Mathua
Mathua is an MIT-licensed open-source adaptive math learning engine covering 1250 concepts from arithmetic through real analysis. It enforces mastery gating (learners must prove speed and accuracy before advancing), sequences content via a prerequisite concept graph, and weaves spaced repetition into every session. Problems are generated on demand via Go functions and never stored. Learner progress is tracked through five mastery states (Unseen, Learning, Practicing, Mastered, Decaying), and the scheduler applies hard rules: prerequisites must be mastered before unlock, no concept appears twice in a row, and roughly 70% of each session is new material. Your team can extend the concept graph by adding JSON nodes and custom problem generators, then validate the graph for cycles before deployment.
Mathua is a lms tool, integrating with GitHub, Discord, MathLive and MathJSON. InnovaAI rates it 2.4 of 10 for agency adoption, best for Product Manager, Software Engineer and Founder roles.
Agency Audit
Mathua is an open-source adaptive math learning engine that gates concept progression behind mastery thresholds, sequences 1250 math topics via prerequisite graphs, and weaves spaced repetition into every session. Internal adoption makes sense for agencies building EdTech platforms, corporate training products, or supplemental math curricula where your team needs to understand how adaptive sequencing and mastery validation work. The MIT license and GitHub integration mean your developers can extend the concept graph and problem generators without vendor lock-in. Best ROI appears for small EdTech-focused shops where 2-3 engineers need hands-on experience with the engine before pitching it to clients.
3recommended
72/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Product Manager handling math curriculum design and sequencing
- Software Engineer handling adaptive learning engine prototyping
- Founder handling mastery-state tracking implementation
- Your agency does not work in EdTech, corporate training, or supplemental education; Mathua has no application outside math learning platforms and will sit unused.
- Your team lacks Go developers or is unfamiliar with JSON schema; extending Mathua's concept graph or adding custom problem generators requires both, and the learning curve adds 2-3 weeks before your team can contribute meaningfully.
- Your product already has a mature adaptive-sequencing engine in production; adopting Mathua would require rearchitecting your mastery-state tracking and prerequisite validation, which is a net cost, not a gain.
Internal Adoption Path
No paid plan published
72 hr/mo
3 seats × 24 hr each
$5,400/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Mathua
Mastery gating with speed thresholds
Concepts unlock only after learners prove both accuracy and retrieval speed (e.g., 10 correct answers in a row at ≤8 seconds). Product managers designing curricula can use this pattern to ensure learners build fluency, not just recall.
Prerequisite concept graph (1250 nodes)
Every math concept maps to its prerequisites as explicit JSON nodes. Engineers can inspect the graph structure, validate it for cycles, and extend it with custom concepts. This eliminates manual prerequisite design for your product team.
Spaced-repetition scheduler
Mastered concepts resurface automatically based on decay and mastery scores; reviews are woven into live sessions, not siloed. Your team can study how the scheduler prioritizes review (70% new, 30% review per session) and adapt the algorithm for your own product.
On-demand problem generation
Problems are generated via Go functions, never stored or memorized. Developers can write custom generators for domain-specific math topics and integrate them into the concept graph without modifying the core engine.
Mastery-state tracking (5-stage pipeline)
Tracks learner progress through Unseen → Learning → Practicing → Mastered → Decaying. Your product team can reference this state machine when designing your own learner-progress UI and backend validation logic.
Weekly leaderboard with composite scoring
Ranks learners by mastered-concept count, speed bonus, and current streak. Useful for your team to test gamification mechanics and understand how to balance multiple scoring dimensions without overwhelming learners.
What Makes Mathua Different
Unique advantages vs similar tools in this niche
Mastery gate requires speed and accuracy together, not accuracy alone
vs Quiz platforms that advance on correct answers regardless of retrieval timeA concept reaches MASTERED only after 10 correct in a row at ≤8s, per the example node arith.add.single.
