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{{T}}

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= E9 M - Recommendation systems and choices =

'''Mini-description:''' Learn how platforms can recommend personalised content and reflect on how recommendations may influence choices. You work mainly in English at a B1-oriented level and use German only as a short support.

[[Datei:Feed comparison-2.svg|500px|rahmenlos|center]]

'''Look first.''' What do you notice? What may happen between a large content pool and your personal feed?

<div class="mw-collapsible mw-collapsed">
'''Deutsch-Hilfe:''' Eine Plattform kann aus sehr vielen Inhalten eine kleinere, personalisierte Auswahl anzeigen. Du sollst verstehen, wie diese Auswahl entstehen kann und wie sie Entscheidungen beeinflussen kann.
</div>

{{BR}}
= Learning path =

'''English first → understand → respond → ask → hint → improve → use again'''

# '''Understand:''' notice the main idea in a picture, video or short text.
# '''Respond:''' give a short reaction in English.
# '''Ask:''' ask when a word, claim or recommendation is unclear.
# '''Hint:''' use sentence starters, context and a short German help box.
# '''Improve:''' revise your answer after feedback.
# '''Use again:''' transfer the language to a new example.

{{BR}}
= I-can goals =

By the end, you can say:

# I can explain in simple English what a recommendation system does.
# I can name signals that may be used for recommendations.
# I can compare two personalised feeds.
# I can discuss benefits and risks without assuming that every recommendation has the same effect.
# I can explain how a recommendation may influence a choice.
# I can ask critical questions about sources, data and AI-generated feedback.
# I can write a coherent short opinion with reasons and an example.
# I can mediate key information from German into English without translating every sentence.

{{BR}}
= What you already know =

Think for one minute.

# Where do you see “For you”, “Recommended”, “You may also like” or similar labels?
# What makes you click on one item but not another?
# Can two people open the same platform and see different content?

'''Rule for this course:''' Use invented examples only. Do not share private social-media histories, real personal profiles, passwords, private messages or other sensitive data.

{{BR}}
= Media entry: one pool, different feeds =

[[Datei:Recommendation Architecture.svg|500px|rahmenlos|center]]

A recommendation system selects and ranks items from a much larger pool. The exact method can differ between platforms.

'''Quick response:''' Complete the sentence: “The system does not show everything. It first …”

<div class="mw-collapsible mw-collapsed">
'''Hint:''' Try: “It first selects possible items and then ranks them.”
</div>

{{BR}}
= Core idea: what is a recommendation system? =

A '''recommendation system''' is software that suggests items which may be relevant to a user. These items can be videos, songs, films, products, articles or posts.

It does not “know you” like a human friend. It works with data and patterns. A system may use information about items, past interactions or patterns from many users.

[[Datei:Collaborative Filtering in Recommender Systems.jpg|500px|rahmenlos|center]]

'''Two simple ideas:'''

{| class="wikitable"
! Method
! Simple explanation
! Example
|-
| '''content-based'''
| It looks for items with similar features.
| You liked a science video, so another science video may appear.
|-
| '''collaborative'''
| It looks for patterns across users or items.
| People with similar viewing patterns also watched another video.
|}

You do '''not''' need the mathematics behind these methods in this course.

<div class="mw-collapsible mw-collapsed">
'''Deutsch-Hilfe:''' content-based = ähnliche Inhalte; collaborative = Muster aus ähnlichem Nutzungsverhalten.
</div>

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= Signals: what can a platform learn from? =

[[Datei:Recommender feedback loop.svg|500px|rahmenlos|center]]

A platform may use '''signals'''. For example, YouTube publicly describes signals such as clicks, watch time, likes, dislikes, sharing and survey responses. Different platforms may use different signals and weight them differently.

'''Important:''' A signal is not the same as a true personal preference. You may click because you are curious, surprised or doing homework.

'''Think-pair-share:''' Which is stronger evidence of interest: one click, a full watch, a repeated search, or a direct rating? Explain your choice.

{{BR}}
= Feedback loop =

A recommendation can change what you see. What you see can change what you click. Your new interaction can then become another signal.

This creates a '''feedback loop''':

'''recommendation → interaction → new data → new recommendation'''

A loop can be useful because suggestions can become more relevant. But repeated similar suggestions may also reduce variety.

[[Datei:Collaborative filtering table.gif|420px|rahmenlos|center]]

{{BR}}
= Choices: influence is not control =

Recommendations can make some options more visible than others. Visibility can influence attention and decisions, but it does not mean that an algorithm fully controls a person’s choice.

