Guide · Learning intelligence
Why retention beats cramming
How spacing and retrieval create durable learning — and what to measure.
Pragnya Analytics
A Learning Intelligence Platform
Learning Intelligence Platform (LIP)
Know the next best learning action — based on how a student thinks.
Pragnya Analytics continuously recommends the right learning action at the right moment, based on how a student thinks — not just what they get wrong. Schools use it to improve reasoning, retention, and transfer with outcome‑verified learning cycles.
What we recommend: what to learn next · when to practice · how deeply to practice · when to revise or slow down — backed by pre/post learning intelligence metrics.
The problem: content-heavy edtech isn’t improving thinking
Schools have more content than ever — and students still struggle to apply it. Completion rates, engagement charts, and even exam scores often hide a deeper issue: weak understanding, shallow reasoning, and low transfer to real problems.
Content ≠ learning
Watching lessons and doing drills can look productive, but it doesn’t reliably build conceptual models, reasoning habits, or retention.
Scores can mislead
A student can score well short‑term through repetition, yet fail to explain “why”, apply concepts in new contexts, or retain learning after a few weeks.
Real-world problem solving breaks
When tasks become novel, multi‑step, or ambiguous, students need reasoning depth and transfer — not just recall.
The solution: Learning Intelligence
Learning Intelligence is the ability to understand, apply, retain, and transfer knowledge — measured over time, not in one‑time tests. Pragnya Analytics improves Learning Intelligence by recommending the next best learning action (and timing), then verifying the outcome with retention and transfer checks.
How our recommendations work
- Baseline diagnostics map misconceptions and reasoning patterns
- We learn from error patterns, transfer performance, and cognitive load signals
- We track learning velocity and adjust the learning rhythm week by week
- We model retention decay to recommend when to revisit (before forgetting becomes a gap)
- We recommend actions: practice, revision, reflection, or challenge — not “more content”
How we improve it (human-in-the-loop)
- Mentor-led cohorts that teach reasoning, not memorization
- Targeted interventions at the exact concept‑skill bottleneck
- Deliberate practice that builds long‑term retention
- Evidence of transfer: can the student use learning elsewhere?
What we are not
Not a video‑content platform. Not exam‑drill focused. Not self‑paced learning only. Not a generic AI tutor. Pragnya is a Learning Intelligence Platform: it measures thinking quality, runs mentor‑led improvement cycles, and proves outcome uplift.
How it works: a 4‑step outcome framework
1) Diagnose Learning Intelligence
Establish a baseline for understanding, reasoning depth, and retention risk — not just syllabus coverage.
2) Recommend the next best learning action
We recommend what to learn next, when to practice, how deeply to practice, and when to revise or slow down — based on learning velocity, error patterns, retention decay, cognitive load, and transfer performance.
3) Mentor‑led cohort execution
Mentors coach reasoning in small groups, apply interventions, and keep students accountable.
4) Measure & prove outcome uplift
Run post‑diagnostics and retention checks (30/60/90 days) to validate growth and guide next steps.
Platform modules (built for measurement)
Diagnostics & baselines
Pinpoint misconceptions, reasoning gaps, and retention risk to establish a learning intelligence baseline.
Retention & transfer practice
Practice sequences designed for long‑term retention, with prompts that test application in new contexts.
Recommendation engine
Turns signals (learning velocity, error patterns, retention decay, cognitive load, transfer) into clear next steps: practice, revise, reflect, or move forward — with mentor oversight.
School & parent reporting
Clear pre/post reports that explain learning progress in outcome metrics — not just time spent.
The Learning Intelligence Platform (LIP)
A coherent system for measurement → recommendation → mentor intervention → proof. Every module exists to guide the next learning decision and show evidence of retention and transfer.
Diagnostics
- Baseline diagnostics that reveal misconceptions and reasoning gaps.
- Explainability for teachers: what’s wrong, why, and what to do next.
- Sets the starting point for measurable improvement.
Learning Intelligence metrics (beyond grades)
Grades are a lagging signal. Learning Intelligence metrics tell you whether understanding is deep, durable, and transferable — and whether an intervention worked.
Concept Transfer Score
Can the student apply a concept in a new situation, with different wording and constraints?
Retention @ 30/60/90 days
Do they still remember and use the concept weeks later — without re-teaching?
Problem‑Solving Depth Index
Tracks reasoning quality: planning, multi‑step structure, and explanation of “why”.
Learning Velocity Uplift
Measures how quickly a student improves after an intervention — not just current level.
Who it’s for
Schools
Pain: engagement and scores rise, but reasoning and retention don’t.
Outcome: measurable uplift in understanding, retention, and transfer — with clear reporting.
Why Pragnya: mentor‑led cohorts + learning intelligence diagnostics (not content consumption).
Parents of high‑potential, underperforming students
Pain: effort is high, results are inconsistent, confidence drops.
Outcome: stronger thinking habits, durable learning, and visible progress over time.
Why Pragnya: we identify the thinking bottleneck and fix it with mentors — then prove it.
Education leaders
Pain: tools report activity, not learning impact.
Outcome: outcome metrics that support program decisions, interventions, and accountability.
Why Pragnya: learning intelligence makes improvement measurable, repeatable, and scalable.
How our recommendations are different
Most “personalization” engines recommend more questions or more content. Learning Intelligence recommends the next best learning action — and the timing — based on readiness and outcomes.
| Traditional edtech recommendations | Pragnya recommendations |
|---|---|
| Based on syllabus completion | Based on thinking readiness |
| More questions when you’re wrong | Targeted cognitive intervention |
| Fixed learning pace | Adaptive learning rhythm |
| Engagement-driven | Outcome-driven |
Research & field notes
How learning intelligence works — and how to build reasoning, retention, and transfer.
Framework · Cohorts
Mentor interventions that work
What mentors do differently when the goal is thinking quality and transfer.
Proof & credibility
We don’t ask schools to trust a dashboard. We run pilots with clear baselines, mentor‑led execution, and post checks that show what improved — and what still needs work.
Pilot design
Baseline diagnostics → targeted cohorts → post‑diagnostics → retention checks. Schools get a transparent outcomes report.
Research-backed methods
Practice design grounded in learning science (retrieval, spacing, worked examples) — implemented with mentor oversight.
Human-in-the-loop
The system identifies what to do next; mentors help students change how they think. That’s how outcomes become reliable.
What early users say
"The baseline report showed exactly where the thinking broke — and the cohort fixed it."
— School leader (Grades 6–8)
"We stopped chasing more practice and started building real understanding."
— Parent, Grade 7
"The outcome metrics made interventions precise — less guesswork, more impact."
— Teacher
Get a Learning Recommendation Profile
For parents: understand what your child should do next — and why — based on how they think (not just mistakes). For schools: run a Learning Intelligence Pilot with baselines, mentor‑led cohorts, and post checks that prove retention and transfer.
CTA options: “See what your child should learn next” · “Get a learning recommendation profile” · “Run a Learning Intelligence Assessment”.