- Amir and Marcus tried to stop a bottle from tipping over. They tested Base Design A (narrow) and Base Design B (wide). The data shows Design A tipped over (yes) and Design B stayed up (yes). A tried to help, but it was weak because it tipped. B was strong because the wide base kept it steady. Marcus said, "Maybe both are the same," but Amir pointed to the test results. Their class needs the base that keeps the bottle standing. Based on the data, what does the data tell us about which design solves the problem better?
Answer: Design B works better because the data shows it stayed up, so the wide base stops tipping.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was preventing a container from tipping. Test data showed: Design A (narrow base) tipped over (yes). Design B (wide base) stayed standing (yes). The students concluded that Design B works better because the data shows it didn't tip over, proving the wide base provides stability. Choice B is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of staying up), explains reasoning (connects data to design feature of wide base to problem solution of preventing tipping), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because the data shows it stayed up (evidence), proving the wide base (design feature) provides stability to keep the bottle from tipping (solves problem)'. Choice A represents the error of selecting the wrong design despite data showing poor performance and reversed logic. This error typically occurs when students misunderstand what the data means, thinking that 'tipped over' is a positive result rather than a failure to solve the problem. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because staying up shows that the wide base stops tipping'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like wide shapes') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- At presentation time, Marcus and Yuki tested two bases to hold up a tall tower. Design A had a narrow base, and it tipped over (yes). Design B had a wide base, and it stayed up (yes). Both designs tried to solve the stability problem, but one worked better. Design B's strength is the wide base helps it stay up, and Design A's weakness is it tips easily. Yuki said, "The evidence tells us which one is safer." Marcus pointed to the word "tipped" on the results sheet. The teacher asked, "How do you know which design works better?" Using the test results, which base design works better and why?
Answer: Design B works better because the data shows it stayed up, so the wide base stopped tipping.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is preventing container from tipping. Test data showed: Design A results: tipped over (yes). Design B results: stayed standing (yes). The students concluded that Design B works better because the data shows it didn't tip over, proving the wide base provides stability. Choice B is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design B works better because the data shows it stayed up (evidence), proving the wide base (design feature) stopped tipping (solves problem)'. Choice A represents reversed logic. This error typically occurs when students confusing designs' results. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Sofia, Chen, and Maya tested two storage shelves for a class library. Design A had 1 shelf and held 8 books. Design B had 3 shelves and held 24 books. A tried to store books but had little space, so it was weak. B was strong because it had more shelves for more books. Chen first thought A was fine, but Sofia pointed to the numbers. The teacher asked, "Which design works better? What does the data tell us?" They want the shelf that solves the storage problem best. Based on the test results, which design best solves the problem?
Answer: Design B works better because the data shows 24 books, so more shelves hold more books.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was storing more books. Test data showed: Design A (1 shelf) held 8 books. Design B (3 shelves) held 24 books. The students concluded that Design B works better because the data shows it held 16 more books (24 vs 8), proving multiple shelves increase storage capacity. Choice A is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of 24 books), explains reasoning (connects data to design feature of more shelves to problem solution of storing more books), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because the data shows it held 24 books (evidence), proving that multiple shelves (design feature) increase storage capacity for the class library (solves problem)'. Choice B represents the error of selecting the wrong design despite data showing poor performance and confusing the designs' results. This error typically occurs when students misread which design had which results or think the smaller number is better without understanding the problem requires storing MORE books. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because 24 books shows that more shelves hold more books'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Sofia and Chen tested two containers to organize crayons and markers. Design A is one box, and the items got mixed (yes). Design B has dividers, and the items stayed separated (yes). A tried to help, but mixing is a weakness. B's dividers are a strength because they keep items apart. They want the best container for keeping supplies neat. Design B works better than Design A because the data shows items stayed separated. The dividers stop mixing, which solves the organizing problem. Using the test results, which design works better?