Ask three questions:

# '''What is shown?'''
# '''What is missing?'''
# '''What can I still choose or search for myself?'''

Useful language:

{| class="wikitable"
! Function
! Language
|-
| possibility
| The recommendation '''may''' influence my choice.
|-
| comparison
| Feed A shows more … than Feed B.
|-
| reason
| I would choose this because …
|-
| uncertainty
| We cannot know from one example whether …
|-
| alternative
| Another option would be …
|}

{{BR}}
= Filter bubbles and variety =

[[Datei:Filter bubble versus echo chamber.svg|500px|rahmenlos|center]]

A '''filter bubble''' is a concept used to describe a situation in which personalised filtering may narrow the information a person sees. Researchers do not agree that every personalised system always creates a strong filter bubble or changes opinions in the same way. Context, platform design and user behaviour matter.

[[Datei:Filter bubbles.svg|420px|rahmenlos|center]]

'''Critical sentence frame:''' “This feed may be narrow because …, but we would need more evidence to know …”

<div class="mw-collapsible mw-collapsed">
'''Deutsch-Hilfe:''' Nicht vorschnell behaupten: „Der Algorithmus macht alle gleich.“ Besser: Möglichkeit, Beobachtung und Beleg unterscheiden.
</div>

{{BR}}
= Listening and viewing 1: two experts =

{{#ev:youtube| https://www.youtube.com/watch?v=XruuRYJqiG8 |500|center}}

This WIRED interview features two members of YouTube’s Search and Discovery team. The speech is natural and moderately fast.

'''Before watching:''' Predict three words you may hear: ''recommend'', ''signal'', ''watch''.

'''While watching:''' Listen for answers to these questions:

# What kinds of user actions are mentioned?
# Why can different people receive different recommendations?
# How do the speakers describe search and recommendation?

'''After watching:''' Give a 30-second summary with this frame: “The speakers explain that … One important signal is … I still want to know …”

'''Listening strategy:''' Do not stop at every unknown word. First listen for repeated key words and examples.

{{BR}}
= Listening and viewing 2: the filter-bubble idea =

{{#ev:youtube| https://www.youtube.com/watch?v=B8ofWFx525s |500|center}}

This TED talk introduced the filter-bubble idea to a wide audience. It is an older talk, so use it as one perspective, not as proof that all current platforms work in exactly the same way.

'''Viewing task:''' Find one claim, one example and one question you would ask today.

'''Source check:''' Separate what the speaker argues from what you can verify with newer sources.

{{BR}}
= Reading: A fictional recommendation test =

'''Profile A – Jordan''' often watches short basketball clips, school science videos and comedy.

'''Profile B – Sam''' often watches cooking tutorials, travel clips and school science videos.

A fictional platform recommends:

{| class="wikitable"
! To Jordan
! To Sam
|-
| “Amazing last-second basketball shots”
| “Five easy pasta sauces”
|-
| “Why volcanoes erupt”
| “Why volcanoes erupt”
|-
| “Best comedy moments”
| “Train travel through Italy”
|}

'''Reading questions:''' What is personalised? What is shared? Which recommendation could come from content similarity? Which could come from a broader user pattern?

'''Important:''' These profiles are invented for learning. Do not replace them with real classmates’ private data.

{{BR}}
= Context vocabulary =

{| class="wikitable"
! Word
! Meaning in this topic
! Example
|-
| '''recommendation'''
| a suggested item
| This recommendation looks relevant.
|-
| '''signal'''
| information used by a system
| A click can be a signal.
|-
| '''rank'''
| put items in an order
| The system ranks possible videos.
|-
| '''feed'''
| a stream of selected content
| My fictional feed shows sports and science.
|-
| '''personalised'''
| adapted to one user or group
| The homepage may be personalised.
|-
| '''pattern'''
| something that repeats
| The model looks for patterns.
|-
| '''bias'''
| a systematic tendency
| A system can reproduce a bias in data.
|-
| '''variety'''
| different types of content
| More variety can show new topics.
|-
| '''choice'''
| a decision between options
| A visible option can affect a choice.
|-
| '''evidence'''
| information supporting a claim
| We need evidence before we generalise.
|}

{{BR}}
= Pronunciation and fluency =

Practise these chunks as whole phrases:

'''a recommendation system''' – stress the key content words.

'''personalised content''' – keep the phrase smooth, not word-by-word.

'''It may influence my choice.''' – stress '''may''', '''influence''' and '''choice'''.

'''I need more evidence.''' – use falling intonation for a clear statement.

'''Fluency challenge:''' Speak for 40 seconds. Use at least three chunks without reading a full script. Clarity is more important than accent.

