- Carlos tested mitten dryers. Problem: dry wet mittens after recess. Design A is a flat tray. Design B is a standing rack with clips. Results: A fits 6 pairs and takes 45 minutes; B fits 10 pairs and takes 30 minutes. Based on the data, what is Design A not good at?
Answer: Design A holds fewer mitten pairs
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of drying wet mittens after recess, with Design A a flat tray and Design B a standing rack with clips; the test data shows Design A fits 6 pairs and takes 45 minutes, while Design B fits 10 pairs and takes 30 minutes. Choice C is correct because the data shows Design A holds only 6 pairs compared to Design B's 10, accurately identifying that it is not good at holding more (holds fewer) as a weakness. Choice A represents a common error of stating something not shown in the data or reversing results, which happens when students claim Design A holds more when the data clearly indicates it holds fewer pairs. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Amir tested book holders. Problem: keep books from falling out of a desk. Design A is a short bookend. Design B is a tall box books slide into. Results: A holds 4 books and can tip, easy to grab, saves desk space; B holds 8 books, stable, harder to slide out, uses more space. What is a weakness of Design A based on the test data?
Answer: Design A holds fewer books
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of keeping books from falling out of a desk, with Design A a short bookend and Design B a tall box; the test data shows Design A holds 4 books and can tip but is easy to grab and saves desk space, while Design B holds 8 books stably but is harder to slide out and uses more space. Choice A is correct because the data shows Design A holds only 4 books compared to Design B's 8, accurately identifying this as a weakness in capacity. Choice B represents a common error of reversing results between designs, which happens when students confuse space usage, claiming Design A uses more when the data indicates it saves space. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Jamal tested two watering bottles. Problem: water plants without spilling. Design A has 1 big hole. Design B has 5 small holes. Test results: A waters 3 plants in 2 minutes, spills sometimes, water in one spot; B takes 5 minutes, never spills, spreads water. What is a weakness of Design B according to the results?
Answer: Design B takes more time
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of watering plants without spilling, with Design A having 1 big hole and Design B having 5 small holes; the test data shows Design A waters 3 plants in 2 minutes but spills sometimes and puts water in one spot, while Design B takes 5 minutes, never spills, and spreads water. Choice A is correct because the data shows Design B takes 5 minutes compared to Design A's 2 minutes, so stating that Design B takes more time accurately identifies a weakness based on the speed criterion. Choice B represents a common error of reversing results between designs, which happens when students confuse which design has which performance, such as attributing spilling to Design B when the data shows it never spills while Design A does sometimes. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Chen tested mitten dryers. Problem: dry wet mittens after recess. Design A is a flat tray. Design B is a standing rack with clips. Look at the test results: A dries in 45 minutes, fits 6 pairs, uses table space; B dries in 30 minutes, fits 10 pairs, saves table space. Which design is better at drying faster according to the test results?
Answer: Design B is better at drying faster
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of drying wet mittens after recess, with Design A a flat tray and Design B a standing rack with clips; the test data shows Design A dries in 45 minutes, fits 6 pairs, and uses table space, while Design B dries in 30 minutes, fits 10 pairs, and saves table space. Choice B is correct because the data shows Design B dries in 30 minutes compared to Design A's 45 minutes, directly supporting that Design B is better at drying faster based on the time criterion. Choice A represents a common error of reversing results between designs, which happens when students misread the data and attribute faster drying to the slower design, ignoring the specific times provided. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Sofia tested two ways to carry art supplies. Problem: carry supplies to the table. Design A is a plastic bag with handles. Design B is a box with dividers. Results: A is light, items can break if dropped, items mix; B is heavier, items protected, items organized. Based on the data, what is Design B good at and not good at?
Answer: Good at protecting, not good at being light
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of carrying art supplies to the table, with Design A being a plastic bag with handles and Design B a box with dividers; the test data shows Design A is light but items can break if dropped and mix, while Design B is heavier but protects items and keeps them organized. Choice B is correct because the data shows Design B protects items (a strength) but is heavier (not good at being light), directly supporting this identification of good and not good aspects. Choice A represents a common error of reversing results between designs, which happens when students swap the attributes, such as claiming Design B is good at being light when the data indicates it is heavier compared to Design A. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Marcus tested two pencil organizers. Problem: keep pencils organized. Design A is one cup. Design B is a tray with sections. Test results: A holds 15, small space, pencils mixed; B holds 20, bigger space, pencils sorted. Which statement correctly compares the strengths and weaknesses?
