- Maya and Marcus look at stability results. Design A (narrow base) tipped over (yes). Design B (wide base) stayed standing (yes). They say they must use evidence from results, not opinions. Looking at the results, what weakness did the testing show about Design A?
Answer: A weakness of Design A is it tipped over because the results say yes.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two bases for stability - A had narrow base, B had wide base. The test data showed: Design A (narrow base) tipped over (yes). Design B (wide base) stayed standing (yes). Based on this data, Design A's weakness is narrow base that allowed tipping (data shows tipped over); Design B's strength is wide base that kept it stable (data shows stayed standing). Choice A is correct because it correctly states what design doesn't do well and provides data support - it identifies that Design A's weakness is it tipped over because the results say yes. Choice B represents reversed evaluation - this error typically occurs when students reverse good and poor performance, saying it stayed standing when the data shows it tipped over. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows tipped over - that means the narrow base is a weakness because it doesn't work well to keep tower stable'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Emma and Amir review results: Design A reached 48 inches high with a 3-inch platform. Design B reached 51 inches high with a 6-inch platform. They say Design A may not be tall enough for some shelves. They use the numbers as evidence. Looking at the results, how do the designs compare based on the test results?
Answer: Design B reached higher because 51 inches is more than 48 inches.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two platform designs for reaching - A was 3 inches high, B was 6 inches high. The test data showed: Design A reached 48 inches high. Design B reached 51 inches high. Based on this data, Design B reached higher because 51 inches is more than 48 inches. Choice B is correct because it makes evidence-based comparison - correctly uses the data showing Design A reached 48 inches while Design B reached 51 inches to identify B reached higher because 51 inches is more than 48 inches. Choice D represents opinion without data. This error typically occurs when students give opinions about safety or appearance instead of using actual test measurements. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 51 is more than 48 - that means B reached higher'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Maya and Marcus compare shelf designs using test data. Design A (one shelf) held 8 books. Design B (three shelves) held 24 books. They say a weakness is when a design holds less than needed. They use the numbers as evidence. Based on the data, what is a weakness of Design A?
Answer: A weakness of Design A is limited storage because the data shows 8 books.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two shelf designs for storage - Design A had one shelf, Design B had three shelves. The test data showed: Design A held 8 books; Design B held 24 books. Based on this data, Design A's weakness is limited storage (data shows 8 books); Design B's strength is more storage (data shows 24). Choice A is correct because it correctly identifies Design A's weakness with evidence; it uses data showing Design A held 8 books to support limited storage. Choice B represents reversed evaluation; this error typically occurs when students reverse good and poor performance. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 8 books - that means limited storage is a weakness because it doesn't work well for holding many'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Yuki and Marcus review reach data: Design A (3 inches) reached 48 inches, and Design B (6 inches) reached 51 inches. They say a strength is what works well, shown by better numbers. They notice the taller platform gave more reach. They write their ideas using the data as evidence. Based on the data, why is Design B's height a strength?
Answer: Because the data shows it reached 51 inches, which is higher.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two platform designs for reaching - Design A was 3 inches high, Design B was 6 inches high. The test data showed: Design A reached 48 inches; Design B reached 51 inches. Based on this data, Design B's strength is its height allowing higher reach (data shows 51 inches, which is higher than 48). Choice A is correct because it correctly identifies Design B's strength with evidence; it uses data showing Design B reached 51 inches, which is higher, to support the strength. Choice C represents opinion without data; this error typically occurs when students focus on appearance instead of function. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 51 inches - that means the height is a strength because it works well to reach higher'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Emma and Jamal review results: Design A had 2 of 3 books fall; Design B had 0 of 3 fall. They say Design B works well because the edges hold books in. Design A does not work well because it has no edges and books slid off. Based on the test results, what is a strength of Design B?
