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School engagement researchers have historically focused on academic engagement or academic-related activities. Although academic engagement is vital to adolescents’ educational success, school is a complex developmental context in which adolescents also engage in social interactions while exploring their interests and developing competencies. In this article, school engagement is re-conceptualized as a multi-contextual construct that includes both academic and social contexts of school.

What are evidence standards for evaluating math and science programs? Watch this webinar recording with experts from the American Institutes for Research (AIR) for an engaging webinar about evidence standards. Presenters discussed the Every Student Succeeds Act (ESSA) Tiers of Evidence and the What Works Clearinghouse (WWC) evidence rating system and how these evidence standards relate to evaluating math and science programs. The presenters discussed how participants can use these resources to strengthen their existing and proposed study designs.

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that the solution solves the problem, and evaluating the solution based on some set of criteria.

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. Supporting Computational Algorithmic Thinking (SCAT) is a longitudinal project that explores the development of CAT capabilities by guiding African American middle-school girls through the iterative game design cycle, resulting in a set of complex games around broad themes.

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that the solution solves the problem, and evaluating the solution based on some set of criteria.

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that it solves the problem, and evaluating the solution based on some set of criteria. CAT has roots in Mathematics, through problem solving and algorithmic thinking. CAT lies at the heart of Computer Science, which is defined as the study of algorithms.

The purpose of this study was to develop and validate a survey of opportunities to participate (OtP) in science that will allow educators and researchers to closely approximate the types of learning opportunities students have in science classrooms. Additionally, we examined whether and how opportunity gaps in science learning may exist across schools with different socioeconomic levels. The OtP in science survey consists of four dimensions that include acquiring foundational knowledge, planning an investigation, conducting an investigation, and using evidence to communicate findings.

Shifting people’s judgments toward the scientific involves teaching them to purposefully evaluate connections between evidence and alternative explanations.

Lombardi, D. (2019). Thinking scientifically in a changing world. Science Brief: Psychological Science Agenda, 33(1). Retrieved from https://www.apa.org/science/about/psa/2019/01/changing-world.aspx

For students to meaningfully engage in science practices, substantive changes need to occur to deeply entrenched instructional approaches, particularly those related to classroom discourse. Because teachers are critical in establishing how students are permitted to interact in the classroom, it is imperative to examine their role in fostering learning environments in which students carry out science practices. This study explores how teachers describe, or frame, expectations for classroom discussions pertaining to the science practice of argumentation.

The Next Generation Science Standards (NGSS) and the Framework for K-12 Science Education (NRC, 2012) on which they are based, describe a new vision for science education that includes having students learn science in a way that more closely aligns to how scientists and engineers work and think. Accomplishing this goal will require teacher educators to make important shifts in the ways they prepare future science teachers (NRC, 2012). Many science teaching methods courses are being reformed to better support future science teachers to meet the ambitious goals of the NGSS.