Answer: Design B works better because the data shows they stayed separated, so dividers stop mixing.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, [problem: organizing supplies without mixing]. Test data showed: Design A [results: items got mixed (yes)]. Design B [results: items stayed separated (yes)]. The students concluded that Design [B] works better because [specific reasoning from stimulus: the data shows items stayed separated, proving the dividers prevent supplies from mixing]. Choice [B] is correct because it [identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion]. For example, [specific complete reasoning: 'Design B is better because test results show items stayed separated (evidence), proving the dividers (design feature) prevent supplies from getting mixed up (solves problem)']. Choice [C] represents [error type: aesthetic reasoning]. This error typically occurs when students [developmental explanation: focus on non-functional aspects like appearance or ease of building]. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Maya and Chen tested trays to carry books to the reading rug. Design A was flat, and Design B had raised edges. The data shows 2 books fell from A and 0 books fell from B. A tried to solve the carrying problem, but it was weak without edges. B was strong because edges helped hold books in place. Chen said, "I thought A was fine," but Maya showed the results. They want the tray that keeps books from falling. From the evidence, why does Design B work better than Design A?
Answer: Design B works better because the data shows 0 books fell, so the edges stopped books from sliding off.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was carrying books without dropping them. Test data showed: Design A (flat tray) had 2 books fall. Design B (tray with edges) had 0 books fall. The students concluded that Design B works better because the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off. Choice A is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of 0 books falling), explains reasoning (connects data to design feature of edges to problem solution of preventing books from sliding), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because the data shows 0 books fell (evidence), so the edges (design feature) stopped books from sliding off when carrying them (solves problem)'. Choice D represents the error of correct design without complete reasoning. This error typically occurs when students identify the right design and mention a feature but don't connect the data to why that feature solves the problem, providing incomplete reasoning that's missing key connections. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because 0 books fell shows that edges keep books from sliding off'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('it is new') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Emma and Jamal tested tray Design A (flat) and Design B (edges) to carry books. The data shows A had 2 books fall and B had 0 fall. A tried to help, but without edges it was weak. B was strong because the edges kept books in. The teacher asked, "Which worked better, and how do you know?" Emma pointed to the data. Jamal said both trays carried books, but one did it better. They wrote a class note: "We recommend one design." Based on the data, which design should they choose and why?
Answer: Design B works better because the data shows 0 books fell, so the edges kept books from sliding off.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was carrying books without dropping them. Test data showed: Design A (flat tray) had 2 of 3 books fall. Design B (tray with edges) had 0 of 3 books fall. The students concluded that Design B works better because the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off. Choice B is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of 0 books falling), explains reasoning (connects data to design feature of edges to problem solution of carrying books), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because the data shows 0 books fell (evidence), which means the raised edges (design feature) successfully prevented books from sliding off when carrying them (solves problem)'. Choice A represents the error of selecting the wrong design despite data showing poor performance. This error typically occurs when students misread the data or think any attempt to solve the problem means the design 'works,' without comparing to see which works BETTER. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because 0 books fell shows that edges keep books from sliding off'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- During a science discussion, Jamal and Maya tested two funnels to pour water into a bottle. Design A had a wide opening and spilled 2 tablespoons. Design B had a narrow opening and spilled 5 tablespoons. Both designs tried to solve the pouring problem, but one spilled less. Design A's strength is the wide opening catches more water, and Design B's weakness is it misses and spills more. Maya said, "Less spilled means it works better for pouring." Jamal looked at the data and agreed. The teacher asked, "Which design works better, and what evidence shows it?" Based on the data, which funnel design works better and why?
Answer: Design A works better because the data shows 2 tablespoons spilled, so the wide opening caught more water.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is pouring without spilling. Test data showed: Design A results: spilled 2 tablespoons. Design B results: spilled 5 tablespoons. The students concluded that Design A works better because the data shows it spilled 3 tablespoons less (2 vs 5), proving the wide opening catches liquid better. Choice B is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design A works better because it spilled 3 tablespoons less (evidence), showing the wide opening (design feature) catches liquid better when pouring into bottles (solves problem)'. Choice A represents wrong design selected. This error typically occurs when students select wrong design despite data showing poor performance. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- In the classroom, Marcus and Yuki tested two containers to organize crayons and markers. Design A was one big box, and the items got mixed up (yes). Design B had dividers, and the items stayed separated (yes). Both designs tried to solve the organization problem, but one kept things apart. Design B's strength is dividers separate items, and Design A's weakness is mixing. Yuki said, "The evidence is on our chart." Marcus said, "Let's pick the one that solves the problem better." The teacher asked, "What evidence shows which design works better?" What evidence shows which design works better?
