Racial and Gender Identity Among Black Adolescent Males: An Intersectionality Perspective

A considerable amount of social identity research has focused on race and racial identity, while gender identity, particularly among Black adolescents, remains underexamined. The current study used survey data from 183 Black adolescent males (13–16 years old) to investigate the development and relation between racial and gender identity centrality and private regard, and how these identities impact adjustment over time. It was found that dimensions of racial and gender identity were strongly correlated. Levels of racial centrality increased over time while gender centrality, and racial and gender private regard declined. In addition, racial and gender identity uniquely contributed to higher levels of psychological well‐being and academic adjustment. These findings are discussed within the context of existing identity theories and intersectionality theory.     

Culturally responsive computing: a theory revisited

Despite multiple efforts and considerable funding, historically marginalized groups (e.g., racial minorities and women) continue not to enter or persist in the most lucrative of fields – technology. Understanding the potency of culturally responsive teaching (CRT), some technology-enrichment programs modified CRP principles to establish a culturally responsive computing (CRC) experience for disenfranchised groups. We draw from our respective praxes developing two such initiatives and reconceptualize CRC as a heuristic. In this theoretical article, we offer a more nuanced vision of CRC considering intersectionality, innovations, and technosocial activism. Implications for the newly defined tenets consider programmatic, theoretical, and methodological concerns.     

Undisclosed Flexibility in Computing and Reporting Structural Equation Models in Communication Science

The use of structural equation modeling (SEM) in communication research has become increasingly popular in recent years, with SEM being commonly used as the primary method of data analysis in studies published within the field’s journals. However, despite its widespread use, there are persistent misapplications of SEM in published empirical studies in the communication sciences. Of these types of misuse, the most troubling are those related to the exploitation of flexibilities in data collection and analysis. Given the number of decisions that must be made when analyzing and reporting structural equation models, decisions which often remain undisclosed, the potential for researchers to exploit flexibilities in SEM research is high. To assess the extent of this problem, as well as the more longstanding types of misuse (i.e., omission of information and disclosed “bad practice”) identified by prior studies, we undertake a methodical review of studies employing structural equation modeling within major journals in the field of communication between the years 2007 and 2011. Articles were coded on the characteristics of the sample, characteristics of the model tested, as well as for various potential problems that we defined a priori. The most serious issues are identified, and recommendations for future standards in SEM research are suggested.     

SPECIFIC COGNITIVE PREDICTORS OF EARLY MATH PROBLEM SOLVING

Development of early math skill depends on a prerequisite level of cognitive development. Identification of specific cognitive skills that are important for math development may not only inform instructional approaches but also inform assessment approaches to identifying children with specific learning problems in math. This study investigated the specific cognitive correlates of math problem solving across early grade levels (1–4) while controlling for basic calculation skills. As expected, basic calculation skill was a significant predictor of math problem solving across the entire sample. However, the addition of cognitive measures almost doubled the variance explained (R² = .61). Additionally, only select cognitive variables contributed to the prediction of math problem solving, and these variables change in importance as children develop higher‐level math skills. Results are discussed within a developmental model, which emphasizes the increasing importance of abstract code representations required in higher levels of math performance.     

M in CoMputational thinking: How long does it take to read a book?

Even at the primary level, computational thinking (CT) can support young students to prepare for participating in futures that are immersed in data. In mathematics classrooms, there are few explanations of the ways CT can support students in formulating and solving complex problems. This paper presents an example of a primary classroom investigation (8-9 year olds) over seven lessons of the problem “How long does it take to read a book?” The aim is to illustrate ways a statistical investigation can provide context for CT and demonstrate how the two complement each other to solve problems involving mathematics. The findings highlight opportunities and challenges that students face across the elements of CT—decomposition, abstraction, pattern recognition and modelling, and generalization and algorithmic thinking, including recommendations for teaching.     

How students reason about matter flows and accumulations in complex biological phenomena: An emerging learning progression for mass balance

In recent years, there has been a strong push to transform STEM education at K-12 and collegiate levels to help students learn to think like scientists. One aspect of this transformation involves redesigning instruction and curricula around fundamental scientific ideas that serve as conceptual scaffolds students can use to build cohesive knowledge structures. In this study, we investigated how students use mass balance reasoning as a conceptual scaffold to gain a deeper understanding of how matter moves through biological systems. Our aim was to lay the groundwork for a mass balance learning progression in physiology. We drew on a general models framework from biology and a covariational reasoning framework from math education to interpret students' mass balance ideas. We used a constant comparative method to identify students' reasoning patterns from 73 interviews conducted with undergraduate biology students. We helped validate the reasoning patterns identified with >8000 written responses collected from students at multiple institutions. From our analyses, we identified two related progress variables that describe key elements of students' performances: the first describes how students identify and use matter flows in biology phenomena; the second characterizes how students use net rate-of-change to predict how matter accumulates in, or disperses from, a compartment. We also present a case study of how we used our emerging mass balance learning progression to inform instructional practices to support students' mass balance reasoning. Our progress variables describe one way students engage in three dimensional learning by showing how student performances associated with the practice of mathematical thinking reveal their understanding of the core concept of matter flows as governed by the crosscutting concept of matter conservation. Though our work is situated in physiology, it extends previous work in climate change education and is applicable to other scientific fields, such as physics, engineering, and geochemistry.