Clarity and rigor review

Blind student pressure-test 1

Perspective: an ordinary, motivated 18-year-old first-year student with no electoral-systems vocabulary or country knowledge. This is an AI source-based reconstruction, not observation of a human learner. I read the permitted HTML, application, styles, data and election module, and the photo metadata. I used no browser, prior reviews, instructor files, or parent-folder content. Pure election-module calculations were checked in isolation. Source behavior below is distinguished from likely novice interpretation; layout, actual interactions, learning and completion time were not measured.

Verdict

The main route supplies enough information to start, reason through each case, write the required work and understand how to submit it. I found no source-demonstrated blocker on the intended first-use route. Two worthwhile refinements remain, principally around recovering from incomplete final work and returning from revisions. Neither warrants weakening the reasoning tasks.

What a new student can understand and do

• First entry and assignment. home() supplies a prominent Start action and optional How it works; the route explicitly says to start with the UK, then Israel and Liberia, and apply the ideas to New Zealand. briefing() translates the project into “Four cases, four short briefs, one final comparison.” Name is optional, pair work still means individual notebooks, priorities have plain-language descriptions, and no political preference earns points. Historical records and models are distinguished before the first investigation. A novice can identify both the immediate action and the eventual deliverable without knowing the countries.

• Opening each investigation. title(), caseTerms() and surprise() supply country, year, a short context, a surprising table and a starting question. “Choose one to continue, even if you are unsure” and “not a scored answer” explain the required choice. The note is explicitly optional. The UK meanings explain seats, MPs, constituencies and plurality before the student must interpret the local contest. A selected starting question is preserved separately from its later draft. The likely novice task is choosing a question to investigate, rather than already solving the case.

• Evidence and checks. evidence() puts the rule and records above a check that explicitly permits a calculator. UK students can connect one local first place to one seat and distinguish rank from winning margin. Israel students can separate validity from eligibility and calculate the minimum whole-vote cutoff. Liberia students can subtract totals while resisting an individual-voter interpretation. New Zealand students receive a four-step rule, then must apply the OR qualification and a new local/list top-up example. Feedback and hints name the needed operation; two unsuccessful complete submissions reveal a worked representation and permit supported progress. The text correctly says exposure does not establish understanding, and the notebook records support.

• Rule labs. lab() explicitly asks students to open at least two settings and explain a consequence. The initially displayed setting is recorded when the lab opens. comparisonCard() keeps the viewed results together. The isolated module check produced UK fractional benchmarks of 219.05, 92.90 and 79.40 for the three displayed parties; Israel's 2%, 3.25% and 5% settings produced 12, 10 and 6 qualifying lists. These are usable comparative facts, without claiming alternative exact seats where none are modeled. Liberia clearly changes from a fixed-October plurality replay to actual November counts. New Zealand preserves the direct local winner when list eligibility changes.

• Case writing and movement. brief() supplies compared results, an expandable record view, a model boundary, a recommendation, a recommendation-specific pressure test, a record selector and a three-part writing plan. The student must explain the two settings, a valued consequence, an accepted cost, and development of the opening thought; New Zealand also calls for an earlier-case connection. A separate sentence names evidence that could challenge the recommendation. Normal filing moves to the next country's First thought, with a sidebar tick and count. Saving and filing are explicitly distinguished. Students can revisit earlier stages and use the Notebook's resume action.

• Final comparison. portfolio() asks students to choose an assembly, a presidency or both, select a priority and two cases, and defend a rule using one figure from each. The plan asks for a shared lesson, a limit, a cost, a fixed input and a possible strategic response. The last two terms are defined at their point of use. The text explicitly handles a comparison between an assembly and a presidency and requires separate rules when designing both. This is a substantive synthesis task; the product does not promise that choosing boxes establishes the quality of the argument.

• Submission. The same notebook exposes TXT, JSON and print. Both the initial briefing and final notebook say nothing is sent automatically. “Mark portfolio complete” records completion; the student is then told to download or print a copy. A first-time student therefore has a clear distinction between saved work, filed case briefs, portfolio completion and actual submission.

Prioritized remaining findings

1. Final completion errors do not identify the missing final field — worthwhile refinement

Source-confirmed evidence: finish() (app.mjs:216) calls validation() with no selector and always gives the same message: “Choose an institution, a goal and two cases. Then add your argument and evidence for reconsideration.” That branch runs whether one field or several are missing. validation() (app.mjs:104) consequently focuses the bottom action message. The final controls generated by portfolio() (app.mjs:181) do not have the field-error elements used by the earlier case-writing controls. By contrast, fileBrief() identifies and focuses a particular missing field.

Likely novice interpretation: after writing a long comparison and overlooking, for example, the institution dropdown, the student receives a list of all requirements without knowing which one was unmet. The wording can suggest that completed work failed to register. This is a recovery problem, not evidence that the assignment itself is unclear.

Concrete fix: test the final requirements individually, name the first missing item and focus its control or case-selection group; add the corresponding inline error. For example, a missing second case should say “Choose one more case for your comparison.”

Rigor safeguard: keep every final requirement, all four filed-case requirements and the instructor's responsibility to assess reasoning. Validate completeness only; do not pretend that automatic completion evaluates the argument.

2. Updating an earlier filed brief can return the student to an already-finished opening task — worthwhile refinement

Source-confirmed evidence: Notebook entries provide “Reopen this case” (entry(), app.mjs:179); editing its explanation makes it unfiled (inputEvent(), app.mjs:254). fileBrief() (app.mjs:212) then always sends an earlier case to caseIndex + 1, phase 0, regardless of whether that next case is filed. Thus revising the UK after all four briefs are complete sends the student to Israel's First thought, although all four are now filed again. The button is “File this brief & continue” after the edit; there is no statement that the destination is an already-completed case.

Likely novice interpretation: a student revising evidence while preparing the final submission may infer that later cases need to be done again. The ticks and Notebook afford a recovery, so this is not a blocker. It is most relevant because revision is an explicitly practiced skill.

Concrete fix: after filing, open the portfolio when all four briefs are filed. Otherwise continue to the next unfiled case, or retain a clear return-to-notebook route for a case reopened from its record. A short “Updated brief filed” confirmation would make the state transition explicit.

Rigor safeguard: retain the requirement to refile edited briefs and preserve their explanation, settings, first thought and support records. Do not automatically mark revised work complete or waive any unfinished case.

Strengths to preserve

• Definitions explain the exact distinctions the student must use: votes versus representatives, plurality versus majority, validity versus qualification, totals versus individual movement, and local seats within a total entitlement. They are short enough to use during the task, with a complete Terms panel available globally.
• The repeated four-stage route, concrete button verbs and case-specific context make beginning and continuing understandable. The New Zealand rule is broken into ordered steps while its check still requires application to a changed top-up example.
• Comparison results and historical figures remain available while writing, so the task does not depend on memorizing numbers from a previous screen.
• The work retains its intellectual demands: arithmetic is interpreted, aggregate claims are challenged, models have explicit limits, and a recommendation must acknowledge a cost and potentially disconfirming evidence.
• Support is transparent and recoverable. Incorrect or incomplete checks do not become permanent dead ends; supported progress is not mislabeled as unaided mastery.
• The final product clearly separates assembly and presidential design, and saved work from actual submission.