Answer: Design B works better because the data says items stayed separated with dividers.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is organizing supplies without mixing. Test data showed: Design A results: items got mixed (yes). Design B results: items stayed separated (yes). The students concluded that Design B works better because the data shows items stayed separated, proving the dividers prevent supplies from mixing. Choice A is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design B is better because test results show items stayed separated (evidence), proving the dividers (design feature) prevent supplies from getting mixed up (solves problem)'. Choice D represents claiming both equal when data shows difference. This error typically occurs when students think all designs are good if they tried to solve problem. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Carlos and Emma wanted to keep crayons and markers from getting mixed up. They tested Container Design A (one box) and Design B (dividers). The data shows in A the items mixed (yes), and in B the items stayed separated (yes). A tried to organize, but it was weak because everything mixed together. B was strong because the dividers kept items apart. Carlos said, "We should use the one that keeps things separate." Emma pointed to the test results. Using the test results, which design should they choose and why?
Answer: Design B works better because the data shows items stayed separated, so dividers stop supplies from mixing.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was organizing supplies without mixing. Test data showed: Design A (one box) had items get mixed (yes). Design B (with dividers) had items stay separated (yes). The students concluded that Design B works better because the data shows items stayed separated, proving the dividers prevent supplies from mixing. Choice B is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of items staying separated), explains reasoning (connects data to design feature of dividers to problem solution of preventing mixing), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because test results show items stayed separated (evidence), proving the dividers (design feature) prevent supplies from getting mixed up (solves problem)'. Choice A represents the error of selecting the wrong design despite data showing poor performance and reversed logic. This error typically occurs when students misunderstand what the data means, thinking that 'items mixed' is a positive result rather than a failure to solve the organizing problem. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because items stayed separated shows that dividers stop supplies from mixing'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('it has more parts') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- In the classroom, Emma and Jamal tried to carry books on two trays. Design A was a flat tray, and 2 books fell off. Design B had raised edges, and 0 books fell off. They said both trays tried to solve the carrying problem, but one worked better. Design B's strength is the edges keep books on, and Design A's weakness is it has no edges. Emma pointed to the data chart and said, "0 fell is better than 2 fell." Jamal first said, "I think A is fine," but then he looked at the numbers. Their teacher asked, "Which worked better, and how do you know?" They wrote a class note: "We recommend one design because of the test data." Based on the data, which tray design works better and why?
Answer: Design B works better because the data shows 0 books fell, so the edges kept books from sliding off.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is carrying books without dropping. Test data showed: Design A results: 2 of 3 books fell. Design B results: 0 of 3 books fell. The students concluded that Design B works better because the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off. Choice B is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design B works better because the data shows 0 books fell (evidence), which means the raised edges (design feature) successfully prevented books from sliding off when carrying them (solves problem)'. Choice A represents reversed logic. This error typically occurs when students confuse designs' results, think all designs are good if they tried to solve problem. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- During design review time, Sofia and Chen tested two containers to organize supplies. Design A was one big box, and the items got mixed up (yes). Design B had dividers, and the items stayed separated (yes). Both designs tried to solve the same organization problem. Design B's strength is dividers keep things apart, and Design A's weakness is items mix together. Chen pointed to the results and said, "Separated helps us find things fast." Sofia said, "Let's choose using evidence, not guessing." Their teacher asked, "Which design should we choose and why?" Based on the test results, which container design should they choose and why?
Answer: Design B works better because the data shows items stayed separated, so the dividers stopped mixing.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is organizing supplies without mixing. Test data showed: Design A results: items got mixed (yes). Design B results: items stayed separated (yes). The students concluded that Design B works better because the data shows items stayed separated, proving the dividers prevent supplies from mixing. Choice B is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design B is better because test results show items stayed separated (evidence), proving the dividers (design feature) prevent supplies from getting mixed up (solves problem)'. Choice C represents aesthetic reasoning. This error typically occurs when students focus on non-functional aspects like appearance or ease of building. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Keisha and Chen tested bases to keep a block statue standing. Design A has a narrow base and it tipped (yes). Design B has a wide base and it stayed up (yes). A tried to stand, but tipping was a weakness. B was strong because it stayed stable. They must pick the base that solves the tipping problem. Design B works better than Design A because the data shows it stayed up. The wide base keeps it from tipping, so it stands safely. Using the test results, which design should they choose and why?
