DataSETUP Modules – DATASETUP

Overview over DataSETUP modules

This table will give you an overview of the DataSETUP modules that will be openly accessible through the Online Learning Platform in the next months.

Module name	Module description
Introduction to Data Science for Future Educators	This module provides pre-service teachers with a foundational understanding of data science, emphasizing its real-world relevance and applications in education. Through interactive activities, videos, and discussions, students explore data science processes, engage with machine learning tools, and critically evaluate ethical issues, helping them develop data literacy and confidence to integrate data science concepts into their future classrooms.
Responsible AI & Data Science: Ethics, Society, and Citizenship	The “Responsible AI & Data Science: Ethics, Society, and Citizenship” module delves into the pivotal role of data science and artificial intelligence (AI) in contemporary society, emphasizing ethical considerations such as fairness, bias, and responsible usage. Students will acquire foundational knowledge in AI-driven decision-making, machine learning processes, and digital citizenship. Through theoretical concepts and case studies, learners will critically examine AI’s impact on education, privacy, and social justice.
Integrating AI/ML-Related Data Science in STEAM Education to Foster Responsible and Active Citizenship	This module builds on the ” Responsible AI & Data Science: Ethics, Society, and Citizenship” module, by focusing on the integration of AI-driven data science in STEAM education. The module aims to equip students – as future educators – with the knowledge, perspectives, and strategies to integrate data science concepts related to AI/ML into STEAM learning, emphasizing equity, privacy, and responsible decision-making. By exploring core data science processes in AI/ML within real-world contexts, the module empowers participants to guide students in critically engaging with AI/ML, questioning AI-driven insights, and understanding the societal implications of AI technologies. Through hands-on activities, theoretical knowledge, and practical applications, the module emphasizes the role of data science literacy as a vital tool for fostering informed, ethical, and equitable participation in an AI-driven society.
Buzzing with Data: Investigating Hive Health through Sensor Data	This module engages pre-service teachers in exploring real sensor data from two honey bee hives to investigate how environmental conditions relate to hive health and sustainability. Students will learn to prepare and merge datasets, formulate statistical questions, analyse and visualise time-series data using CODAP, and communicate their findings through data storytelling. By working with authentic “messy” data, they will reflect on both the challenges of data science and the potential of sustainability contexts for teaching.
Preparing and Exploring Messy Secondary Data on Mental Health	This module engages pre-service teachers in exploring a real-world issue—Mental Health in the Tech World—through the data science process and practices introduced in EDUCATE: The DataSETUP Conceptual Framework. Using CODAP, participants will develop skills in preparing and analyzing secondary data, formulating statistical questions, creating visualizations, and communicating evidence-based recommendations. The module emphasizes critical interrogation of data, including identifying variables, recognizing data types and distributions, and addressing ethical considerations and limitations of the data source.
How do Young People Consume News?	In this module, students will analyze real-world data and analyze how young people consume news. They will investigate trends and patterns in news consumption habits, including which platforms are most popular, the frequency of engagement with news, and preferred types of news. The objective is for students to draw meaningful conclusions about their peers’ news consumption behaviors and understand how these habits influence the spread and reception of information in society. By the end of this module, students will be equipped to offer practical recommendations for themselves and others, enhancing their critical thinking and analytical skills.
Exploring Rock, Paper, Scissors with Teachable Machine. Educational insights from AI explorations	The aim of this module is to introduce pre-service teachers to fundamental concepts of Artificial Intelligence (AI) and data science through hands-on activities. Specifically, the module aims to: Familiarize participants with the process of creating, cleaning, and using datasets to train AI models. Expose participants to the practical application of data science in educational settings by utilizing tools like Teachable Machine. Encourage critical reflection on the ethical implications and limitations of AI and data science technologies in a real-world context. Provide participants with insights into how AI can support problem-solving and decision-making processes while fostering a deeper understanding of the data science pipeline.
Exploring Solar Phenomena with Data Science: Invisible storms, real-world impact	This module introduces pre-service teachers to data science through the study of Coronal Mass Ejections (CMEs), a real-world phenomenon with significant impacts on modern infrastructure. Participants will explore, evaluate, and analyze publicly available CME datasets using tools such as CODAP to conduct exploratory data analysis, create visualizations, and make predictions. The module also emphasizes critical evaluation of data sources, awareness of biases, ethical considerations, and data governance policies.
Data Science and Applications in Educational Research	Within this module, central concepts of educational data science are introduced. An emphasis is put von learning analytics and its applications in educational research.
From Images to Data: Investigating “Access to Essential Resources” with Dollar Street	This module engages pre-service teachers in the full data science cycle using real-world image-based data, moving from question formulation to variable construction, analysis, and communication. By working with complex, semi-structured images from Dollar Street, participants experience how visual information must be transformed into analyzable variables through decisions about what to observe, measure, and represent.
Gaming Habits of Young People	In this module, students will analyze real-world data and young people’s gaming habits. They will investigate trends and patterns in gaming habits, including which platforms are most popular, the frequency of engagement with digital games, and the most preferred genres. The objective is for students to draw meaningful conclusions about their peers’ gaming habits and understand how these habits influence the spread and reception of gaming in society. By the end of this module, students will be equipped to offer practical recommendations for themselves and others, enhancing their critical thinking and analytical skills.
PISA data and what we make of it	In the module “PISA data and what we make of it”, pre-service teachers are confronted with a newspaper article that reports on PISA 2022 results in a drastic manner. After reconstructing the statistical claims in the article, pre-service teachers carry out their own data analysis of a random sample of the PISA 2022 data (student survey and cognitive item data) and respond to the newspaper with a letter to the editor in which they address occurring problems in the communication of statistical results in the before-mentioned newspaper article.