Teach mathematics and science using materials for the weather-focused Community Collaborative Rain, Hail, & Snow Network project.
Teach mathematics and science using materials for the weather-focused Community Collaborative Rain, Hail, & Snow Network project.
This article portrays how citizen science (CS) projects can be integrated into elementary classrooms to enhance students’ sensemaking skills and connect to real-world science problems. For the last several years, we have been involved in a study, Teacher Learning for Effective School-Based Citizen Science (TL4CS), that developed materials for elementary school teachers to engage their students in data collection, analysis, and interpretation for two existing CS projects: Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) and the Lost Ladybug Project (LLP).
This article portrays how citizen science (CS) projects can be integrated into elementary classrooms to enhance students’ sensemaking skills and connect to real-world science problems.
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores.
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy.
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores.
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI’s scoring process mirrors that of humans or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students’ written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy.
The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. Founded in 1928, NARST is an international organization of thousands of members focused on improving science education through research. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences.
The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences. Despite the growth, limited studies have shed light on how the community members integrate AI into science education and the challenges. We systematically reviewed 36 AI-related papers presented at the 2024 NARST conference to address this gap.
The disproportionate impacts of societal challenges (e.g., climate change, air and water pollution) on minoritized groups expose systemic injustices and compels STEM educators to reframe the role of STEM education in society. In this article, we describe traditional approaches, contemporary approaches, and our proposed future approach in science and STEM education with a focus on equity and justice. First, we begin with conceptual framing for equity and justice.
The disproportionate impacts of societal challenges (e.g., climate change, air and water pollution) on minoritized groups expose systemic injustices and compels STEM educators to reframe the role of STEM education in society. In this article, we describe traditional approaches, contemporary approaches, and our proposed future approach in science and STEM education with a focus on equity and justice.
The Language, Culture, and Knowledge-building through Science (LaCuKnoS) project tests and refines a model of science teaching and learning that brings together current research on the role of language in science communication, the role of cultural and community connections in science engagement, and the ways people apply science knowledge to their daily decision making. One key component of the model brings families together as co-learners and co-teachers through family learning experiences.
The Language, Culture, and Knowledge-building through Science (LaCuKnoS) project tests and refines a model of science teaching and learning that brings together current research on the role of language in science communication, the role of cultural and community connections in science engagement, and the ways people apply science knowledge to their daily decision making. One key component of the model brings families together as co-learners and co-teachers through family learning experiences. We describe our work to promote more robust family conversations about science in our lives within an existing research practice partnership, using a two-tiered qualitative conversational analysis to compare the family conversations that result from three family engagement models: (a) family science festivals; (b) family science workshops; and (c) family science home learning.
The Exploring Coasts and Coastal Change in Hawai‘i unit supports middle school haumana (students) in developing multi-perspective understanding and personal stances about coastal change in their community. The unit was collaboratively developed by a partnership among educators bringing together Indigenous and Western ways of knowing and learning.
The Exploring Coasts and Coastal Change in Hawai‘i unit supports middle school haumana (students) in developing multi-perspective understanding and personal stances about coastal change in their community. The unit was collaboratively developed by a partnership among educators bringing together Indigenous and Western ways of knowing and learning. Through lessons that take place over three weeks, haumana (students) undertake a place-based learning experience addressing physical, biological, and social facets of their changing coasts and engage the perspectives of ‘ohana (family), community members, and scientists.
As educators, we recognize that commercially prepared curricula advertised as “NGSS aligned” do not necessarily emphasize student sensemaking. In this article, we describe our process of modifying such curricula by reflecting on previous instruction and planning for future instruction that centers student sensemaking in a middle school unit on chemical reactions. We highlight the ways that a set of publicly available pedagogical tools (known as the ASET SEP Tools) focused our discourse on a shared vision of sensemaking that is appropriate to expect of middle school students.
As educators, we recognize that commercially prepared curricula advertised as “NGSS aligned” do not necessarily emphasize student sensemaking. In this article, we describe our process of modifying such curricula by reflecting on previous instruction and planning for future instruction that centers student sensemaking in a middle school unit on chemical reactions.
This study is part of a larger project on culturally and linguistically responsive instruction for multilingual learners (MLs) in biology (CLIMB), aimed at enhancing MLs’ engagement in science practices, biology content, and language development. We developed the CLIMB observation protocol to capture how teachers implement responsive instruction, integrating language development, biology content, and science practices.
This study is part of a larger project on culturally and linguistically responsive instruction for multilingual learners (MLs) in biology (CLIMB), aimed at enhancing MLs’ engagement in science practices, biology content, and language development. We developed the CLIMB observation protocol to capture how teachers implement responsive instruction, integrating language development, biology content, and science practices. The protocol is comprised of six elements that align with research-backed practices to support MLs: Attention to Language, Multiple Modalities, Collaboration, Affirming Identities, Funds of Knowledge, and Sociopolitical Consciousness.
