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Mindsweeper Teacher
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Yu Xiao Ting
GCSE Foundation · Diagnostic v3 · 7 May 2026
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LONGITUDINAL · MINDSWEEPER ASSESSMENT

Mindsweeper sessions

Every Mindsweeper assessment for this student, in order — with the M³ trajectory (Motivation · Metacognition · Resilient disposition) and how their knowledge gaps change over time.

Student dashboard · Monday · Week 1

Yu Xiao Ting

8-week intervention plan in progress · currently in Phase 1 (repair bottleneck + consolidate unstable content) · 12 days to next diagnostic checkpoint

Verified. All CCSS standards verified live via Learning Commons MCP · Last checked: 20 May 14:32 · Standards progression chain uses SAP Coherence Map · Learning components use ANet decomposition
Latest composite score
56%
↓ 15pt vs Test 1 (scope expanded)
Topics needing intervention
4
1 bottleneck · 2 Edexcel-only
Strategies this week
3
Worked Ex · Math Talks · Offload
Next checkpoint
D-12
End of Week 2 · target ≥ 75%
PHASE 1 · This week's focus
Repair bottleneck node 7.EE.A.2
7.EE.A.2 (the "+5% = ×1.05" equivalence) is the root cause across Xiao Ting's entire knowledge graph. It propagates downward to 4 high-school standards (SAP fan-out verified), three of which correspond to items she has already lost marks on. Repairing this single conceptual gate will simultaneously reduce cognitive load across three downstream item families: compound interest, reverse percentage, and quadratic inequalities.
Worked Examples (with error-example analysis) Math Talks (verbal reasoning) Cognitive Offloading (external working memory)
Items requiring attention
View all →
  • D-12 Week 2 diagnostic checkpoint: 8 mixed items — 4 percent / 2 compound interest / 2 quadratic inequalities; target ≥ 75% with full working shown SCHED
  • This week Error-example deck (6 cards) must be ready — Card #2 (a − 0.15a = 0.85a) is the core item TODO
  • This week Bearings + probability bilingual vocabulary cards (Phase 2 prep) — 5 min/day PREP
Recent activity
Full log →
  • 7 MAY Diagnostic report v3 generated · bottleneck revised from 8.EE.A.1 to 7.EE.A.2 (API-verified) REPORT
  • 1 APR Test 2 entered: 14/25 (56%) · scope expanded to full GCSE Foundation TEST
  • 25 MAR Test 1 entered: 32/45 (71%) TEST
  • 19 MAR Peer teaching observation: notably engaged when teaching a classmate OBS
  • 12 MAR Initial diagnostic: 32% · working memory overload and test anxiety as primary signals INIT
LONGITUDINAL · TRAJECTORY

Progress trajectory

From the 11 March baseline (32%) to the present, with intervention milestones and diagnostic checkpoints overlaid. This is the central view of Mindsweeper's longitudinal tracking model.

11 MARInitial diagnostic · 32%Baseline test; identified working memory overload and test anxiety as the two principal signals
25 MARTest 1 · 71%Marked improvement after two weeks of targeted training
1 APRTest 2 · 56%Scope expanded to the full GCSE Foundation set; the decline reflects expanded scope, not regression
7 MAYDiagnostic report v3Bottleneck node 7.EE.A.2 confirmed; the 8-week plan begins
EXECUTION · 8 WEEKS

Intervention execution tracker

Execution view derived from the §⑧ plan. Tick off completed strategies; progress is automatically aggregated to the dashboard.

Phase 1 · Repair bottleneck + consolidate unstable content

Focus: 7.EE.A.2 (multiplier-rate equivalence) plus reverse percentage, compound interest, and quadratic inequalities. Repairing this conceptual gate simultaneously reduces cognitive load across the three downstream item families.

