Science

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.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures.

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.

We need STEM knowledge programs in formal and informal settings that guide learners in applying STEM learning to the creation of solutions.

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional learning and teaching.

Teachers experience challenges in effectively using formative assessment practices in their classrooms. In the US, only 28% of elementary teachers report using formative assessment. This study highlights the need to design resources to meet teacher needs and support teachers in making sense of assessment information to inform three-dimensional 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.