Curriculum

The need for data literacy is an increasingly pressing priority in society, but most of the work in data-centred education has focused on developing skills at the middle school, secondary, and post-secondary levels, with little attention on the potential for engaging elementary-aged students in reasoning with and about data. This paper reports findings from a foundational study to explore the natural strengths, skills, and strategies that upper elementary students bring to reasoning about data-centred problems.

Teacher-facilitated whole-class conversations can help elementary students apply the full power of the NGSS science and engineering practices to an engineering design process. In this article we describe and provide examples for five kinds of Design Talks. Each type of Design Talk centers on a different framing question and is facilitated by specific prompts that help students voice their ideas and make connections to others' ideas.

Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR.

Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR.

Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR.

Foregrounding climate education in formal science learning environments provides students with opportunities to develop critical climate-related knowledge and skills. However, research has shown many challenges to teaching and learning about Earth’s climate and global climate change (GCC). This longitudinal study aims to establish how secondary science teachers, over time, implement model-based climate curricula in support of students’ climate and GCC education by utilizing EzGCM. The model (EzGCM) is a data-driven, computer-based climate modeling tool use to explore global climate data.

Foregrounding climate education in formal science learning environments provides students with opportunities to develop critical climate-related knowledge and skills. However, research has shown many challenges to teaching and learning about Earth’s climate and global climate change (GCC). This longitudinal study aims to establish how secondary science teachers, over time, implement model-based climate curricula in support of students’ climate and GCC education by utilizing EzGCM. The model (EzGCM) is a data-driven, computer-based climate modeling tool use to explore global climate data.

Global climate change (GCC) is one of the greatest challenges of our age and a highly significant socio-scientific issue (SSI). Developing secondary students’ understanding about the Earth’s climate and GCC is critical for empowering future citizens and a key focus of the Next Generation Science Standards. In this cross-sectional study, we investigated secondary students’ evidence-based reasoning about GCC grounded in a curricular intervention involving the use of a data-driven, computer-based global climate model—EzGCM—over 3 years with four teachers who adapted the module in their own courses.

Global climate change (GCC) is one of the greatest challenges of our age and a highly significant socio-scientific issue (SSI). Developing secondary students’ understanding about the Earth’s climate and GCC is critical for empowering future citizens and a key focus of the Next Generation Science Standards. In this cross-sectional study, we investigated secondary students’ evidence-based reasoning about GCC grounded in a curricular intervention involving the use of a data-driven, computer-based global climate model—EzGCM—over 3 years with four teachers who adapted the module in their own courses.

Science education literature has highlighted socio-scientific issues (SSI) as an effective pedagogy for teaching science in a social and political context. SSI links science education and real-world problems to engage students in real-world issues, making it ideal for teaching global climate change (GCC). Additionally, technological advances have created a unique opportunity for teaching climate by making previously inaccessible computer-based computational models and data visualizations accessible to the typical K-12 learning environment. Here, we present the findings from the 2020–2021 school year pre-/post-implementation of a 3-week, model-based climate education curriculum module (EzGCM).