Answer: A saves space, but B holds more
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of keeping pencils organized, with Design A one cup and Design B a tray with sections; the test data shows Design A holds 15 pencils in small space but pencils mixed, while Design B holds 20 in bigger space with pencils sorted. Choice B is correct because the data supports Design A saves space (a strength) but holds fewer (implying B holds more as its strength), correctly comparing trade-offs in space and capacity. Choice D represents a common error of claiming one design is better without specifying the criterion or ignoring trade-offs, which happens when students overlook data showing weaknesses and state one is perfect despite evidence of limitations. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Keisha tested book holders. Problem: keep books from falling out of a desk. Design A is a short bookend. Design B is a tall box. Results: A easy to grab, holds 4, saves space; B holds 8, stable, harder to slide out, uses more space. Look at the test results. Which design is better at holding more books?
Answer: Design B is better at holding more books
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of keeping books from falling out of a desk, with Design A a short bookend and Design B a tall box; the test data shows Design A is easy to grab, holds 4 books, and saves space, while Design B holds 8 books, is stable, harder to slide out, and uses more space. Choice B is correct because the data shows Design B holds 8 books compared to Design A's 4, directly supporting that Design B is better at holding more books based on capacity. Choice A represents a common error of reversing results between designs, which happens when students misattribute higher capacity to the design that actually holds fewer books according to the data. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Maya tested rainwater collectors. Problem: collect rainwater for a garden. Design A is a wide shallow pan. Design B is a narrow tall bucket. Test results: A catches more rain per storm, loses water fast, stores less; B catches less rain per storm, loses water slowly, stores more. Compare the two designs. What does Design A do better than Design B?
Answer: Design A catches more rain per storm
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of collecting rainwater for a garden, with Design A a wide shallow pan and Design B a narrow tall bucket; the test data shows Design A catches more rain per storm but loses water fast and stores less, while Design B catches less rain per storm but loses water slowly and stores more. Choice C is correct because the data directly states Design A catches more rain per storm than Design B, supporting that it does this better. Choice A represents a common error of stating something not shown in the data or reversing results, which happens when students ignore the specific criteria and claim Design A stores more when the data shows it stores less. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Yuki tested two watering bottles. Problem: water plants without spilling. Design A has 1 big hole. Design B has 5 small holes. Results: A is faster but spills sometimes; B is slower but never spills. What is a strength of Design B based on the test data?
Answer: Design B never spills during watering
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of watering plants without spilling, with Design A having 1 big hole and Design B 5 small holes; the test data shows Design A is faster but spills sometimes, while Design B is slower but never spills. Choice C is correct because the data indicates Design B never spills during watering, directly supporting this as a strength in preventing spills. Choice B represents a common error of reversing results between designs, which happens when students attribute a weakness of Design A (spills sometimes) to Design B, ignoring the data that shows B never spills. To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
- Emma tested two pencil organizers. Problem: keep pencils organized. Design A is one cup. Design B is a tray with sections. Look at the test results: A holds 15, uses small space, pencils mix; B holds 20, uses bigger space, pencils stay sorted. What is a strength of Design A based on the test data?
Answer: Design A uses less desk space
This question tests 2nd grade ability to compare test data and identify strengths and weaknesses of designs (NGSS K-2-ETS1-3: Analyze data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of how each performs). When we test two designs that solve the same problem, we collect data to compare how well each performs. A strength is something a design does well—an aspect where it performs successfully. A weakness is something a design doesn't do as well—an area where it could be improved. Often, designs have trade-offs: Design A might be better at one thing (like speed) while Design B is better at another thing (like control). By comparing test data across multiple criteria (speed, capacity, ease of use, space needed, cost, durability), we can identify what each design is good at and not good at. This helps us choose the right design for a specific situation or improve designs by addressing weaknesses. In this scenario, both designs solve the problem of keeping pencils organized, with Design A being one cup and Design B a tray with sections; the test data shows Design A holds 15 pencils, uses small space, but pencils mix, while Design B holds 20 pencils, uses bigger space, and keeps pencils sorted. Choice B is correct because the data shows Design A uses small space, making it a strength in terms of using less desk space compared to Design B's bigger space. Choice A represents a common error of reversing results between designs, which happens when students mistakenly attribute a strength of one design to the other, such as claiming Design A holds more when the data shows it holds fewer (15 vs. 20). To help students compare designs using test data: Model using comparison language—'Design A is better at [criterion] because [data], but Design B is better at [criterion] because [data].' Create comparison charts with rows for criteria and columns for each design's results. Practice identifying strengths: 'Look at this result—Design A holds 20 pencils. That's more than Design B's 15. So a strength of Design A is greater capacity.' Then identify corresponding weakness for the other design. Teach that trade-offs are normal: usually no design is best at everything. Use color coding: highlight Design A's strengths in one color, Design B's in another, to visualize trade-offs. Have students practice with the frame: 'Based on the data, Design [A/B] is good at [criterion supported by data] but not good at [criterion supported by data].' Watch for students who ignore data to state preferences, reverse results between designs, or claim one design is perfect when data shows weaknesses. Emphasize: use evidence from tests, not opinions.
