Science // Scientific Inquiry

Comparing Design Solutions

Analyse data from tests of two objects designed to solve the same problem to compare the strengths and weaknesses of each design

Ages
5 to 8
Type
Method
Builds on
2 topics
Leads to
1 topic
Standards
1 code

Quick check

If the learner tests two different paper aeroplane designs, can they compare the results and explain which one flew better and why?

Ask this to see whether the topic is secure.

Topic // Mastery

What mastery looks like

What a learner can do once this topic is secure.
  • Test two designs that address the same problem and collect data on their performance
  • Compare the results, identifying strengths and weaknesses of each design
  • Use data as evidence to recommend which design works better and suggest improvements

Topic // Map

Where this fits

What to learn first and what this topic opens up, with the reason for each link. Required links come first.
  • RequiredMust come first
  • HelpfulHelps, but not essential

Before this

2 topics
  • Recording DataRequired

    Scientific Inquiry // Ages 5 to 7

    Must gather/record data before analysing data from design tests

  • Modelling with SketchesRequired

    Scientific Inquiry // Ages 5 to 8

    Must create models before comparing test results of two designs

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Comparing Design Solutions

Ages 5 to 8 // Method

Leads to

1 topic
  • Simple Design ProblemsRequired

    Scientific Inquiry // Ages 8 to 11

    Must analyse simple design comparisons before formally defining design problems with criteria/constraints

Topic // Research-informed

Teaching notes

From the Marble Skill Taxonomy. These notes have not been validated in classrooms, so treat them as a starting point for your own judgement.

The approach

For ages 5–8, teach evidence-based comparison of two engineering designs that solve the same problem: test both the SAME way, record each one's performance, then recommend the better-performing design by citing the recorded data (not preference or looks), name a strength AND weakness of each, and suggest one improvement. The central move beyond a single test is that the recommendation must rest on what the numbers/observations showed. The same test–record–compare loop works with other same-problem contexts (two bridge shapes, two boat hulls, two shade structures). The strongest version has children generate the head-to-head data through their own fair tests; pre-authored scenes, dialogue, and scored questions can support interpretation and vocabulary but do not let the child produce the comparison themselves.

Common misconceptions

  • Recommending the design you liked or built rather than the one the data supports.
  • Testing the two designs differently, making the comparison unfair.
  • Seeing only a 'winner' and missing that each design has strengths and weaknesses.
  • Judging on appearance rather than measured performance.
  • Not recording results, so the comparison rests on memory or feeling.
  • Thinking the better design needs no improvement.

Useful representations

  • Two designs tested the same way on a shared problem
  • Side-by-side results chart (Design A vs Design B)
  • Strength/weakness tags on each design
  • Evidence-based recommendation ('better because the data showed…')
  • An improvement note for the chosen design

Teaching practices

  • Test both designs the SAME way (same load, same drop) so the comparison is fair and the data is meaningful.
  • Record each design's performance simply (a number, tally or mark) so the comparison rests on evidence, not memory or preference.
  • Draw out strengths AND weaknesses of each design, so children see trade-offs rather than a single 'winner'.
  • Insist the recommendation cite the data ('the arch held more bricks') rather than 'I like it / I made it'.
  • Follow the comparison with a 'how could we improve it?' step, seeding iteration.
  • Use problems where each design wins on a different measure, so comparison becomes multi-dimensional.

Activities to try

  1. 1

    Best Design Wins: testing two bridges with a shared load

    Why it works: The recorded data — not your favourite or the one you built — shows which design performed better on the measure you tested, and you can point to the numbers to prove it.

    1. Set up two designs for the same problem — e.g. a flat bridge and an arched bridge made from the same material and spanning the same gap (or, as alternatives, two boat hulls floated the same way, or two shade structures compared by how much light gets through — judged by the cast shadow or transmitted light patch on the ground — and/or by how cool the shaded spot stays, checked with a thermometer or hand touch over the same time; never by looking toward the sun or holding material up to the eyes).
    2. State out loud what you will keep the same for both (same gap, same way of adding weight, same exposure time) before you start, so the test is fair.
    3. Add weights (toy bricks or marbles) to the first design one at a time, and record the number it held before it sagged or dropped (e.g. flat bridge held 8).
    4. Test the second design exactly the same way and record its number (e.g. arch held 12).
    5. Fill both numbers into a side-by-side comparison chart (Design A row vs Design B row).
  2. 2

    Trade-offs: when each design wins on a different measure

    Why it works: Real designs have trade-offs: one design can be strong on one measure and weak on another, so the recommendation depends on which measure matters for the problem.

    1. Choose a problem where each design can win on a different measure — e.g. one bridge holds more weight but the other is quicker to build or uses less paper.
    2. Test and record each design on BOTH measures (e.g. weight held, and time or material used).
    3. Tag each design with a strength and a weakness based on the records.
    4. Explain why there is no single 'best' — each design has trade-offs — and recommend one for a stated purpose, citing which measure mattered most.
  3. 3

    Read the results chart and defend the pick (interpretation practice)

    Why it works: An evidence-based recommendation reads the recorded results and names strengths and weaknesses, rather than relying on appearance or preference.

    1. Look at a small side-by-side results table for two designs tested on the same problem.
    2. Decide which design the data supports as better for the stated problem.
    3. Reject a 'liked it more' or 'looks stronger' reason and give the data-based reason instead.
    4. Match each design to its strength and its weakness from the table; sort listed features into strengths vs weaknesses.

Topic // Standards

Curriculum standards

Codes for the curriculum statements this topic maps to. Each publisher owns its standard text.
Next Generation Science Standards (NGSS) K-5
K-2-ETS1-3

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