What is ST Math?
ST Math is grounded in a learning-sciences approach that uses space and time, rather than language, as the primary medium for expressing mathematical ideas. Students learn through a cycle of prediction, action, and feedback — refining thinking through perception–action loops that strengthen and reorganize underlying math schemas.
Game-based learning
Visual puzzles reduce anxiety and invite perseverance through meaningful challenge and formative feedback.
Designed so game mechanics are tied to the math, not a "gamification layer."Mastery-based progression
Students advance by demonstrating understanding, not by screen time, with self-pacing that meets learners where they are.
Supports incremental success and productive struggle.Concrete-to-abstract learning
Digital manipulatives help students reason visually first, then transition toward more symbolic representations — building deeper conceptual understanding.
Helps bridge intuitive understanding to formal math.Creative reasoning
Instead of revealing solutions, the environment guides students to construct strategies and meaning — building flexible and transferable understanding.
Aligned with deeper conceptual learning goals.Explore further resources and learn about our unique spatial-temporal math learning approach.
Learn more about ST MathWhat we are
looking for
We continuously partner to answer rigorous questions regarding mathematics learning through spatial-temporal, mastery-oriented mechanics — seeking insights into achievement growth and cognitive engagement at scale.
Efficacy, impact, and generalization
Under what conditions does ST Math produce the largest gains on state math assessments (by grade band, baseline achievement, or school context)?
How do impacts differ when implemented with fidelity versus partial implementation, and what outcomes are most sensitive to fidelity thresholds?
Do gains persist into subsequent years, and do they translate to success in later math courses or problem-solving outcomes beyond standardized tests?
Implementation, dosage, and the "how"
What patterns of usage (minutes/week, progression rate, content completion) best predict achievement gains — and for whom?
How much of the variation in outcomes is attributable to teacher- and school-level implementation differences, and which practices reliably improve usage and learning?
What implementation supports (coaching, feedback dashboards, nudges, scheduling structures) most effectively increase high-quality usage?
Learner experience, motivation, and self-beliefs
How does ST Math influence math self-beliefs, and does that pathway help explain achievement growth — especially for students who start behind?
Which in-game experiences (productive struggle, feedback, mastery pacing) are most associated with perseverance and confidence over time?
Mechanisms, equity, and measurement
Which cognitive mechanisms best explain learning gains (schema building, formative feedback, mastery progression, creative reasoning) — and how can we measure them directly?
Are effects consistent across student subgroups, and what supports narrow opportunity gaps most effectively?
What new learning analytics can be developed from gameplay data (productive struggle, bottleneck detection, early-warning measures)?
Interested in
collaborating?
Reach out so we can align on scope, data access, study design, and dissemination opportunities.