'''Recording rule:''' Recording is voluntary. Keep recordings private unless you actively choose to share them. Do not upload them to an external AI service without permission.

{{BR}}
= Speaking: compare, react, negotiate =

Work with the two fictional feeds.

'''Round 1 – Compare:''' “Both feeds show …, but Feed A … whereas Feed B …”

'''Round 2 – React spontaneously:''' Your partner says, “Personalisation is always helpful.” Reply with one agreement point and one limitation.

'''Round 3 – Negotiate:''' Design a better feed together. It should contain familiar content, one surprising item and one reliable information source.

'''Round 4 – Present:''' Give a one-minute explanation of your choices.

<div class="mw-collapsible mw-collapsed">
'''Hint ladder:'''
# Start: “We kept … because …”
# Add contrast: “However, we also added …”
# Add caution: “This may help, but …”
</div>

{{BR}}
= Functional grammar =

Use '''may, might, can''' to avoid claims that are too strong.

{| class="wikitable"
! Too strong
! Better for careful reasoning
|-
| The algorithm changes your opinion.
| The recommendation '''may''' influence what you notice.
|-
| One click means you like the topic.
| One click '''can''' be a signal, but it may have other reasons.
|-
| Personalisation creates a filter bubble.
| Personalisation '''might''' reduce variety in some situations.
|}

Use an '''if-clause''' for cause and effect:

“If I repeatedly watch similar videos, the system may recommend more similar content.”

{{BR}}
= Writing: short opinion =

Write 120–160 words:

'''“Personalised recommendations are useful, but users should stay active decision-makers.” Discuss.'''

Use this structure:

# opening opinion
# one benefit
# one risk or limitation
# one example
# one practical strategy
# short conclusion

'''Keep your own text.''' If you use AI feedback, compare it with your original version. Accept only changes you understand. Check factual claims and sources yourself.

<div class="mw-collapsible mw-collapsed">
'''Writing help:''' Useful connectors: ''firstly, however, for example, because, therefore, in my view, on the other hand''.
</div>

{{BR}}
= Mediation =

'''Situation:''' An English-speaking exchange student asks what your school means by this German notice. Explain the key message in English. Do not translate sentence by sentence.

'''German source:''' „Empfehlungen auf Plattformen können personalisiert sein. Prüfe deshalb, warum dir etwas angezeigt wird, suche bei wichtigen Themen auch selbst nach weiteren Quellen und teile im Unterricht keine privaten Nutzungsdaten.“

'''Your task:''' Give a 40–60 word English explanation for the exchange student.

<div class="mw-collapsible mw-collapsed">
'''Hint:''' Start with: “The notice says that personalised recommendations can shape what you see. It advises students to …”
</div>

{{BR}}
= Ask better questions =

Instead of asking only “Is this recommendation good?”, ask:

# What information might the system be using?
# What goal might the ranking have?
# What other content could have been shown?
# Is the source reliable?
# Can I change settings, search directly or ask for another view?
# What evidence supports the claim I am seeing?

{{BR}}
= Feedback and error analysis =

After a speaking or writing task, mark one sentence in each category:

{| class="wikitable"
! Check
! Question
|-
| '''meaning'''
| Did I answer the task clearly?
|-
| '''evidence'''
| Did I separate observation from assumption?
|-
| '''language'''
| Did I use may, might or can where needed?
|-
| '''cohesion'''
| Did I connect ideas with because, however or for example?
|-
| '''source check'''
| Did I verify important AI or platform claims?
|}

'''Improve one thing, not everything.''' Then use the improved sentence again in a new example.

{{BR}}
= Safe and responsible use =

Use fictional profiles and invented recommendation data in class. Do not collect private social-media histories from classmates. Do not upload another person’s work, image, voice or data to an external AI system without permission. AI-generated explanations, facts, sources and feedback can be wrong, so check them. Keep your own original text so that you can see what you changed and why.

{{BR}}
= Can-do check =

{| class="wikitable"
! I can …
! Yes
! Not yet
|-
| explain a recommendation system simply
| ☐
| ☐
|-
| name and question recommendation signals
| ☐
| ☐
|-
| compare two feeds
| ☐
| ☐
|-
| discuss influence without overclaiming
| ☐
| ☐
|-
| give a short spoken response
| ☐
| ☐
|-
| write a coherent opinion
| ☐
| ☐
|-
| mediate a short German message into English
| ☐
| ☐
|}

{{BR}}
= Interaktive Aufgaben =

{{BR}}
== Quiz: Teste Dein Wissen ==

{{MC}}

'''What does a recommendation system mainly do?'''
(It suggests items that may be relevant to a user)
(!It shows every available item)
(!It removes all user choice)
(!It only works with written text)

