The Mental Model of Great Teaching: A Takeaway from Nick Hart's BSME TeacherCon Talk

This past Saturday, I had the privilege of attending the BSME TeacherCon, where Nick Hart delivered a thought-provoking talk on the mental model of great teaching. As educators, we often focus on isolated strategies or individual lesson plans, but Hart's session made me think deeply about the broader structure and sequencing of learning. He challenged us to think about the why behind our teaching methods and how we can align our instructional strategies with the cognitive processes our students experience. This connects to a broader theme I explore in why repetition and habit lead teachers to plateau.

From Hart's talk, I distilled a key takeaway: great teaching follows a clear progression that mirrors the stages of learning. It isn't about using a single, magical strategy but about knowing when to use the right approaches at different points in the lesson or unit. This understanding forms a mental model for structuring lessons effectively.

Here's the model I've developed based on that takeaway, aligning key teaching strategies with cognitive processes:

The Research Behind Mental Models in Teaching

The idea that expert teachers operate from internalised mental models is not new, but it has gained significant traction in recent years through the work of cognitive scientists and education researchers. Barak Rosenshine's Principles of Instruction, published in American Educator, distilled decades of research into a set of strategies that effective teachers use consistently. What Rosenshine observed -- and what Hart's talk reinforced -- is that these strategies are not applied randomly. Expert teachers sequence them deliberately, matching the right approach to the right moment in the learning process.

More recently, the Education Endowment Foundation's Teaching and Learning Toolkit has provided a meta-analytic evidence base that helps teachers understand which strategies yield the greatest gains and under what conditions. Metacognition and self-regulation, for instance, consistently rank among the highest-impact approaches -- and they feature prominently in the later stages of the mental model I outline below. Feedback, another high-impact strategy, is most effective when timed correctly within the learning sequence rather than applied generically.

What struck me about Hart's presentation was how naturally it synthesised these research threads. Rather than presenting a checklist of strategies, he encouraged us to think about the underlying cognitive architecture of a lesson -- what is happening in the learner's mind at each stage, and how our instructional choices either support or hinder that process.

1. Introduction & Engagement (Activating Prior Knowledge)

At the start of a lesson, the goal is to stimulate curiosity and recall prior learning. Inquiry prompts, questioning techniques, and low-stakes assessments help activate students' previous knowledge. This primes them for deeper learning as they connect new content to their existing understanding.

Why: This stage is rooted in constructivist theory. Activating prior knowledge gives students a foundation to build on, making new information easier to process and retain.

2. Explicit Instruction (Building Understanding)

Once students are engaged, clear explanations of new material are critical. Direct instruction with scaffolding and reducing cognitive load are essential strategies at this point. Breaking down complex information into manageable chunks helps ensure students can process and comprehend new concepts.

Why: Cognitive load theory emphasises the importance of reducing extraneous distractions and presenting information in a way that allows working memory to function efficiently. Clear, direct instruction is the foundation for this process.

3. Active Processing (Deepening Understanding)

After initial instruction, it's time for students to process and make sense of the content. Generative learning tasks—like creating mind maps or engaging in probing questions—help students organise and internalise information, moving it from short-term to long-term memory.

Why: Research shows that students learn more deeply when they actively engage with content through techniques like summarising, elaboration, and questioning.

4. Practice & Application (Reinforcement and Skill Development)

Once students have built a solid understanding, they need opportunities to practice and apply what they've learned. Low-stakes assessment, retrieval practice, and inquiry-based investigations provide them with the chance to apply knowledge and test their skills in meaningful contexts. For a deeper look at these strategies, see our post on evidence-based methods for improving student learning outcomes.

Why: Application and practice allow students to consolidate their knowledge and develop a stronger command of the material. This stage moves students from "understanding" to "applying" in Bloom's taxonomy.

5. Inquiry & Exploration (Deeper Learning and Creativity)

Once students demonstrate understanding, deeper learning happens when they engage in open-ended tasks. Inquiry-based learning and project-based investigations encourage autonomy and critical thinking, allowing students to explore complex problems creatively.

Why: Encouraging higher-order thinking promotes creativity, problem-solving, and the development of key 21st-century skills, such as collaboration and communication.

6. Review & Reflection (Metacognition and Feedback)

At the end of a lesson or unit, review and reflection are essential. Retrieval practices -- like summarising tasks and feedback loops -- allow students to revisit key ideas and engage in metacognition. The art of professional noticing is closely related here. This is the time to reflect on progress and plan for future improvements.

Why: Metacognitive reflection helps students assess their understanding and identify areas for improvement, fostering self-regulation.

7. Long-Term Memory and Mastery

Mastery is achieved not through cramming but through spaced retrieval practice and ongoing feedback. By revisiting content over time, students move beyond rote memorization to fluency and application in varied contexts.

Why: Research shows that spaced retrieval and regular practice over time are key to retaining knowledge in long-term memory, supporting mastery of key concepts.

Mapping Strategies to Stages: A Summary

One of the challenges teachers face when reading about evidence-based strategies is knowing when to deploy them. A retrieval practice starter is different from a retrieval practice review task at the end of a unit, even though both fall under the same umbrella. The table below maps commonly used strategies to the stages where they tend to be most effective.

This is not intended as a rigid prescription. Some lessons will spend most of their time in stages two and three; others -- particularly towards the end of a unit -- will focus almost entirely on stages five through seven. The value of the model lies in helping teachers make these decisions consciously rather than defaulting to habit.

Adapting the Model for Different Contexts

One question that came up during the TeacherCon session was whether this kind of model works equally well across subjects and age groups. In my experience, the underlying cognitive principles are universal, but the emphasis shifts considerably. In early years settings, for instance, the boundary between explicit instruction and active processing is much more fluid -- young children learn best through play-based exploration that blends both stages simultaneously. In secondary science, by contrast, there are moments where extended explicit instruction is essential before any meaningful practice can begin, particularly when introducing abstract concepts like chemical bonding or electromagnetic waves.

The model also needs adapting for different cultural and institutional contexts. In the international schools I work with across the Middle East, class sizes, language backgrounds, and student mobility patterns all influence how much time a teacher can spend at each stage. A teacher working with a highly transient student population, for example, may need to spend more time on stage one -- activating and assessing prior knowledge -- because they cannot assume shared foundational understanding. These contextual adjustments are where professional judgement becomes irreplaceable, and no mental model can substitute for knowing your students.

Building a Mental Model for Teachers

This framework reflects my mental model for structuring lessons. It's important to recognise that great teaching isn't about rigidly following a formula, but about applying the right strategy at the right time for your students. My key takeaway from Nick Hart's talk is that "when we think of lessons as part of a larger learning journey, we can better align our methods with the stages of students' cognitive development." But it's also crucial to remember that teaching is a deeply personal and contextual practice—what works for one educator may not work for another.

This is the approach that resonates with me and will hopefully help me guide my students toward deeper learning, but it's not the only way. I encourage you to reflect on your own teaching and what works best for your students, your subjects, and your own style. Deliberate practice is a useful companion concept here. We all have unique contexts that shape how we teach, and there's power in embracing that diversity.