2/9 strategies executedWeek 1 · Day 3
LVNWorked Examples
Targets: Working Memory · Long-term Memory
Present fully worked examples first, then have the student attempt independently. LVN Math 7-10 indicates that error-example analysis is especially effective for conceptual development — present a flawed example (using 0.15 instead of 0.85) and have her identify and articulate where it went wrong.
Concrete actions: A 6-card mini-deck. Card 1: a + 0.05a = 1.05a (canonical 7.EE.A.2 example). Card 2: a − 0.15a = 0.85a (decay variant). Card 3: 18000 × 0.85³ (correct version of PT1-Q22b). Cards 4-6 introduce further variants. Gradually fade scaffolding: full solution → middle steps blank → final step blank → independent completion.
Research basis: Sweller (1988) cognitive load theory; Renkl (2014) worked example effect
LVNMath Talks
Targets: Math Communication · Long-term Memory · Self-efficacy
Have the student verbalise her mathematical reasoning. LVN Math Talks notes that articulating reasoning aloud consolidates long-term memory more reliably than silent practice.
Concrete actions: 15 minutes per session. Sample prompt: "Why is the multiplier for a 15% decrease 0.85 rather than 0.15? Explain it to me as if you were teaching a younger student." If she gets stuck, do not interrupt — wait for her to self-correct; that struggle is where learning happens.
Research basis: Chi & Wylie (2014) ICAP framework; Schoenfeld (1992)
LVNCognitive Offloading
Target: Working Memory
Explicitly teach her to use paper as external working memory. Tully et al. (2025) show that paper-based offloading yields the greatest benefit for students with prior knowledge in place — precisely Xiao Ting's profile.
Concrete actions: A three-step rule for every multi-step problem: (1) write the givens; (2) write the requirement; (3) state the method choice before computing. Apply this rule to every multi-step problem for two weeks. The goal is to automate external offloading before time pressure activates.
Research basis: Risko & Gilbert (2016); Tully et al. (2025) Br J Educ Psychol
Diagnostic checkpoint (end of Week 2): 8 mixed items — 4 percentage change, 2 compound interest, 2 quadratic inequalities. Target ≥ 75%, timed conditions, all working shown.
Phase 2 has not yet started
Unlocks at Week 3 · Focus: bearings, similar solids, quadratic formula, independent events with complement
Phase 3 runs in parallel with Phases 1-2
Metacognitive calibration training · Focus: turn the predictive bias documented in §⑦ into a measurable skill
Phase 4 — integration period
Weeks 7-8 · Full-length CEM simulations + longitudinal evidence presentation
REPORT v3 · COVER

Diagnostic report cover

Student profile, testing history, and report generation metadata

API-verified. Every CCSS code has been verified through the three-step Learning Commons workflow. v1 initially misidentified 8.EE.A.1 as the bottleneck; the API corrected this to 7.EE.A.2.
Student profile
Maintained by Paula Zheng
Name
Yu Xiao Ting
Course
Edexcel GCSE 9-1 Foundation
Target
Perse Year 9 · CEM
Diagnostic history
PT1 (25 Mar) 71% → PT2 (1 Apr) 56%
Report generated
7 May 2026 · v3 (EN)
Next checkpoint
End of Week 2 · 8-item mixed diagnostic
Topics needing intervention
4
Topics to watch
4
Topics consolidated
4
§① CURRENT PERFORMANCE

Topic-level scores

Aggregated across both tests. Topic names follow Edexcel GCSE 9-1 Foundation conventions; standard codes are CCSS-M (verified via Learning Commons). Bearings and similar solids are Edexcel-only with no clean CCSS anchor, and are flagged accordingly.

All 12 Intervene 4 Watch 4 Consolidated 4 Click any row to expand evidence
§② PERFORMANCE vs KNOWLEDGE GAP

Not every lost mark is a knowledge gap

Conventional diagnostics treat every wrong answer as a knowledge gap. Cross-test analysis shows that a substantial portion of Xiao Ting's lost marks stem from execution failure rather than conceptual gaps. Separating the two prevents the intervention plan from re-teaching material she has already mastered.

