Learning science

What this means in practice:

No single mode dominates.
Each part of the model has a clear role, and the system makes it simple for teachers to balance multiple modes coherently.

Instructive doesn’t ask schools to abandon explicit teaching, or to replace it with something else.
It provides a structure that allows explicit teaching, practice, retrieval, collaboration, and intervention to work together as part of a balanced whole.

The ideas behind learning science are well established [3], [12].

What matters is not whether schools recognise them, but how those ideas shape teaching in practice.

In Instructive, learning science doesn’t sit on top of the model as a label.
It shapes the structure, sequencing, and boundaries of the system itself.

Learning science tells us:
Working memory is limited, especially when students are encountering new ideas for the first time [3], [12].

What this means for the model:

• Whole-class explicit teaching is deliberately:
  • Short
  • Focused
  • Carefully scoped
• New ideas are introduced clearly, without unnecessary complexity
• Explanations aim to establish shared understanding, not to do all the learning at once

Design consequence:
Explicit teaching is used to create clarity, not to carry the entire cognitive load of the lesson

Learning science tells us:
Without revisiting ideas, forgetting is the default; even meaningful, well-understood learning is subject to forgetting unless it is revisited [12], [14], [15].

What this means for the model:

• Key concepts are revisited over time
• Review is spaced rather than massed
• Retrieval is woven into everyday classroom routines

Design consequence:
Retention is planned for systematically, rather than left to chance or revision weeks.

With Instructive, students complete embedded retrieval activities, revisiting earlier learning. These ‘Warm-ups’ are:

• Brief, focused and once per day
• Spaced over time, and interleaved
• Designed to prompt recall and build fluency

This approach reflects well-established findings from learning science: forgetting is normal unless ideas are revisited [14], [15]; retrieving information strengthens long-term memory [15], [16]; and spacing practice over time improves retention and transfer [12], [15].

Learning science tells us:
New understanding builds on existing knowledge [12], [17], with particular implications for Australian maths classrooms, where students often operate across a spread of attainment equivalent to at least five years [18], [19].

What this means for the model:

• Students do not start from the same place
• Assumptions about shared readiness are risky
• Missing foundations increase cognitive load and lead to fragile understanding

Design consequence:
The model includes structured opportunities for students to consolidate or build prerequisite knowledge before moving on.

Learning science tells us:
Research distinguishes between how novices and more confident learners benefit from instruction [12], [17].

What this means for the model:

• Some students need more structure and support
• Others need challenge, application, and extension
• A single, uniform lesson risks overload for some students; disengagement for others

Design consequence:
Learning is distributed across different modes, rather than concentrated into one experience for everyone at once.

Learning science does not prescribe a single teaching method.
It sets constraints on how teaching must be designed to be effective.

Instructive’s model is built deliberately and consistently around those constraints.

When learning science shapes the design of the system:

Concerns about explicit teaching are often framed as matters of style or preference.
At their core, they are design problems.

When explicit teaching is delivered as a single, uniform lesson for all students at once, it clashes with what learning science tells us about learning in heterogeneous classrooms [12], [17].

One-size-fits-all explicit teaching assumes that:

• Students are broadly ready for the same next idea
• Shared explanations will land similarly for most learners
• A single level of practice will be appropriate

In real classrooms, these assumptions rarely hold [18], [19].

Learning depends on prior knowledge [12], [17] — and prior knowledge varies widely [18], [19].

In Australian maths classrooms:

• Students often operate across multiple developmental stages [18], [19]
• Prerequisite knowledge is uneven [18], [19]
• Gaps compound as content becomes more hierarchical [17], [19], [20], [21]

A single instructional pathway cannot meet all learners’ cognitive needs at the same time.

When instruction is misaligned with readiness:

• Some students experience cognitive overload [3], [17]
• Others disengage because the work is too easy [12], [17]
• Misconceptions form when ideas are introduced too early [17], [19]

This is not a failure of explicit teaching as defined in the research.
It is a failure of how it is deployed in mixed-ability settings.

Learning science does not say:

• “Explicit teaching doesn’t work”, or
• “Whole-class instruction should be abandoned”

It does say:

• Guidance must match learners’ current knowledge [12], [17]
• Cognitive load must be managed relative to readiness [3], [17]
• Instructional support must change as expertise develops [10], [17]

In heterogeneous classrooms, this cannot be achieved through a single, uniform lesson alone.

These challenges exist across subjects — but they are especially pronounced in mathematics. Mathematics is strongly hierarchical:

• New ideas depend directly on earlier concepts [19], [20]
• Missing foundations block access to later learning [17], [19], [20]
• Surface participation can mask fragile understanding [19], [22]
• Students cannot always productively attempt the same task if prerequisite knowledge is missing.

One-size-fits-all explicit teaching is not just unpopular with some teachers.
It is inconsistent with the learning science it claims to be based on in genuinely mixed-ability classrooms.

To remain research-aligned, explicit teaching must be:

• Situated within differentiated learning pathways that reflect students’ current knowledge
• Responsive in its level of guidance, practice, and challenge
• Embedded within a broader instructional system that supports consolidation, intervention, and extension

When explicit teaching sits within a system that also supports:

• Differentiation
• Consolidation
• Intervention
• Application and extension

it becomes:

• Safer for learners
• More effective for teachers
• More sustainable at scale

This is the core problem Instructive is designed to solve.

We understand more than ever about how learning works. In theory, this should make teaching and school leadership more effective than at any point in the past.

In practice, implementation is hard. When explicit teaching is interpreted too narrowly, it can slip into one-size-fits-all instruction. When schools try to combine too many initiatives without structure, success depends on individual effort and unsustainable teacher heroics. And despite decades of reform, outcomes in mathematics remain a persistent challenge for many students.

Instructive was created by Australian teachers to bridge this gap. It brings together well-established, evidence-based practices — explicit teaching, structured practice and retrieval, rich learning experiences, timely intervention, and differentiation — into a single, coherent model. Supported by clear data and thoughtful automation, it helps schools move beyond narrow implementations and individual heroics, making strong, balanced teaching possible every day, for every classroom.