Research papers

Instructive is built on established research and tested using real school data.

Independent analysis of national NAPLAN results and multiple school-led studies using PAT Maths show that students using Instructive learn significantly faster than expected.

This page summarises the key findings and explains why these results are plausible, given the way Instructive is designed and implemented.

Evidence from national assessment data (NAPLAN)

In 2025, Brody Hannan of the University of Southampton conducted an independent analysis with NAPLAN data and Instructive data, analysing over 10,827 students across 152 schools.

The findings can be interpreted as follows:

Attainment data from Instructive – which is up-to-date every few weeks – correlates well with NAPLAN scores [1].

Students learn more than 2.0 years of maths each year with Instructive. Within the study, each year of use puts students more than a full grade level above their peers. This analysis is based on established methodologies for mapping equivalent year levels onto NAPLAN scores; and uses attainment measures which correlate with NAPLAN [2].

Everyone benefits from using Instructive. Schools whose students completed more modules in Instructive tended to achieve higher results on NAPLAN. This effect was found across all types of schools, regardless of wealth [3].

Instructive may close the wealth gap between schools. Use of Instructive “moderated” the usual association between socio-educational advantage (ICSEA) and NAPLAN performance; reducing it to only a slight effect [4].

Brody Hannan presents detailed findings in this academic talk with the Southampton Education School.

Key findings in detail:

[1] Instructive attainment data correlates significantly with NAPLAN performance (moderate correlation with r = 0.61) – quite high correlation for social science.

[2] Students’ prior experience with the Instructive platform matched with end-of-year achievement levels which were over 1.0 year levels higher per year of use. Each additional year of Instructive usage is associated with a 1.170 (0.094) increase in Equivalent Year Level (p<0.001).

[3] Schools whose students completed more modules in Instructive tended to achieve higher results on NAPLAN. This effect was found across all ICSEA quartiles.

[4] Schools with higher use of Instructive were associated with a weaker connection between ICSEA and NAPLAN outcomes. That is, in schools with high Instructive usage, ICSEA was a weaker predictor of NAPLAN outcomes.

School-led studies using PAT Maths

Across three independent school-led studies, student growth was analysed using PAT Maths data before and after sustained use of Instructive (formerly Maths Pathway). In each case, observed growth substantially exceeded expected baseline growth for the same year level.

Despite being conducted in different schools and contexts, the three studies show a consistent pattern:

Students demonstrated approximately 1.4–1.6 years of growth per year, equating to around 50% faster learning than baseline expectations.

growth-in-1-year-as-measured-by-PAT-Maths-Year7-8-graph

These studies are particularly informative because they use a familiar, standardised assessment instrument (PAT Maths), focus on rate of learning, and explicitly examine the relationship between student growth and program dosage.

References:

Steinbergs, T., & Smith, J. (2024). Maths Pathway at Bayside Christian College: Analysis with PAT-M data. Zenodo. https://doi.org/10.5281/zenodo.13864326

Colledge, S. M., Walker, N. D., & Smith, J. M. (2024). Individualised learning at Geneva Christian College: Analysis with PAT-M data. Zenodo. https://doi.org/10.5281/zenodo.14015766

Brittain, A. K., & Smith, J. M. (2024). Balancing instructional modes using PAT Maths data: Analysis at SCOTS PGC College. Zenodo. https://doi.org/10.5281/zenodo.14189711

Plausibility of the results

The findings from NAPLAN and PAT Maths are strong. Just as importantly, they are consistent with what we already know about how students learn mathematics, and with the way Instructive has been designed and refined over time.

Instructive does not rely on a novel or untested theory of learning. Instead, it brings together well-established research on effective mathematics instruction with disciplined, data-informed content design.

“Before (Instructive), classroom management was nearly impossible due to the spread of ability, but with (Instructive) we can work 1:1 with students while everyone else can still work productively on maths they’re ready to learn.”
Jane Ryan, Secondary Mathematics Teacher

Built on established learning science

At a system level, the Instructive model is grounded in established research on learning and cognition. Key principles include:

managing cognitive load so students are not overwhelmed by missing prerequisite knowledge
building durable understanding through spaced retrieval and repeated application
using mastery-based progression rather than age-based pacing
combining explicit instruction with carefully structured independent practice
using frequent formative assessment to guide next steps in learning

These principles are well supported in the research literature and are widely reflected in effective classroom practice. Instructive’s contribution is not to reinvent them, but to embed them coherently into a single, day-to-day teaching and learning model that can operate at scale.

For a fuller discussion of the research foundations underpinning the overall model, see more on Learning science.

High-quality learning materials, designed and refined through data

Strong outcomes also depend on the quality of the learning materials themselves.

Instructive’s modules are designed using a disciplined approach to pedagogical content knowledge and scaffolding. Each module targets a specific piece of mathematics, anticipates common misconceptions, and structures learning so that new ideas are introduced in manageable steps and integrated with what students already know.
Crucially, this design work is not static. Module content is iteratively refined using data from student interactions, including patterns of error, time on task, and performance on subsequent assessments. This allows weaknesses in explanations, sequencing, or scaffolding to be identified and addressed over time.
In this way, established learning science provides the guiding principles, while ongoing data analysis supports continual improvement in execution and quality.

A detailed account of this process — including how modules are written, reviewed, and iterated — is available in this summary paper:

Sundar, K. (2021). Leveraging learning analytics for better educational content design: Analysis of the Maths Pathway approach.

What the evidence shows.

Across national assessment data and independent school studies, the evidence is consistent: students using Instructive learn mathematics faster than expected.

These outcomes are not accidental. They align with learning science and are supported by a disciplined approach to content design, scaffolding, and ongoing refinement using student data.

Taken together, the evidence and the underlying design provide a clear, credible basis for describing Instructive as research-based and evidence-backed.