No other specific remaining issue in the permitted source justified a change from this novice perspective. Browser layout and actual novice performance still require separate evidence.

Student 1 discussion

I agree with student 3 that the support-state defect was an integrity problem, rather than merely unexpected timing. Faculty 3's copy-only alternative would not have resolved the branch that recorded a worked example without displaying one. The revised separation of failed submissions, permission and confirmed display addresses the stronger finding. This agreement is based on source, not observed student learning.

Both findings from my blind review are addressed in the current source. finish() now identifies the actual missing institution, priority, case selection, argument or reconsideration evidence, and passes its selector to validation(). portfolio() supplies corresponding persistent inline errors; the existing styles make them visible when unhidden. fileBrief() now returns to the portfolio when no briefs remain unfiled; otherwise it resumes the first unfinished case at its saved stage. Revising an earlier brief therefore no longer inherently reopens the next completed case's opening task.

One minor refinement remains: case-selection validation marks the first checkbox aria-invalid, but inputEvent() removes that attribute only from the checkbox subsequently changed. Selecting different cases can hide the group error while leaving the original checkbox marked invalid. Clear validation attributes across that group when its selection changes. This is a recoverable accessibility inconsistency, not a publication blocker.

The revisions preserve the intellectual work: students still explain mechanisms, compare evidence, accept costs, identify limits and propose reconsideration evidence. Inquiry-development wording also fits an optional initial hunch without forcing an opinion change. I found no remaining publication blocker in this targeted source review. Browser behavior and actual novice comprehension remain unverified by me.

Blind student-perspective source review

Reviewed the frozen blind_product after reading REVIEW_SCOPE.txt. Perspective: an 18-year-old first-year student with basic arithmetic and percentages, little confidence reading quantitative tables, and no prior political knowledge. This is an AI design review from permitted source files and isolated pure-code probes. No browser, other reviews, instructor material, or human observations were used. No product files were changed.

No completion blocker or incorrect mathematical result was found in the reviewed student path. Two wording refinements remain. Both concern information the product already supplies; the recommendations make that information more explicit where the student uses it.

1. Make each table header name its quantity and, for percentages, its denominator

Priority / status: First refinement; not a blocker. Classification: observed label ambiguity, with inferred novice impact; not a source-data or engine bug.

Exact evidence: historicalTable() in app.mjs:139–142 uses ['List','Valid votes','Share','Actual seats'] for Israel and ['Party','Party share','Electorate','List','Total'] for the full New Zealand results. In New Zealand, the quick meaning of *electorate* is a local voting district, while the table's numeric Electorate column contains the number of electorate seats won. The adjacent List and Total columns also contain seats without saying so in their headers. record() for Liberia (app.mjs:151) labels the two count columns 10 Oct and 14 Nov, followed by Net change.

Inference: A hesitant table reader must reconstruct units from captions, rules and surrounding prose before comparing a percentage with a count. The New Zealand table is particularly consequential: distinguishing party-vote share, local seats and list seats is the central task, and the short column labels make that task unnecessarily harder. The surrounding explanations and quick meanings already help; this is a request to carry their precision into the numbers themselves.

Concrete fix: Use Share of valid votes for Israel's percentage column. For New Zealand, use Party-vote share, Electorate seats, List seats, and Total seats (and identify valid party votes as the percentage denominator in a short caption/note). For Liberia's comparison, use 10 Oct votes, 14 Nov votes, and Net change (votes). These are label edits; no new panel or tutorial is needed.

Rigor protection: Keep votes, percentages and seats separate. Keep the existing New Zealand rule and top-up explanation, the Israel validity/qualification distinction, and Liberia's warning that net changes do not identify individual transfers. Do not replace exact counts with rounded verbal descriptions.

2. Preserve the changed eligibility result in New Zealand's saved comparison wording

Priority / status: Second refinement; not a blocker. Classification: observed omission in a summary, with inferred novice impact; not an allocation or historical-data bug.

Exact evidence: nzLab() (app.mjs:169) explicitly shows that the Māori Party qualifies for list allocation when the electorate pathway is retained and does not qualify when it is removed. Its direct electorate seat is retained in both settings. However, the removed-pathway branch of formatLab() (app.mjs:177) saves only: 5% national threshold for list allocation, preserving local winners: The Māori Party retains its direct electorate seat; exact alternative totals are not modeled. It does not explicitly save No for list eligibility or say that New Zealand First still meets neither route. comparisonCard(), the case brief, final comparison evidence and notebook exports all reuse this summary. The retained-pathway summary does name the Māori Party's qualification and New Zealand First's zero seats.

Inference: While composing the case or final argument, the student has an asymmetric comparison: one setting names qualification and the other mainly names the unchanged local seat and the model limit. A novice may miss the precise consequence that changed, or read “exact alternative totals are not modeled” as preventing any clear conclusion about list eligibility. The main lab already makes the distinction correctly.

Concrete fix: Make the removed-pathway summary explicit, for example: “At a 5% national threshold for list allocation, the Māori Party does not qualify for list seats; its one directly elected electorate seat remains. New Zealand First still does not qualify. Other parties' alternative seat totals are not calculated.” Retain the existing distinction between an actual 2020 seat result and this eligibility comparison.

Rigor protection: Do not invent a full alternative allocation, change other parties' entitlements, erase the direct electorate winner, or imply that these fixed votes predict behavior after a real reform. This change repeats the existing lab result at the point of writing.

Coverage and successful checks

• United Kingdom: The student path distinguishes national vote totals from local victories, explains repeated ballots in the local table, separates second-place ranks from margins, labels fractional seats as a benchmark, and supplies its denominator and formula. Pure-code checks reproduce the displayed fractional benchmarks: Labour 219.05, Reform 92.90 and Liberal Democrats 79.40. No additional issue found.
• Israel: Valid versus invalid votes, qualification versus exact allocation, and the valid-vote denominator are explained. Exact probes give cutoffs 95,295 / 154,855 / 238,238 and qualifying-list counts 12 / 10 / 6 for 2% / 3.25% / 5%. The cutoff question requires meaningful ceiling arithmetic; feedback and the worked example explain why rounding up is necessary. Preserve that demand.
• Liberia: The product clearly distinguishes a new ballot from automatic transfers, a net gain from individual movements, and historical runoff comparison from a controlled causal estimate. Probes confirm Boakai's +17,520, Weah's −10,173 and the −226,121 valid-vote change. No reasoning or mathematical correction requested.
• New Zealand: The two votes, OR eligibility rule, preservation of direct seats, and electorate/list components of one entitlement are explicit. The Green model check fixes total entitlement at ten and asks for the remaining seven list seats after three local wins. Qualification probes reproduce the displayed Māori Party change and New Zealand First's nonqualification.
• Writing and final argument: The product already preserves compared results beside the writing task, offers a three-part case argument plan, permits revisiting the opening thought, distinguishes assembly and presidency, and asks for evidence from two cases, a shared lesson, comparison limit, accepted cost, fixed input, strategic response and reconsideration evidence. These demands are academically meaningful and sufficiently signposted in the source; no request to remove them or add an already implemented scaffold.

