DGB 1: Personalised Search Results
Search engines and other digital services may adapt results and recommendations to individual users. This personalisation can be useful, but it also influences which information becomes visible and often relies on data about usage and interests.
Curriculum reference
[Bearbeiten]| Area | Classification |
|---|---|
| Competence area | Information |
| Focus | Personalised search routines |
| Competence | Recognise factors that can influence personalised results and assess benefits, risks and limits. |
Learning objectives
[Bearbeiten]Students can:
- explain personalisation in simple terms;
- name factors that may influence search results or recommendations;
- describe possible links between search history, location, language settings and personalised results;
- recognise possible benefits of personalisation;
- describe risks such as reduced diversity, profiling or commercial influence;
- explain the basic idea of a filter bubble;
- reflect on when personalised results are useful and when they should be viewed critically;
- identify settings related to history, cookies or personalisation.
Learning content
[Bearbeiten]What is personalisation?
[Bearbeiten]Personalisation means that a digital service adapts content, recommendations or search results to a user or context. Two people can therefore receive different results even when they enter similar queries.
Which data can play a role?
[Bearbeiten]Possible factors include previous searches, clicked results, location, language settings, device information, cookies, account activity and inferred interests. Not every service uses all of these factors, and the exact methods differ.
Possible benefits
[Bearbeiten]Personalisation can make results more relevant to a location or previous interests, reduce the amount of irrelevant information and provide useful recommendations. A local search, for example, can prioritise nearby results.
Risks and limitations
[Bearbeiten]If a system mainly selects content that matches previous behaviour, the diversity of visible information may decrease. The term filter bubble describes the risk of seeing mainly similar perspectives. Personalisation also often requires data about use and interests.
Another factor is the business model of many services. Recommendations and search results may be linked to advertising, attention and sales. Personalisation is therefore not automatically neutral.
Possible risks include reduced diversity, privacy issues, profiling, commercial influence and becoming overly dependent on recommendations.
Check results consciously
[Bearbeiten]Users can counterbalance personalisation by comparing several sources, changing search terms, searching without an account where appropriate and deliberately looking for different perspectives.
Settings and control
[Bearbeiten]Browsers and online services may offer settings for history, cookies, activity data or personalised recommendations. These settings can provide some control, although they do not always reveal every factor used by a service.
Classroom activities
[Bearbeiten]Compare results
[Bearbeiten]Task: Compare search results produced in two suitable test situations. Note similarities and differences without using private accounts or personal data.
Match possible factors
[Bearbeiten]Task: Match examples such as location, language, search history and cookies to ways in which they could influence results.
Benefit or risk?
[Bearbeiten]Everyday situations such as a local restaurant search, a video recommendation or a product search are assessed.
Task: For each situation, name one possible benefit and one possible risk of personalisation.
Expected outcome: Balanced judgements rather than automatic approval or rejection.
Explore settings
[Bearbeiten]In a suitable school browser or demonstration account, examine privacy and search settings.
Task: Find settings related to history, cookies or personalisation and describe their stated effect. Do not change private accounts without permission.
Expected outcome: Awareness of available controls and their limits.
Self-check
[Bearbeiten]- I can explain personalisation.
- I can name factors that may influence search results.
- I can explain the idea of a filter bubble.
- I can identify both benefits and risks of personalised results.
- I can use settings and source comparison more consciously.