Who Walked Here?

Concept

Footprint size tells you who walked by, but footprint depth tells you something size can't — how heavy they are. Kid discovers that a big animal can leave a shallow print (if it's light) and a tiny animal can leave a deep print (if it's heavy).

The Experience

Kid taps four leaf-covered patches in mud one at a time to clear them, revealing four differently sized and shaped footprints.

Kid taps each footprint to see how deep it goes, with a cross-section diagram showing an arrow measuring depth — one print goes deep, one is shallow, one is barely there, and one is deep again.

Four animal silhouettes — Tortoise, Monkey, Elephant, Sloth — appear with the instruction "Drag each animal to its print" and the reassurance "Any guess is okay." Kid drags each one to a print based on size alone.

The game tests each pairing one at a time with an animated "steps in" sequence and a Deep/Shallow prediction: the elephant correctly makes a deep print, the monkey a shallow one — but the sloth, despite being big, makes a shallow print, and the tortoise, despite being tiny, makes a deep one.

Kid is asked directly: "What makes a print DEEP?" with options Big animals / Heavy animals / Fast animals. Picking "Heavy animals" is confirmed correct, and the game states the rule plainly: "HEAVY presses DEEP."

Kid re-drags all four animals to their prints a second time, now knowing the rule — this reveals the sloth actually belongs on the wide, shallow print ("big but light") and the monkey on the smallest print ("small and light"), while the elephant and tortoise keep their original matches.

Closing summary: "Big doesn't mean heavy!" — the sloth labeled "BIG BUT LIGHT" and the tortoise "TINY BUT HEAVY," with the rule "Heavy goes deep. Light stays shallow," tied to real life: "A boot in snow, a bike in mud, YOU in wet sand."

What Math Does The Kid Do?

• Observes and compares a physical measurement (depth) across multiple specimens.
• Forms a hypothesis by matching size to depth, then revises it when evidence contradicts it.
• Distinguishes between two variables (size vs. weight) that can independently affect an outcome.
• Selects the correct causal explanation from three plausible-sounding options.
• Applies a learned rule to correctly re-sort all four data points in a second round.