~7
marks
Of the 70 marks across both tests, roughly 7 (~10%) were lost to execution failure despite intact conceptual understanding. Correcting this category alone would lift PT1 to ~80% and PT2 to ~75% — closer to her actual ceiling.
ItemCategoryWhat went wrongEvidence of intact concept
PT1-Q11ArithmeticReverse-percentage method correct; chained multiplication (×1.025 × 1.032) miscalculatedMethod steps visible. Multi-step computation without a calculator induced working memory overload
PT1-Q18bGraph-reading precisionRead x-intercepts as −0.7 and 4.8 (true values ≈ −0.65, 4.65); wrote several values and crossed them out(0, −3) and vertex (2, −7) both correct — the graph-reading concept is present; precision is insufficient
PT1-Q21aWorking not shownFactoring steps illegible (2 marks lost), but (b) gave x = 5 or −2(b) fully correct — she knew it but did not record the working
PT1-Q22bArithmeticWrote the correct compound-interest formula 18000 × 0.85³, but computed it as 9900 (should be ≈ 11054)Method chain correct — compound-interest formula applied correctly
PT2-Q1Second-guessingCircled A (x > 5, correct) and C (x < 5); final answer CFirst circle was correct — she overturned her own answer under uncertainty
PT2-Q10Second-guessingCircled B (9, correct) and D (5); final answer DShe knew the answer — second-guessing led to the wrong final choice
PT2-Q15Notation misreadRead "y:x" as "x:y" and selected 3:8 instead of 8:3Algebraic manipulation 3y = 8x correct — only the ratio order was reversed
PT2-Q22cCascading errorMethod correct (volume × unit price); the volume input came from the incorrect result of (b)The "volume × unit price = total cost" model itself is correct
§③ CONSOLIDATION GRADIENT

Consolidation gradient

Not all "knowing" is equivalent. Before designing interventions, we need to distinguish the maturity of the underlying conceptual representations.

Robust
Correct across ≥ 2 independent items and conditions, including under time pressure.
Emerging
Correct under standard conditions; no observed breakdown under pressure.
Recognised but unstable
The student can write the correct formula or method (conceptual representation is present), but execution is unstable. PT1-Q11 and PT1-Q22b fall in this band.
Introduced not internalised
Cannot map the problem onto the correct procedure; the conceptual structure has not yet formed. Bearings and similar solids fall in this band.
§④ KNOWLEDGE TREE · BACKWARD PROGRESSION

Knowledge tree — backward traversal

Trace prerequisites downward from the current grade band (9-10). Click any node to view the evidence, learning components, and the official standard statement. Clicking the bottleneck node 7.EE.A.2 automatically reveals its forward fan-out.

Critical Unstable Robust Prereq Bottleneck
Grade 9-10 · currentWhere the visible gaps live
HSA-SSE.B.3.c
Compound interest · decay
0% · PT1-Q22
Edexcel · no CCSS
Similar solids · k²/k³
0% · PT2-Q22
HSA-REI.B.4.b
Quadratic equations
partial · PT2-Q23
Edexcel · no CCSS
Bearings
0% · PT2-Q3
Grade 8Prerequisites here are robust
8.EE.A.1
Integer exponents
✓ PT1-Q4
8.G.B.7
Pythagorean theorem
✓ PT2-Q13, Q23a
8.F.A.1
Functions and evaluation
✓ PT1-Q9
8.F.B.4
Linear rate of change
untested
Grade 7 · root layerWhere the rotten root lives
7.EE.A.2 ROOT
Rewrite expressions to reveal relationships
fan-out = 4 · core gap
7.RP.A.2
Proportional relationships
✓ ×4 items
7.RP.A.3
Multi-step ratio and percent
unstable · PT1-Q11
7.SP.C.8
Compound event probability
50% · PT2
Grade 6SAP: stop here, do not regress further
6.RP.A.1
Ratio concept
✓ PT2-Q4, Q21a
6.NS.B.4
HCF and LCM
✓ PT1-Q2, Q3
6.RP.A.3.d
Unit conversion via ratio
partial · PT2-Q19c
6.G.A.2
Volume formulas
untested
Click any node to view the official standard statement, evidence from this diagnostic, and learning components (ANet decomposition).
§⑤ BOTTLENECK ANALYSIS

Bottleneck node analysis

In a standards progression graph, certain prerequisites act as gateways to multiple downstream standards. Repairing these nodes propagates positive effects downstream along the dependency chain.

🔴 BOTTLENECK · 7.EE.A.2
Translating "additive stacking" into "multiplicative stacking"
The official standard statement already describes Xiao Ting's core error: "Understand that rewriting an expression in different forms in a problem context can shed light on the problem and how the quantities in it are related. For example, a + 0.05a = 1.05a means that 'increase by 5%' is the same as 'multiply by 1.05.'"

