Your Information Footprint is Larger Than You Think

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Your Information Footprint is Larger Than You Think


Content Source:

International Computer Science Institute
Type: Lesson/Unit Plan


The lesson elements in this module teach students about the privacy principle “Your information is larger than you think”. They are designed to be independent and flexible, so you can incorporate them into any size lesson plan. Student lesson is available at

Summary of Learning Objectives: Students can enumerate ways their online and offline activities contribute to their information “footprint”; students can use privacy settings and critical thinking skills to limit the exposure of their footprint.

Target Age: High school, college undergraduate.

Learning Objectives: 
  1. Students can give examples of ways their online and offline activities generate digital footprints, within each of the following broad categories: intentional posting/online activities, metadata attached to posts, information transmitted by devices, and others collecting or posting information about them.
  2. For at least one example of an activity that generates digital footprints, students can explain (at least in non-technical terms) how that activity generates those footprints.
  3. Students can enumerate some factors that affect how many people or entities can see the data in their information footprint, including (minimally) privacy settings and third-party data sharing, and give examples of potential negative consequences of exposure.
  4. Students can explain how the amount of information available about them, and how many people have access to it, is affected by the mining of data from different sources to form a picture of each person and can give examples of inferences that can be drawn by data-mining.
  5. Students can give examples of available privacy settings for apps, online services, and devices they use frequently, and explain why they would choose particular settings based on their information-sharing preferences.
  6. Students can suggest some potential uses apps and online services might have for particular types of personal data they typically request access to and evaluate whether those uses would likely be beneficial, neutral, or harmful to the student.
Content Standard(s):
Digital Literacy and Computer Science
DLIT (2018)
Grade: 9-12
11) Model and demonstrate behaviors that are safe, legal, and ethical while living, learning, and working in an interconnected digital world.

a. Recognize user tracking methods and hazards.

Examples: Cookies, WiFi packet sniffing.

b. Understand how to apply techniques to mitigate effects of user tracking methods.

c. Understand the ramifications of end-user license agreements and terms of service associated with granting rights to personal data and media to other entities.

d. Explain the relationship between online privacy and personal security.

Examples: Convenience and accessibility, data mining, digital marketing, online wallets, theft of personal information.

e. Identify physical, legal, and ethical consequences of inappropriate digital behaviors.

Examples: Cyberbullying/harassment, inappropriate sexual communications.

f. Explain strategies to lessen the impact of negative digital behaviors and assess when to apply them.

Digital Literacy and Computer Science
DLIT (2018)
Grade: 9-12
16) Identify laws regarding the use of technology and their consequences and implications.

Examples: Unmanned vehicles, net neutrality/common carriers, hacking, intellectual property, piracy, plagiarism.

Digital Literacy and Computer Science
DLIT (2018)
Grade: 9-12
19) Prove that digital identity is a reflection of persistent, publicly available artifacts.

Digital Literacy and Computer Science
DLIT (2018)
Grade: 9-12
20) Evaluate strategies to manage digital identity and reputation with awareness of the permanent impact of actions in a digital world.

Digital Literacy and Computer Science
DLIT (2018)
Grade: 9-12
23) Debate the positive and negative effects of computing innovations in personal, ethical, social, economic, and cultural spheres.

Examples: Artificial Intelligence/machine learning, mobile applications, automation of traditional occupational skills.

Tags: data sharing, digital footprint, end user licensing agreement, EULA, metadata, negative consequences data mining, privacy settings
License Type: Attribution
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Author: Aimee Bates