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Add English DGB 4 translation

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{{DISPLAYTITLE:DGB 4: Handling User Data}}
← [[DGB 4. Klasse/en|Digital Literacy – Year 4]]

Apps, websites and connected devices can collect far more than a name or e-mail address.
Clicks, search activity, location, contacts, device identifiers and inferred interests can be
combined into profiles. Students examine how this happens, which legitimate purposes exist and
where negligence, misuse or surveillance create risks.

== Curriculum reference ==

{| class="wikitable"
! Area !! Classification
|-
| '''Competence area''' || Information
|-
| '''Perspectives''' || G, I
|-
| '''Focus''' || Collection, analysis and linking of user data
|-
| '''Competence''' || Understand how user data is collected and combined, recognise risks of negligence, misuse and surveillance, and make data-conscious decisions.
|}

== Learning objectives ==

Students can:

* identify different types of user data;
* explain common ways in which websites, apps and devices collect data;
* describe how separate data sets can be linked into profiles;
* distinguish necessary processing from optional tracking in simple cases;
* recognise risks caused by negligent handling of data;
* recognise risks of misuse, identity theft and unauthorised disclosure;
* explain how surveillance can influence behaviour;
* check app permissions and privacy settings;
* apply data minimisation and careful sharing in everyday situations;
* respect the data and privacy of other people.

== Learning content ==

=== Collecting user data ===

Data may be entered deliberately, generated by actions or observed automatically. Examples
include account details, clicks, search queries, location, device information, interaction times and
purchase history.

=== Analysing user data ===

Providers can use data to improve services, detect fraud, personalise content or sell advertising.
Analysis can also infer interests or characteristics that users never stated directly.

=== Linking data ===

The combination of several harmless-looking data points can reveal much more than each item
alone. Linking account data, location and behavioural patterns may create a detailed profile.

=== Risks through negligence ===

Public sharing, weak passwords, excessive permissions, careless forwarding or unprotected
devices can expose personal or third-party data even without malicious intent.

=== Misuse and identity theft ===

Stolen or improperly used data can enable impersonation, fraud, harassment or targeted
manipulation. The consequences depend on the sensitivity and context of the data.

=== Surveillance and behavioural effects ===

When people believe they are being monitored, they may change behaviour or avoid legitimate
activities. This is sometimes called a chilling effect and shows that privacy affects freedom as well
as secrecy.

=== Responsible handling ===

Use data minimisation, review permissions, use strong unique passwords and multi-factor
authentication where possible, choose appropriate audiences and avoid collecting or forwarding
data that is not needed.

== Classroom activities ==

=== Everyday data hunt ===

Identify what types of data a fictional app could collect during normal use.

'''Expected outcome:''' A data inventory.

=== From click to profile ===

Connect individual actions to possible inferences and advertising categories.

'''Expected outcome:''' A profile-building diagram.

=== Negligent or responsible ===

Classify everyday data-handling scenarios and improve the risky ones.

'''Expected outcome:''' A corrected scenario set.

=== Recognise misuse ===

Analyse fictional incidents involving leaked data, impersonation or unauthorised sharing.

'''Expected outcome:''' An incident analysis.

=== Surveillance and behaviour ===

Discuss how visible monitoring could change behaviour in school or public spaces.

'''Expected outcome:''' A reasoned discussion.

=== Check privacy settings ===

Review permissions in a safe test environment and remove unnecessary access.

'''Expected outcome:''' A settings record.

=== Role-play a data conflict ===

Debate a case from user, provider, friend and data-protection perspectives.

'''Expected outcome:''' A stakeholder map.

=== Create a personal protection concept ===

Develop practical rules for accounts, permissions, sharing and backups.

'''Expected outcome:''' A protection guide.

== Self-check ==

* I can identify different categories of user data.
* I understand that combining data can reveal additional information.
* I can recognise negligence, misuse and surveillance risks.
* I check permissions and privacy settings deliberately.
* I protect other people's data as well as my own.
* I can explain why data minimisation matters.

== See also ==

[[DGB 4: Grenzen und Moeglichkeiten von KI/en|AI – Opportunities and Limitations]]
[[DGB 4: Datensicherung und Wiederherstellung/en|Backups and Data Recovery]]
[[DGB 4: Technische Konfigurationen und Selbstbestimmung/en|Technical Settings and Informational Self-Determination]]

[[Kategorie:Digitale Grundbildung]]
[[Kategorie:DGB 4]]

DGB 4: Umgang mit Nutzerdaten/en - Versionsgeschichte

Administrator: Add English DGB 4 translation