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The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses reveal that students tend to neglect ethical concerns in their data analysis. Based on the data we collected from questionnaires distributed to undergraduate science and engineering students, this paper expands the discussion beyond human concerns and ethics in data science education. As we will show, students tend to neglect the context when attempting to solve data science questions. We argue that gaps in understanding the context relating to the data result in gaps in the analysis as well as in the interpretation of the data. Thus, we propose anthropological thinking as a pedagogy to overcome the context neglect. Placing the spotlight on the context promotes a holistic understanding of the phenomenon being analyzed, as it includes important considerations that do not necessarily fit the more commonly used term human concerns. [ABSTRACT FROM AUTHOR]

Computational thinking is accepted today as a collection of cognitive and social skills required for functioning in the 21st century. The paper presents a conceptual view at computational thinking that encompass concepts, problem-solving skills, application skills, and social skills. To impart those perceptions and skills the paper proposes the Four Pedagogies for Developing Computational Thinking (4P4CT) framework, which relies on active learning, project-based learning, product-based learning, and context-based learning, and advocates implementing computational thinking across all the education system in all subject matters at all ages by all teachers. The framework is presented and its implementation in an academic course for preservice K-12 teachers, taught so far in 16 classes attended by 409 preservice K-12 teachers, is described in detail. To support the effective development of the expected competences among preservice teachers, two types of empirical qualitative evidence, related to student outcomes, are presented: (a) simulations of computational processes, and (b) reflections that indicate a change in preservice teachers' perceptions and the application of computational thinking in their future teaching work.Graphical abstract: Computational thinking is accepted today as a collection of cognitive and social skills required for functioning in the 21st century. The paper presents a conceptual view at computational thinking that encompass concepts, problem-solving skills, application skills, and social skills. To impart those perceptions and skills the paper proposes the Four Pedagogies for Developing Computational Thinking (4P4CT) framework, which relies on active learning, project-based learning, product-based learning, and context-based learning, and advocates implementing computational thinking across all the education system in all subject matters at all ages by all teachers. The framework is presented and its implementation in an academic course for preservice K-12 teachers, taught so far in 16 classes attended by 409 preservice K-12 teachers, is described in detail. To support the effective development of the expected competences among preservice teachers, two types of empirical qualitative evidence, related to student outcomes, are presented: (a) simulations of computational processes, and (b) reflections that indicate a change in preservice teachers' perceptions and the application of computational thinking in their future teaching work. [ABSTRACT FROM AUTHOR]