Student 2 discussion

I agree with student 5's distinction between clarifying the task and reducing its intellectual demand, and with faculty 1's recommendation to preserve the institutional and causal limits. The revised final guide now asks how each rule produces its result before asking for a shared lesson and comparison limit. That clarifies the order of reasoning without supplying an argument.

My two original refinements are resolved in the inspected source. historicalTable() now labels New Zealand's percentage as “Share of valid party votes” and identifies electorate, list and total seats, with an accompanying statement that the columns count MPs. Israel identifies the valid-vote denominator; Liberia's comparison names votes and net-change units. formatLab() now explicitly preserves Māori Party list nonqualification, its retained direct seat, and New Zealand First's continuing nonqualification when the exception is removed. These summaries reach the existing writing and export paths. The qualifications about other parties' uncalculated alternative totals remain intact.

One remaining refinement concerns exposure claims: brief() still says “A worked example was shown” whenever supported && !checked. A restored legacy notebook can have legacySupport=true and workedShown=false, so this sentence overstates what the revised state records establish. Match checkStatus()'s qualified legacy wording instead. This is a source-observed reporting inconsistency, not evidence of student misunderstanding.

The revisions preserve ceiling arithmetic, entitlement reasoning, accepted costs and comparison limits. I found no new numerical or task-completion publication blocker. Correct the legacy sentence for reporting accuracy. CSS retains horizontal table overflow, but actual wrapping, browser behavior and novice comprehension remain unverified by this source-only discussion.

Blind student pressure test — independent reviewer 3

Scope: REVIEW_SCOPE.txt plus the complete permitted index.html, app.mjs, style.css, data.json, photos.json and elections.mjs. No instructor files, parent reports, other reviews, product edits or browser use. This is an AI novice-perspective source review, not an observed student study. Phone reading, pausing and returning, revising answers, and skipping between stages inform the inferences below. Findings marked observed were established from the actual source and isolated execution of its functions; no viewport, measured time or student quotation is claimed.

Prioritized findings

1. High — successful rechecks can create false worked-example records and unlock the supported path

Observed source behavior. app.mjs check() (200–208) increments c.attempts for every submitted check, including successful ones. Both evidence() (164) and nextEvidence() (211) treat total attempts >= 2 as the condition for a worked step. invalidateCheck() (248) enables the continue button on that same total even if c.supported is false. The stated policy in the check support text is two unsuccessful submitted attempts.

Reproduce, without editing product source.

• In the UK case, submit the two correct answers.
• Press the offered “Check again” button with the same answers. Both submissions pass.
• Change the first answer, without submitting it.
• The next button now says “Check the edited answers first →” but is enabled. Click it.
• nextEvidence() sets c.supported=true and opens the lab. The current check has not passed and no worked example was displayed between the edit and navigation. checkStatus() (61), the brief callout (173), and TXT export (213) nevertheless describe a worked example as shown.

An isolated execution of the source functions produced: before continue, attempts = 2, submissions = [true, true], checked = false, supported = false; the next button had disabled = false; after continue, supported = true. A simpler branch (one pass, one failed resubmission) also set supported = true with only one failed submission. Conversely, after two passes and an edit, clicking Hint invokes a render containing worked() through the total-attempt condition while leaving c.supported=false; a subsequent successful submission can record workedBefore=false despite that display.

Inferred novice consequence. A returning student who uses the invited recheck can receive an instruction to recheck while the enabled button permits leaving. Their notebook can then claim instructional support they did not receive, or fail to record support that was shown. This affects the honesty of the learning-process record and the integrity of the supported progression rule.

Fix and rigor safeguard. Count unsuccessful submissions separately from total submissions. Use that count consistently for eligibility. Record actual worked-example display through one shared path, and require that recorded display or a current successful check to continue. Preserve the current rule that editing an answer invalidates its pass and filed status; keep the two-failure support route, worked example, open explanation and instructor review. Check the two-successes-then-edit, one-success-one-failure, two-failures, and hint-after-edit paths.

Blocker distinction. This is a substantive state-integrity defect in revision paths, not an unconditional inability to complete the activity.

2. Medium — final completion does not identify the missing final requirement

Observed source behavior. The final comparative brief has five required components: institution, priority, two cases, argument, and evidence for reconsideration. finish() (216) sends the same message for any missing component: “Choose an institution, a goal and two cases. Then add your argument and evidence for reconsideration.” It calls validation() (104) with no selector. Consequently the focus and scroll target is the bottom #action-message, and the final inputs have no corresponding data-error-for elements. This differs from fileBrief() (212), which identifies and focuses the first missing case-brief field.

Reproduce. File all four cases, complete the final priority, two case selections, argument and reconsideration, but leave the institution unselected. Press “Mark portfolio complete.” The generic message lists all requirements and remains at the bottom; it does not name or focus the sole missing field. Isolated execution confirmed the validation call contained only that general message.

Inferred novice consequence. On a phone, a student returning to a long final draft may have to search above a completed argument for the one omitted selection and may read the message as requiring changes to already completed work. This is unnecessary procedural uncertainty at the final step.

Fix and rigor safeguard. Validate the first missing final requirement by name and focus it, with an adjacent error that clears on input. Treat selecting fewer than two cases similarly. Retain every current completion requirement and the explicit distinction between completion and instructor assessment of quality.

Blocker distinction. Recoverable procedural friction; completing the missing field works. No evidence of lost writing or a completion dead end.

Strengths worth preserving

• The same four named phases, country identity, direct case navigation and guarded stage changes make the route recoverable. Browser history is explicitly handled (191–194, 265), while saved lastPhase supports reopening a case.
• Plain-language definitions preserve disciplinary terms. Quick meanings support hover, focus and tap, and tapping opens a native dialog; rule explanations and prompts distinguish an assembly from choosing one president.
• Historical data and model boundaries are explicit. The UK benchmark is fractional; Israel changes eligibility only; Liberia compares different historical ballots; New Zealand preserves the directly elected MP and treats list seats as a top-up.
• The brief contains the two compared results, accessible historical figures and rules, a saved opening-thought snapshot, a cost prompt, and next-evidence prompt. Students can retain or revise their first thought with reasons.
• Answer edits invalidate checks and filed status; writing edits return filed briefs to saved drafts. Progress counts filed briefs, and the interface distinguishes browser saving from filing or submitting.
• Local-save failure and unusable saved-data states have visible warnings. JSON restoration previews replacement and downloads the current notebook before replacement. TXT, JSON and print exports are available before final completion, with an explicit statement that the app does not submit work automatically.
• Checks accept formatted numbers, do not count missing answers as attempts, provide specific repairs, and generally distinguish current draft from submitted work. Open explanations remain subject to instructor assessment.

