Analysing Public Confidence in Social Organisations Using R

School of Information Technology

About this use case

Students conduct a data analysis to investigate factors influencing people’s confidence in different social organisations. Each student is assigned specific variables and a country of focus. They apply statistical techniques, create visualisations and interpret the results to identify patterns and differences in real-world social data. Generative AI may support coding tasks but not the analysis or interpretation of findings.

Learning outcomes:

Transform real-world problems into questions that can be addressed using data analytics techniques.
Cleanse and prepare data for analysis.
Analyse large datasets using statistical, graphical and machine-learning techniques.
Validate and critically assess analytical results.
Interpret findings and communicate them to a broad audience.

- Help students build practical experience in data manipulation, visualisation and analysis
- Applying R to a real-world social research problem.
1. Assign students a country of focus and specific variables to investigate.
2. Guide students in selecting and applying appropriate statistical techniques.
3. Provide opportunities to practise data cleaning, visualisation and interpretation in R.
4. Explain that generative AI may be used only to locate relevant R functions and coding examples.
5. Provide clear instructions on documenting generative AI use in the report.
6. Reinforce ethical and responsible AI use during workshops and tutorials.
7. Direct students to relevant LearnHQ guidance.
- Improved ability to prepare and analyse real-world datasets.
- Stronger skills in applying statistical and graphical techniques.
- Greater confidence in using R for social research.
- Improved ability to identify patterns and differences in data.
- Stronger critical evaluation and interpretation of analytical results.
- Greater awareness of appropriate and transparent generative AI use.

Try it out

This use case requires a medium level of effort to implement.

- Select a real-world dataset and assign students a defined country, population or context together with specific variables to investigate.
- Ask students to clean and prepare the data, apply appropriate statistical techniques, create visualisations and interpret the results.
- Clearly define the permitted use of generative AI and require students to document how it supported their coding process.
- Set clear boundaries between coding support and analytical thinking.
- Generative AI may help students locate R functions and examples, but the selection of methods, validation of results, interpretation and communication of findings should remain the students’ own work.
- Support these expectations through assessment instructions, workshops, tutorials and institutional guidance.
Educator involvement through collaboration
Consultation sessions provide visibility of learning by allowing students to verbalise their understanding, clarify misconceptions, and demonstrate their grasp of key concepts through discussion.

Supporting resources

Here are some additional resources that you can browse to help you from TeachHQ:

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Sidebar

Unit: FIT3152	Condition: Individual
Class size: 200–300	Typology: Written
AI approach: Using and applying AI tools effectively	AI statement: Guided use
Visibility of Learning: Educator involvement through collaboration

About the educator

Validated by

Shannon Ng, Education Designer, Education Excellence