Emerging Data Trends

Lesson Overview

Year Level: Year 12
Subject: Technology – Data Analytics
Curriculum Area: VCE Applied Computing – Unit 3, Outcome 1 (U3O1): Data Analytics
Duration: 75 minutes
Class Size: 20 students

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Learning Objectives

By the end of this lesson, students will:

• Understand emerging trends in data analytics, with a focus on AI-driven analytics.
• Identify solution requirements and data needs for AI-based analytics problems.

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Lesson Structure

Introduction (10 minutes)

Purpose

• Capture student interest and contextualise the lesson content.

Activities

1. Lesson Welcome (2 minutes)

   • Briefly outline the structure of the lesson and the importance of understanding AI-driven trends in data analytics.

   Example script:
   "In this lesson, we’re going to explore how artificial intelligence (AI) is redefining the field of data analytics. This is a critical area of learning not only for your upcoming VCE SAC for U3O1, but also for staying informed about technologies shaping the future."

2. U3O1 SAC Reminder (2 minutes)

   • Announce the SAC dates and any conditions related to school guidelines. Ensure students are clear on their expectations for the assessment.

3. Emerging Trends Kickoff (6 minutes)

   • Provide a brief introduction to major AI trends transforming data analytics, including:

     • Predictive Modelling: Using AI to predict customer behaviours or business outcomes.
     • Generative AI: Tools like ChatGPT that can generate written reports, code, or visuals based on data prompts.
     • Natural Language Processing (NLP): AI interpreting and analysing human language data.

   • Use relatable, real-world examples from Australian industries (e.g., mining companies using predictive analytics, healthcare using NLP to analyse patient feedback, or retail leveraging generative AI for targeted advertising).

Resources

• PowerPoint slide with images and graphs representing AI trends.

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Discussion on Emerging Trends (20 minutes)

Purpose

• Deepen understanding through interaction and exploration of prior knowledge.

Activities

1. Slideshow Presentation (10 minutes)

   • Discuss and visually present the emerging AI-driven trends in data analytics. Include examples such as:
     • AI applications in Australian agriculture (e.g., precision farming through predictive modelling).
     • Retail analytics transforming customer recommendations using AI.
     • Generative AI applications in creating data-driven marketing campaigns.
     • Natural language processing helping customer service chatbots to improve efficiency.

2. Interactive Discussion (10 minutes)

   • Pose open-ended questions to encourage critical thinking:
     • "How do you think predictive modelling might make industries more efficient?"
     • "What ethical considerations could arise with generative AI in data analytics?"
   • Encourage students to share personal experiences or notable news articles they’ve come across.
   • Chart student input on the whiteboard for reference during the rest of the lesson.

Resources

• Interactive whiteboard or butcher paper for shared brainstorming.

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Activity: Identifying Solution Requirements (20 minutes)

Purpose

• Practise applying theoretical concepts to real-world problems.

Activities

1. Introduction to Solution Requirements (5 minutes)

   • Explain that solution requirements refer to the questions, goals, and data needs that drive a data analytics project.
   • Use the example of a cafe chain wanting to use AI to reduce food wastage and predict menu demand accurately.
   • Key questions:
     • What data is needed? (e.g., inventory levels, sales patterns, customer orders)
     • What solution features are necessary? (e.g., AI model predicting demand for salads vs pastries)

2. Pair Work (15 minutes)

   • Distribute a printed scenario describing a business problem:
     Scenario: A small fashion retailer in Melbourne wants to use AI to optimise its online store by providing customers personalised clothing recommendations and increasing sales.

   • Students work in pairs to identify:

     • What data would the AI need? (e.g., previous purchase history, website browsing behaviour, seasonality trends).
     • What output should the solution provide? (e.g., personalised clothing suggestions for customers, insights on popular items).

   • Rotate around the class to guide discussions and clarify misconceptions.

   • At the end, ask a few pairs to share their answers.

Resources

• Printed scenario handouts.
• Whiteboard for recording answers during the class sharing session.

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Summary and Homework (10 minutes)

Purpose

• Consolidate learning and set work to encourage independent exploration.

Activities

1. Summary of Key Points (5 minutes)

   • Recap the significant trends explored: Predictive Modelling, Generative AI, and Natural Language Processing.
   • Emphasise how AI is both a tool for efficiency and a field requiring ethical responsibility.

2. Homework (5 minutes)

   • Homework Task:
     Research three tools that integrate AI for data visualisation and summarise how prompts (like questions or instructions) can create visual outputs. Examples include:
     • ChatGPT (with code interpretation plugins).
     • Bard (integrated with Google tools).
     • Tableau’s AI features.
     • Students should be prepared to discuss their findings in the next class.

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Assessment Strategies

• Observe participation during the pair activity and discussion to gauge understanding.
• Evaluate homework to assess research skills, depth of understanding, and ability to articulate findings.

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Teacher Notes

• Avoid overloading students with too much technical jargon. Use plain language for clarity.
• Aim to foster curiosity and excitement about AI technologies; this may be new territory for some students.
• Encourage critical thinking about ethical considerations in AI (e.g., privacy concerns, biases in algorithms).

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Extension and Enrichment

For advanced learners:

• Challenge them to explore specific AI algorithms (e.g., neural networks, decision trees) and how they apply to data analytics.
• Invite them to consider career opportunities in AI and data science within Australia.