Creating Pie Charts and Scatter Plots

Active learning works well for pie charts and scatter plots because students often struggle to see the difference between effective and misleading visualizations. By handling real data in hands-on tasks, they immediately spot why some charts confuse or mislead, making the concepts stick far better than passive lessons.

• Class 11
• Computer Science
• 4 activities
• 30 min–45 min

Learning Objectives

1. Create a pie chart using Python to represent the proportional distribution of a dataset.
2. Construct a scatter plot using Python to visualize the relationship between two numerical variables.
3. Analyze the effectiveness of a pie chart for representing data with more than ten categories.
4. Evaluate the correlation shown in a scatter plot and describe the nature of the relationship.
5. Critique the ethical implications of using pie charts to represent sensitive data, such as election results.

Decision Matrix

⏱ 35 min·Pairs

Pair Programming: Survey Pie Chart

Pairs survey class preferences on subjects, tally percentages, and code a pie chart using matplotlib.pyplot.pie(). They add labels, explode a slice, and present findings. Discuss why colours aid readability.

Prepare & details

Justify the use of a pie chart for representing parts of a whole.

Facilitation Tip: During Pair Programming: Survey Pie Chart, ensure both students alternate roles every 10 minutes so that both engage with coding and discussion equally.

Setup: Works in standard classroom rows with individual worksheets; group comparison phase benefits from rearranging desks into clusters of 4–6. Wall space or the blackboard can display inter-group criteria comparisons during debrief.

Materials: Printed A4 matrix worksheets (individual scoring + group summary), Chit slips for anonymous criteria generation, Group role cards (Criteria Chair, Scorer, Evidence Finder, Presenter, Time-keeper), Blackboard or whiteboard for shared criteria display

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Decision Matrix

⏱ 45 min·Small Groups

Small Groups: Height-Weight Scatter Plot

Groups measure heights and weights of members, input data into lists, and plot with plt.scatter(). Add trend line using numpy.polyfit(), interpret strength of relationship. Share plots on projector.

Prepare & details

Construct a scatter plot to visualize the relationship between two numerical variables.

Facilitation Tip: For Small Groups: Height-Weight Scatter Plot, provide graph paper for manual plotting before moving to Python to strengthen conceptual understanding.

Setup: Works in standard classroom rows with individual worksheets; group comparison phase benefits from rearranging desks into clusters of 4–6. Wall space or the blackboard can display inter-group criteria comparisons during debrief.

Materials: Printed A4 matrix worksheets (individual scoring + group summary), Chit slips for anonymous criteria generation, Group role cards (Criteria Chair, Scorer, Evidence Finder, Presenter, Time-keeper), Blackboard or whiteboard for shared criteria display

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Decision Matrix

⏱ 30 min·Whole Class

Whole Class: Chart Limitation Challenge

Display same dataset as pie and bar charts coded by teacher. Class votes on clarity for 12 categories, codes alternatives in shared Jupyter notebook. Debate justifications.

Prepare & details

Analyze the limitations of pie charts for comparing multiple categories.

Facilitation Tip: In Whole Class: Chart Limitation Challenge, display student-generated pies on the board and let peers vote on the clearest one to encourage critical observation.

Setup: Works in standard classroom rows with individual worksheets; group comparison phase benefits from rearranging desks into clusters of 4–6. Wall space or the blackboard can display inter-group criteria comparisons during debrief.

Materials: Printed A4 matrix worksheets (individual scoring + group summary), Chit slips for anonymous criteria generation, Group role cards (Criteria Chair, Scorer, Evidence Finder, Presenter, Time-keeper), Blackboard or whiteboard for shared criteria display

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Decision Matrix

⏱ 40 min·Individual

Individual: Custom Dataset Exploration

Students choose ethical dataset like crime rates versus literacy, code both charts, note limitations in a report. Submit code and annotated screenshots for feedback.

Prepare & details

Justify the use of a pie chart for representing parts of a whole.

Facilitation Tip: For Individual: Custom Dataset Exploration, ask each student to present their chart to the class in 90 seconds to build confidence in explaining their work.

Setup: Works in standard classroom rows with individual worksheets; group comparison phase benefits from rearranging desks into clusters of 4–6. Wall space or the blackboard can display inter-group criteria comparisons during debrief.

Materials: Printed A4 matrix worksheets (individual scoring + group summary), Chit slips for anonymous criteria generation, Group role cards (Criteria Chair, Scorer, Evidence Finder, Presenter, Time-keeper), Blackboard or whiteboard for shared criteria display

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Teaching This Topic

Experienced teachers approach this topic by starting with quick, messy data that students need to organise before visualising. They avoid teaching chart types in isolation and instead connect each choice to a real-world question. Teachers also use quick visual checks, like holding up pies at arm’s length, to show how humans perceive angles rather than areas. Research suggests that students learn chart design best when they experience the frustration of a confusing chart and then fix it themselves.

What to Expect

After completing these activities, students should confidently choose the right chart type for given data, justify their choices with clear reasons, and identify common pitfalls in chart design. They should also analyse relationships in scatter plots without assuming cause and effect.

Watch Out for These Misconceptions

Common Misconception

During Pair Programming: Survey Pie Chart, watch for students assuming all pie charts are equally clear regardless of slice count.

What to Teach Instead

Ask students to code pies with 5, 10, and 15 categories from the same survey, then compare readability in pairs. Discuss when to switch to a bar chart and document their reasoning in their notebooks.

Common Misconception

During Small Groups: Height-Weight Scatter Plot, watch for students interpreting any pattern in scatter plots as a cause-effect relationship.

What to Teach Instead

Provide a spurious dataset like ‘hours of sleep vs number of books read’ and ask groups to plot it. During debrief, guide them to question correlation versus causation with prompts like ‘Does more sleep make you read more books?’.

Common Misconception

During Whole Class: Chart Limitation Challenge, watch for students believing that pie slice area directly reflects proportion without considering angle perception.

What to Teach Instead

Display pies with identical angles but different slice areas and vice versa. Have students manually measure angles with protractors and adjust their code to ensure slice angles match proportions accurately.

Assessment Ideas

Quick Check

After Pair Programming: Survey Pie Chart, give students a small dataset with 12 categories. Ask them to generate a pie chart and justify in two sentences whether a pie chart is appropriate or if a bar chart would be better for clarity.

Exit Ticket

During Small Groups: Height-Weight Scatter Plot, ask students to write two sentences describing the relationship they observe and one key limitation of scatter plots in representing that relationship.

Discussion Prompt

After Whole Class: Chart Limitation Challenge, present two pie charts: one with 8 slices for budget allocation and another with 15 slices for election results. Ask students to discuss in pairs which chart is more effective and what ethical concerns arise from using pie charts for election data.

Extensions & Scaffolding

• Challenge: Ask students to find a misleading pie chart online, recreate it properly, and write a short report on how the original chart misled viewers.
• Scaffolding: Provide a partially completed Jupyter notebook with datasets pre-loaded and basic code stubs for pie charts and scatter plots.
• Deeper exploration: Have students collect data from different sources, compare pie charts with bar charts for the same data, and present findings on readability vs accuracy.

Key Vocabulary

Pie Chart	A circular chart divided into slices, where each slice represents a proportion or percentage of the whole dataset.
Scatter Plot	A graph that uses dots to represent the values obtained for two different numerical variables, showing the relationship between them.
Categorical Data	Data that can be divided into distinct groups or categories, often represented by labels or names.
Numerical Variable	A variable that represents a quantity that can be measured or counted, typically expressed as a number.
Correlation	A statistical measure that describes the extent to which two variables change together, indicating a linear relationship.