Overall judgment

No unconditional first-pass navigation, saving, or export blocker was established from the permitted source. The supported-progress revision defect should be corrected because it can misstate what happened and relax the stated progression condition. Final-field validation is a smaller but concrete clarity improvement. No additional faults are asserted merely to fill a review quota; no visual clipping, empirical student behavior or measured duration is inferred.

Student 3 discussion

I agree with faculty_2 that assistance chronology required correction and with student_1 that final-form recovery and refiling needed clearer destinations. These changes address demonstrated procedures without adding another writing task.

I checked the revised actual handlers with the existing source harness. Redundant successful submissions no longer increment attempts. Two successes followed by an edit cannot unlock support, including through Hint or reopening Evidence. The wrong → pass → edit → reopen → pass sequence retains one failure and records no worked exposure. Pass → fail → fail enables the supported path; rendering records exposure once, and a subsequent pass snapshots “shown.” With the worked marker deliberately withheld in the harness, advancement remains blocked until the marker is available. The ordinary two-failure route works in all four cases, and incomplete answers still count as zero attempts.

The concrete refinement is consistency for older notebooks. The updated brief() callout now distinguishes confirmed display from earlier support with unknown timing. Importing a valid completed legacy notebook preserved all four filed briefs and completion while retaining legacySupport=true and workedShown=false; its brief, notebook entry and TXT qualify the uncertainty. Historical completion should remain preserved without inventing exposure.

The revised finish() also names and targets the particular missing final requirement. Mechanism explanation, comparative figures, accepted costs, model limits and instructor assessment remain intact. Recording display still does not establish reading or understanding.

I found no remaining publication blocker in these checked branches. This is source and isolated-handler verification, not browser, accessibility, classroom-comprehension or timing evidence.

Independent blind novice pressure-test — student 4

This is an AI design review from the perspective of an ordinary 18-year-old novice who is willing to think hard. It is not an observation of students, a classroom pilot, or evidence of measured learning. I read REVIEW_SCOPE.txt first and inspected the permitted student source, data and assets. I used no browser, instructor material, previous reviews or other agents' reports, and made no product changes. Pure-code spot checks confirmed the displayed Israeli threshold comparisons and New Zealand eligibility change.

The product provides a credible intellectual task: explain how an electoral rule produces a result, identify what the evidence cannot establish, and defend a political choice with a cost. I found no blocker in that task from this review. Two remaining refinements are below; neither calls for easier political answers or longer compulsory writing.

Remaining findings

1. Refinement — make one causal judgment part of using a lab

Observed source behavior. modelButtons() offers settings whose consequences appear immediately; the set-model handler changes c.lab, records it in c.tested, and renders the output. nextEvidence() also records the initial setting. lab-next gates progress on c.tested.length >= 2. lab() accurately explains that opening a setting records viewing, while the brief supplies the explanation. Thus the product already distinguishes viewing from understanding; it does not falsely grade two clicks as reasoning. However, the lab interaction itself contains no prediction or test of a causal claim before the result is supplied. The opening questions in TEACHING select an investigation question, rather than predict a particular lab consequence. New Zealand's CHECKS.nz2020.q3 is a valuable exception elsewhere: changing local wins while holding the total at ten requires an actual counterfactual calculation.

Likely effect — inference. An engaged novice can reason through the labs using the prompts and later brief. A novice focused on getting through the controls can also experience the interaction as revealing two ready-made outputs, with the causal work deferred entirely to composition. This is a limited engagement opportunity, not a claim that the existing brief fails to require reasoning.

Concrete fix. Add one short, non-gating, one-click causal prediction to a lab, reusing the current outputs as feedback. For Israel, for example: with the same votes, could raising the threshold reduce the number of valid votes supporting nonqualifying lists? Invite a prediction, reveal the comparison, and ask the learner to check the direction against the figures. Do not add another required paragraph, political score, mandatory attempt cycle or barrier to viewing settings. A similarly small counterfactual can replace a recall-level check if adding a control would crowd the lab.

Likely effect of fix — inference. It would place an act of causal reasoning inside the model interaction and give the learner a claim to test, while leaving the defended recommendation open.

2. Refinement — clarify whether exploring Israel's third setting creates more writing work

Observed text and behavior. title() describes the brief as using “your two compared settings.” lab() asks for “at least two settings.” Israel's israelLab() supplies three, and comparisonCard() retains every viewed setting. The first item in brief() then says to compare a figure or result “from each setting.” After exploring 2%, 3.25% and 5%, the available instructions therefore refer both to two settings and to every setting. The same brief still requests a short paragraph.

Likely effect — inference. A conscientious novice who explores all three can reasonably infer that doing so has added a required third comparison to an already multi-part paragraph. That may turn exploration into a perceived composition penalty.

Concrete fix. Use the same scope wording in the brief subtitle and guide: “Choose at least two of your viewed settings. Compare a figure or result from each of those settings.” Keep all viewed results available, and keep the third setting optional evidence.

Likely effect of fix — inference. It would make the writing task more predictable without reducing the number of available settings, the causal explanation or the trade-off requirement.

Strengths to preserve

• A clear purpose and legitimate disagreement. briefing(), brief() and portfolio() connect the work to a recommendation supported by evidence, explicitly withhold political preference points, and distinguish an assembly from choosing one president. The choices are consequential without a prescribed preferred reform.
• Checks that distinguish evidence from overclaiming. CHECKS.uk2024.q2 distinguishes second place from a narrow loss; Liberia's second check distinguishes a net gain from identified individual transfers. These are meaningful reasoning demands for a novice, beyond recognizing a rule name.
• Feedback distinguishes supported progress from passing. evidence(), worked(), checkStatus() and the notebook preserve whether the check passed and whether a hint or worked example preceded it. The text explicitly says that exposure does not establish unaided understanding. The supported route should remain available.
• The difficult model boundaries are explicit and useful. modelBoundary(), ukLab(), liberiaLab() and nzLab() separate a fractional benchmark from whole-seat allocation, two real voting dates from an isolated rule effect, and eligibility from exact alternative seat totals. The Māori Party's directly elected MP is retained in both lab settings. These limits help reasoning rather than requiring the novice to guess what the software models.
• Composition is scaffolded without a word-count hurdle. comparisonCard() and the expandable figures in brief() keep evidence accessible. The numbered argument guide, recommendation-specific cost prompt, optional opening note and short separate next-evidence field provide structure. Preserve these; the two refinements above do not require more compulsory prose.
• Technical terms have usable meanings. Case-specific caseTerms() and the full meanings() glossary explain seats, denominators, thresholds, entitlements and top-ups while keeping the disciplinary vocabulary. The photographs have dated contextual captions and the abstract rule studies are identified as conceptual illustrations.

The main uncertainty remaining after this source-only review is how actual learners use the prompts and evidence while composing. The findings above describe observable design behavior and inferred risks, not established novice reactions.

I agree with the focused revision after reading the docket, student 2 and faculty 3, and inspecting the revised source and relevant CSS. Student 2's explicit units and saved New Zealand eligibility distinction improve the evidence available during writing. Faculty 3's distinction between developing an inquiry and documenting actual claim revision is sound; the optional opening hunch should remain optional.

