An official website of the United States government
The integration of science and engineering practices in K-12 science education is currently an area of growing national interest in the United States, as evidenced in the recently published document titled A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. However, to date, little is known about the extent to which these practices are covered in the widely used K-12 engineering programs. As a response to the dearth of research in this area, this study investigated the nature and extent to which science and engineering practices are covered in the widely used K-12 engineering programs in the United States. Nine programs that are widely used in the United States were analyzed via document content analysis method using the K-12 science education framework. The results revealed important findings showing the similarities and disparities in the coverage of science and engineering practices in the analyzed programs, grade levels, and in different science discipline units. This study is significant because an understanding of the current status of science and engineering practices coverage would be helpful to educators and curriculum designers as they strive to further the development of integrated science and engineering curricula, as well as shaping the scope and sequence of engineering design thinking learning activities in the K-12 science curriculum. Key Words: engineering practices; science practices; K-12 engineering education; K-12 science curriculum
more »
« less
This content will become publicly available on July 1, 2026
EXPLORING A US FRAMEWORK OF LEARNING PROGRESSIONS FOR K-12 DATA SCIENCE EDUCATION
There is a need for a framework that conceptualizes data science learning at the K-12 level which could serve as a guide to policy makers, practitioners and researchers alike. In an attempt to build such a framework, the Concord Consortium and Data Science 4 Everyone joined together, with seed funding from NSF and the Valhalla Foundation to facilitate a series of workshops across the field with the goal of building consensus Learning Progressions (LP) for K-12 DSE.
more »
« less
- Award ID(s):
- 2325871
- PAR ID:
- 10625739
- Publisher / Repository:
- OpenReview
- Date Published:
- Format(s):
- Medium: X
- Location:
- Salzburg Austria
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Contemporary science is a field that is becoming increasingly computational. Today’s scientists not only leverage computational tools to conduct their investigations, they often must contribute to the design of the computational tools for their specific research. From a science education perspective, for students to learn authentic science practices, students must learn to use the tools of the trade. This necessity in science education has shaped recent K–12 science standards including the Next Generation Science Standards, which explicitly mention the use of computational tools and simulations. These standards, in particular, have gone further and mandated thatcomputational thinkingbe taught and leveraged as a practice of science. While computational thinking is not a new term, its inclusion in K–12 science standards has led to confusion about what the term means in the context of science learning and to questions about how to differentiate computational thinking from other commonly taught cognitive skills in science like problem-solving, mathematical reasoning, and critical thinking. In this paper, we propose a definition ofcomputational thinking for science(CT-S) and a framework for its operationalization in K–12 science education. We situate our definition and framework in Activity Theory, from the learning sciences, in order to position computational thinking as an input to and outcome of science learning that is mediated by computational tools.more » « less
-
Despite significant advances in machine learning (ML) applications within science, there is a notable gap in its integration into K-12 education to enhance data literacy and scientific inquiry (SI) skills. To address this gap, we enable K-12 teachers with limited technical expertise to apply ML for pattern discovery and explore how ML can empower educators in teaching SI. We design a web-based tool, ML4SI, for teachers to create ML-supported SI learning activities. This tool can also facilitate collecting data about the interaction between ML techniques and SI learning. A pilot study with three K-12 teachers provides insights to prepare the next generation for the era of big data through ML-supported SI learning.more » « less
-
Data science is revolutionizing academia and industry, creating a high demand for a workforce fluent in this field. While the availability of data science courses has increased recently, few curricula rigorously build on mathematical logic. The LogicDS Project addresses this gap by engaging high school students from rural communities in an online data science course integrating mathematics, statistics, and programming into a unified framework based on logic and reasoning. A one-week course, consisting of six lessons, was developed and 110 participants were recruited. Pre- and post-intervention data, along with students' LMS activity logs, were collected to analyze engagement. Results indicate that the Logic-Based framework effectively engages students from diverse backgrounds, with participants finding the course valuable for learning data science skills. Notably, entropy analysis of student activity logs correlated with other mixed methods analyses, providing insights into engaging K-12 students in data science education.more » « less
-
What do we know about data science learning at the grades K–12 (precollegiate) level? This article answers this question by using the notion of agency to provide a framework to review the diverse research agendas and learning environments relevant to data science education. Examining research on data science education published in three recent special issues, we highlight key findings from scholars working in different communities using this lens. Then, we present the results of a co-citation coupling analysis for articles published in one of three recent data science education special issues with research spanning various levels and contexts. This co-citation analysis showed that while there are some common touchpoints, research on data science learning is taking place in a siloed manner. Based on our review of the literature through the lens of agency and our analysis, we discuss how the data science education community can synthesize research across disciplinary and grade-level divides.more » « less
