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This study uses a mixed-method sequential exploratory design to examine influences on urban adolescents’ engagement and disengagement in school. First, we interviewed 22 middle and high school students who varied in their level of engagement and disengagement. Support from adults and peers, opportunities to make choices, and external incentives aligned with greater engagement. In contrast, a strict disciplinary structure, an irrelevant and boring curriculum, disengaged peers, and lack of respect by adults coincided with greater disengagement.

Emotions are central to how students experience mathematics, yet we know little about how specific instructional practices relate to students’ emotions in mathematics learning. We examined how dialogic instruction, a socially dynamic form of instruction, was associated with four learning emotions in mathematics: enjoyment, pride, anger, and boredom. We also examined whether these associations differed by student gender and prior mathematics achievement.

Student-centered instruction is featured in reforms that aim to improve excellence and equity in mathematics education. Although research on stereotype threat suggests that student-centered instruction may have differential effects on racial minority students, the relationship between student-centered mathematics instruction and student engagement remains understudied.

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.