Kindergarten – Grade 2
Sorting, grouping, tallying and simple picture graphs about the class and school.
FutureTech & Computing
Data Literacy & Computational Thinking
Grade bands served: Kindergarten through Grade 12
Data Literacy teaches learners to work with information systematically: collect data honestly, organise it, summarise it, visualise it and question it. Computational thinking — decomposition, pattern recognition, abstraction and algorithms — runs alongside as a general problem-solving method used in every subject. High-school learners handle real datasets, build dashboards and study how sampling, framing and misleading charts distort public understanding.
Why this subject matters
Decisions in health, agriculture, business and government are made with data. Learners who read data well are harder to mislead and more employable.
Overall learning goals
- • Collect and organise data accurately.
- • Summarise data with appropriate measures.
- • Choose and build clear visualisations.
- • Question the source, sampling and framing of data.
- • Apply computational thinking to structure problems.
Core competencies & skills
- • Data collection and cleaning
- • Summary statistics
- • Visualisation
- • Critical interpretation
- • Decomposition and abstraction
Scope by grade band
Grades 3–5
Tables, bar and line charts, averages, surveys and interpreting graphs found in real life.
Grades 6–8
Spreadsheet analysis, mean, median and range, sampling, correlation intuition and misleading-chart critique.
Grades 9–12
Real datasets, cleaning, pivot analysis, dashboards, statistical claims, and introductory data-science concepts.
Typical learning activities
- • Class surveys
- • Spreadsheet analysis labs
- • Chart critique sessions
- • Data storytelling presentations
- • Unplugged computational-thinking puzzles
Project examples
- • Class attendance or weather data chart
- • School-wide survey with analysis and recommendations
- • Community health or market-price data dashboard
- • Data-driven policy recommendation for local leaders
Assessment methods
- • Data projects with written interpretation
- • Spreadsheet practicals
- • Chart critique tasks
- • Examinations
Real-world & career connections
- • Data analysis
- • Monitoring and evaluation
- • Business intelligence
- • Research and public health
FutureTech & AI integration
Data literacy is the bridge to AI: learners see that model quality depends on data quality, and audit datasets for bias and gaps.
Expected learner outcomes
- • Graduates analyse a real dataset and present findings.
- • Graduates detect misleading data presentation.
Where it appears by grade
Related subjects
Computer Science
How computers work, from instructions to algorithms and systems.
Artificial Intelligence Literacy
What AI is, how it works, where it fails, and how to use it responsibly.
Coding & Software Development
Block coding to Python, web development and real software projects.
Robotics & Engineering
Design thinking, build challenges, mechanisms, sensors and control.