On PT1-Q22a, Xiao Ting wrote 0.15 instead of 0.85 (the decay multiplier). This is a textbook 7.EE.A.2 failure — she has not yet internalised the equivalence between the additive language ("decrease by 15%") and the multiplicative language ("× 0.85"). This single conceptual gateway underlies the vast majority of her downstream high-school SSE work.
SAP-verified forward fan-out · 4 standards
7.EE.A.2 · Grade 7 · Rewriting expressions to reveal relationships
↓ ↓ ↓ ↓
HSA-SSE.A.1
Interpret expressions in context
HSA-SSE.B.3.a
Factor to reveal zeros
HSA-SSE.B.3.b
Complete the square
HSA-SSE.B.3.c
Compound interest / decay (Q22)
Verified via Learning Commons: find_standards_progression_from_standard(direction='forward'), UUID = 6b9d6fe1-d7cc-11e8-824f-0242ac160002. 7.EE.A.2 has no upstream prerequisite in the SAP map; it is a clean root node.
🟣 SECONDARY · 6.RP.A.3.d

"Rate as multiplier" in unit conversion

Speed conversion m/s → km/h requires ×3.6 (marks lost on PT2-Q19c). This is the same conceptual move as the bottleneck — translating a rate into a multiplier. It can be trained alongside 7.EE.A.2 during Phase 1.

§⑥ INTERVENABLE FACTORS

Which factors are limiting performance

Based on the Digital Promise Learner Variability Navigator (LVN) Math 7-10 model. The lead factor (cognitive load) is anchored on the strongest observation from the 12 March initial diagnostic.

🧠
Working memory overload under non-calculator conditions Trainable · moderate
COGNITION · WORKING MEMORY · LEAD FACTOR
The core factor in this case and the strongest signal from the 12 March initial diagnostic: "Frequent sighing, sensitive to ambient noise; basic arithmetic and procedural execution consumed most of her cognitive resources, leaving insufficient capacity for logical reasoning." Per the Baddeley & Hitch working memory model, mental arithmetic under time pressure simultaneously occupies the central executive (decision-making), the phonological loop (verbal computation), and the visuospatial sketchpad (figure processing). When all three are loaded, the executive resource for self-monitoring is exhausted, producing the three observed error classes: (1) multi-step arithmetic collapse; (2) MCQ revisions (chose and changed); (3) mental computation leaving no written trace.
Items directly explained
PT1-Q11PT1-Q21aPT1-Q22bPT2-Q1PT2-Q10PT2-Q22c
😰
Test anxiety Trainable · moderate
EMOTION · MATH ANXIETY
Anxiety consumes working memory resources (Ashcraft & Kirk, 2001). Xiao Ting exhibits a "double-pick" pattern — initially correct, then changed to wrong upon second-guessing. Expressive writing (Ramirez & Beilock, 2011) is a well-evidenced, low-cost intervention.
🎯
Metacognitive calibration Trainable · high
METACOGNITION · SELF-ASSESSMENT
Systematic predictive bias: she underestimates recently mastered content and overestimates familiar but unstable content. A 4-6 week structured "predict-verify" protocol responds strongly to this pattern. This is the most direct metacognitive intervention lever.
⏱
Stamina / 35-minute fatigue threshold Trainable · moderate
SELF-REGULATION · STAMINA
The 12 March observation showed clear attentional decay after 35 minutes. The full-length CEM runs 67 minutes; Phase 4 schedules progressive duration training (30 → 40 → 50 → 67 min).
🗣
Mathematical English vocabulary Trainable · high
VOCABULARY · LANGUAGE
Cambridge CEM / Perse admissions require precision in English mathematical vocabulary. Bilingual flashcards for bearings and probability terms, 5 min/day. Schleppegrell (2007), Cummins (2008) BICS/CALP.
§⑦ CHARACTER PROFILE · CALIBRATION

Learner traits and metacognitive calibration

A working profile inferred from approximately four weeks of observation (not a diagnostic conclusion). OCEAN inferences from a single snapshot serve only as working hypotheses and require validation through further observation.

Conscientiousness High

Step completeness (except PT1-Q21a), assignment return rate, and spontaneous note-taking habits are all high. Strong willingness to redo missed items.

Evidence: PT1 self-assessment cover · every assignment returned · error notebook

Neuroticism Moderate

Test anxiety is significant but not extreme. The "double-pick" pattern, the 35-minute fatigue threshold, and pre-test emotional expression ("more or less") are the principal signals.

