Resolve takes any lesson from a high-quality mathematics curriculum and produces a complete diagnostic package — clarified lesson, research-derived assessment instrument, and typed intervention modules — from a single cognitive-diagnostic model that makes all three coherent with each other.
Most instructional cycles are sequences in name only. Lessons are taught, assessments are given, interventions are assigned — but the three steps share no common model of what students know or where they are. The assessment was not derived from the instruction. The intervention was not derived from the assessment. The result is motion without inference.
Each step is performed in good faith. Each step is, in isolation, defensible. But without a shared cognitive architecture underneath them, the data the assessment generates cannot be traced to what was taught, and the intervention cannot be matched to what the assessment found. The cycle runs — and nothing accumulates.
Resolve builds each output from a single psychometrician-authored cognitive-diagnostic model for the standard being taught. The clarified lesson surfaces the model's cognitive architecture for the teacher. The assessment measures against it. The intervention addresses the specific failure it identifies. Each cycle produces a sharper picture of where each student is — and why.
Every lesson Resolve processes produces the same three artifacts — each derived from the same cognitive-diagnostic model, each designed to hand off cleanly to the next.
Resolve applies the cognitive-diagnostic model to the existing HQIM lesson — surfacing the mathematical intent embedded in its structure, adding scaffolds at the points where students are most likely to lose access to the concept, and clarifying the instructional path without reconstructing the lesson. The curriculum mostly stays. What changes is its legibility — for the teacher preparing it, and for the students receiving it.
The exit ticket is replaced with an instrument derived from a research-defined misconception framework — documented cognitive error types per standard, each representing a coherent, internally consistent alternative model of the mathematics that students actually hold. Every wrong-answer option is a hypothesis about a specific student's cognitive model — not a plausible distractor, a research-defined diagnosis.
Because the misconception framework identifies not just what the error is but what kind of error it is, the intervention is matched to the cognitive failure type — not just the topic. Each type has a distinct, evidence-based instructional template. Resolve generates targeted 20-minute modules automatically from the diagnostic result. Teachers don't choose the intervention. The model does.
The full introduction covers the cognitive-diagnostic model in depth — the pipeline architecture, the assessment design principles, the intervention framework, and what it means for a cycle to actually build rather than repeat. If you're evaluating Resolve for a curriculum organization, district, or research partnership, start here.
Read the Full Introduction →Resolve's cognitive-diagnostic model was designed with a specific architectural property: every student response it generates is labeled against a research-defined misconception framework — latent states defined before data is collected, not inferred from behavioral proxies afterward. That structure makes Resolve's output useful well beyond the immediate instructional cycle.
Most adaptive systems are trained on behavioral proxies where latent student states are inferred post-hoc. Resolve inverts this — misconception codes and cognitive step sequences define the latent states before a single data point is collected.
| Without Resolve | With Resolve |
|---|---|
| Right/wrong sequences, time-on-task, clickstreams | Response sequences labeled against psychometrician-defined misconception codes from the empirical research literature |
| Latent states inferred post-hoc from behavioral patterns | Latent states defined in advance by a cognitive architecture — before a single data point is collected |
| Black-box mastery probability | Interpretable state: which misconception is active, at which construct level, at which cognitive step |
| Training corpus assembled from proxies | Theoretically-labeled corpus generated automatically — data collection begins when the pipeline runs |
The fastest path to understanding Resolve is reviewing a complete lesson package — clarified lesson, diagnostic assessment, and typed intervention modules — built from a psychometrician-authored cognitive-diagnostic model for a Grades 3–8 mathematics standard.
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