mayer defines learning as "actively building knowledge representations in working memory by applying appropriate cognitive processes" (p. 186). Notice that this definition says nothing about whether students are talking, moving, collaborating, or attempting to discover solutions. Instead, the emphasis is on the quality of the cognitive processing taking place. For Mayer, active learning occurs whenever learners engage in the cognitive processes necessary for learning, whereas passive learning occurs when those processes break down or fail to occur. Thus, active learning is always the goal, while passive learning should be avoided.

This leads Mayer to make an important distinction that is frequently overlooked in discussions of engagement: the distinction between active learning and active instruction. Active instruction refers to instructional methods that require students to engage in overt behavioral activity during learning, such as discovering a solution to a problem or participating in an inquiry task. Passive instruction, by contrast, refers to instructional methods in which learners are not required to engage in those behaviors, such as listening to an explanation, reading a text, or watching a teacher model a procedure.

With these distinctions in place, we can better understand what Mayer calls the constructivist teaching fallacy: "the idea that active instructional methods (e.g., discovery) are required to produce active learning (i.e., engaging in appropriate cognitive processing during learning)" (p. 185). In other words, the fallacy is the assumption that instructional methods that appear behaviorally active necessarily produce cognitive engagement.

This misconception has important implications for classroom practice. Consider mathematics instruction. Many educators are reluctant to provide students with fully worked examples before asking them to solve problems independently. Instead, they prefer to have students grapple with problems first, observing what strategies students already possess or what solution methods they might invent. Because the worked-example approach appears more passive, it is often assumed to produce less learning. After all, solving problems seems to immediately engage students in the cognitively demanding work of reasoning through a solution.

Yet this assumption runs counter to a substantial body of empirical evidence. Across decades of experimental research, novice learners who study worked examples consistently outperform comparable students who are asked to solve problems without prior guidance (Sweller & Cooper, 1985; Sweller, 1999). Similar findings have been documented across numerous domains (see Kirschner, Sweller, & Clark, 2006, for a review), providing substantial evidence that explicit instructional approaches are generally more effective than minimally guided approaches for novice learners. If active instruction were necessary to produce active learning, these findings would be difficult to explain.

Why Cognitive Engagement Matters

If visible activity is not a reliable indicator of learning, then what is? The answer lies in understanding human cognitive architecture.

One of the strongest predictors of learning is what learners already know. Prior knowledge provides the framework through which new information is interpreted, organized, and remembered. As Kirschner and Hendrick (2020) succinctly put it, “what you know determines what you learn” (p. 116).

From an information-processing perspective, long-term memory serves as the storehouse of knowledge, and the ultimate goal of instruction is to build and reorganize that knowledge. Learning, therefore, occurs when new information is successfully integrated into long-term memory.

Before new information can be learned, however, learners must first attend to it. Attention serves as the gateway to working memory: information that is not attended to is unlikely to be processed, learned, or remembered (Dehaene, 2020; Leong et al., 2017). Once information enters working memory—the mental workspace responsible for processing new information—it encounters another important limitation. Working memory can actively process only a few novel elements at one time and can maintain that information for only a short period unless it is rehearsed or meaningfully connected to existing knowledge stored in long-term memory (Cowan, 2001).

The figure above helps clarify why behavioral engagement is an incomplete measure of learning. Students may appear highly engaged—talking, moving, collaborating, or manipulating materials—but unless their attention is directed toward relevant information, that information is unlikely to move beyond sensory memory into working memory and, therefore, cannot be learned. Likewise, even when information enters working memory, learning can be inhibited if the task overwhelms students' limited cognitive capacity.

This is why behavioral engagement and cognitive engagement are not the same thing. It also helps explain why the worked-example studies discussed earlier consistently favor explicit guidance for novice learners. By reducing unnecessary cognitive demands, worked examples allow students to devote their limited cognitive resources to accurately and efficiently acquiring the target concepts and procedures. Put simply, explicit instruction succeeds where less guided approaches often fail because it enables students to think about the right things for longer periods of time. And what students repeatedly think about, process, and practice is ultimately what becomes lasting learning.

The Constructivist Research Fallacy

Sometimes advocates for a particular instructional framework or strategy make strong claims that adopting that approach substantially increases student learning. Often, they cite studies to support these claims, giving the impression that the approach rests on strong empirical evidence. Yet things are not always what they seem.

Take, for example, one popular instructional approach featured in Peter Liljedahl's book Building Thinking Classrooms. Liljedahl recommends that students engage in mathematical thinking tasks in randomly assigned groups while working on vertical, non-permanent surfaces, such as whiteboards, rather than sitting at their desks working in notebooks. When one walks into a classroom that has adopted this recommendation, it is easy to conclude that a great deal of learning is taking place. Students are standing, discussing ideas, watching and commenting on one another's work, and collaborating as they solve problems. Indeed, Liljedahl cites research showing that this structure increases student discussion, participation, and other measures related to engagement (Liljedahl, 2016).