Evidence: PT2-Q1, Q10 double-picks · 12 March observation

Openness High

Absorbs new content quickly; willing to attempt unfamiliar item types. Her course overview also shows interest in politics and history — supporting genuine intellectual curiosity.

Evidence: 19 March peer-teaching performance · course overview

Growth vs fixed mindset Transitional

Shifting toward a growth orientation; not yet stable. Growth signals: re-engages with errors, accepts peer teaching, absorbs new content quickly. Fixed signals: defensive pre-test prediction ("average level, easy to reach"), unspecific post-test goals.

Evidence: post-test (d) reflection · pre-test "average" anchoring

Motivation type Mixed

External motivation is clear (the Perse Year 9 target), but not purely extrinsic. The PT1 cover self-assessment — "didn't take the long-answer items seriously" — reflects an internal standard: she has her own definition of "doing well."

Evidence: PT1 cover self-assessment · breadth of intellectual interest

Self-assessment accuracy Systematic bias

Asymmetric calibration: underestimates recently mastered content (trigonometry 1/5 → 100%); overestimates familiar but fragile content (inequalities 3/5 → 50%). This is the most direct lever for metacognitive intervention.

Evidence: see calibration table below

📐 Self-assessment calibration (Test 2)

Topic
Predicted
Actual
Gap
Calibration insight
Trigonometry (Pythagoras + ratios)
1/5
100%
-3
Severe underestimate. Newly mastered content has not yet been integrated into the "I can do this" self-schema.
Inequalities and algebra
3/5
50%
+1
Familiar but unstable content is easy to overestimate.
Probability
4/5
50%
+2
Overconfident. Basic probability is stable; complement/tree are not yet consolidated.
Ratio and proportion
4/5
100%
0
Calibration accurate — her most stable area.
Volume and surface area (including similar solids)
3/5
~33%
+2
Did not distinguish the two sub-difficulties of "basic volume" and "similar solids".
§⑧ INTERVENTION PLAN · READ-ONLY

Eight-week, four-phase intervention plan

This is the plan as written in the report. For execution tracking, go to the "Intervention tracker" section in the left navigation, where strategies can be checked off, reflections recorded, and progress monitored.

Each strategy is tagged with its LVN name, the corresponding intervenable factor, and the research basis. The plan addresses content, study habits, mathematical thinking, and learning method. It is compatible with the eight-week transition programme from the 12 March report, with Mindsweeper-side priority refinements.

PHASE 1 · WK 1-2
Repair bottleneck
7.EE.A.2 + reverse percentage, compound interest, quadratic inequalities
PHASE 2 · WK 3-4
Conceptual scaffolding
bearings, similar solids, quadratic formula, indep events
PHASE 3 · WK 5-6
Metacognitive calibration
Runs in parallel with Phases 1-2 · predict-verify training
PHASE 4 · WK 7-8
Integration + simulation
Interleaved practice, stamina, full-length CEM simulation
§⑨ METHODOLOGY

Sources, decision criteria, and limitations

The basis and boundaries of every judgement, transparent for peer review.

Knowledge Graph MCP query workflow
Each standard is resolved through three live API calls:
(1) find_standard_statement with the Multi-State jurisdiction. Key finding: high-school standards (Grades 9-12) require the HS prefix.
(2) find_learning_components_from_standard using the CASE network UUID. Key finding: the official ANet decomposition differs substantially from an intuitive partition. HSA-REI.B.4.b has 7 official LCs, and "method selection" is its own component.
(3) find_standards_progression_from_standard with forward/backward direction. Key correction: the v1 hypothesis that 8.EE.A.1 was the bottleneck was empirically rejected — its forward chain points to logarithms and scientific notation. The verified bottleneck is 7.EE.A.2, with a fan-out of 4 into the SSE family.
Consolidation gradient grading criteria
Robust: correct across ≥ 2 independent items and conditions.
Emerging: correct under standard conditions.
Recognised but unstable: conceptual representation present but execution unstable.
Introduced not internalised: conceptual structure not yet formed.
Performance vs knowledge gap attribution
Four signals: (1) method visible on paper; (2) a similar item answered correctly earlier; (3) error pattern matches the cognitive-overload signature; (4) cross-test counter-evidence. The framework is based on Newman's (1977) error-analysis hierarchy.
Limitations of CCSS-Edexcel mapping
Bearings was initially mapped to 7.G.A.2, which the API showed to be inappropriate. Similar solids was initially mapped to HSG-MG.A.1, which the API also rejected. CCSS contains no standard that specifically covers these two Edexcel-only topics. Methodological implication: when Mindsweeper uses CCSS as the backbone, Edexcel-only topics should be marked as placeholders.
Scope and limitations
① The standards backbone is CCSS-M; the student's actual curriculum is Edexcel GCSE 9-1 Foundation.
② The standards progression chain reflects logical prerequisite relationships, not necessarily instructional order.
③ Trait and factor analyses are based on roughly four weeks of data and constitute working hypotheses rather than settled conclusions.
④ Performance-versus-knowledge attribution involves judgement; every attribution is accompanied by explicit evidence.
⑤ LVN strategies have population-level research support; individual responses vary.
TOOL · NEW DIAGNOSTIC INPUT