My two main findings have been addressed proportionately. labPrompt() now asks a case-specific causal question, and lab() places it before the setting controls under “Before you switch.” The Israel cue asks which lists gain or lose eligibility and what stays fixed; New Zealand separates the local seat from the list pathway. .lab-prediction gives this short cue a distinct background and border. The handler still reveals settings directly: there is no new field, response requirement or gate. I accept this lighter approach over my original suggestion of another one-click response. It invites thought without creating another completion task. Whether learners actually use the cue remains unknown.

title() now says “at least two settings you viewed,” and brief() begins its comparison instruction with “Choose at least two settings you viewed.” Together these resolve the apparent requirement to write about every Israeli setting merely because it was explored. All viewed comparisons remain available as evidence.

Rigor is preserved: students still explain mechanisms with figures, defend a consequence, accept a cost and identify limits. The changes add no compulsory prose or preferred political answer. I find no publication blocker in these focused revisions. This is source verification, not browser verification or evidence of novice comprehension, workload or timing.

Independent novice review — student 5

Reviewed the permitted student product in blind_product: REVIEW_SCOPE.txt, index.html, app.mjs, style.css, data.json, photos.json, elections.mjs, and the supplied image assets and credits. No browser, instructor documents, parent-folder contents, prior reviews or other reviewers' reports were used. The lens is an ordinary 18-year-old first-year student with normal English reading and no comparative-politics vocabulary. This is an AI source-based review, not an observation of a human student or a measurement of learning or time.

Judgment: no blocking clarity defect found. The three prioritized findings below are refinements. The observed evidence is the wording and source sequence; the likely reading difficulties are explicitly inferences, not demonstrated student reactions. I found no source bug that prevents the required reasoning.

Strengths to preserve

• The term help already exists at the point of each case, with hover, keyboard focus and tap support (term(), caseTerms(), CASE_TERMS, app.mjs:20–56, and casePage(), line 176). Definitions make useful distinctions: a seat counts a representative, qualification is separate from exact seat allocation, and a list top-up fills the remainder of an entitlement. These are substantive explanations, not merely expansions of acronyms.
• The sequence asks for an unscored starting question, then provides a rule and records, a bounded check, a comparison, and a reasoned recommendation. surprise() explicitly says uncertainty is acceptable (line 147). evidence() permits a calculator while still requiring interpretation and explains the supported route after two unsuccessful submissions (line 164). This keeps the intellectual task clear without treating a mistaken opening choice as failure.
• The brief is already scaffolded. brief() supplies compared results, expandable figures and rules, a relevant cost prompt, a three-part argument guide, and a return to the opening thought (line 173). I would retain the requirement to explain how a rule produces a result, cite figures, accept a cost, and identify evidence that could challenge the recommendation.
• The model boundaries are unusually useful. modelBoundary() (line 62), the case labs (lines 166–169), and the final institution distinction in portfolio() (line 181) prevent simple but misleading claims about alternative seats, voter transfers or a presidency being the same design problem as an assembly.

Prioritized refinements

1. Make the third UK recommendation describe the allocation decision without suggesting an already-defined party ballot

Evidence observed: TEACHING.uk2024.options (app.mjs:13) offers “Explore allocation primarily through proportional party votes”. In contrast, historicalTable() explicitly explains that the UK national totals “are not a separate party ballot” (line 138). TERMS['party-vote'] gives “party vote” a specific New Zealand meaning: a separate vote for a party (line 39). The UK lab calculates a national vote-share benchmark and states that a real reform still needs further design (line 166).

Novice inference: I could understand the comparison of vote shares and seat shares, yet be unsure whether this choice asks me to endorse a new ballot, an allocation principle, or both. That uncertainty concerns the object of the recommendation, not the desirability of proportionality.

Concrete alternative: replace the option with “Use parties’ national vote shares as the main basis for seat totals.” Keep the existing explanation that the benchmark does not allocate whole seats and that the relationship between places, candidates and party representation needs a separate design.

Rigor safeguard: this clarifies the principle being defended; it does not equate fractional benchmark seats with an implementable reform, assume unchanged votes under reform, or remove the local-representation cost. No extra field or required design exercise is needed.

Priority/status: highest of these refinements; wording ambiguity, not a proven source bug or blocker.

2. Separate the causal explanation from the comparison lesson within the existing final writing guide

Evidence observed: the second item in portfolio()'s “Plan your comparison” (app.mjs:181) says “Use one figure from each case to explain a mechanism, a useful shared lesson, and a limit to the comparison.” This single instruction contains a result-to-rule explanation and two different comparative judgments. The surrounding text already distinguishes assemblies from presidencies and tells students which lesson can transfer; this is not missing comparison support.

Novice inference: I might put two numbers beside each other and call that the “shared lesson,” without understanding that I must first explain how each rule produced its result and then explain why the cases can be compared. “A mechanism” also leaves slightly unclear whether I must explain one case or both.

Concrete alternative for that same guide item: “Use a figure from each case. Explain how each rule produced that result. Then say what the cases teach in common and one reason they cannot be treated as equivalent.” Retain the adjacent instruction about which lesson transfers between an assembly and a presidency.

Rigor safeguard: this preserves both mechanisms, both figures, the shared lesson and the comparison limit. It requires the same connected argument in the existing text box; it adds no checklist, form field or predetermined substantive conclusion.

Priority/status: second; a cognitive-sequence refinement inferred from compressed wording, not an observed inability to compare cases.

3. Phrase the Israel calculation as the list's decision problem

Evidence observed: CHECKS.israel2022.q1 (app.mjs:159) asks “Using 4,764,742 valid votes, what is the minimum whole-vote cutoff at 3.25%?” The surrounding quick meanings and rule correctly explain the threshold. The question's repair text correctly gives the multiplication and explains why to round up; that support already exists.

Novice inference: “minimum whole-vote cutoff” stacks three labels around an otherwise familiar percentage calculation. I could spend effort decoding what the answer represents before doing the arithmetic.

Concrete alternative: “Out of 4,764,742 valid votes, what is the fewest votes a list needs to reach the 3.25% threshold? Give a whole number.” Keep the current feedback about rounding up if the submitted answer is wrong.

Rigor safeguard: the student still has to choose the valid-vote denominator, calculate the percentage and reason about the smallest qualifying integer. The answer and rounding instruction are not supplied in advance.

Priority/status: low; plain-language refinement. The current question is answerable and is not a blocker.

Boundary of this review

These findings do not justify removing technical vocabulary, weakening numerical checks, shortening the argument requirements, or replacing defensible tradeoffs with preference selection. The existing term buttons, worked examples, saved comparisons, model limits and writing guides already address those needs. Browser behavior and actual novice comprehension remain for separate verification.

Student 5 — discussion

I agree with student_1 that precise final-form recovery and returning to the portfolio after refiling improve navigation without weakening the assignment. Revised finish() names the missing requirement, and fileBrief() returns to the portfolio when all cases are filed. Faculty_2’s assistance-chronology finding also warranted correction: check() now counts unsuccessful submissions separately, and render() records the example after its element exists.

