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Three-dimensional (3D) questions assess students' understanding of science concepts through the integration of three key dimensions. A well-designed 3D question requires students to apply scientific practices and crosscutting concepts to demonstrate a deep understanding of the disciplinary content.
Effective 3D questions are built around a scientifically accurate, real-world phenomenon that students can analyze and explain.
✅ Use local or familiar contexts to increase student engagement and relevance.
✅ Ensure the phenomenon requires students to apply knowledge rather than recall facts.
✅ Example: Instead of asking students to define photosynthesis, present data on plant growth under different light conditions and ask them to explain the results.
Examples: Lake effect snow causing flooding, Superstorm Sandy, wild fires in Long Island, frequency of tornadoes, drought in summer, flooding in Herkimer due to severity of thunderstorms, marine fossils in the Adirondacks.
Performance Expectations (PEs) define what students should know and be able to do at each grade level.
✅ Use the action verbs from the PEs (e.g., analyze, model, predict) to frame questions.
✅ Ensure that the question measures the full intent of the PE, not just isolated facts.
✅ Example: If the PE calls for students to "analyze data to support a claim," the question should require analysis, not just recognition of data.
3D assessments should reflect the balance of question types found in state exams.
✅ Use multiple-choice questions to assess foundational understanding and patterns.
✅ Use constructed response questions to evaluate reasoning, evidence use, and explanation.
✅ Example: Ask students to identify patterns in a data table (multiple choice) and then explain why those patterns occur (constructed response).
A well-constructed 3D question cluster should have a range of complexity as students work through it.
✅ Include "identify" type questions to establish understanding.
✅ Assess higher-order thinking questions requiring analysis and explanation.
✅ Example: Start with a question about interpreting a data trend, then ask students to predict how changing a variable would affect the trend.
Students should engage with scientific data and use it to support their claims.
✅ Provide data sets, graphs, tables, and charts as part of the question.
✅ Require students to select and justify which data supports their claim.
✅ Example: Present a graph showing population changes in an ecosystem and ask students to explain how predator-prey relationships are influencing the data.
Students should not be able to find the answer directly in the text or data provided.
✅ Design questions that require analysis and reasoning rather than simple extraction of information (DBQ style questions).
✅ Example: Provide a description of a scientific phenomenon and ask students to predict an outcome rather than identify a detail from the text.
Multiple-choice questions should be designed with care to avoid misleading or irrelevant options.
✅ Eliminate "junk choices" (obviously wrong answers).
✅ Ensure all options are scientifically accurate but only one is fully supported by the data.
✅ Example: If a question asks about the effect of temperature on gas volume, all answer choices should reflect accurate scientific relationships.
Encourage students to apply big-picture scientific ideas to their analysis.
✅ Include prompts that focus on crosscutting concepts such as:
Cause and effect
Patterns
Systems and system models
✅ Example: Ask students to explain how a change in temperature (cause) affects the behavior of molecules (effect).
Questions should require students to explain why the evidence supports a claim.
✅ Use the Claim-Evidence-Reasoning (CER) model to structure questions.
✅ Include prompts like:
“Explain why the data supports your conclusion.”
“Use evidence to justify your answer.”
✅ Example: Present a data table on plant growth under different soil types and ask students to identify the best soil and explain why that soil produced better growth.
Questions should reflect a variety of cognitive demands.
✅ Include some lower-level questions that assess understanding and recall.
✅ Include higher-level questions that require analysis, reasoning, and prediction.
✅ Example: Include a mixture of multiple-choice question asking students to identify the evidence that supports a claim, aligned to a constructed response question asking them to apply that evidence/concept to a new scenario.
✔️ Collaborate: Work with colleagues to align questions to the PEs/PLDs and ensure consistent rigor.
✔️ Pilot and Revise: Try your questions with students and analyze their responses to identify areas of confusion or misalignment.
✔️ Use Feedback: Reflect on how students performed and adjust future questions to better target learning goals. Make adjustments to instruction (scaffolding) to support skill building in students.