Answer: Design B works better because it stayed up, so the wide base stops tipping.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, [problem: preventing container from tipping]. Test data showed: Design A [results: tipped over (yes)]. Design B [results: stayed standing (yes)]. The students concluded that Design [B] works better because [specific reasoning from stimulus: the data shows it didn't tip over, proving the wide base provides stability]. Choice [B] is correct because it [identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion]. For example, [specific complete reasoning: 'Design B works better because the data shows it stayed up (evidence), which means the wide base (design feature) prevents tipping (solves problem)']. Choice [A] represents [error type: reversed logic]. This error typically occurs when students [developmental explanation: confuse designs' results]. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Amir and Yuki tested two shelves to store classroom books. Design A had 1 shelf and held 8 books. Design B had 3 shelves and held 24 books. A tried to store books, but it was weak because it held fewer. B was strong because more shelves made more space. Yuki said, "We should pick the one that holds more books." Amir pointed to the data to be sure. They need the best design for the storage problem. What evidence shows which design works better?
Answer: Design B works better because it held 24 books, while Design A held 8.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was storing more books. Test data showed: Design A (1 shelf) held 8 books. Design B (3 shelves) held 24 books. The students concluded that Design B works better because the data shows it held 16 more books (24 vs 8), proving multiple shelves increase storage capacity. Choice B is correct because it identifies the correct design based on data (Design B held 24 books while Design A held 8), provides specific evidence from the test results, and clearly states which design performed better. The statement 'Design B works better because it held 24 books, while Design A held 8' gives a complete comparison showing B's superior performance. Choice D represents the error of confusing the designs' results by switching which design had which data. This error typically occurs when students mix up the test results, attributing Design A's performance to Design B and vice versa, showing they haven't carefully read the data. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because 24 books shows that more shelves hold more books'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('three is my favorite number') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Jamal and Sofia needed to reach a high shelf. They tested Platform Design A (3 inches) and Platform Design B (6 inches). The data shows A helped them reach 48 inches and B helped them reach 51 inches. A tried to help, but it was weak because it gave less height. B was strong because it was taller and helped them reach higher. Sofia said, "Let's use the evidence." Jamal agreed after seeing the numbers. Based on the data, which design should they choose and why?
Answer: Design B works better because the data shows 51 inches, so extra height helps reach the shelf.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was reaching high shelves. Test data showed: Design A (3-inch platform) reached 48 inches. Design B (6-inch platform) reached 51 inches. The students concluded that Design B works better because the data shows it reached 3 inches higher (51 vs 48), proving extra height helps reach higher shelves. Choice B is correct because it identifies the correct design based on data (Design B with better performance), provides evidence (cites specific test results of 51 inches), explains reasoning (connects data to design feature of extra height to problem solution of reaching shelves), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design B works better because it reached 51 inches (evidence), showing that the 6-inch height (design feature) helps students reach higher shelves (solves problem)'. Choice A represents the error of selecting the wrong design despite data showing poor performance and confusing which number is higher. This error typically occurs when students misread numbers or don't understand that 51 is greater than 48, leading them to pick the design that actually performed worse. To help students make evidence-based conclusions: Use three-part frame: 'Design B works better because 51 inches shows that extra height helps reach higher'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('bigger is always best') and evidence-based conclusion ('B works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- At design review time, Sofia and Amir tested two trays for carrying books. Design A was flat, and 2 books fell off. Design B had raised edges, and 0 books fell off. Both designs tried to solve the carrying problem, but the data shows a difference. Design B's strength is the edges help stop sliding, and Design A's weakness is no edges. Amir said, "We should use evidence from the test." Sofia pointed to the number 0 and smiled. The teacher asked, "Why does one design work better than the other?" Why does Design B work better than Design A?
