Progress Measurement

1. Hypothesis

Support-adjusted mastery and reasoning growth will predict future success better than raw item scores.

2. Experiment design

Design: diagnostic and adaptive-practice study; baseline, active comparison, calibrated AI condition, user-controlled condition, failure or handback scenario and delayed follow-up Methods: diagnostic assessment; item-level telemetry; worked-example experiments; adaptive-practice trials; interviews; longitudinal progression analysis; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief Independent variables: assessment mix; support level; time; curriculum Dependent variables: growth; transfer; independence; prediction Confounders: baseline capability; prior exposure; age; motivation; context; technology access; implementation fidelity Measures: growth model fit; support-adjusted score; reasoning rubric; forecast accuracy Success criteria: Statistically and practically meaningful improvement; preserved or improved human skill and agency; acceptable burden; no disproportionate subgroup harm; reproducible performance; effective handback, correction and recovery. Failure conditions: No meaningful benefit; gains mask reduced understanding or skill; dependency, fatigue, stress or exclusion exceeds benefit; effects fail to transfer or persist; user control is ineffective; correction or handback fails.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Progress Measurement as a testable research proposition. Problem basis: Current approaches to progress measurement are fragmented, poorly calibrated or insufficiently measured, making it difficult to distinguish real benefit from substitution, novelty or surveillance effects. Directional expectation: If the hypothesis is supported, the intervention condition should improve growth; transfer; independence; prediction while avoiding material deterioration in independence, confidence calibration or delayed performance. Proposed observations: growth model fit; support-adjusted score; reasoning rubric; forecast accuracy. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 36/100; Novelty 78/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Progress Measurement warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “Support-adjusted mastery and reasoning growth will predict future success better than raw item scores.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Number sense; conceptual change; worked-example effect; productive struggle; mathematical anxiety; adaptive practice. It also records: OECD PISA Mathematics — https://www.oecd.org/pisa/; AERO — https://www.edresearch.edu.au/; UNESCO Education — https://www.unesco.org/; NCTM — https://www.nctm.org/. Evidence register status: “Seeded; authoritative source register refreshed; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Number sense; conceptual change; worked-example effect; productive struggle; mathematical anxiety; adaptive practice. References: OECD PISA Mathematics — https://www.oecd.org/pisa/; AERO — https://www.edresearch.edu.au/; UNESCO Education — https://www.unesco.org/; NCTM — https://www.nctm.org/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.