Answer: A strength of Design B is its edges because the data shows 0 fell.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two tray designs for carrying books - A had no edges, B had edges. The test data showed: Design A had 2 of 3 books fall. Design B had 0 of 3 books fall. Based on this data, Design B's strength is the edges that prevented books from falling (data shows 0 fell); Design A's weakness is lack of edges that allowed books to slide off (data shows 2 fell). Choice A is correct because it correctly states what design does well and provides data support - it identifies that Design B's strength is the edges because data shows 0 books fell (all stayed on). Choice B represents opinion without data. This error typically occurs when students give opinions instead of using data, thinking 'prettier' means it works better without looking at test results. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 0 books fell - that means the edges are a strength because they work well to keep books on'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Marcus and Yuki compare two containers using results. Design A (one box) had items get mixed (yes). Design B (box with dividers) had items stay separated (yes). They say evidence comes from the test results. Based on the data, what is a weakness of Design A?
Answer: A weakness of Design A is items mixed because the results say yes.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two container designs for organizing supplies - A had one box, B had box with dividers. The test data showed: Design A (one box) had items get mixed (yes). Design B (box with dividers) had items stay separated (yes). Based on this data, Design A's weakness is single space that allowed mixing; Design B's strength is dividers that kept items separated. Choice B is correct because it correctly states what design doesn't do well and provides data support - it identifies that Design A's weakness is items mixed because the results say yes. Choice A represents wrong feature - this error typically occurs when students confuse which design had which feature, saying Design A had dividers when it was actually one box. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows items mixed - that means one box is a weakness because it doesn't work well to keep items organized'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Emma and Chen look at stability results together. Design A has a narrow base and tipped over (yes). Design B has a wide base and stayed standing (yes). They say a weakness is what doesn't work well, shown by the results. They connect the narrow base to tipping. From the testing, what weakness did Design A show?
Answer: A weakness of Design A is tipping over because the results say yes.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two base designs for stability - Design A had narrow base, Design B had wide base. The test data showed: Design A tipped over (yes); Design B stayed standing (yes). Based on this data, Design A's weakness is tipping over due to narrow base (data shows yes); Design B's strength is staying standing (data shows yes). Choice A is correct because it correctly states what design doesn't do well and provides data support; it identifies Design A's weakness as tipping over because data shows yes. Choice B represents reversed evaluation; this error typically occurs when students reverse good and poor performance. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows tipped over yes - that means tipping is a weakness because it doesn't work well for stability'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Amir and Maya study container tests. Design A was one box, and items got mixed (yes). Design B had dividers, and items stayed separated (yes). They use the data to find strengths and weaknesses. From the testing, why are dividers a strength in Design B?
Answer: Dividers are a strength because the data shows items stayed separated in Design B.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two container designs for organizing supplies - A had one space, B had dividers. The test data showed: Design A items got mixed together (yes). Design B items stayed separated (yes). Based on this data, Design B's strength is dividers that kept items separated; Design A's weakness is single space that allowed mixing. Choice A is correct because it correctly states what design does well and provides data support - it identifies that dividers are a strength because data shows items stayed separated in Design B. Choice B represents reversed evaluation - this error typically occurs when students call good performance a weakness or vice versa, saying items got mixed when the data shows they stayed separated. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows items stayed separated - that means the dividers are a strength because they work well to keep items organized'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Keisha and Carlos review results: Design A reached 48 inches high using a 3-inch platform. Design B reached 51 inches high using a 6-inch platform. They say higher reach works well for high shelves. They use the data to compare. Based on the data, which design helped reach higher?
Answer: Based on data, Design B helped reach higher because it reached 51 inches.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two platform designs for reaching - A was 3 inches high, B was 6 inches high. The test data showed: Design A reached 48 inches high. Design B reached 51 inches high. Based on this data, Design B's strength is providing more height (data shows reached 51 inches); Design A's weakness is providing less height (data shows reached only 48 inches). Choice B is correct because it makes evidence-based comparison - uses data from both designs to compare performance, correctly identifying that Design B helped reach higher because data shows it reached 51 inches (versus 48 for Design A). Choice D represents aesthetic focus. This error typically occurs when students focus on non-functional features like color instead of using test measurements. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows B reached 51 inches and A reached 48 - that means B helped reach higher'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Yuki and Carlos test two shelves for book storage. Design A (one shelf) held 8 books. Design B (three shelves) held 24 books. They use the data to name strengths and weaknesses. Based on the data, what is a strength of Design B?
Answer: A strength of Design B is it held 24 books because the data shows 24.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two shelves for book storage - A had one shelf, B had three shelves. The test data showed: Design A (one shelf) held 8 books. Design B (three shelves) held 24 books. Based on this data, Design B's strength is multiple shelves that held more books (data shows 24); Design A's weakness is single shelf that held fewer books (data shows 8). Choice A is correct because it correctly states what design does well and provides data support - it identifies that Design B's strength is it held 24 books because the data shows 24. Choice B represents wrong data - this error typically occurs when students confuse which design had which results, saying Design B held 8 when it actually held 24. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 24 books - that means three shelves is a strength because they work well to hold many books'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Jamal and Sofia test two bases for a tower. Design A had a narrow base and tipped over (yes). Design B had a wide base and stayed standing (yes). They use data to compare strengths and weaknesses. From the testing, what does the data show about the wide base?
Answer: The data shows the wide base is a strength because it stayed standing.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two bases for a tower - A had narrow base, B had wide base. The test data showed: Design A tipped over (yes). Design B stayed standing (yes). Based on this data, Design B's strength is the wide base that kept it stable (data shows stayed standing); Design A's weakness is narrow base that allowed tipping (data shows tipped over). Choice A is correct because it correctly states what design does well and provides data support - it identifies that the wide base is a strength because data shows it stayed standing. Choice B represents reversed evaluation - this error typically occurs when students call good performance a weakness or vice versa, saying the wide base is a weakness when it actually worked well. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows stayed standing - that means the wide base is a strength because it works well to keep tower stable'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- In the classroom, Carlos and Keisha test two base designs for stability. The results show Design A (narrow base) tipped over: yes. Design B (wide base) stayed standing: yes. They say tipping over means it doesn't work well. They compare using the same push each time. Based on the test results, what is a strength of Design B?
Answer: A strength of Design B is the wide base because it stayed standing.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two base designs for stability - Design A had narrow base, Design B had wide base. The test data showed: Design A tipped over (yes); Design B stayed standing (yes). Based on this data, Design B's strength is the wide base that prevented tipping (data shows stayed standing); Design A's weakness is narrow base that caused tipping (data shows tipped over). Choice A is correct because it correctly states what design does well and provides data support; it identifies Design B's strength as the wide base because data shows it stayed standing. Choice D represents reversed evaluation; this error typically occurs when students call good performance a weakness or vice versa. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows stayed standing - that means the wide base is a strength because it works well to keep stable'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Jamal and Sofia review results: Design A has a narrow base and it tipped over (yes). Design B has a wide base and it stayed standing (yes). They connect the base shape to what happened. Based on the data, what weakness did the testing show about Design A?
Answer: A weakness of Design A is narrow base because the data shows it tipped over.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two platform designs for stability - A had narrow base, B had wide base. The test data showed: Design A tipped over (yes). Design B stayed standing (yes). Based on this data, Design A's weakness is narrow base that made it unstable (data shows it tipped over); Design B's strength is the wide base that kept it stable (data shows it stayed standing). Choice A is correct because it identifies weakness with evidence - correctly states what design feature doesn't work well and provides data support, identifying that Design A's weakness is narrow base because data shows it tipped over. Choice C represents aesthetic focus. This error typically occurs when students focus on non-functional features like shininess instead of using test data about actual performance. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows it tipped over - that means the narrow base is a weakness because it doesn't work well to keep it stable'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Sofia and Chen look at results: Design A had 2 of 3 books fall; Design B had 0 of 3 fall. They write that edges help keep books on the tray. They also note Design A has no edges, so books slide off. Based on the data, what weakness did the testing show about Design A?