However, what Liljedahl's research does not demonstrate is that this "active instructional method" improves what matters most—learning outcomes. Slightly adapting Mayer's terminology, I call this the constructivist research fallacy: mistaking evidence of increased behavioral engagement for evidence of increased learning. In other words, because an instructional method visibly increases student activity—and research may even confirm that it does so—it is easy to assume that the method also increases the cognitive processing necessary for learning. Yet all we can conclude from such evidence is that the method increases behavioral engagement. And, as we have seen, behavioral engagement can be a poor proxy for actual learning.

None of this is to suggest that structures such as the one described above should never be used. They may have an appropriate place within instruction. The point is simply that visible activity should not be confused with meaningful learning, just as research demonstrating increases in behavioral engagement should not be confused with research demonstrating increases in learning outcomes.

Designing for Cognitive Engagement

Instead of prioritizing structures designed to increase behavioral engagement, teachers should first ensure that the instructional conditions necessary for cognitive engagement are firmly in place.

The following recommendations are grounded in what we know about human cognitive architecture and how novice learners acquire new knowledge.

1. Protect Students' Cognitive Resources - Before students can engage deeply with academic content, they must be able to devote their attention to it.

   • Establish consistent routines and procedures. Predictable classroom routines reduce unnecessary cognitive demands and allow students to focus their attention on learning rather than figuring out what they are supposed to be doing. A chaotic classroom environment fractures attention, leaving fewer cognitive resources available for learning.

   • Practice routines until they become automatic. Routines should not simply be explained; they should be practiced until they become habitual. Transitions between activities should be seamless, requiring little conscious thought from students. The same is true of collaborative structures. Turn-and-talks, partner work, and other discussion routines should be rehearsed until students can participate almost automatically. The less mental effort students expend managing the routine itself, the more working-memory resources remain available for the learning task.

     
     

2. Direct Students' Cognitive Resources Toward Learning - Instruction should be designed to ensure that students' limited cognitive resources are channeled appropriately. This means not only helping students think about the right things but also ensuring that all students are doing the thinking.

   • Provide clear explanations and models before independent work. When introducing a novel concept or procedure, begin by explicitly explaining the concept and modeling the procedure before asking students to apply it independently. Instruction should be intentionally sequenced and broken into manageable chunks so that the number of interacting elements students must process at one time does not overwhelm working memory. As students' knowledge within a specific domain increases, these supports can be gradually faded.

   • Create a culture in which everyone is expected to think. Whole-group participation should be promoted through frequent opportunities to respond, followed by timely affirmative and corrective feedback. Accountability structures such as cold calling and no opt out help ensure that every student remains mentally engaged, rather than allowing only a small number of volunteers to carry the cognitive load. Too often, a handful of students do the thinking while the rest are on what I like to call "cognitive vacations." A strong culture of participation communicates that thinking is everyone's responsibility.

     
     

3. Sustain Cognitive Processing Until Learning Occurs - Learning requires more than initial attention and processing. Students must repeatedly retrieve, use, and refine new knowledge if it is to become durable.

   • Follow instruction with deliberate, spaced practice. Retrieval brings knowledge back into working memory, where it can be strengthened, elaborated, and connected to new learning. Without opportunities for spaced retrieval, even well-learned information is likely to fade from long-term memory and be forgotten.

A Final Thought on Productive Struggle

One final point deserves emphasis: struggle is not synonymous with learning.

When novice learners are asked to solve problems or complete complex learning tasks before they have been explicitly taught the necessary knowledge and procedures, they will often struggle. For many educators, this struggle is viewed as evidence that students are engaged in deep thinking and that meaningful learning is taking place. However, as discussed above, the research on minimally guided instruction for novice learners does not support this conclusion. In my experience, what is often described as productive struggle is simply struggle, particularly for students who lack important prior knowledge related to the content or task.

As we have seen, what students already know largely determines what they are able to learn next. Without sufficiently developed knowledge structures in long-term memory, novice learners lack the foundation needed to navigate complex problems successfully. Rather than engaging in the cognitive processes that promote learning, they often become overwhelmed by the demands placed on their limited working memory.

The consequences are predictable. Students become frustrated, lose confidence, and begin to disengage. Despite the teacher's well-intentioned encouragement—"Keep going." "You're in the learning pit." "Don't give up." "This is what real learning looks like."—many students are not experiencing productive cognitive engagement. They are experiencing cognitive overload.

Ironically, these lessons often end the same way. After students have struggled unsuccessfully, the teacher eventually steps in to explain the concept, model the procedure, and provide the guidance that novices needed from the outset.

If engagement is ultimately about the cognitive processes that produce learning, then we should stop judging engagement by how busy students appear or how much they struggle. The better question is this: Does this instructional design direct students’ limited cognitive resources toward building new knowledge? If the answer is yes, then meaningful engagement—and meaningful learning—is far more likely to occur.

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Resources

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922

Dehaene, S. (2020). How we learn: Why brains learn better than any machine . . . for now. Viking.

Kirschner, P. A., & Hendrick, C. (2020). How learning happens: Seminal works in educational psychology and what they mean in practice. Routledge.

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1

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Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59-89.