Data Investigation

Overview

This lesson plan is designed for Year 11 students in New Zealand, focusing on the statistical enquiry process, specifically cleaning and preparing data. This aligns with the new 1.1 standard in the New Zealand math curriculum.

Curriculum Area and Level

• Curriculum Area: Mathematics and Statistics
• Level: Year 11 (Level 6 of the New Zealand Curriculum)

Lesson Duration

• Total Time: 60 minutes
• Class Size: 30 students

Learning Objectives

• Understand and apply the statistical enquiry cycle.
• Clean and prepare data for analysis.
• Develop skills in evaluating data quality and ethical data collection.

Materials Needed

• Laptops or tablets with spreadsheet software.
• Projector and screen for demonstration.
• Sample datasets (digital).

Lesson Outline

Introduction (10 minutes)

• Objective: Introduce the topic of data cleaning and its importance.
• Activity:
  • Begin with a discussion on why data needs cleaning. Use real-world examples such as survey data with missing entries or inconsistent formatting.
  • Engage students with a question: "What problems might arise from using unclean data?"

Main Activity (35 minutes)

Step 1: Explain the Statistical Enquiry Cycle (10 minutes)

• Objective: Familiarize students with the cycle: Problem, Plan, Data, Analysis, Conclusion.
• Activity:
  • Use a visual diagram to outline each step.
  • Highlight the data cleaning process within the 'Data' step.

Step 2: Hands-On Data Cleaning (25 minutes)

• Objective: Practice cleaning a dataset.
• Activity:
  • Part A (10 minutes): Demonstrate basic cleaning techniques using spreadsheet software (e.g., removing duplicates, correcting data types).
  • Part B (15 minutes): Students work in pairs to clean a provided dataset, focusing on identifying and correcting errors.

Conclusion and Reflection (10 minutes)

• Objective: Reflect on the process and its applications.
• Activity:
  • Discuss as a class the challenges faced during the cleaning process and solutions found.
  • Encourage students to think about how cleaned data can lead to more accurate conclusions.

Supporting Dyslexic Students

• Visual Aids: Use diagrams and color-coded charts to explain processes.
• Clear Instructions: Provide step-by-step written instructions and checklists.
• Peer Support: Pair students to encourage collaborative learning and support.

Assessment

• Formative: Observe students during the hands-on activity, noting their ability to identify and correct errors.
• Summative: Collect the cleaned datasets and review them for accuracy and completeness.

Follow-Up

• Discuss ethical considerations in data collection and the importance of viewing data as taonga (treasure), incorporating cultural perspectives.

This lesson plan aims to engage and support all learners, with specific strategies for students with dyslexia, ensuring a comprehensive understanding of data handling.

References

[1] NZ-math-2025-curriculum-draft.pdf