My main wording findings are addressed in revised app.mjs. The UK option now says “Use parties’ national vote shares as the main basis for seat totals” (line 13), identifying the allocation principle without implying an already-defined separate ballot. The Israel question asks for the fewest qualifying votes (line 165), preserving the percentage and rounding reasoning. In portfolio() (line 188), students explain how each case’s rule turns votes into a winner, seats or eligibility before stating the shared lesson and comparison limit. Assembly and presidency remain explicitly distinct.

One concrete refinement was warranted during discussion: brief() initially claimed that a worked example was shown for legacy support with unknown exposure. The latest source conditions that claim on workedShown. An isolated string-render probe confirms that legacy-only support receives the timing qualification; confirmed display receives the shown claim.

The changes preserve figures, mechanisms, accepted costs, fixed inputs, possible responses and instructor judgment. The before-switch cue has styling but adds no required response. I found no remaining publication blocker in these inspected wording paths. This does not establish browser layout, actual novice comprehension or complete migration correctness; my verification was source-based, without browser use.

Independent blind faculty review: comparative electoral systems

Verdict: No content blocker and no adverse finding that warrants a required product change in this review. The student product preserves substantial intellectual demand while taking unusually good care with units, denominators, institutional stages and causal limits. I would retain those distinctions rather than simplify them away.

Scope and evidentiary limits

I read the permitted index.html, app.mjs, style.css, data.json, photos.json and elections.mjs, and ran isolated pure-code calculations against the frozen engine. I did not use browser UI, inspect instructor documents, inspect other reviews, modify the product or consult prior review context. The assessments of potential first-year comprehension below are expert inferences from the text and task structure, not observations of students or measured learning. Independent web-retrieval attempts returned tool errors; I therefore do not claim a fresh external certification of the source documents or their accessibility.

Prioritized adverse findings

None. I found no reproducible mathematical error, institutional misstatement or unsupported causal claim in the student-facing comparisons that should block use. The following are evidenced strengths, not a list of changes to add.

Findings worth preserving

• The UK comparison measures proportional distance without passing off a fractional reference as an electoral reform. ukLab() labels the second setting “FIXED-VOTE MODEL · fractional benchmark,” gives the exact denominator, describes fractional seats explicitly, and states that a reform needs a whole-seat allocation method and a design for local representation. historicalTable('uk2024') also explains that the national totals aggregate local candidate votes rather than a separate party ballot or everyone's first preference. The worked local examples and CHECKS.uk2024.q2 distinguish rank from margin. ukLab() already states that three selected constituencies cannot explain the nationwide geographic pattern. Those qualifications are sufficient for the claim actually being made; requiring a complete geographic explanation would expand the investigation rather than repair an overclaim.

• Israel cleanly separates ballot validity, list qualification and seat allocation. record('israel2022'), CHECKS.israel2022, israelLab() and thresholdAnalysis() consistently use all valid votes as the threshold denominator. The engine uses an integer ceiling, not a rounded percentage test. The Hadash-Ta’al/Labor comparison establishes that passing the line does not determine exact seats. The lab counts qualifying lists, explicitly notes that one list may contain several parties, and refrains from equating more qualifying lists with longer bargaining or an unstable government. This is an appropriate introductory treatment of Bader–Ofer without pretending to simulate its complete allocation and surplus-agreement process.

• Liberia treats the two elections as different observations and avoids the ecological inference trap. liberiaLab() distinguishes a plurality replay of October's recorded votes from the actual November runoff. modelBoundary('liberia2023') explicitly says that comparing the two dated outcomes does not isolate changing one rule. CHECKS.liberia2023.q2, worked('liberia2023') and runoffChanges() all distinguish a net increase from identified individual transfers. The valid-vote totals appear alongside the winner's percentages; the prose confines the majority to participating valid votes in that round. The brief requires students to acknowledge that November also had different votes. These are substantive safeguards already present in the task, not merely disclaimers in a remote source note.

• New Zealand provides a valid transfer exercise rather than a relabelled PR example. displayRule('nz2020') supplies both ballot types, local plurality, the 5%-OR-electorate route and the relationship between total entitlement and top-ups. The current check uses the more demanding hypothetical “10 total, 3 electorate” calculation, requiring 7 list seats instead of simple addition of the displayed historical components. nzLab() preserves a direct electorate MP in both eligibility settings and declines to invent alternative whole-seat totals. record('nz2020') already introduces the Māori-electorate context and overhang limit. portfolio() then requires students to specify assembly, presidency or both, and to identify the limit of a comparison across those institutional jobs. This is a sound separation of generalizable lessons from incompatible outcome measures.

Reproducible arithmetic audit

The following results were generated from the frozen elections.mjs exports:

I also checked every UK benchmark pool's grouped vote total against its display group; all match. The complete benchmark inputs sum to 28,809,340, and each of Hamilton, d’Hondt and Sainte-Laguë conserves 650 seats. These whole-seat helpers are separate from the student-facing fractional benchmark.

First-year clarity and intellectual demand

Observed in the source: quick case-specific meanings and a full glossary; explicit definitions of denominator, entitlement, top-up, fixed input and strategic response; calculator permission for the numerical checks; specific repair feedback; a worked example after two unsuccessful submitted attempts; compared figures carried into the writing screen; and a writing plan that requires mechanism, evidence, an accepted cost and reconsideration. A passed bounded check is distinguished from an instructor's evaluation of an argument, and worked-example exposure is not labelled unaided understanding.

Expert inference: these supports make the language and operations plausible for ordinary 18-year-old first-years without removing the work of inference and judgment. The central challenges remain appropriately demanding: distinguish an eligibility rule from an allocation rule; explain why a ranking is not a margin; reject individual-flow claims from totals; transfer the relationship between local seats and overall entitlement; and compare institutions using compatible units. Classroom completion, usability and actual understanding remain empirical questions outside this source review. No unsupported estimate of those outcomes is offered here.

I agree with Student 2’s refinements and Faculty 2’s assistance-chronology finding. My original review found the electoral comparisons sound; their reports identify narrower presentation and process-integrity issues without weakening that judgment.

In the revised source, historicalTable() names Israel’s valid-vote denominator and New Zealand’s electorate, list and total seats. Liberia’s comparison explicitly labels vote counts and net change. formatLab() now preserves the removed-exemption consequence: Māori does not qualify for list seats, its direct electorate seat remains, and New Zealand First still does not qualify. This improves the saved comparison without inventing a complete alternative allocation.

The revised check() counts unsuccessful submissions separately; canAdvance() requires a passed check or supported permission with confirmed display/qualified legacy evidence. render() records worked display after the example exists. This addresses Faculty 2’s identified mismatch at source level; I have not browser-verified it.

One remaining refinement is warranted: brief() still says “A worked example was shown” whenever c.supported && !c.checked. A restored legacy record can satisfy that condition with legacySupport=true and workedShown=false, while checkStatus() correctly says exposure timing was not retained. Branch the brief callout on those states too. This is a narrow consistency issue, not an electoral-content blocker.

