Learning Analytics
Measuring learning behaviour through decisions, effort, stability, and recovery — not demographics or comparison.
Measuring Learning, Not Learners
Most learning platforms measure outcomes. Phlow Academy measures thinking.
Rather than focusing on who a learner is — their age, location, or background — Phlow focuses on how learning unfolds over time: the decisions students make, the effort those decisions require, and how understanding stabilises or improves.
Learning analytics in Phlow exist to support mastery, not to categorise learners.
Designed Without Demographic Assumptions
When a student creates a Phlow Academy account, only the essentials are collected: a username and password. This is intentional.
Phlow does not record a learner’s age, gender, location, or background. By design, the system cannot segment learners by identity or generate population-level comparisons based on demographic attributes.
This avoids two common pitfalls in educational analytics: drawing conclusions from proxy variables that do not cause learning, and allowing statistical groupings to overshadow individual understanding.
By removing demographic data entirely, Phlow ensures that every learner is evaluated on how they think — not on characteristics they cannot control.
Decisions as the Foundation of Measurement
At the heart of Phlow’s analytics is a simple shift in perspective.
Progress is not measured by questions completed or content covered. It is measured by decisions made.
A decision is any point where a learner must commit to an answer — whether that involves recognising a value, choosing an operation, interpreting a diagram, or confirming a result.
Some Phlows contain a single decision. Others contain several, chained together. By tracking decisions rather than questions, Phlow can analyse learning consistently across very different Phlow structures and difficulty levels.
Measuring Cognitive Demand with Base Decision Value (BDV)
Not all decisions place the same cognitive demand on a learner.
To account for this, Phlow assigns each decision type a Base Decision Value (BDV) — a neutral benchmark representing the inherent cognitive demand of that decision for an average learner.
BDV is not a level, a difficulty label, or a step count. It reflects the kind of thinking required.
Recognising a number, comparing quantities, choosing an operation, or interpreting a symbol all carry different cognitive demands — even when they appear within the same level or question.
BDV allows Phlow to reason about thinking itself, rather than relying on surface indicators such as topic or length.
Personalising Cognitive Effort
When a learner answers a decision, the system does more than record correctness.
It considers the Base Decision Value of that decision alongside how that learner typically handles similar decisions. For one learner, a correct answer may simply confirm fluency. For another, the same correct answer may represent meaningful cognitive effort.
Both signals matter — but in different ways.
This allows Phlow to recognise improvement, stability, and genuine learning effort, rather than over-rewarding easy wins or penalising productive struggle.
From Decisions to Learner Profiles
Rather than grouping learners by age or background, Phlow builds learner profiles based on observed learning behaviour.
These profiles emerge from patterns such as: how learners respond to different decision types, how stable understanding is over time, where errors tend to occur, how learners recover after mistakes, and which kinds of support lead to improvement.
Learner profiles are not fixed labels. They evolve as learning evolves. A learner may move between profiles as confidence grows, new challenges arise, or understanding deepens.
Learning from Errors, Not Just Outcomes
Errors are not treated as failure in Phlow.
Because learning is analysed at the decision level, the system can distinguish between different kinds of errors — such as conceptual misunderstandings, execution slips, or cognitive overload.
This allows support to be targeted precisely, rather than applied broadly or repeatedly without effect.
Analytics That Improve Over Time
Phlow’s learning analytics are designed to grow more accurate as the platform is used.
As more decisions are made, patterns become clearer. As more learner profiles form, support becomes more precise. As understanding of learning behaviour deepens, the quality of guidance improves for everyone.
Early analytics are still meaningful — they are simply lower-confidence signals that strengthen with evidence over time.
Analytics in Phlow are not about surveillance or comparison. They exist to make learning fairer, more responsive, and more humane.
Explore the Learning Analytics Framework
For readers who want to explore the underlying ideas in more depth, the following reports are available: