Comparing Design Strengths
Help Questions
1st Grade Science › Comparing Design Strengths
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?
Dividers are a strength because Design A stayed separated in the test.
Dividers are a strength because the data shows items stayed separated in Design B.
Dividers are a strength because they make the box look pretty.
Dividers are a strength because the data shows items got mixed in Design B.
Explanation
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.
Yuki and Carlos look at shelf tests for storing books. Design A (one shelf): holds 8 books. Design B (three shelves): holds 24 books. They say more books held means it works well for storage. Based on the data, which design holds more books?
Design B holds more books because the results show 24 books.
Design A holds more books because it has one shelf.
Both hold 8 books because shelves are the same.
Design A holds more books because the results show 24 books.
Explanation
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 storing books - Design A had one shelf, Design B had three shelves. The test data showed: Design A holds 8 books, Design B holds 24 books. Based on this data, Design B's strength is holding more books (24); Design A's weakness is holding fewer (8). Choice B is correct because it makes evidence-based comparison; it uses data from both designs to compare performance, correctly identifying Design B holds more with 24 books. Choice C represents wrong feature without data connection, saying Design A holds more based on one shelf but ignoring actual numbers. This error typically occurs when students identify features but don't connect to results, or confuse which design performed better. 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 more shelves are a strength because it holds more'); 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?
A strength of Design B is it looks nicer, so it must work well.
A strength of Design B is 3 books fell because edges push books off.
A strength of Design B is being flat because the data shows 2 books fell.
A strength of Design B is the edges because the data shows 0 books fell.
Explanation
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.
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?
A weakness of Design A is limited storage because the data shows 8 books.
A weakness of Design A is holding 24 books because it is too strong.
A weakness of Design A is it is made fast, not from results.
A weakness of Design A is three shelves because it is tall.
Explanation
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.
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?
A weakness of Design B is less spilling because the data shows 2 tablespoons.
A weakness of Design B is more spilling because the data shows 5 tablespoons.
A weakness of Design B is being wide, so it catches too much.
A weakness of Design B is it looks small, not from data.
Explanation
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.
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?
A strength of Design B is it is shiny, so it works well.
A strength of Design B is it tipped over because the data says yes.
A strength of Design B is the wide base because it stayed standing.
A strength of Design B is the narrow base because it tipped over.
Explanation
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.
During science talk, Amir and Maya compare two reach platforms. The results say Design A is 3 inches high and helped reach 48 inches. Design B is 6 inches high and helped reach 51 inches. They use the same measuring tape for both tests. They say higher reach means it works better for reaching. Looking at the results, which design reached higher?
Design A, because the results tell us it reached 51 inches.
Both designs, because they reached the same height in the data.
Design B, because the results tell us it reached 51 inches.
Design A, because it is smaller and that is always better.
Explanation
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 high; Design B reached 51 inches high. Based on this data, Design B's strength is providing more height (data shows 51 inches); Design A's weakness is providing less height (data shows 48 inches). Choice B is correct because it correctly identifies Design B's strength of reaching higher and provides data support; it uses data from both designs to compare performance, correctly stating Design B reached 51 inches. Choice C represents opinion without data; this error typically occurs when students give opinions instead of using 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 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 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?
A weakness of Design A is staying standing because the results say yes.
A weakness of Design A is it looks boring, not the data.
A weakness of Design A is being wide, so it takes up space.
A weakness of Design A is tipping over because the results say yes.
Explanation
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.
During design review, Jamal and Sofia test shelf storage. Design A has one shelf and holds 8 books. Design B has three shelves and holds 24 books. They say holding more books is a strength for storage. They use the same size books for both tests. Looking at the results, what does the data show about Design B?
Design B holds 80 books because three shelves means 80.
Design B holds fewer books because the data shows 8 books.
Design B holds more books because the data shows 24 books.
Design B holds more books because it is painted red.
Explanation
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 B's strength is holding more books (data shows 24); Design A's weakness is holding fewer (data shows 8). Choice A is correct because it correctly identifies Design B's strength with evidence; it uses data showing Design B held 24 books to support holding more. Choice C represents irrelevant feature; this error typically occurs when students focus on non-functional features. 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 holding more is a strength because it works well for storage'); 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?
Both designs, because 2 and 5 are the same.
Design A, because the data shows it spilled 2 tablespoons.
Design B, because narrow openings always spill less.
Design B, because the data shows it spilled 2 tablespoons.
Explanation
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.