{{E}}
<br>

{{MC}}

'''Which item can be a recommendation signal?'''
(Watch time)
(!Screen colour)
(!Battery shape)
(!Keyboard size)

{{E}}
<br>

{{MC}}

'''What is a careful way to describe influence?'''
(A recommendation may influence what I notice)
(!A recommendation always controls my decision)
(!A recommendation proves my opinion)
(!A recommendation makes all users identical)

{{E}}
<br>

{{MC}}

'''What does content-based recommendation focus on?'''
(Similar features of items)
(!Only the user’s age)
(!Random numbers only)
(!The device brand only)

{{E}}
<br>

{{MC}}

'''What does collaborative filtering use in a simple explanation?'''
(Patterns across users or items)
(!Only one fixed list for everyone)
(!Only spelling rules)
(!Only the newest item)

{{E}}
<br>

{{MC}}

'''Why can one click be difficult to interpret?'''
(It can have different reasons)
(!It always means strong interest)
(!It cannot be recorded)
(!It always means dislike)

{{E}}
<br>

{{MC}}

'''What is a feedback loop in recommendation?'''
(Interactions can affect later recommendations)
(!Recommendations never change)
(!Users see no content)
(!Only teachers can create signals)

{{E}}
<br>

{{MC}}

'''What is a useful source-checking question?'''
(What evidence supports this claim)
(!How colourful is the page)
(!How fast can I share it)
(!How many emojis are visible)

{{E}}
<br>

{{MC}}

'''Which modal verb helps express uncertainty?'''
(may)
(!must always)
(!proves)
(!guarantees)

{{E}}
<br>

{{MC}}

'''What is good classroom practice for this topic?'''
(Use fictional profiles instead of private social-media data)
(!Share classmates’ private histories)
(!Upload recordings without asking)
(!Copy AI feedback without checking it)

{{E}}
<br>

{{BR}}
== Memory ==

<div class="memo-quiz">
{|
|-
| recommendation || suggested item
|-
| signal || information used by a system
|-
| ranking || ordering possible items
|-
| variety || different kinds of content
|-
| evidence || support for a claim
|-
| feedback loop || interaction affects later suggestions
|}
{{E}}
<br>

{{BR}}
== Drag and Drop ==

<div class="lueckentext-quiz">

{| class="wikitable"
! Ordne die richtigen Begriffe zu.
! Thema
|-
| '''Signal'''
| A click or watch time
|-
| '''Ranking'''
| Putting items in an order
|-
| '''Personalisation'''
| Adapting suggestions to a user
|-
| '''Variety'''
| Showing different types of content
|-
| '''Evidence'''
| Information that supports a claim

|}
{{E}}

<br>
<br />

{{BR}}
== Kreuzworträtsel ==

<div class="kreuzwort-quiz">
{|
|-
| signal || What word means information that can be used by a recommendation system?
|-
| ranking || What word means putting possible items into an order?
|-
| profile || What word can describe a set of information linked to a user?
|-
| choice || What word means a decision between options?
|-
| bubble || What word completes the term filter ...?
|-
| feedback || What word completes the term ... loop?
|}
{{E}}
<br>

{{BR}}
== LearningApps ==

<iframe> https://learningapps.org/index.php?s=Recommendation+systems+and+choices </iframe>

{{BR}}
== Lückentext ==

<quiz display=simple>
{'''Vervollständige den Text.'''<br>
|type="{}"}
A recommendation system suggests { content } that may be relevant to a user.
A click or watch time can act as a { signal }.
The system may { rank } possible items before showing them.
Similar user or item patterns can support { collaborative } filtering.
Repeated interaction can create a feedback { loop }.
Personalised suggestions may influence a user’s { choice }.
A careful speaker uses words such as { may } instead of making an absolute claim.
Before sharing an important claim, you should check the { evidence }.
</quiz>

<br>

{{BR}}
= Offene Aufgaben =

{{BR}}
=== Leicht ===

# [[Recommendation spotting]]: Find three recommendation labels on fictional screenshots or teacher-provided examples and explain what each label suggests.
# [[Signal cards]]: Create six cards with possible signals and sort them into “strong clue”, “weak clue” or “depends”.
# [[Feed comparison]]: Compare the two fictional profiles from the reading and write five sentences using ''both'', ''but'' and ''whereas''.
# [[Question maker]]: Write four critical questions you could ask before following a recommendation.