Scheduler enforces prerequisite, no-repeat, and 70% new-material rules
vs Linear course players that replay the same drill setThe scheduler enforces three hard rules: prerequisites must be mastered before a concept unlocks, the same concept never appears twice in a row and roughly 70% of each session is new material.
Curriculum is a validated JSON graph with Go problem generators
vs Closed LMS content locked to a vendor's authoring toolEvery concept is a JSON node. Every problem is a Go generator function. Every contribution goes through a graph validator that rejects cycles and orphaned nodes automatically.
Value Equation
Outcome-likelihood-time-effort assessment for Mathua
Value math requires real pricing
The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. Mathua has no published pricing, so we hold this section until real numbers are available.
Contact MathuaPricing
Pricing data not yet available for Mathua.
Reality Check
Mathua is purpose-built for math learning; it has no value for agencies outside EdTech, corporate training, or supplemental education verticals. Adoption requires your team to learn the concept-graph JSON schema and Go generator functions, which adds 1-2 weeks of onboarding friction. The leaderboard and mastery-state tracking are useful only if your team is actively building or testing a math product.
High effort: requires technical configuration and team training
How This Accelerates White-Label Services
Who It's For
- ✓edtech-agencies-building-tutoring-platforms
- ✓corporate-training-providers
- ✓supplemental-math-education-products
- ✓open-source-friendly-development-shops
Acceleration Steps
- 1Schedule onboarding with the vendor
- 2Configure gate concept progression behind proven mastery of prerequisites
- 3Connect GitHub
- 4Launch your first client project
Academy for Mathua
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Mathua Agency Implementation, Building Mastery-Gated Math Curricula
Learn to design and deploy adaptive math learning products using Mathua's prerequisite concept graph, mastery gating, and spaced-repetition scheduler. This course teaches agencies how to extend the 1250-concept curriculum, configure speed thresholds for fluency, and deliver productized math learning services to schools and tutoring platforms.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Adoption Debt RatioConcept
Adoption Debt Ratio measures the gap between the training seats an agency sells or provisions and the learners who actually log in during a 30-day window. It matters because LMS economics invert on this number: license cost is fixed per seat while the value of a client enablement retainer depends entirely on completion and behavior change. A program at 20 percent monthly active use is an expensive content repository; the same content at 65 percent is a defensible retainer line. The ratio also sets renewal risk, since a client who cannot see learner activity in the dashboard has no reason to keep paying. Practical example: platforms such as LearnUpon and TalentLMS bundle AI course authoring that claims faster course creation, which raises output volume without touching adoption. Agencies that track the ratio before scaling content production avoid shipping libraries nobody opens.
- Content Half-Life TestConcept
Content Half-Life Test is a screening rule for LMS engagements: before scoping a build, estimate how many months a course stays accurate, then match the platform and the retainer to that clock. Compliance modules can hold for 18 to 24 months; product training for a fast-moving SaaS client may decay in 90 days. The framework matters because agencies price LMS work as a one-time build when the real cost is refresh labor, and a stale catalog is exactly the expensive, unused repository the category warns about. Absorb LMS markets AI agents that draft courses from documents in under an hour, and LearnUpon's Create+ claims course creation 90% faster at 10% of the cost, which changes refresh economics but does not remove the review burden. Run the test per client: short half-life content justifies a managed retainer, long half-life content justifies a fixed build fee.
- Seat-to-Skill Conversion RateConcept
Seat-to-skill conversion measures how many licensed LMS seats translate into verified capability gains within a defined window, usually 90 days. It reframes LMS spend as a yield problem rather than a procurement decision: a 500-seat deployment where 60 learners complete a certification path is a 12 percent conversion rate, and that number predicts renewal risk better than login counts. Agencies feel this twice. Internal enablement seats carry the cost directly, while client-facing training retainers get judged on completion and outcome data the client can audit. The pressure is tightening: Forrester's 2027 predictions series argues the financial grace period for AI-adjacent tooling is ending, with CMOs asked to show P&L results instead of pilot activity. An LMS that reports 80 percent enrollment but 15 percent completion will not survive that review. Platforms such as Docebo and LearnUpon expose skills intelligence and completion analytics that make the ratio calculable; the discipline is choosing to report it.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- LMS Rule: Price the Content Build Before You Price the LicenseEvaluation Rule
Scope the content production and refresh budget before you scope the platform license, and write both into the retainer.
