The DataSETUP modules cover a wide range of topics in data science education and are aimed at learners with different backgrounds and levels of experience. To help you find the module that best fits your needs, the table below provides an overview of the prior knowledge required for each module.
In addition to the prerequisites, the table lists supplementary learning resources for each module. These resources can help learners refresh existing knowledge, close possible gaps, or explore certain topics in more depth. They are intended as optional support and can contribute to successfully completing the respective module.
The table is designed to support module selection for different target groups:
- Teachers who would like to expand their data science competencies can use the table to identify modules that match their current knowledge and to prepare specifically for a module of their choice.
- Teacher educators looking for a suitable course for their students can use the table to assess which modules fit the prior knowledge of their learners and how these modules can be integrated into their own courses.
For a detailed overview of each module, please visit Material > DataSETUP Modules.
Module | Prerequisites | Learning Resources |
|---|---|---|
| Integrating AI/ML-Related Data Science in STEAM Education to Foster Responsible and Active Citizenship | Data types (categorical vs. numerical) Descriptive statistics Basic probability (in AI predictions PPDAC cycle AI/ML fundamentals Basic use of CODAP Basic use of Teachable Machine | Teachable Machine (Tool) Generative AI: Output & Probabilities (Video) How AI Works – Machine Learning (Video) |
| Responsible AI & Data Science: Ethics, Society, and Citizenship | Data types (categorical vs. numerical) Basic probability (in AI predictions) PPDAC cycle AI/ML fundamentals Basic use of Teachable Machine | Teachable Machine (Tool) CODAP Tutorials (Tutorial) AI for Oceans – Code.org, grades 3–12 (Learning module) How AI Makes Decisions – Code.org, grades 4–6 (Learning module) |
| Data Science and Applications in Educational Research | to be added | to be added |
| Introduction to Data Science for Future Educators | No prerequisites | We are already using AI more than we realise (Video) Machine learning and Big data for Music discovery (Video) Use of machine learning by Netflix (Video) Overview of Netflix and Spotify like recommendation engines (Podcast recommendation) Algorithmic bias (Website – background information) |
| Buzzing with Data: Investigating Hive Health through Sensor Data | Basic use of CODAP | CODAP Overview (Video) Why Bees Matter (Handout) Importance of Bees (Video; start at 1:54mins) The Power of Pollinators (Video) I started Beekeeping; How Bees make honey; How bees build their hive; How do bees build honey comb (Video shorts) |
| Preparing and Exploring Messy Secondary Data on Mental Health | Group comparisons Association between two categorical variables Data cleaning in CODAP Two-way dot plots in CODAP | CODAP: Add Counts/Percentages to Categorical Graphs (Tutorial) CODAP Overview (Video) |
| From Images to Data: Investigating “Access to Essential Resources” with Dollar Street | Descriptive statistics Basic use of CODAP | CODAP: Add Counts/Percentages to Categorical Graphs (Tutorial) CODAP Overview (Video) |
| How do Young People Consume News? | Creating & interpreting data visualizations Percentages Group comparisons Confident use of CODAP | CODAP Tutorial (Video) CODAP Overview (Video) |
| Gaming Habits of Young People | Creating & interpreting data visualizations Percentages Group comparisons Confident use of CODAP | CODAP Tutorial (Video) CODAP Overview (Video) |
| PISA Data and What We Make of It | Descriptive statistics Creating & interpreting data visualizations Percentages Group comparisons PPDAC cycle Confident use of CODAP or SPSS | CODAP How-to Guides (Tutorial) CODAP Tutorials (Tutorial) SPSS Resources (IBM Website – Tutorials and Manuals) DataSETUP Framework (EDUCATE) (Framework) Leavy et al. (2026) (EDUCATE, the DataSETUP framework: Literature) GAISE II, pp. 13–16 (Statistical investigative questions: Literature) Arnold & Franklin (2021) (Statistical investigative questions: Literature) Frischemeier & Leavy (2020) (Statistical investigative questions: Literature) |
| Exploring Rock, Paper, Scissors with Teachable Machine | Basic Python programming Following step-by-step instructions | Teachable Machine (Tool) Rock-Paper-Scissors Dataset (Kaggle) (Dataset) Teachable Machine Tutorial: Bananameter (Tutorial) AI: Training Data & Bias (Video) 3 Types of Bias in AI (Video) DataSETUP Framework (EDUCATE) (Framework) Leavy et al. (2026) (EDUCATE, the DataSETUP framework: Literature) Fergusson & Pfannkuch (2024) (Images as data: Literature) |
| Exploring Coronal Mass Ejections (CMEs): A Data Science Approach | Descriptive statistics Creating & interpreting data visualizations Trigonometric functions Pattern recognition & prediction (e.g., periodicity) Confident use of CODAP | CODAP Tutorials (Tutorial) CODAP: Getting Started with Tables (Tutorial) CODAP: Entering/Importing Data (Tutorial) What Are Coronal Mass Ejections from the Sun? (0:58) (Video) What Are Coronal Mass Ejections? – Britannica (1:59) (Video) Further Video on CMEs (EN) (Video) Further Video on CMEs 1 (GR) (Video) Further Video on CMEs 2 (GR) (Video) |