{{BR}}
=== Standard ===

# [[Mini debate]]: Discuss “Personalisation saves time” with one benefit, one limitation and one example.
# [[Design a feed]]: Build a fictional six-item feed for a made-up learner and explain your ranking in English.
# [[Listening response]]: Use one of the embedded videos and record a private 60-second summary or present it live; recording is optional.
# [[Mediation challenge]]: Write an English message that explains the German school notice to an exchange student without translating line by line.

{{BR}}
=== Schwer ===

# [[Recommendation experiment]]: With teacher-provided fictional data, change one signal and predict how the ranking might change. Explain why your result is only a hypothesis.
# [[Source triangle]]: Compare a platform source, a Wikipedia article and one independent educational source. Note where they agree and where they use different language.
# [[Fair feed proposal]]: Design rules for a feed that balances relevance, variety and user control, then defend the trade-offs.
# [[One-minute panel]]: In groups, take roles as user, platform designer and media educator. Negotiate one change that could make recommendations more transparent.

{{:Offene Aufgabe - MOOC erstellen}}

{{BR}}
= Lernkontrolle =

# [[Transfer to shopping]]: A fictional shop recommends only products similar to the last item clicked. Explain one benefit, one possible problem and one way the user could broaden the search.
# [[Evidence check]]: A student says, “My feed changed after one click, so the platform knows exactly what I want.” Explain why this conclusion is too strong and rewrite it more carefully.
# [[Feed design]]: Choose five items from a teacher-provided content pool for two different fictional users. Explain your ranking criteria and what information you deliberately did not use.
# [[Decision pathway]]: Describe how a recommendation could influence a choice from first view to final decision. Include at least one point where the user can actively change direction.
# [[Platform comparison]]: Two fictional platforms use different signals. Explain how this could produce different feeds even for the same person.

<br>
<br>

{{BR}}
= Lernnachweis =

For a successful learning record, show that you can:

# explain the basic purpose of a recommendation system in clear English.
# use key vocabulary such as ''signal'', ''ranking'', ''feed'', ''personalised'', ''choice'' and ''evidence''.
# compare recommendations and explain possible reasons for differences.
# distinguish a fact, an observation, an assumption and an opinion.
# use careful language such as ''may'', ''might'' and ''can''.
# participate in a short discussion and react to another view.
# write a structured 120–160 word opinion.
# mediate key information from German into English.
# explain safe handling of personal data, recordings and AI feedback.
# verify important claims with suitable sources.

<br>
<br>

{{BR}}
= Sources and fact check =

# [https://de.wikipedia.org/wiki/Empfehlungsdienst Wikipedia: Empfehlungsdienst] – overview of recommendation systems.
# [https://en.wikipedia.org/wiki/Recommender_system Wikipedia: Recommender system] – English background reading.
# [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ YouTube: On YouTube’s recommendation system] – platform description of recommendation signals and goals.
# [https://commons.wikimedia.org/wiki/Category:Recommender_systems Wikimedia Commons: Recommender systems] – freely licensed learning media.
# [https://commons.wikimedia.org/wiki/Category:Filter_bubbles Wikimedia Commons: Filter bubbles] – freely licensed diagrams for critical discussion.

{{BR}}
= OERs zum Thema =

<iframe> https://de.m.wikipedia.org/wiki/Empfehlungsdienst </iframe>

<iframe> https://en.m.wikipedia.org/wiki/Filter_bubble </iframe>

{{BR}}
= Follow-up course =

Continue with [[E9 M - Algorithms, feeds and responsibility]] to analyse ranking goals, transparency and responsible platform design in more depth.

{{BR}}
= Verknüpfte Lernbereiche =

{| align=center
{{:D-Tab}}
'''[[E9 M - Recommendation systems and choices]]'''
# [[Englisch]]
# [[Medienbildung]]
# [[Künstliche Intelligenz]]
# [[Algorithmen]]
# [[Empfehlungsdienst]]
# [[Filterblase]]
# [[Informationskompetenz]]
# [[Datenschutz]]
|}

[[Kategorie:Englisch]]
[[Kategorie:Klasse 9]]
[[Kategorie:M-Niveau]]
[[Kategorie:Baden-Württemberg]]
[[Kategorie:Medienbildung]]
[[Kategorie:Künstliche Intelligenz]]
[[Kategorie:Digitale Welt]]
[[Kategorie:Informationskompetenz]]

{{BR}}
= aiMOOC-Projekte =
[[Kategorie:AI_MOOC]] [[Kategorie:GPT aiMOOC]]
{{MT}}

E9 M - Recommendation systems and choices - Versionsgeschichte

Glanz: aiMOOC über GPT aiMOOC Action erstellt