Answer: Design B works better because the data shows 0 books fell, so the edges stopped books from sliding off.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem is carrying books without dropping. Test data showed: Design A results: 2 books fell. Design B results: 0 books fell. The students concluded that Design B works better because the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off. Choice A is correct because it identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion. For example, 'Design B works better because the data shows 0 books fell (evidence), which means the raised edges (design feature) successfully prevented books from sliding off when carrying them (solves problem)'. Choice B represents reversed logic. This error typically occurs when students confusing designs' results. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Emma and Jamal tested trays to carry books. Design A is flat and 2 books fell. Design B has edges and 0 books fell. A tried to help, but no edges was a weakness. B's edges were a strength. They need to pick one tray for carrying books. Design B works better than Design A because the data shows 0 books fell. The edges stopped books from sliding off. This solves the problem of carrying books safely. Based on the data, which design should they choose and why?
Answer: Design B works better because the data shows 0 fell, so edges stop sliding.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, [problem: carrying books without dropping]. Test data showed: Design A [results: 2 of 3 books fell]. Design B [results: 0 of 3 books fell]. The students concluded that Design [B] works better because [specific reasoning from stimulus: the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off]. Choice [B] is correct because it [identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion]. For example, [specific complete reasoning: 'Design B works better because the data shows 0 books fell (evidence), which means the raised edges (design feature) successfully prevented books from sliding off when carrying them (solves problem)']. Choice [C] represents [error type: opinion without evidence]. This error typically occurs when students [developmental explanation: don't use data to decide, pick design they prefer instead of one that performed better]. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Emma and Chen tested funnels to pour juice without spilling. Design A has a wide top and spilled 2 tablespoons. Design B has a narrow top and spilled 5 tablespoons. A was strong because it caught more juice. B was weak because more juice missed the cup. They want the funnel that solves the spilling problem best. Design A works better than Design B because the data shows 2 spilled, not 5. The wide top catches more juice, so less spills out. From the evidence, which design works better?
Answer: Design A works better because the data shows 2 spilled, so the wide top catches more.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, [problem: pouring without spilling]. Test data showed: Design A [results: spilled 2 tablespoons]. Design B [results: spilled 5 tablespoons]. The students concluded that Design [A] works better because [specific reasoning from stimulus: the data shows it spilled 3 tablespoons less (2 vs 5), proving the wide opening catches liquid better]. Choice [B] is correct because it [identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion]. For example, [specific complete reasoning: 'Design A works better because it spilled 3 tablespoons less (evidence), showing the wide opening (design feature) catches liquid better when pouring into bottles (solves problem)']. Choice [A] represents [error type: confusing designs' results]. This error typically occurs when students [developmental explanation: confuse designs' results]. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Jamal and Maya tested trays to carry books to the library. Design A is flat and 2 books fell. Design B has edges and 0 books fell. A tried to work, but no edges was a weakness. B was strong because edges kept books on the tray. They want to choose the tray that works better. Design B works better than Design A because the data shows 0 books fell. The edges stop sliding, so it solves the carrying problem. What evidence shows which design works better?
Answer: Design B had 0 books fall, and Design A had 2 fall.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, [problem: carrying books without dropping]. Test data showed: Design A [results: 2 books fell]. Design B [results: 0 books fell]. The students concluded that Design [B] works better because [specific reasoning from stimulus: the data shows 0 books fell, proving the raised edges successfully prevented books from sliding off]. Choice [C] is correct because it [identifies correct design based on data: chooses design with better performance; provides evidence: cites specific test results; explains reasoning: connects data to design feature to problem solution; makes complete argument: includes all three parts of evidence-based conclusion]. For example, [specific complete reasoning: 'Design B works better because the data shows 0 books fell (evidence), which means the raised edges (design feature) successfully prevented books from sliding off when carrying them (solves problem)']. Choice [A] represents [error type: aesthetic reasoning]. This error typically occurs when students [developmental explanation: focus on non-functional aspects like appearance or ease of building]. To help students make evidence-based conclusions: Use three-part frame: 'Design [X] works better because [data evidence] shows that [design feature] [solves problem successfully]'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like A better') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Yuki and Keisha needed to pour water into a bottle without spilling. They tested Funnel Design A (wide) and Funnel Design B (narrow). The data shows A spilled 2 tablespoons and B spilled 5 tablespoons. Both funnels tried to help, but one worked better. A was strong because the wide opening caught more water. B was weak because more water missed and spilled. Keisha pointed to the numbers and said, "The data tells us." Based on the data, which design works better?
