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Add English DGB 3 translation

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{{DISPLAYTITLE:DGB 3: Patterns in Data Visualisations}}
← [[DGB 3. Klasse/en|Digital Literacy – Year 3]]

Charts and graphs can reveal trends, fluctuations, outliers and recurring patterns. Useful interpretation requires careful reading of titles, axes, units, scales and legends before any conclusion or prediction is made.

== Curriculum reference ==

{| class="wikitable"
! Area !! Classification
|-
| '''Competence area''' || Information
|-
| '''Perspectives''' || T, I
|-
| '''Focus''' || Recognising and describing patterns in data visualisations and using them for predictions
|-
| '''Competence''' || Recognise and describe patterns in diagrams and graphs, derive simple predictions and reflect on uncertainty and possible misinterpretation.
|}

== Learning objectives ==

Students can:

* read titles, axes, units, scales and legends accurately;
* distinguish bar, column, line and other suitable visualisations;
* recognise trends, fluctuations, outliers and recurring patterns;
* describe patterns precisely in words;
* sort data and create simple charts in Excel or Calc;
* derive simple predictions from stable patterns and justify them;
* name uncertainty and limits of predictions;
* identify visual designs that may exaggerate or hide differences.

== Learning content ==

=== Basics of data visualisation ===

A chart summarises selected data visually. Before interpreting shapes, read what is measured, the units, time period, categories and scale.

=== Common types of charts ===

Bar and column charts compare categories, line charts often show change over time, and other chart types serve different purposes. A chart type should match the data and the question.

=== Recognising patterns ===

Patterns include increases, decreases, cycles, clusters, plateaus and outliers. A pattern is an observation in the available data, not automatically an explanation of why it occurs.

=== Describing and comparing patterns ===

Precise language such as 'gradual increase', 'sharp drop', 'roughly stable' or 'one clear outlier' is more useful than vague statements.

=== From patterns to predictions ===

A prediction extends a pattern beyond the observed values. It is more credible when the pattern is stable and the prediction remains close to the observed range.

=== Limits and uncertainty ===

Unexpected events, changing conditions and small or biased data sets can make predictions unreliable. Predictions should therefore include uncertainty rather than pretending to be certain.

=== Everyday and social examples ===

Weather data, transport counts, energy use, learning results and population statistics all contain patterns that can be analysed, but each context requires appropriate caution.

=== Questioning visualisations critically ===

Truncated axes, uneven intervals, selective time ranges and decorative effects can alter the impression. Critical reading checks whether the visual form fairly represents the numbers.

== Classroom activities ==

=== Read a chart systematically ===

Identify title, axes, units, scale, legend and main pattern.

'''Expected outcome:''' A complete chart-reading record.

=== Mark patterns ===

Highlight trends, outliers and recurring changes on prepared charts.

'''Expected outcome:''' An annotated chart.

=== Describe trends precisely ===

Write factual descriptions without explaining causes yet.

'''Expected outcome:''' A set of precise statements.

=== Compare visualisations ===

Compare two charts of the same data and explain which is clearer or more misleading.

'''Expected outcome:''' A design comparison.

=== Make cautious predictions ===

Extend a stable pattern and state why the prediction might fail.

'''Expected outcome:''' A prediction with limitations.

=== Test prediction limits ===

Change one assumption or add an unexpected event and revise the prediction.

'''Expected outcome:''' A revised explanation.

=== Detect misleading graphics ===

Find scale, axis or selection choices that distort the impression.

'''Expected outcome:''' A critical checklist.

=== Collect and visualise data ===

Create a small anonymous data set in Excel or Calc and produce a suitable chart.

'''Expected outcome:''' A chart with correct labels.

=== Interpret a real-life chart ===

Analyse a chart from school, media or public information and separate observation from interpretation.

'''Expected outcome:''' A short analysis.

=== Reflect on uncertainty ===

Explain why data patterns are useful without being guarantees of the future.

'''Expected outcome:''' A reasoned reflection.

== Self-check ==

* I can read axes, units, scales and legends.
* I can identify and describe common patterns.
* I can create a suitable simple chart.
* I can make a cautious prediction from data.
* I can explain why predictions have limits.
* I can identify misleading features in visualisations.

== See also ==

* [[DGB 3: Informations- und Datensuche/en|Independent Information and Data Search]]
* [[DGB 3: Daten Ursache Wirkung Vorhersagen/en|Data, Cause and Effect, and Predictions]]
* [[DGB 3: Cloudbasierte Systeme/en|Cloud-based Systems]]

[[Kategorie:Digitale Grundbildung]]
[[Kategorie:DGB 3]]

DGB 3: Muster in Datendarstellungen/en - Versionsgeschichte

Administrator: Add English DGB 3 translation