Curriculum

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student-drawn models and their written descriptions of those models.

To build a sustainable future, science and engineering education programmes should emphasise scientific investigation, collaboration across traditional science topics and disciplines, and engineering design, including design and evaluation of solutions. We adopted a qualitative case study design to address the research question, What is the process of team co-construction of instructional materials that emphasize learning through both science investigation and engineering design? The paper outlines the first year of our team co-construction activities involving the design, implementation, and evaluation of instructional materials for secondary science.

Do your students ever share ideas that are only peripherally related to the discussion you are having? We discuss ways to minimize and deal with such contributions.

Do your students ever share ideas that are only peripherally related to the discussion you are having? We discuss ways to minimize and deal with such contributions.

Do your students ever share ideas that are only peripherally related to the discussion you are having? We discuss ways to minimize and deal with such contributions.

In this paper, we present an overview of the design research used to develop a digital collaborative environment with an embedded problem-based curriculum. We then discuss the student and teacher features of the environment that promote inquiry-based learning and teaching.

Bioinformatics—a rapidly developing discipline that integrates mathematical and computational techniques with biological knowledge for applications in medicine, the environment, and other important aspects of life—is an example of an emerging field that illustrates the need for a greater focus on STEM integration in K12 education. Studies on teaching bioinformatics in high school reveal difficulties that arise from a lack of curricular resources and teacher knowledge to effectively integrate disciplinary content. In this study, we investigated challenges teachers experienced in teaching a problem-based bioinformatics unit after participating in professional development (PD) activities that were carefully constructed using research-based effective PD characteristics.

While tragedy has struck an inordinate number of students in the past several years, not all areas of the country are at risk for every natural hazard all the time. To avoid having students feel like Chicken Little under a falling sky, the GeoHazard project uses simulations, data, experimentation, and scientific argumentation to teach about risk and uncertainty. We have created three scaffolded online modules focused on hurricanes, wildfires, and inland flooding to help teach these concepts. Through investigations using both simulations and real-world data, these curriculum units introduce students to the scientific factors responsible for these hazards and provide practice in interpreting forecasts.