Answer: Design A works better because the data shows 2 tablespoons spilled, so the wide opening catches more water.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was pouring without spilling. Test data showed: Design A (wide funnel) spilled 2 tablespoons. Design B (narrow funnel) spilled 5 tablespoons. The students concluded that Design A works better because the data shows it spilled 3 tablespoons less (2 vs 5), proving the wide opening catches liquid better. Choice B is correct because it identifies the correct design based on data (Design A with better performance), provides evidence (cites specific test results of 2 tablespoons spilled), explains reasoning (connects data to design feature of wide opening to problem solution of catching water), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design A works better because the data shows it spilled only 2 tablespoons (evidence), showing the wide opening (design feature) catches more water when pouring into bottles (solves problem)'. Choice A represents the error of selecting the wrong design despite data showing poor performance and reversed logic. This error typically occurs when students misread the data or think that higher numbers mean better performance, not understanding that MORE spilling means WORSE performance. To help students make evidence-based conclusions: Use three-part frame: 'Design A works better because 2 tablespoons spilled shows that the wide opening catches more water'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I would pick it') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
- Emma and Marcus tested funnels to pour juice without spilling. Funnel Design A had a wide opening, and Design B had a narrow opening. The data shows A spilled 2 tablespoons and B spilled 5 tablespoons. A was strong because it caught more juice. B was weak because more juice missed and spilled out. Marcus said, "I thought narrow would be better," but Emma showed the numbers. They want to choose the funnel that solves the pouring problem best. Based on the data, which design should they choose and why?
Answer: Design A works better because the data shows 2 tablespoons spilled, so the wide opening catches more juice.
This question tests the 1st grade skill of using evidence from data to determine which design works better and explaining why (K-2-ETS1-3: Analyze data from tests to compare strengths and weaknesses and determine which design works better). After testing two designs and analyzing the data to find strengths and weaknesses, we use all that evidence to make a conclusion about which design works better for solving the problem. An evidence-based conclusion includes three parts: (1) stating which design works better, (2) providing specific data evidence (test results, numbers, observations), and (3) explaining the reasoning that connects the data to the design's features and to solving the problem. We don't choose based on which design we like, which looks nicer, or which was easier to build - we choose based on which one PERFORMED BETTER according to the test data. The design that works better is the one that solves the problem more successfully, as proven by the evidence from testing. In this scenario, the problem was pouring without spilling. Test data showed: Design A (wide funnel) spilled 2 tablespoons. Design B (narrow funnel) spilled 5 tablespoons. The students concluded that Design A works better because the data shows it spilled 3 tablespoons less (2 vs 5), proving the wide opening catches liquid better. Choice B is correct because it identifies the correct design based on data (Design A with better performance), provides evidence (cites specific test results of 2 tablespoons spilled), explains reasoning (connects data to design feature of wide opening to problem solution of catching juice), and makes a complete argument including all three parts of an evidence-based conclusion. For example, 'Design A works better because the data shows 2 tablespoons spilled (evidence), so the wide opening (design feature) catches more juice when pouring into bottles (solves problem)'. Choice D represents the error of evidence without connection. This error typically occurs when students mention the correct data but then claim it doesn't matter for the problem, not understanding that less spilling is exactly what solves the pouring problem - they have the evidence but don't connect it properly. To help students make evidence-based conclusions: Use three-part frame: 'Design A works better because 2 tablespoons spilled shows that the wide opening catches more juice'; practice complete reasoning chains (data → what it means → how design causes that → why that solves problem); emphasize difference between opinion ('I like wide funnels best') and evidence-based conclusion ('A works better because data shows...'); review data and strengths/weaknesses before concluding; ask guiding questions: 'Which design had better results?' 'What data tells you that?' 'Which design feature caused those good results?' 'How does that solve the problem?'. Watch for: students choosing based on preference not data, stating conclusion without reasoning, using data but not connecting to why that matters, selecting wrong design despite data showing poor performance, thinking if a design works at all it's 'good enough' without comparing to see which works BETTER, or providing partial reasoning that's missing key connections. Key concept: Data-driven decision making = using test results to choose the design that solves the problem best, and explaining your reasoning with evidence.
