Raghavi Ravi is a data science student and a researcher specializing in the intersection of data-driven methodologies and STEM education. She earned a B.E. in Electrical and Electronics Engineering from Anna University, PSG iTech, India, in 2021. Currently, she is pursuing an M.S. in Data Science at Michigan State University.
Barbara Schneider is the John A. Hannah University Distinguished Professor in the College of Education and the Department of Sociology. She uses a sociological lens to understand societal conditions and interpersonal interactions that create norms and values for enhancing human and social capital. Her research focuses on how the social contexts of schools and families influence the academic and social well-being of adolescents as they move into adulthood.
The Health in Our Hands project is proud to present two final 'health summits', which feature 7th and 8th grade students presenting their research on community health issues. The Diabetes health summit will be held on Thursday, December 12, and the Substance Abuse Disorder summit will be held on Tuesday, December 17. Both events will run from 12:15 until 1:30 and are located at Mott Community College, 1401 E. Court St., Flint, MI 48503-2089.
Scientific modelling plays a crucial role in students’ science learning. Modelling proficiency and literacy development reinforce each other. This study investigates the relationship between teacher support of student literacy development and their modelling proficiency in the context of elementary project-based learning science environments. To explore the relationship, we sampled 557 students from 24 classrooms in 12 different schools. Data were analysed by multilevel mixed linear regression model analysis.
Having a negative attitude toward science plays a major factor in elementary teachers avoiding teaching science in elementary school. This mixed methods study examined changes in pre-service elementary teachers’ (PSETs) attitudes toward teaching science. PSETs (n=59) engaged in a semester-long university course in the Southeastern United States. The course focused on demonstrating and applying knowledge of scientific concepts and inquiry-based teaching. PSETs took a Dimension of Attitudes toward Science (DAS) questionnaire before and after the course.
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning—a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the measurement of knowledge and understanding to the measurement of performance and knowledge in use.
The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students’ contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the teaching and learning of science consistent with contemporary calls.
There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts.
An ability to engage in system thinking is necessary to understand complex problems. While many pre-college students use system modeling tools, there is limited evidence of student reasoning about causal relationships that interact in diverging and converging chains, and how these affect system behavior. A chemistry unit on gas phenomena was implemented in two successive years with 73 high school students. Although the phenomena could be explained with simple linear causal reasoning, many student models included surprising and problematic causal chains and non-linear patterns.
