The skill being assessed is 3-PS2-2: Use motion patterns to predict future motion. Patterns facilitate prediction as observations show consistent relationships, like proportions, extendable to new cases with similar setups. The ball bounces 50 cm from 100 cm drop, 75 cm from 150 cm, and 100 cm from 200 cm, following a pattern of bouncing half the drop height. Choice A properly applies this ratio, predicting 125 cm from 250 cm, which matches the proportional trend. Distractors incorrectly assume bounces exceed drops, repeat previous heights, or use isolated data points without the half-rule. Teach by calculating ratios of bounce to drop for each trial to confirm the pattern. Then, apply the ratio to the new drop height and assess if the prediction is logical for energy loss in bounces.

This question tests 3-PS2-2: using motion patterns to predict future motion. When we observe repeated motion under changing conditions, we can identify patterns and extend them to make predictions about what will happen next. The pattern shows the toy car stopping distance decreases by 1 meter each time as the surface gets rougher: smooth (3m) → slightly rough (2m) → rough (1m), decreasing by 1m at each step. Following this pattern, on very rough surface the car would stop at 0m (1m - 1m = 0m), meaning it wouldn't move at all. Option B incorrectly uses only the smooth surface data, option C uses only the last measurement without considering the pattern, and option D makes an arbitrary guess. To solve pattern problems, first identify what changes between observations (surface roughness), then calculate the consistent change in the outcome (stopping distance decreases by 1m), and apply this rule to predict the next value.

This question tests 3-PS2-2: using motion patterns to predict future motion. Understanding proportional relationships between distance and time helps predict motion for longer distances. The pattern shows time increases by 4 seconds for each additional meter: 1m→4s, 2m→8s, 3m→12s, maintaining a constant rate of 4 seconds per meter. For a 4-meter track, the time would be 16 seconds (4m × 4s/m = 16s). Option B just repeats the last time, option C incorrectly assumes faster motion on longer tracks, and option D uses data from only one trial. When dealing with proportional patterns, calculate the rate (time per distance), verify it remains constant across all data points, then multiply by the new distance.

This question tests 3-PS2-2: using motion patterns to predict future motion. When we observe repeated motion under changing conditions, we can identify patterns and extend them to make predictions about what will happen next. The pattern shows the toy car stopping distance decreases by 1 meter each time as the surface gets rougher: smooth (3m) → slightly rough (2m) → rough (1m), decreasing by 1m at each step. Following this pattern, on very rough surface the car would stop at 0m (1m - 1m = 0m), meaning it wouldn't move at all. Option B incorrectly uses only the smooth surface data, option C uses only the last measurement without considering the pattern, and option D makes an arbitrary guess. To solve pattern problems, first identify what changes between observations (surface roughness), then calculate the consistent change in the outcome (stopping distance decreases by 1m), and apply this rule to predict the next value.

This question tests 3-PS2-2: using motion patterns to predict future motion. By observing how motion changes with different starting conditions, we can identify mathematical relationships and extend them to new situations. The pattern shows rolling distance increases by 80 cm for each 20 cm increase in ramp height: 20cm→80cm, 40cm→160cm, 60cm→240cm. Following this pattern, an 80 cm high ramp (60+20) would result in 320 cm rolling distance (240+80). Option B just repeats the last measurement, option C uses only the first data point, and option D miscalculates the pattern as adding 160 cm. To solve these problems, first identify the relationship between input (height) and output (distance), calculate the consistent change (80 cm per 20 cm height), then apply this ratio to make predictions.

The skill being assessed is 3-PS2-2: Use motion patterns to predict future motion. Patterns allow prediction because they reveal trends from observations, like steady increases, which can be projected forward if conditions are comparable. The ball speeds are 2 m/s on low angle, 4 m/s on medium, and 6 m/s on high, indicating a pattern of increasing by 2 m/s per angle step. Choice B correctly extends this by adding another 2 m/s for very high, predicting 8 m/s, aligning with the arithmetic progression. Distractors fail by averaging without basis, repeating a prior speed, or ignoring the incremental pattern. A strategy is to identify the pattern by subtracting consecutive speeds to find the difference. Apply the same difference to predict the next and evaluate if it makes sense with increasing steepness.

This question aligns with the skill 3-PS2-2: Use motion patterns to predict future motion. Patterns in motion allow us to predict future outcomes because repeated observations reveal consistent trends, and under similar conditions, we can expect similar results that can be extended forward. In this case, the stopping time increases by 10 s with each stronger push: gentle at 10 s, medium at 20 s, strong at 30 s. The correct answer, 40 s, works because it extends the pattern by adding another 10 s for the very strong push, aligning with the trend. Distractors fail by averaging times, assuming constancy, or incorrectly suggesting shorter times without following the increase. To teach this, first identify the pattern type by finding differences between stopping times. Then, apply the same addition rule to predict the next time, and check if the prediction is reasonable given the push strength.

This question assesses using motion patterns to predict future motion (3-PS2-2). Patterns in motion data allow us to make reliable predictions because similar conditions typically produce similar results. The data shows a clear proportional pattern: when the ramp is 20 cm high, the ball rolls 80 cm (4 times the height); at 40 cm high, it rolls 160 cm (4 times); at 60 cm high, it rolls 240 cm (4 times). Answer B correctly identifies and applies this pattern - an 80 cm ramp would result in the ball rolling 320 cm (80 × 4). Answer A incorrectly assumes a constant addition pattern, Answer C fails to extend the pattern, and Answer D incorrectly predicts the distance would decrease. To identify proportional patterns, divide the output by the input for each data point (80÷20=4, 160÷40=4, 240÷60=4). When the ratio is constant, multiply the new input by this ratio to predict the output.

This question tests the skill of using motion patterns to predict future motion (3-PS2-2). Patterns in motion data allow us to predict future outcomes when similar conditions are repeated. The data shows a proportional relationship where the ball rolls 4 times the ramp height: 20 cm ramp→80 cm (4×20), 40 cm→160 cm (4×40), 60 cm→240 cm (4×60). Answer B correctly identifies this pattern and predicts 320 cm for an 80 cm ramp (4×80=320). The distractors fail because A assumes a constant increase of 20 cm which doesn't match the data, C incorrectly assumes no change from the last measurement, and D misidentifies the pattern as adding 40 cm each time. When analyzing patterns, calculate the relationship between input and output values - here dividing distance by height consistently gives 4, revealing the proportional pattern.

This question tests the ability to use motion patterns to predict future motion (3-PS2-2). When we observe how motion changes under different conditions, we can identify patterns that help predict future behavior. The data shows that as push strength increases (gentle→medium→strong), the swing time increases in a linear pattern: 10 s, 20 s, 30 s, with each step adding 10 seconds. Answer B correctly extends this pattern by adding another 10 seconds to reach 40 s for a very strong push. The incorrect answers don't follow the established pattern - A (25 s) uses an incorrect increment, C (10 s) goes back to the beginning value, and D (30 s) just repeats the last measurement. When analyzing motion patterns, first identify the type of change (here, constant addition), calculate the exact amount of change between steps, then apply this same rule consistently to predict the next value.