Enter a new test

Once submitted, the system uses the Learning Commons API to automatically verify each item's CCSS anchor, retrieve learning components, and compute the progression chain, generating a draft report. The teacher reviews and edits before publishing.

Item entry

What happens next: after you click "Generate draft report", the system runs three Learning Commons API calls for each CCSS code, classifies the error descriptions automatically using the §② attribution taxonomy, identifies candidate bottleneck nodes, and finally produces a draft with the same structure as this report. The teacher can edit any field before publishing.
TOOL · STANDARD VERIFICATION

Learning Commons verification

All CCSS standards, learning components, and progression chains cited in this report originate from Learning Commons MCP. From here you can batch re-verify; the system automatically diffs against the previous run.

All 14 Verified 12 Needs review 0 No anchor 2 Last run: 20 May 14:32
7.EE.A.2 Rewrite expressions to reveal relationships 5/20 14:32 verified
HSA-SSE.B.3.c Properties of exponents for exponential functions 5/20 14:32 verified
HSA-REI.B.4.b Solving quadratic equations (7 LCs) 5/20 14:32 verified
Edexcel · bearings No CCSS anchor — flagged — no anchor
Edexcel · similar solids No CCSS anchor — flagged — no anchor
TOOL · PARENT-FACING VIEW

Parent-facing preview

The same diagnostic data in a different register. No CCSS codes or jargon stacking — the focus is on "what this means and what we will do in the next two weeks". Requires teacher review before being sent.

PREVIEW This is what parents will see when they open the link · as of 25 May · reviewed by Paula Zheng
Dear Yu family,
The diagnostics over the past eight weeks give us a clear picture: Xiao Ting's mathematical foundation is actually quite solid — algebraic manipulation, Pythagoras, ratio, and functions are all in good shape. The places where she is currently losing marks are not "things she doesn't know"; they trace to one specific conceptual gate and a handful of execution issues during testing.
Core finding: what Xiao Ting needs to address is not a long list of topics but one key "translation" move — translating additive language such as "decrease by 15%" into multiplicative language such as "multiply by 0.85". Repairing this single concept simultaneously resolves four downstream item families: compound interest, reverse percentage, quadratic equations, and similar.
What we will do in the next two weeks (Phase 1)
  1. 1Use a 6-card error-example deck to train the "+5% is equivalent to ×1.05" conversion. Each time she begins with a flawed solution and has to identify where it went wrong — substantially more effective than handing her the correct solution to memorise.
  2. 2Fifteen minutes per session of verbal reasoning. Have her explain aloud "why the multiplier is 0.85 and not 0.15". Do not interrupt when she gets stuck — the struggle itself is where learning happens.
  3. 3Establish a "three-step paper rule": for any multi-step problem, first write "given — required — method", then begin computing. The goal is for this to become automatic within two weeks.
How parents can support
No need for additional drill volume. Xiao Ting already does enough problems; what she needs is to work each one through thoroughly. When she gets an item wrong, rather than asking "why did you get it wrong?" ask "if you did it again, what would you do differently?" These are metacognitive prompts and have very strong research support.

At the end of Phase 1 (around 8 June) there will be an 8-item diagnostic checkpoint with a target of ≥ 75%. I will send a brief progress update at that point.
Would you like to see more detail? The teacher-facing report contains per-item attribution, the standards progression graph, the underlying research, and other technical detail. If you would like a walkthrough, we can schedule a 30-minute video call.
— Paula