| Aim(s) of the module | This module, “Introduction to Data Science for Future Educators,” is designed to provide pre-service teachers with a foundational understanding of data science and its relevance to their lives and to education. Through interactive discussions, practical activities, and engaging case studies, participants will explore the core components of data science, distinguish it from traditional statistical inquiry, and understand how these concepts apply to real-world scenarios. By the end of the module, participants will have a greater understanding of data science and develop greater awareness of its relevance and potential in education and the wider world. |
| Justification | An introduction to data science is essential for pre-service teachers as it equips them with the foundational understanding of what data science entails and its significance in their world. As future educators, it is crucial for them to grasp the nature of data science, including its role in collecting, analyzing, and interpreting data to make informed decisions. By first exploring the ‘whys’ and ‘hows’ of data science in this module, pre-service teachers are introduced to the processes and practices of data science and appreciate its relevance in classrooms and broader society. This context helps develop a positive attitude toward the subject and prepares them for application-focused modules, where they will actively engage in data analysis. With this deeper understanding, teachers are more likely to approach data science with confidence and curiosity, enabling them to incorporate data-driven approaches in their classrooms. |
| Learning outcomes | Students will be able toExplain Data Science: Students will be able to explain what data science is and its relevance to their world Explore some fundamental data science practices: Students will be introduced to some practices unique to data science through reference to real world examplesEvaluate Ethical Issues in Data Science: Students will be able to identify and critically evaluate ethical considerations in data science, including bias, and responsible data use. |
| Key phrases | Data science processes; data science practices; machine learning; data governance and ethics |
| Prerequisite knowledge | None |
| Expected module duration | 3 hours 15 mins |
| Materials and tools | A computer/tablet with internet and working camera (for each small group of students); Youtube videos; printouts of ‘Biting monkey problem’, magnetic board or blackboard |
| Further remarksList and justify the main teaching approaches, e.g., group work, class discussion, other teaching techniques, such as case studies, assignment, etc. | The module employs a combination of hands-on activities, small-group discussions, and video-based learning to support students in developing an understanding of key data science concepts. Students will work in small groups to engage in two activities—one paper-based and one digital task requiring the use of a laptop. These activities are designed to make complex ideas more tangible, helping students build intuition about data science processes through active engagement. Small-group discussions are incorporated to encourage reflection and meaning-making, allowing students to articulate their insights, clarify misunderstandings, and connect their learning to real-world contexts. Additionally, the use of short videos featuring real-world data scientists and everyday people sharing their experiences, making data science feel more relevant and relatable to students’ lives. |
Introduction to Data Science for Future Educators (Instructor)
The first module of the DataSetup curriculum
Free
0 Quizzes
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