Year Level

Year 8

Duration

50 minutes

Curriculum Alignment

South Australian Curriculum: Digital Technologies Years 7-8

AC9TDI8P01: Acquire, store and validate data from a range of sources using software, including spreadsheets and databases. Evaluate data for accuracy and timeliness.
AC9TDI8P14: Investigate and manage digital footprints and assess if data is essential to their purpose.
AC9TDI8P09: Design, implement and modify programs involving control structures and functions in Python.
AC9TDI8P05: Design algorithms involving nested control structures represented via flowcharts and pseudocode.
AC9TDI8P12: Collaborate effectively online, plan and manage individual and group projects using digital tools.
Knowledge of how computers transmit data between networks.

Learning Objectives

By the end of this lesson, students will be able to:

Identify and evaluate different types of data for accuracy and timeliness.
Process, analyse, and visualise data using Microsoft Excel and Power BI.
Explain the fundamentals of how data is transmitted between computer networks.
Design simple algorithms using flowcharts and pseudocode.
Write basic Python programs with nested control structures.
Collaborate effectively in pairs or small groups.
Demonstrate problem-solving and self-management skills throughout the lesson.

Resources and Tools

Computers with Microsoft Excel and Microsoft Power BI installed.
Python IDE or an online Python environment (e.g., Thonny or Replit configured for classroom use).
Projector and whiteboard.
Printed handouts: Algorithm flowchart template, pseudocode guide, data set for analysis.
Timer or stopwatch.
Pre-prepared data set (relevant and timely; e.g., school canteen choices, local weather data, or student survey data).

Lesson Breakdown

Time (min)	Activity	Description	Curriculum Links
0-5	Introduction and Objectives	Briefly introduce today's objectives connecting to real-world data use and programming skills. Show quick real example of data visualisation in Power BI.	AC9TDI8P01, AC9TDI8P14
5-15	Understanding Data Types and Evaluation	- Introduce types of data (categorical, numerical, temporal). - Discuss accuracy, timeliness, relevance with real examples. - Class brainstorm on how incorrect or outdated data affects outcomes.	AC9TDI8P01_E2
15-25	Data Processing and Visualisation in Excel & Power BI	- Students open provided dataset in Excel. - Guided steps on sorting, filtering, and creating charts (bar/pie). - Switch to Power BI for interactive dashboard creation demo by teacher. - Students explore simple filters and visuals.	AC9TDI8P01_E3, AC9TDI8P01_E4
25-30	Breakout Discussion: Data Accuracy and Ethics	- Small groups discuss how data might be misused or misrepresented. - Groups record findings on shared doc and share key points.	AC9TDI8P14
30-40	Programming Algorithms and Python Basics	- Introduce flowcharts and pseudocode using nested controls. - Show example related to data validation (e.g., checking if data is within expected range). - Students design flowchart for a simple algorithm (e.g., deciding if data is valid). - Transition to Python: write and run a simple nested IF statement program relating to data (e.g., grading scores).	AC9TDI8P05, AC9TDI8P09
40-47	Networking Data Transmission Introduction	- Teacher-led explanation with visuals: How computers communicate data over networks (packets, IP addresses, simple concept of protocols). - Q&A session to clarify concepts.	General knowledge aligned with curriculum emphases.
47-50	Reflection and Self-Assessment	- Students individually reflect on what they learned and record one skill they developed and one question they still have. - Quick peer review pair-share for feedback. - Teacher sets extension tasks for interested students (e.g., build more complex Python program, deeper Power BI dashboard).	AC9TDI8P12

Teaching Strategies

Explicit instruction combined with active, hands-on learning.
Use of real-world datasets relevant to students' context for engagement.
Pair and group activities to foster collaboration.
Use of questioning to develop critical thinking and evaluation skills.
Incremental scaffolding from data concepts to programming.
Embed digital citizenship and data ethics discussion to heighten awareness.

Assessment

Formative Assessment

Observation of students working collaboratively in Excel and Python tasks.
Review flowchart designs for correct use of nested structure.
Participation in group discussion about data ethics.
Reflections: accuracy of self-assessment and depth of questions.

Summative Assessment Ideas (post-lesson)

Task students to import a new dataset, clean data, visualize and interpret it.
Write a Python program that reads data, applies decision structures and outputs results.
Present a mini project on data ethics and digital footprint impact.

Differentiation and Support

Provide simplified datasets and flowcharts for students requiring support.
Challenge advanced students with creating additional data visuals or more complex programming challenges.
Use peer tutoring within groups.
Clear, step-by-step handouts for software use.

Extensions and Cross-Curriculum Links

Link coding concepts to Maths: nested conditions and logical reasoning.
Use Geography or Science datasets to integrate learning (e.g., weather patterns, population).
Encourage exploration of automation and AI impact on data processing and ethics.

Notes for Teachers

This lesson plan is designed to "wow" with the integration of industry-standard tools like Power BI tailored for year 8 capability.
Incorporate use of AI tools (as per AC9TDI8P12_E5) by encouraging students to ask AI for pseudocode or algorithm steps as a classroom demo to boost engagement and highlight future technology trends.
Emphasize the importance of accuracy and ethical handling of data as a digital citizen skill critical in today's world.

Summary

This 50-minute lesson on Data Skills Challenge actively engages Year 8 students in Digital Technologies by aligning with South Australian Curriculum requirements. It combines evaluation of data, hands-on software skills in Excel and Power BI, programming in Python, and understanding data communication in networks, all scaffolded with collaboration and ethical considerations to build comprehensive digital competence.

References

South Australian Curriculum documents:

AC9TDI8P01 – Data acquisition and validation
AC9TDI8P09 – Programming with control structures (Python)
AC9TDI8P05 – Algorithm design and flowcharts
AC9TDI8P12 – Collaboration and project management
AC9TDI8P14 – Digital footprints and data management
As sourced from South Australian Curriculum Technologies documents

Data Skills Challenge

Teaching Instructions