The reasoning cue and final plan retain mechanism, evidence, cost, fixed-input and comparison-limit demands. UK fractions remain a benchmark; Liberia’s two dates remain a non-isolated comparison; NZ direct seats remain protected. I find no new institutional or causal publication blocker. Actual first-year comprehension and completion remain untested by these source reviews.

Independent blind faculty review 2

Scope: the frozen student product only, following REVIEW_SCOPE.txt. I inspected index.html, app.mjs, style.css, data.json, photos.json, and elections.mjs. I used isolated source/function probes and arithmetic checks; no browser, instructor material, prior reports, other reviewers' work, app edits, or subagents. Findings below describe code behavior, not observed student reactions or measured learning.

Judgment

The student product is methodologically careful. I found one remaining, reproducible defect in assistance chronology. I did not find an additional evidenced defect warranting another requirement. In particular, completion is already distinguished from mastery; record exposure is already distinguished from understanding; and open explanations remain subject to instructor assessment. Adding automated prose grading, more mandatory fields, or stronger anti-cheating gates would not follow from this review.

Remaining finding

F2-1 — A worked example can be displayed before a submission while the notebook records no worked example before that submission

Priority/risk: P2, medium. This does not break the electoral arithmetic or make the exercise unusable. It makes a specific assessment-process category unreliable when a learner revisits a previously passed check. Resolve before treating the exported support chronology as evidence about assistance.

Exact implementation evidence:

• app.mjs:164, evidence(), displays worked(id) whenever c.attempts>=2&&!c.checked.
• app.mjs:203–206, check(), increments all submitted attempts, records workedBefore:c.supported, and sets c.supported=true only after an unsuccessful submission with total attempts at least two.
• app.mjs:248, invalidateCheck(), clears c.checked after an answer edit without setting worked exposure. Reopening Evidence renders the worked example through the separate condition above.
• app.mjs:61, checkStatus(), consequently reports: “Check passed without a recorded hint or worked example before that submission.” plainNotebook() exports that status at line 213.

Reproducible ordinary UI sequence, derived from the handlers:

• Open the UK Evidence check. Submit q1 = “Multiply its national share by 650” and q2 = “98 second places; margins are needed to assess ‘narrowly’.” One answer is wrong; this is attempt 1.
• Correct q1 to “Count its candidates’ local first places” and submit. Attempt 2 passes. supported remains false, correctly so at this point.
• Change q1 back to a wrong answer, then click the Evidence & rule phase tab again, or reload while on Evidence. The example headed “A different way to see it” is now displayed because the total attempt count is two and the edited check is not passed. There has been only one unsuccessful submitted attempt. supported is still false, and no worked_step_viewed event has been recorded.
• Correct q1 and resubmit. The passing submission records workedBefore:false although the example was displayed before it.

Isolated probe result: after step 3, {attempts:2, workedRendered:true, supported:false}; after step 4, {passed:true, hintBefore:false, workedBefore:false} and the quoted status above. A related probe passes once, edits to a wrong answer, and submits once: support is enabled after one unsuccessful submission because the gate counts all attempts. This also conflicts with the visible promise, “After two unsuccessful submitted attempts”.

Concrete fix: use the number of unsuccessful submitted attempts for that promised gate, and record worked-example exposure through one canonical path whenever the example is actually shown, including on revisit/restore. Snapshot that exposure into later submissions. Keep exposure separate from understanding, as the current explanatory copy already does. A focused regression probe should cover wrong → pass → edit → reopen → pass, plus pass → wrong, and preserve the existing normal two-failures path. This requires no additional student form or task.

Inference, not observation: a student who revisits a check could receive assistance earlier than expected, and an instructor could misread the support category. I have not observed how often learners follow this route.

Strengths supported by the source and probes

• The UK lab explicitly calls its numbers a fractional benchmark, shows the denominator and recorded candidate-vote meaning, and declines to claim an alternative whole-seat allocation or a reform forecast. The selected constituencies are explicitly illustrations, not an explanation of the entire geographic outcome. ukLab(), historicalTable(), and modelBoundary() preserve these distinctions.
• The Israel lab uses the complete 40-list partition. Exact probes returned 12/10/6 qualifying lists at 2%/3.25%/5%, with cutoffs 95,295/154,855/238,238. At the historical threshold, 417,400 valid votes (8.76018%) support nonqualifying lists. The UI separates validity, eligibility, and exact seat allocation, and warns that lists need not equal individual parties or governing partners.
• Liberia supplies both denominator changes and the limitation of aggregate evidence. The recorded net changes are Boakai +17,520 and Weah −10,173; valid votes fall by 226,121. record(), the checks, the lab, and the brief all resist the inference that the net gain identifies individual switchers. The cross-date comparison is explicitly not an isolated causal effect of changing the selection rule.
• New Zealand preserves direct electorate winners under the alternative eligibility setting, keeps electorate and list seats within one entitlement, and labels overhang as outside this 2020 exercise. The check includes a genuine numerical application: 10 total seats with three electorate wins requires seven list seats. The engine reproduces the stated 2020 entitlements and list components.
• Submitted opening thoughts, current drafts, submitted check answers, lab settings, and filed work are separately represented. “Opening a setting records that you viewed it” avoids equating a click with analysis. The portfolio explicitly says that completion records work while the instructor assesses argument quality. Those existing boundaries should be retained.

Verification boundary: all three national vote partitions and seat sums reconcile; UK benchmark pools sum to 28,809,340; the displayed UK fractional benchmarks agree with exact vote calculations; the Israel fixed-denominator/new-voter gaps evaluate to 4,062/4,198; and the Liberia/NZ checks above agree internally. This is source-level verification, not a classroom pilot or a full external re-audit of each historical source.

I agree with the revision’s separation of support permission, displayed worked examples, and legacy records. Student 3’s reproductions establish a state-integrity problem; I would not accept Faculty 3’s original suggestion that changing the trigger wording alone was sufficient, because false exposure records also occurred.

My main finding is closed for fresh work in the revised source. check() counts unsuccessful submissions and ignores redundant successful rechecks; canAdvance() governs progression consistently. Source-execution probes confirmed that pass → one failure remains unsupported, and wrong → pass → edit/reopen does not display an example before the second actual failure. A presence-aware selector probe confirmed that absent worked markup cannot set workedShown or authorize supported progression; after markup exists, the display is recorded once. The next passing submission snapshots supportEvidence:'shown'; legacy support snapshots 'legacy-recorded'. These are source checks, not browser verification.

I requested one precise refinement: brief() initially told legacy-supported learners “A worked example was shown,” even when exposure was unknown. Root corrected that callout during discussion. I reran the restored-filed-notebook branch: legacySupport:true, workedShown:false, and the filed status remained valid; the reopened brief now explicitly says exposure timing was not retained. That refinement is closed. Preserving the existing filed work while qualifying its evidence is appropriate.

The changes preserve rigor: students still interpret figures, explain mechanisms, accept costs, compare institutions and identify limits. Inquiry-development wording now matches the optional opening hunch. No additional assessment gate is warranted. I found no further source-level publication blocker; actual novice understanding and timing remain unmeasured.

