Mario Kart Data Challenge

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Mario Kart
Data Challenge

Technology 7–8 · Stage 4

4Students

5Rounds

20Data Points

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⭐

🏎️

🎨

🏁 Round 1

👩‍🏫

Ms Rodriguez

▼ click anywhere to continue

📗 Spreadsheet Update

📗 MarioKart_TimeTrials_Group1.xlsx

Round Data ✓

	A — Student Name	B — Round 1	C — Round 2	D — Round 3	E — Round 4	F — Round 5	G — Best (s)	H — Average (s)

💡 …

📊 Line Graph

Group 1 — Time Trial Performance

📊 Complete Analysis — All 20 Data Points

📗 MarioKart_TimeTrials_Group1.xlsx — Complete Dataset

Student	R1 (s)	R2 (s)	R3 (s)	R4 (s)	R5 (s)	=MIN()	=AVERAGE()	Improvement	% Better
🎮 Jake	112.34	109.87	108.23	107.56	105.12	105.12	108.62	↓ 7.22s	6.4%
⭐ Emily	123.17	120.45	117.88	116.23	114.67	114.67	118.48	↓ 8.50s	6.9%
🏎️ Marcus	135.08	131.23	128.67	125.44	122.89	122.89	128.66	↓ 12.19s	9.0%
🎨 Chloe	118.42	116.78	115.34	114.89	113.45	113.45	115.78	↓ 4.97s	4.2%
Group Avg	122.25	119.58	117.53	116.03	114.03	—	117.89	↓ 8.22s	6.7%

🏆 Performance Leaders

Fastest overall: Jake — 105.12s (R5)
Most improved (abs): Marcus — ↓12.19s
Most improved (%): Marcus — 9.0%
Most consistent: Chloe — smallest range (4.97s)
Fastest R1 → R5 rate: Marcus (steepest gradient)

📈 Trends & Patterns

All 4 students improved every single round — no regressions
Group average dropped 8.22s across 5 rounds (6.7%)
Improvement rate slowed in R4–R5: learning plateau
Jake's curve flattened earliest — near his performance ceiling
Marcus's steep curve reflects greatest room to improve

📐 Why 20 Data Points?

Data scientists recommend a minimum of 5 readings per variable to identify reliable trends. With 4 students × 5 rounds:

Enough to calculate meaningful averages
Enough to visualise learning curves on a line graph
Achievable within a single lesson
Sufficient to identify and discuss outliers

🔍 Discussion Questions

Why does Marcus show the biggest % improvement despite the slowest times?
What does a flattening line graph tell us about learning limits?
How would the graph change if we added a 6th round?
Is absolute or percentage improvement a fairer comparison? Why?

📋 How This Activity Addresses the Syllabus

🔧 Technology 7–8 (2023) — Stage 4

This activity directly addresses key Stage 4 outcomes:

TE4-SDP-01 — Students explain relationships between sustainability, design and production; analysing performance data over 5 rounds models iterative improvement processes
TE4-DIG-01 — Students use digital technologies (Nintendo Switch, Excel) to collect, organise and present data
TE4-DIG-02 — Students store, share and communicate digital information when producing project work (the shared spreadsheet)
Fulfils the Digital & Communication Technologies focus area requirement to "research, collect and share information using digital tools"

📐 Cross-KLA: Data Representation (Stage 4)

Directly meets cross-curriculum numeracy outcomes from the NSW data literacy document:

Students record data in tables with correct units (mm:ss.ms → seconds conversion)
Students create line graphs — a core Stage 4 graphing type — to represent multiple datasets
Students compare multiple line graphs to identify similarities, differences and trends across 5 rounds
Students use spreadsheet formulas: =AVERAGE(), =MIN() and calculate % improvement
Students interpret downward slopes and learning plateaus — connecting graph shape to real meaning

🤝 Collaborative Learning Design

The group structure embeds the syllabus expectation to "work collaboratively to plan projects and share information":

One shared spreadsheet = collaborative data production
Jake explains unit conversion to Emily — peer scaffolding modelled explicitly
Emily's axis question models authentic inquiry during graph interpretation
Chloe's percentage improvement suggestion models higher-order statistical thinking
Mixed ability group supports expert/novice learning discourse
Group average row demonstrates collective data analysis

💡 Teaching & Learning Design Principles

The 5-round structure reflects evidence-based design choices:

Authenticity — students generate their own real data rather than using given datasets
Sample size literacy — 20 data points explicitly discussed: 5 readings per variable for reliable trends
Iterative improvement — mirrors the Technology syllabus design and production cycle
Visual novel format — models the thinking and questioning that expert learners do during data activities
Scaffolding — spreadsheet builds visually round by round; graphs progressively add data

🤔 Three Questions for You, Teacher!

1

Would you want students to run this activity live in real-time, or use the visual novel as a modelled example before they do their own data collection?

The VN could serve as a worked example first — showing students exactly what to record and how — before they do their own 5-round time trial with their actual group data.

2

Should the line graph screen include interactive elements — for example, clicking a student's line to highlight their data, or toggling the group average on/off?

Adding interactivity to the graph could deepen the data interpretation phase and let students explore the dataset at their own pace.

3

Would you like to add a differentiation layer — for example, extension questions for students who finish early, or scaffolded sentence starters for interpreting the graph?

The analysis screen currently shows all four insights at once. These could be revealed progressively, or different versions shown depending on student readiness level.