Predicting Motion Patterns

Pattern-seeking for 8–9 year-olds: children take repeated measurements of an object's motion under varied conditions (classically, how far a toy car rolls from different ramp heights), organise the readings into a table and graph, identify the relationship (e.g. 'the steeper the ramp, the further it rolls', for the heights tested), and then use that pattern to predict motion in an untested case — before testing the prediction. The pedagogically ideal vehicle is a real hands-on investigation where children do the measuring, repeat readings, and meet genuinely uncontrollable variables themselves; an on-screen ramp-and-car simulation is intended to rehearse the data→pattern→prediction reasoning as an alternative but does not build real-apparatus measurement craft. The core objective is that a prediction is an evidence-based inference anchored in collected data, not a guess, and that pattern-seeking is real science alongside the fair test.

• Thinking one reading is enough — not repeating measurements, so noise is mistaken for pattern.
• Believing only 'fair tests' produce trustworthy science, so pattern-seeking feels 'not real science' (AKSIS: fair tests dominate >50% of practical work).
• Treating a prediction as a guess rather than an inference justified by the collected data/pattern.
• Expecting an exact value rather than a trend-based prediction ('about this far / further than before').
• Not separating the condition being changed from the quantity being measured when recording.
• (Content) believing all objects experience the same air resistance regardless of size or shape (Oak National Academy).

• Ramp-and-car apparatus (height/gradient as the changed condition; distance rolled as the measure)
• Data table pairing the condition with the measured motion (e.g. ramp height vs distance)
• Line/scatter graph revealing the trend across readings
• The 'big question' framing the enquiry
• A prediction marker on the graph/number line for an untested condition, later checked against the actual reading

• Frame the enquiry with a 'big question' ('does a steeper ramp make the car roll further?') and let children handle real apparatus — ramps, toy cars, metre rules, stopwatches.
• Record measurements systematically in a table, then plot them (bar/line/scatter as appropriate) so the pattern becomes visible; take repeat readings for reliability (upper-KS2 working scientifically).
• Use the 'what do you notice?' prompt to elicit the relationship from the data, rather than stating it for the children (Science-Sparks; Ogden Trust).
• Make prediction explicit and evidence-based: use the established pattern to predict an UNTESTED condition, then run it and compare — 'predict, then test' (Ogden Trust).
• Name the enquiry type honestly as pattern-seeking and don't default everything to a fair test; pattern-seeking often serves as a preliminary enquiry that generates predictions and new questions (PSTT; Ogden Trust).
• Build measurement progression — accurate lengths (to the nearest mm/cm), times in seconds — and discuss variables that cannot be controlled and how they might affect the data.