@article{55667,
  abstract     = {{<jats:p>This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test data. They learned to manually construct decision trees to classify food items as recommendable or not. They utilized data cards with a heuristic that is a simplified form of a machine learning algorithm. We report on evidence that this topic is teachable to middle school students, along with insights for refining our teaching approach and broader implications for teaching machine learning at the school level.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Podworny, Susanne and Biehler, Rolf}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  number       = {{1}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning to Construct Data-Based Decision Trees Using Data Cards as the First Introduction to Machine Learning in Middle School}}},
  doi          = {{10.52041/serj.v23i1.450}},
  volume       = {{23}},
  year         = {{2024}},
}

@article{32335,
  abstract     = {{Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.}},
  author       = {{PODWORNY, SUSANNE and Hüsing, Sven and SCHULTE, CARSTEN}},
  issn         = {{1570-1824}},
  journal      = {{STATISTICS EDUCATION RESEARCH JOURNAL}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{48108,
  abstract     = {{<jats:p>Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.</jats:p>}},
  author       = {{PODWORNY, SUSANNE and HÜSING, SVEN and SCHULTE, CARSTEN}},
  issn         = {{1570-1824}},
  journal      = {{STATISTICS EDUCATION RESEARCH JOURNAL}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{34920,
  abstract     = {{<jats:p>A very warm welcome to this Special Issue of the Statistics Education Research Journal (SERJ) on data science education. Our hope is to give an overview of selected theoretical thoughts and empirical studies on data science education from a statistics education research perspective. Data science education is rapidly developing but research into data science education is still in its infancy. The current issue presents a snapshot of this developing field.</jats:p>}},
  author       = {{Biehler, Rolf and De Veaux, Richard and Engel, Joachim and Kazak, Sibel and Frischemeier, Daniel}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Editorial: Research on Data Science Education}}},
  doi          = {{10.52041/serj.v21i2.606}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{35672,
  abstract     = {{<jats:p>This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}},
  doi          = {{10.52041/serj.v21i2.61}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{56706,
  abstract     = {{<jats:p>Group comparisons offer students opportunities to reason about many fundamental statistical concepts like center, variation, or distribution. When doing such activities using large, real datasets, technology becomes and essential tool for exploring the data. With its large variety of features and its user-friendly handling, TinkerPlotsTM --as a software for learners and teachers--can facilitate the process of comparing distributions. In this article we focus on eight preservice teachers´  reasoning when comparing groups with TinkerPlots. We present ideas on the design of a course to develop statistical reasoning with TinkerPlots, present a framework to rate learners´  performance when comparing groups with TinkerPlots, and present results of a laboratory study about preservice teachers´  reasoning when comparing groups with TinkerPlots. Findings suggest that the TinkerPlots tool and design of the course supported these preservice teachers´  reasoning and that more learning opportunities are needed to increase their group comparison elements´  repertoire and interpretation in context.
First published May 2018 at Statistics Education Research Journal Archives</jats:p>}},
  author       = {{Frischemeier, Daniel and Biehler, Rolf}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  number       = {{1}},
  pages        = {{35--60}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Preservice teachers´ comparing groups with TinkerPlots - An exploratory video study}}},
  doi          = {{10.52041/serj.v17i1.175}},
  volume       = {{17}},
  year         = {{2018}},
}