Independent blind faculty review 3

Verdict: The student product supports a demanding introductory investigation without requiring prior country knowledge. I found no pedagogical blocker. Two small refinements remain; neither warrants reducing the explanation, comparison, or evidence demands.

This is an AI source and content analysis, not a classroom study or human faculty observation. I read REVIEW_SCOPE.txt, the permitted student source files, their records, and the image assets. I did not browse, open instructor materials or earlier reports, edit the app, or observe students. Behavior described below is either directly specified in source or checked with isolated calculations. Possible learner effects are inferences, not measured findings.

What is working

• Learning and assessment are substantially aligned. CHECKS (app.mjs:157–162) samples a rule operation, exact threshold arithmetic, a net-change calculation, and two limits on inference. The current New Zealand check uses a new hypothetical: preserve Green's ten-seat entitlement while increasing electorate wins to three. That requires applying the part/whole relationship rather than adding the historical table again. The written briefs then require explanation, compared evidence, a valued consequence, an accepted cost, and a reason for retaining or developing the first thought (brief, line 173). Political choices do not receive an automatic correct/incorrect judgment.
• The writing demands have useful scaffolds without supplying a finished argument. The brief has a three-part plan, a separate next-evidence sentence, the compared results beside the writing task, and expandable original figures and rules. “Use as many sentences as you need” avoids a word-count ceiling that would conflict with the reasoning load. These supports organize the work; students still must connect numbers to a mechanism and defend a trade-off. There is no warranted reason to replace the briefs with more multiple-choice responses.
• Support is distinguished from independent success. worked (line 163) offers another representation and a further oral application prompt; checkStatus (line 61), brief (line 173), and plainNotebook (line 213) distinguish a passed check from a worked example merely shown. The latter explicitly states that exposure does not establish use or understanding. Supported progress still leads to an individual explanation. I would not require another pass simply to protect the appearance of rigor: the open explanation remains the relevant assessed evidence.
• The synthesis now asks a valid comparative question. portfolio (line 181) separates an assembly from a presidency, requires one figure from each selected case, asks for a shared lesson and a limit, and requires a fixed input, possible strategic response, accepted cost, and reconsideration evidence. It explicitly requires separate rules if the student designs both institutions. This supports transfer while making the category difference part of the reasoning task. Completion is described as recording work, with quality left for instructor review.
• Evidence interpretation is carefully bounded. UK second place is separated from closeness; national candidate totals are distinguished from a separate party ballot; Israel's threshold controls qualification rather than exact seat allocation; Liberia's aggregate changes do not identify individual transfers; and the New Zealand exception does not erase the directly elected MP when removed. The Liberia lab visibly changes its label from a replay of October ballots to actual counts from different dates. modelBoundary and the writing page repeat the limits at the point where students might otherwise overclaim.
• The arithmetic supports these distinctions. Isolated execution of elections.mjs confirmed complete vote partitions for the UK (28,809,340), Israel (4,764,742), and New Zealand (2,886,420). Israel's displayed 2%, 3.25%, and 5% settings produce respectively 12, 10, and 6 qualifying lists; the 3.25% cutoff is 154,855. Liberia's +17,520 Boakai net change and −226,121 valid-vote change reconcile. The 2020 New Zealand demonstration reproduces all supplied qualifying parties' total and list seats. These checks verify internal arithmetic, not the external sources' historical authority.

Remaining refinements

1. The revision skill label is stronger than the compulsory opening record

Classification: Low-priority learning/assessment alignment refinement; not a blocker.

Source evidence: home (app.mjs:133) advertises “Revising a claim”; skills (line 197) describes connecting a changed explanation to evidence. However, surprise (line 147) requires only a choice of what question to investigate. The hunch is optional and may itself be only a question. brief (line 173) appropriately tells students that an opening question need not be proved right or wrong.

Observed limit: A fully compliant notebook can preserve no initial explanatory claim to compare with the later explanation. It can show development from a question to an explanation, and reasoned reconsideration, but it cannot necessarily substantiate revision of an earlier claim. This is a limit of the evidence elicited, not evidence that students failed to think or revise.

Warranted correction: Align the skill wording with the actual question-first design: use “Reconsidering an explanation” or “Developing an explanation,” and describe explaining how evidence strengthened, developed, or changed it. Keep the optional hunch, saved opening choice, and demanding final explanation. If an instructor separately requires evidence of actual conceptual revision, a brief initial explanatory hunch would be needed; the current app alone should not promise that stronger observation.

2. “Two unsuccessful attempts” does not describe the implemented support trigger on revisits

Classification: Low-priority clarity refinement; not a blocker or a recommendation to restrict support.

Source evidence: evidence (app.mjs:164) and nextEvidence (line 211) promise the worked example after “two unsuccessful submitted attempts.” check (lines 203–206) increments the total c.attempts on every complete submission, including successful ones, then shows support whenever the current submission fails and that total is at least two. invalidateCheck (line 248) permits editing a previously passed answer without resetting that total.

Observed behavior from source: A student can pass on submission one, revisit and edit an answer, fail on submission two, and receive the worked example after only one unsuccessful submission. The notebook still correctly distinguishes example exposure from a passed check. The possible learner effect is a small mismatch between the stated support rule and what appears; no learning harm is demonstrated.

Warranted correction: Preserve the existing generous support path and change the copy consistently to “after two submitted attempts, if the current check has not passed.” Alternatively, explicitly count failed submissions if two failures is the intended policy. The wording correction is sufficient; an additional assessment gate is unwarranted.

Limits of this judgment

The source supports a coherent, appropriately challenging introductory sequence. It does not establish actual ease of use, writing quality, learning gains, or completion time. Those require observed use and review of real student work. Browser layout and interaction testing were outside this review's scope.

Faculty 3 discussion

Student 3's reproduction establishes a more consequential defect than my original support-copy refinement: successful rechecks could authorize unsupported progress and misstate example exposure. I agree that correcting the state logic was necessary. In revised app.mjs, check() increments failedAttempts only on unsuccessful complete submissions; canAdvance() requires a current pass or supported permission with confirmed display or qualified legacy support. render() records workedShown only after .worked exists, and submission snapshots distinguish shown, none, and legacy-recorded. These source changes address the identified branches without treating example exposure as mastery. I did not verify browser behavior.

My inquiry-alignment concern is also addressed: skills() now asks students to explain how evidence answered an opening question or strengthened or changed an idea. That fits the optional hunch and preserves reasoned retention of an initial view. It avoids requiring a fabricated change of mind.

I agree with Student 5's distinction between clearer instructions and lower demands. The revised Israel question still requires percentage calculation and upward rounding; the UK recommendation identifies an allocation principle. The final guide retains both case mechanisms, evidence, a shared lesson, and a limit.

The labPrompt() cue and .lab-prediction styling place a short reasoning invitation before switching, with no added field or gate. Its educational value remains an inference: the app cannot establish that students paused or reasoned. That limitation warrants accurate reporting, not another compulsory response. Existing briefs still carry the substantive explanation, cost, and evidence demands. I find no remaining publication blocker within this source-review scope; actual reading, interaction, and learning remain unestablished.