As education becomes increasingly shaped by data-driven systems, generative AI, and algorithmic decision-making, teachers need competencies that go beyond general digital skills. Digital traces generated through digital learning activities can make aspects of learning visible that may otherwise be difficult for teachers to observe. When combined with classroom observations and other sources of data, these digital traces can provide additional insights into students’ needs and help teachers reflect on and adapt their teaching approaches. The project addresses the need to make data and algorithm literacy more explicit in teacher education, including the ability to understand and critically examine how data are collected and interpreted, how algorithmic systems influence educational processes, and what implications these developments may have for learners and teachers.

The project synthesises and structures existing knowledge on data and algorithm literacy and translates it into educational objectives tailored to teacher educators. Expert interviews and workshops within the DEEP community help contextualise and refine these objectives and identify relevant action for teacher educators to suppport teachers in developing the necessary skills.

Based on this work, the project develops an educational resource for teacher educators that brings together relevant competencies, educational objectives, and practical materials for addressing data and algorithmic literacy in teacher education. By strengthening the data and algorithmic literacy of teacher educators, the project contributes to preparing future teachers to engage with data and algorithmic systems in educational practice in a critical, reflective, and responsible way.