Answer: A weakness of Design A is no edges because the data shows 2 fell.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two tray designs for carrying books - A had no edges, B had edges. The test data showed: Design A had 2 of 3 books fall. Design B had 0 of 3 books fall. Based on this data, Design A's weakness is lack of edges that allowed books to slide off (data shows 2 fell); Design B's strength is the edges that prevented books from falling (data shows 0 fell). Choice B is correct because it correctly states what design doesn't do well and provides data support - it identifies that Design A's weakness is no edges because data shows 2 of 3 books fell off. Choice C represents aesthetic focus. This error typically occurs when students focus on non-functional features like color instead of using test data about performance. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 2 books fell - that means no edges is a weakness because they don't work well to keep books on'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- During design review, Emma and Jamal read results: Tray A (flat) had 2 of 3 books fall; Tray B (edges) had 0 of 3 books fall. They say Tray B works well because the edges help hold books on. They write that Tray A doesn't work well because it has no edges and books can slide off. They compare both designs using the same test. Based on the test results, what is a strength of Design B?
Answer: A strength of Design B is the edges because the data shows 0 books fell.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two tray designs for carrying books - Design A had flat board, Design B had raised edges. The test data showed: Design A 2 of 3 books fell; Design B 0 of 3 books fell. Based on this data, Design B's strength is the raised edges that prevented books from falling (data shows 0 fell); Design A's weakness is lack of edges that allowed books to slide off (data shows 2 fell). Choice A is correct because it correctly states what design does well and provides data support; it correctly identifies that Design B's strength is the edges because data shows 0 books fell (all stayed on). Choice B represents wrong feature; this error typically occurs when students confuse which design had which results. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 0 books fell - that means the edges are a strength because they work well to keep books on'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Marcus and Yuki review results: Design A is one shelf and holds 8 books. Design B is three shelves and holds 24 books. They say Design B works well because it has more shelves. They say Design A does not work well for lots of books. Based on the test results, what is a weakness of Design A?
Answer: A weakness of Design A is it holds only 8 books because the data shows 8.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two shelf designs for holding books - A had one shelf, B had three shelves. The test data showed: Design A holds 8 books. Design B holds 24 books. Based on this data, Design A's weakness is holding fewer books (data shows only 8 books); Design B's strength is holding more books (data shows 24 books). Choice A is correct because it identifies weakness with evidence - correctly states what design doesn't do well and provides data support, identifying that Design A's weakness is it holds only 8 books because the data shows 8. Choice D represents wrong data. This error typically occurs when students confuse which design had which results, mixing up the test data between designs. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows A holds only 8 books - that means holding few books is a weakness'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Keisha and Amir test two funnels for pouring. Design A (wide opening) spilled 2 tablespoons. Design B (narrow opening) spilled 5 tablespoons. They say less spilled means it works well. They use the numbers as evidence. Based on the data, which design spilled less?
Answer: Design A spilled less because the data shows 2 tablespoons.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two funnels for pouring - A had wide opening, B had narrow opening. The test data showed: Design A (wide opening) spilled 2 tablespoons. Design B (narrow opening) spilled 5 tablespoons. Based on this data, Design A's strength is wide opening that spilled less (data shows 2 tablespoons); Design B's weakness is narrow opening that spilled more (data shows 5 tablespoons). Choice B is correct because it correctly uses the data from both designs to compare performance - it correctly identifies that Design A spilled less because data shows 2 tablespoons. Choice A represents wrong data - this error typically occurs when students confuse which design had which results, saying Design B spilled less when it actually spilled more. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 2 tablespoons - that means Design A is a strength because it spills less liquid'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Emma and Jamal review results: Design A had 2 of 3 books fall; Design B had 0 of 3 fall. They say Design B's strength is the edges because the data shows none fell. They say Design A's weakness is no edges because the results tell us 2 books fell. They compare fairly because both carried 3 books. Based on the test results, what is a strength of Design B?
