Proposal Description

TThe rapid advancement of data-driven machine learning (ML) technologies and their ubiquitous applications in various domains require a comprehensive understanding of not only the technical foundations but also the societal and ethical implications.

This session will discuss novel educational frameworks aimed at secondary school students and teachers to enhance their understanding of the multifaceted nature of data science, with a specific focus on machine learning. It will also discuss innovative research and development projects for secondary school students and their teachers.

The following questions will be discussed

· What are the social benefits of ML, and what are the risks and perils for society?

· How can the algorithmic and mathematical core of ML methods such as decision trees, artificial neural networks, and k-nearest neighbors be made accessible to students?

· What are new perspectives on modeling (multivariate modeling, algorithmic modeling, predictive modeling, training and test data, overfitting)

· What kind of tools are suitable to support these processes (unplugged data cards, games, CODAP with ARBOR plug-ins, pedagogically designed Jupyter notebooks)?

· What contexts, applications, questions, and data sets are appropriate for secondary education?

· How can generative AI support teaching and learning?

The invited speakers are selected from innovative projects that cover this spectrum of aspects. Through a combination of theoretical instruction, practical exercises, and case studies, students in these projects are equipped with the necessary skills to critically analyze and engage with ML applications. They will highlight the structure of their curricula, the pedagogical approaches adopted, and the results of pilot implementations in different educational settings.

This session will contribute to the emerging discourse on data science education by providing insights into effective strategies for preparing the next generation of informed citizens capable of navigating the complexities of a data-driven world.