<jats:p>Decision-making processes are often based on data and data-driven machine learning methods in different areas such as recommender systems, medicine, criminalistics, etc. Well-informed citizens need at least a minimal understanding and critical reflection of corresponding data-driven machine learning methods. Decision trees are a method that can foster a preformal understanding of machine learning. We developed an exploratory teaching unit introducing decision trees in grade 6 along the question “How can Artificial Intelligence help us decide whether food is rather recommendable or not?” Students’ performances in an assessment task and self-assessment show that young learners can use a decision tree to classify new items and that they found the corresponding teaching unit informative.</jats:p>

<jats:p>We report on our work with students in our data science courses, focusing on the analysis of students’ results. This study represents an in-depth analysis of students’ creation and documentation of machine learning models. The students were supported by educationally designed Jupyter Notebooks, which are used as worked examples. Using the worked example, students document their results in a so-called computational essay. We examine which aspects of creating computational essays are difficult for students to find out how worked examples should be designed to support students without being too prescriptive. We analyze the computational essays produced by students and draw consequences for redesigning our worked example.</jats:p>

<jats:p>Data Science has become an increasingly important aspect of our everyday lives as we gain a lot of different insights from data analyses, for example in the context of environmental issues. In order to make the process of data analyses comprehensible for lower secondary school students, we developed a data analysis project for computer science classes, focusing on gaining insights from environmental data by using the concept of epistemic programming. In this article, we report on the second implementation of this project, which was conducted in a ninth-grade computer science class. Concretely, we want to examine, how far the students were able to create computational essays to conduct reproducible data analyses on their own. In this regard, the computational essays created with the help of the professional tool Jupyter Notebooks will be examined in terms of aspects of reproducibility.</jats:p>