Adaptive difficulty
Content and challenge adjust to how that learner is performing.
Apps and platformsEducation
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TFESpersonalisedlearninggames
Learning games that adapt to each child, adjusting difficulty and content to how that learner is actually doing.
Engagement
TFES personalised learning games
Children in the same classroom learn at very different speeds. One worksheet cannot fit them all — so TFES needed practice that adapts to how each learner is actually doing.
Each child practises at the right level, not the class average — difficulty that adapts through play.
Who personalisation serves
Learners
Practice at the right level, with games they choose to come back to.
Teachers
Spot struggling learners early through play data, not only end-of-term tests.
Schools
Personalised practice that fits the same sixteen-school network as the LMS.
What the games do
Three paths
Different learners, different routes — not one average worksheet for the room.
Early signals
Struggle shows up in play, while there is still time to help.
Network scale
Built for the TFES schools, not a one-off classroom experiment.
How we approached it
Ground personalisation in how children actually practise — then scale it.
Step 01 / 04
Respect different speeds
The design started from Bloom’s case for one-to-one tutoring: level matters more than lockstep.
Step 02 / 04
Make practice desirable
Games children return to beat drills they avoid — engagement is part of the pedagogy.
Step 03 / 04
Feed insight back
Play generates the early signal teachers need without another paper test.
Step 04 / 04
Align to the network
Same sixteen schools as the LMS — personalisation sits inside the wider TFES stack.
Outcome
Each child practises at the right level, struggling learners are spotted early through play, and practice becomes something children choose to continue.
The need
Children in the same classroom learn at very different speeds, and one worksheet cannot fit them all.
What we built
Learning games that adapt to each child, adjusting difficulty and content to how that learner is actually doing.
Each child practises at the right level, not the class average
Struggling learners are spotted early through play
Practice that children choose to come back to
Proof metrics
Industry benchmark
2 sigma
gain for one-to-one tutored students over a standard class — the case for personalised learning
Source · Bloom, 1984
Verified
16 schools
in the network the games are built for
How to read the numbers
- Verified. Measured on the named deployment.
- Industry benchmark. Published third-party reference; our delivery tracked in line with them.
- By design. A property built into the product.
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