Day 11: Data Collection and Privacy

Learning Objectives

• IOC-1.J: Explain how the collection of data affects privacy.

Essential Questions

• How do computing innovations collect and use data?
• What privacy implications arise from data collection?
• What are the trade-offs between functionality and privacy?

Materials Needed

• Presentation slides on data collection methods and purposes
• Sample privacy policies
• Data collection audit worksheet
• Personal data inventory template
• Exit ticket templates

Vocabulary

• Data collection
• Privacy
• Personally identifiable information (PII)
• Data mining
• Tracking
• Cookies
• Metadata
• Data broker
• Surveillance
• Consent

Procedure (50 minutes)

Opening (8 minutes)

1. Review and Week 3 Introduction (3 minutes)

   • Review intellectual property concepts from previous lesson
   • Introduce Week 3 focus on privacy, data, and final project
   • Connect to today's focus on data collection and privacy

2. Warm-up Activity (5 minutes)

   • Ask students to list all the ways they think their data was collected in the past 24 hours
   • Create a class compilation of data collection examples
   • Categorize by type of data and collection method
   • Introduce the pervasiveness of data collection in modern computing

Main Activities (32 minutes)

3. Lecture: Data Collection Methods and Purposes (12 minutes)

   • Explain common data collection methods:
     • Direct user input (forms, profiles, surveys)
     • Automated tracking (cookies, pixels, fingerprinting)
     • Sensors and IoT devices
     • Location tracking (GPS, cell towers, WiFi)
     • Purchase and transaction records
     • Social media activity and connections
     • Cross-platform tracking
   • Discuss types of data commonly collected:
     • Personally identifiable information (PII)
     • Behavioral data (clicks, views, time spent)
     • Location data
     • Device information
     • Content and communications
     • Metadata (time stamps, device info, etc.)
   • Explain purposes of data collection:
     • Service functionality and improvement
     • Personalization and user experience
     • Targeted advertising and marketing
     • Product development
     • Research and analytics
     • Security and fraud prevention
     • Sale to third parties
   • Discuss how data collection has evolved:
     • Increasing volume and variety
     • Greater persistence and searchability
     • More sophisticated analysis capabilities
     • Growing commercial value

4. Investigation: Privacy Policies of Common Applications (10 minutes)

   • Divide students into small groups
   • Assign each group a popular application or service
   • Groups examine the privacy policy to identify:
     • What data is collected
     • How data is collected
     • How data is used
     • Who data is shared with
     • What choices users have
     • How long data is retained
   • Groups create a summary of key findings
   • Share and compare findings across different applications
   • Discuss patterns and differences in data practices

5. Discussion: Trade-offs Between Functionality and Privacy (10 minutes)

   • Explore the relationship between data collection and service functionality:
     • How data enables personalization
     • How data improves service quality
     • How data supports free services (advertising model)
     • How data creates network effects
   • Discuss privacy costs of these benefits:
     • Loss of control over personal information
     • Potential for unwanted profiling and discrimination
     • Security risks from data breaches
     • Chilling effects on behavior
     • Aggregation of data across sources
   • Analyze specific examples of functionality-privacy trade-offs:
     • Location services
     • Voice assistants
     • Personalized recommendations
     • Social media
     • Search engines
   • Discuss how users can make informed decisions about these trade-offs

Closing (10 minutes)

6. Activity: Personal Data Footprint Analysis (7 minutes)

   • Guide students through analyzing their own data footprint:
     • What services have their personal information?
     • What permissions have they granted to apps?
     • What tracking might be happening without their knowledge?
     • What data might be inferred about them?
   • Students identify their biggest sources of data collection
   • Students consider what surprised them about their data footprint
   • Discuss strategies for managing personal data exposure

7. Exit Ticket and Preview (3 minutes)

   • Students begin an audit of data collection practices for an app or website
   • Audit should analyze what data is collected, how it's used, and privacy implications
   • Preview that next class will focus on privacy concerns and protections

Assessment

• Formative: Quality of privacy policy investigation and discussion participation
• Exit Ticket: Thoroughness and insight in data collection audit

Differentiation

For Advanced Students

• Ask them to analyze technical aspects of data collection (APIs, tracking methods)
• Have them compare privacy regulations across different jurisdictions
• Challenge them to design a privacy-preserving alternative to a data-intensive service

For Struggling Students

• Focus on more obvious forms of data collection
• Provide a structured template for the privacy policy analysis
• Use more concrete examples and visual aids

Homework/Extension

• Complete the data collection audit for an app or website
• Track personal data requests for 24 hours and analyze patterns
• Research a company's data collection practices and business model

Teacher Notes

• Keep discussions focused on understanding rather than fear
• Use current examples that students will recognize
• Make connections to students' experiences as technology users
• Consider privacy implications of any classroom technology being used
• Emphasize that awareness is the first step toward informed decisions