Group observable answer patterns into testable misconception hypotheses without labeling learners. 제공할 내용: Question and expected reasoning, Anonymous student answers, Teaching and data context. 예상 결과: An evidence table of error patterns, alternative explanations, instructional responses, and rechecks.
1
맥락 추가
텍스트는 이 브라우저에 유지됩니다. AILesson Prompts는 이를 모델이나 서버로 보내지 않습니다.
2
프롬프트
채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다
Analyze the anonymous student answers against the supplied task and reasoning.
Question, objective, correct reasoning, rubric, methods, and key:
[task]
Anonymous answers, shown work, choices, confidence, and blanks:
[answers]
Instruction, language/access supports, completeness, privacy, and next teaching opportunity:
[context]
Preserve every answer ID. Code observable features before inferring misconceptions. Distinguish conceptual model, procedure, arithmetic, vocabulary, representation, instruction interpretation, transcription, and incomplete evidence. A wrong answer may have multiple explanations; a correct answer may use faulty reasoning. Do not infer intelligence, effort, disability, motive, demographic traits, or a stable learner type. Treat blanks separately and never assume why they are blank.
Create an evidence table with pattern ID, exact response evidence, count and denominator, affected objective component, misconception hypothesis, credible alternatives, confidence with reason, confirming diagnostic question, and teaching response. Keep overlapping patterns visible; do not force one student into one group. Prioritize by prevalence, prerequisite impact, persistence, and teaching leverage without invented scores. Propose whole-class, small-group, and individual responses only where evidence supports them, plus a parallel recheck and success criteria. End with ID reconciliation, outliers/counterexamples, privacy check, and what the data cannot establish.
기본적으로 비공개프롬프트 구성은 브라우저에서 로컬로 이루어집니다. 조직에서 허용하지 않는 한 기밀 정보를 AI 서비스에 입력하지 마세요.
입력에서 결과까지
적용 예시
구체적인 맥락이 이 레시피를 바로 사용할 수 있는 결과로 바꾸는 방법을 확인하세요
실제 입력
Question and expected reasoning
Question: For 2,3,4,100, calculate the median and choose mean or median for a typical value, explaining why. Objective: order data; for even n average the two middle values; choose based on purpose/extremes; do not say an accurate mean is mathematically wrong. Key: median 3.5; median is usually more representative here if 100 is verified, with limitation. Two points calculation, two reasoning.
Anonymous student answers
A01: median 4; 'middle number.' A02: 3.5; median because 100 pulls mean. A03: 27.25; 'mean is always average so use it.' A04: 3.5; 'median always wins if an outlier exists.' A05: blank. A06: sorted 2,3,4,100 then wrote (4+100)/2=52. A07: 3.5; chose mean because 'median ignores data.' A08: median 3.5; says verify whether 100 is an entry error first, then choose based on purpose. A09: 4; chose median because 100 is large. A10: 3.5 with no choice/explanation.
Teaching and data context
All ten answers are from the same lesson and complete class sample. Ordering and even-count rule were modeled once. English task; bilingual glossary was available. IDs are anonymous. Fifteen minutes for reteaching tomorrow; no demographic or accommodation data should be inferred.
예시 출력
Coverage: A01-A10 all retained; A05 is blank, not coded as a misconception.
P1 even-count middle procedure: A01/A09 choose one middle value; A06 chooses the upper two after ordering—3/10 observed. Hypothesis: incomplete identification of the two central positions, moderate confidence; alternatives include transcription or wording confusion. Diagnostic: “After crossing off one value from each end, which values remain, and what operation follows?” Response: concrete card pairing, then two parallel even-count sets. Success: independently selects the correct central pair and averages both.
P2 unconditional choice rules: A03 says always mean; A04 says always median; A07 treats use of all values as sufficient—3/10, overlapping conceptual pattern. A02 is correct but does not mention verification/limitation; A08 is a counterexample demonstrating full conditional reasoning. Diagnostic: compare one verified high value with one data-entry error and ask what changes. Reteach purpose, data quality, representativeness, and limitation; success requires a conditional justification without “always.”
P3 incomplete explanation: A10 gives correct calculation but no selection; evidence is missing, not proof of misconception. Prompt A10 with the reasoning stem rather than reteach calculation. A03's 27.25 is the correct mean but answers the wrong requested calculation; classify instruction/measure selection, not arithmetic failure. A05 needs a neutral alternative response opportunity.
15-minute plan: 5 minutes whole-class even-count cards because P1 affects a prerequisite; 6 minutes contrast cases for conditional choice; 4 minutes individual parallel exit item. Do not group by stable labels. Recheck success: median calculation plus purpose-based choice, evidence, and one limitation. Data cannot establish why A05 is blank, whether glossary use mattered, or stable ability.
효과가 있는 이유
1
Coding answer features before causes reduces the chance that one visible error becomes an unsupported learner diagnosis
2
Diagnostic questions distinguish look-alike errors so teaching can target the actual missing reasoning
결과 확인
Is every misconception hypothesis grounded in specific anonymous answer evidence
Are alternative explanations, overlap, blanks, and correct answers with weak reasoning retained
Does each teaching response include a diagnostic or parallel recheck
안심하고 사용하세요
자주 묻는 질문
이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변
What should I prepare before using “Analyze common misconceptions in student answers”?
For “Analyze common misconceptions in student answers,” prepare Question and expected reasoning, Anonymous student answers, and Teaching and data context. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Analyze common misconceptions in student answers” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—An evidence table of error patterns, alternative explanations, instructional responses, and rechecks—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Analyze common misconceptions in student answers”?
The published test record for “Analyze common misconceptions in student answers” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.
ChatGPT