- LMS Rule: Count Active Learners Before You Count SeatsEvaluation Rule
Size the platform and the retainer against verified 90-day completion volume, then buy seats to that number rather than to the client's org chart.
- LMS Tools Decision: Training-as-a-Retainer vs Internal Onboarding UtilityDecision Framework
IF a client will pay monthly for structured learning (customer education, partner certification, compliance recertification) and the agency can own content production, THEN treat the LMS as a billable delivery surface with white-label portals and per-seat pricing baked into the retainer. IF the only buyer is the agency's own HR function, THEN buy the cheapest portal that covers onboarding and compliance and stop shopping on AI course-generation features.
- Why LMS Tools Stall in Client Onboarding ProgramsFailure Pattern
- The Content Vacuum Trap: Why LMS Tools Become Expensive Empty RoomsFailure Pattern
Delivery system
Blueprints and procedures for running it as a service.
- Client Onboarding Academy Build (10-15 days)Implementation Blueprint
Agencies productize LMS Tools into a fixed-scope onboarding academy: a branded, measurable training path that shortens client ramp time and turns onboarding into a retained deliverable rather than a one-off kickoff call.
- Learner Activation Audit (Onboarding)Operating Procedure
- Training Content Inventory and Gap Map (Delivery)Operating Procedure
- Client Training Program Handoff (Handoff)Operating Procedure
13 modules selected for Mathua
Frequently Asked Questions
Answers about pricing, setup, implementation
Mathua is an open-source adaptive math learning engine that gates concept progression behind mastery thresholds, sequences 1250 math topics via a prerequisite concept graph, and weaves spaced repetition into every session. Problems are generated on demand via Go functions, and learner progress is tracked through five mastery states (Unseen, Learning, Practicing, Mastered, Decaying). Your team can extend the concept graph by adding JSON nodes and custom problem generators without vendor lock-in.
Mathua is free and open-source under the MIT license. There is no per-seat pricing, no paywall on the concept graph, and no account required to start learning. Your team can fork the repository on GitHub and run it locally or self-host it without licensing fees.
Product managers designing math curricula benefit most by using the 1250-concept graph and mastery-gating logic as a reference model for their own adaptive sequencing. Engineers building EdTech platforms can study the prerequisite-validation and spaced-repetition algorithms and port them into their product. Founders pitching adaptive learning solutions to clients can use Mathua as a working prototype to demonstrate mastery gating and fluency-based progression without building from scratch.
For a product manager designing a math curriculum from scratch, Mathua saves 6-10 hours per week by providing a validated 1250-concept foundation and prerequisite graph, eliminating manual sequencing work. For an engineer prototyping an adaptive engine, Mathua saves 15-20 hours per week by providing working mastery-state tracking and spaced-repetition scheduling code, cutting prototype time by 3-4 weeks. Savings depend on whether your team is building a math product; if not, Mathua has no time value.
For a team with Go experience and familiarity with JSON schema, initial setup and local deployment takes 2-3 hours. Understanding the concept-graph structure and mastery-state machine takes an additional 1-2 weeks of hands-on work. If your team needs to extend the concept graph or write custom problem generators, add 1-2 weeks per feature. No vendor onboarding is required because Mathua is self-hosted.
Mathua integrates with GitHub (for concept-graph and generator-function version control), Discord (for community support), MathLive (for rendering math notation), and MathJSON (for structured math representation). If your product uses a different math-rendering library or learner-management system, you will need to write custom adapters. The MIT license allows you to fork and modify Mathua without restrictions.