Answer: A strength of Design B is the edges because the data shows 0 books fell.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two tray designs for carrying books - A had flat board, B had raised edges. The test data showed: Design A had 2 of 3 books fall. Design B had 0 of 3 books fall. Based on this data, Design B's strength is the raised edges that prevented books from falling (data shows 0 fell); Design A's weakness is lack of edges that allowed books to slide off (data shows 2 fell). Choice A is correct because it correctly states what design does well and provides data support - it identifies that Design B's strength is the edges because data shows 0 books fell (all stayed on). Choice B represents wrong feature - this error typically occurs when students confuse which design had which results, calling Design B flat when it actually had edges. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 0 books fell - that means the edges are a strength because they work well to keep books on'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- At the science table, Amir and Yuki test two funnel designs. Design A has a wide opening and spilled 2 tablespoons. Design B has a narrow opening and spilled 5 tablespoons. They say less spilling means it works well. They compare both designs with the same pour. Based on the data, which design spilled less?
Answer: Design A, because the data shows it spilled 2 tablespoons.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two funnel designs for pouring - Design A had wide opening, Design B had narrow opening. The test data showed: Design A spilled 2 tablespoons; Design B spilled 5 tablespoons. Based on this data, Design A's strength is less spilling (data shows 2); Design B's weakness is more spilling (data shows 5). Choice B is correct because it correctly identifies Design A's strength of spilling less with evidence; it uses data showing Design A spilled 2 tablespoons, which is less than 5. Choice A represents confusing designs; this error typically occurs when students confuse which design had which result. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 2 tablespoons spilled - that means less spilling is a strength because it works well for pouring'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
- Keisha and Carlos read the funnel results. Design A (wide opening) spilled 2 tablespoons. Design B (narrow opening) spilled 5 tablespoons. They say a weakness is what doesn't work well, shown by more spilling. They connect the narrow opening to missing the cup. From the testing, what weakness did Design B show?
Answer: A weakness of Design B is more spilling because the data shows 5 tablespoons.
This question tests the 1st grade skill of analyzing data from tests of two objects to compare the strengths and weaknesses of how each performs (K-2-ETS1-3: Analyze data to compare strengths and weaknesses). After testing two designs, we analyze (look carefully at) the data to find strengths and weaknesses. Strengths are what a design does well - where it performs successfully based on test results (fewer items fell, reached higher, held more, didn't tip, kept items separated, spilled less). Weaknesses are what a design doesn't do well - where it performs poorly based on test results (more items fell, didn't reach as high, held less, tipped over, items mixed together, spilled more). We identify strengths and weaknesses BY LOOKING AT THE DATA - not by guessing or opinions. The data (test results) gives us evidence about how each design performed, and we use that evidence to say what each design's strengths and weaknesses are. In this scenario, two funnel designs for pouring - Design A had wide opening, Design B had narrow opening. The test data showed: Design A spilled 2 tablespoons; Design B spilled 5 tablespoons. Based on this data, Design B's weakness is more spilling due to narrow opening (data shows 5); Design A's strength is less spilling (data shows 2). Choice A is correct because it correctly identifies Design B's weakness with evidence; it states more spilling as weakness because data shows 5 tablespoons. Choice B represents wrong data; this error typically occurs when students use wrong data. To help students analyze data for strengths and weaknesses: Create two-column chart (Strengths | Weaknesses) for each design; explicitly connect data to evaluation ('The data shows 5 tablespoons - that means more spilling is a weakness because it doesn't work well for pouring'); practice strength/weakness language (strength = what works well, weakness = what doesn't work well); emphasize evidence ('How do we know? The data shows...'); look at specific results (numbers, yes/no, measurements) to identify performance; compare design features to their results. Watch for: students who give opinions without data, who state results without labeling as strength/weakness, who reverse good and poor performance, who focus on appearance instead of function, who confuse which design had which result, or who think strength/weakness is about whether they like it instead of whether data shows it works well. Key concept: Data tells us about performance → good performance = strength, poor performance = weakness → we base our evaluation on evidence from testing, not on opinions.
