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.

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 storing more books. Test data showed: Design A results: held 8 books. Design B results: 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 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 24 books (evidence), so more shelves (design feature) give more space for storage (solves problem)'. Choice B represents reversed logic. This error typically occurs when students use wrong numbers. 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.

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 reaching high shelves. Test data showed: Design A results: reached 48 inches. Design B results: 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 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 reached 51 inches (evidence), which means the taller platform (design feature) helped reach higher shelves (solves problem)'. Choice A represents reversed logic. This error typically occurs when students use wrong numbers. 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.

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.

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.

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.

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.

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 storing more books. Test data showed: Design A results: held 8 books. Design B results: 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 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 held 24 books (evidence), proving multiple shelves (design feature) increase storage capacity (solves problem)'. Choice B represents reversed logic. This error typically occurs when students use wrong numbers. 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.

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.

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.