“Not All Help Helps”: Rethinking the Expertise Reversal Effect Through Task Complexity

“Not All Help Helps”: Rethinking the Expertise Reversal Effect Through Task Complexity

An engaging perspective for education professionals

Picture this: you’re in a Year 7 classroom during a science lesson on circuits. Two students—Yusuf and Ava—are sitting side by side. Both are tackling the same question: 
“Design a circuit that includes a switch, a buzzer, and two bulbs, and explain how current flows through it.”

Ava has only recently grasped the basics of series and parallel circuits. She’s still shaky on the idea of current flow. Yusuf, on the other hand, has a strong conceptual understanding already—he spent time tinkering with electronics at home and can visualise the flow of electrons almost intuitively.

You offer them both a scaffolded worksheet. It breaks the task down: draw the circuit step-by-step, identify components, label direction of current, and finally explain.

Ava breathes a sigh of relief and dives in, building confidence with each step.
Yusuf rolls his eyes.

He starts skipping steps, gets distracted, and eventually makes a careless error—not because he doesn’t know the content, but because the support has become noise. It’s holding him back.

This is the Expertise Reversal Effect (ERE) in action. And as simple as this moment seems, it unlocks some powerful insights about what adaptive teaching really means—and where traditional models of differentiation fall short.

The Classic Definition: Help that Backfires

The ERE (Kalyuga et al., 2003) is a well-established phenomenon that shows instructional support benefits novices but may hinder experts. As students gain expertise, support that once reduced cognitive load becomes redundant. And redundancy isn’t just inefficient—it adds extraneous cognitive load, which can block learning.

In Ava’s case, the step-by-step scaffolds were unnecessary. Her brain was trying to reconcile instructions she didn’t need with the mental model she already had. That tension used up working memory, leaving less room for actual reasoning and reflection.

But here’s where this theory gets even more interesting—and more useful for teachers.

The Power of the Task: It’s Not Just About the Learner

Recent studies, especially from Chen, Sweller, and others (2017; 2018), suggest the Expertise Reversal Effect is better understood through the lens of Cognitive Load Theory—particularly element interactivity.

Let’s unpack that.

Element interactivity refers to how many concepts or steps need to be considered at once to solve a problem. If a task involves lots of interacting ideas—like understanding current, resistance, parallel paths, and battery orientation all at once—it has high element interactivity.

• For Yusuf, the complexity isn’t a barrier—it’s a challenge he’s equipped to tackle.
• For Ava, that complexity is overwhelming unless it’s broken down.

So, what’s happening here isn’t just about different “ability levels.” It’s about where each learner is in relation to the demands of this particular task.

That’s a key insight: adaptive teaching isn’t just about who the student is—it’s about the interaction between the student and the task at this moment in time.

Expertise Reversal as a Variant of the Element Interactivity Effect

In this view, ERE isn’t a standalone phenomenon. It’s actually a variant of a broader principle: as learners become more skilled, they perceive tasks as simpler because they chunk information into schemas. That reduces perceived complexity.

Yusef doesn’t see four circuit components. He sees a familiar pattern.

Ava still sees four confusing pieces.

So when we give the same scaffold to both, Yusef experiences unnecessary interference—extraneous load—while Ava experiences productive support—reduced intrinsic load.

This perspective, grounded in the work Chen et al. (2017), highlights how task complexity moderates the ERE. In simpler tasks, the cost of giving “too much” help is minimal. But as complexity increases, getting support wrong—either by over-scaffolding or under-supporting—has a much greater impact.

Why This Challenges Traditional Differentiation

In many classrooms, differentiation is treated as a fixed pathway:

• “Low ability” = more support
• “High ability” = more challenge

But this assumes both that learners are static and that tasks are neutral.

That’s not what the research suggests.

In reality:

• Expertise is dynamic—Yusuf won’t be a novice forever.
• Tasks vary in intrinsic difficulty—some will spike in complexity even for otherwise confident learners.

This means support must be fluid. One-size-fits-all grouping strategies (or even rigid tiered scaffolds) risk becoming blind to the actual learning moment. A “high” student might still need help on a high-interactivity task. A “low” student might not need hand-holding if the task aligns well with their prior learning.

It’s not that traditional differentiation is wrong—but it’s incomplete. What we need is responsive teaching, where formative assessment, classroom dialogue, and teacher intuition help judge both task complexity and learner readiness in real time.

Practical Strategies for the Classroom

So how can we apply this nuanced understanding of ERE and element interactivity? Here are a few actionable ideas:

1. Design scaffolds as opt-in, not one-size-fits-all
Offer step-by-step guidance, but empower students to choose whether to use it—especially in mixed-attainment groups.
2. Vary support within the same task
You don’t need different tasks for different students. Instead, vary how much guidance is embedded. Provide prompts, sentence starters, or checklists that can be ignored or embraced based on need.
3. Use formative assessment to gauge moment-by-moment expertise
Mini whiteboards, cold calling, and peer explanations aren’t just about checking answers—they reveal how complex the task feels to each student.
4. Don’t assume challenge = independence
In high-interactivity tasks, even high-attaining students may benefit from revisiting worked examples or collaborative modelling.
5. Embrace dynamic roles. 
Let students move between novice and expert roles within a lesson. Today’s Yusuf might teach tomorrow’s Ava how to approach a new concept she hasn’t encountered.

Final Thought: The Art of Just Enough

Ultimately, the Expertise Reversal Effect reminds us that good teaching isn’t about offering help or withholding it—it’s about giving just enough. Not too much to bore or overload. Not too little to leave students floundering.

That sweet spot is hard to find. But with an awareness of task complexity and the changing nature of expertise, we’re better equipped to find it—not just once, but again and again, every lesson.

References


Chen, O., Kalyuga, S., & Sweller, J. (2017). The Expertise Reversal Effect is a Variant of the More General Element Interactivity Effect. Educational Psychology Review, 29(2), 393–405. https://doi.org/10.1007/s10648-016-9359-1


Chen, O., Castro-Alonso, J. C., Paas, F., & Sweller, J. (2018). Extending Cognitive Load Theory to Incorporate Working Memory Resource Depletion: Evidence from the Spacing Effect. Educational Psychology Review, 30(2), 483–501. https://doi.org/10.1007/s10648-017-9426-2


Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The Expertise Reversal Effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4