Individuals with Mathematics Learning Disabilities have persistent mathematics underperformance but vary with respect to their cognitive profiles. The present study examined mathematics ability and achievement, and associated mathematics-specific numerical skills and domain-general cognitive abilities, in young children with Turner syndrome compared to their matched peers. We utilized two independent peer groups so that group comparisons would account for verbal skills, a hypothesized strength of girls with Turner syndrome, and nonsymbolic magnitude comparison skills, a hypothesized difference of girls with Turner syndrome. This individual matching approach afforded characterization of mathematics profiles of girls with Turner syndrome and girls without Turner syndrome that share potential key features of the Turner syndrome phenotype. Results indicated differences in mathematics ability and nonsymbolic magnitude comparison tasks between girls with Turner syndrome and peers with similar levels of verbal skill. Mathematics ability and mathematics achievement scores of girls with Turner syndrome did not differ significantly from their peers with similar levels of accuracy on a nonsymbolic magnitude comparison task. Cognitive correlates of mathematics outcomes showed disparate patterns across groups. These quantitative and qualitative differences across profiles enhance our understanding of variation in mathematics ability in early childhood and inform how mathematics skills develop in young children with or without Turner syndrome.

U.S. charter schools are publicly funded through state school finance formulas that often mirror the traditional public school finance systems. While charter school advocates and critics disagree over whether charters receive an equitable share of funding, few discussions are based on rigorous analyses of funding and expenditures. Most prior analyses, especially those presented in policy briefs or white papers, examine average funding differences without exploring underlying cost factors between the two sectors. Our purpose is to demonstrate how careful analysis of charter school funding with appropriate methodological approaches can shed light on disagreements about charter school finance policy. Using detailed school finance data from Texas as a case study, we find that after accounting for differences in accounting structures and cost factors, charter schools receive significantly more state and local funding compared to traditional public schools with similar structural characteristics and student demographics. However, many small charter schools are actually underfunded relative to their traditional public school counterparts. Policy simulations demonstrate that on average, each student who transfers to a charter school increases the cost to the state by $1,500. We discuss the implications of these findings for both school finance policy in Texas and nationally.

The underrepresentation of non-male and non-White individuals in engineering continues to be a persistent and critical problem [1-3]. A widespread and commonly accepted approach to recruit and retain diverse individuals in engineering is to provide multiple pathways into engineering degree programs, such as offering introductory courses at community colleges or regional campuses. Although these pathways are intended to promote diversity, they are similar in structure to the educational tracking practices common within the K-12 context, which extant research has shown perpetuate social inequalities [4]. Specifically, students in less prestigious tracks have lower educational aspirations and less favorable self-beliefs [5]. As such, the objective of this research is to understand the beliefs and identities with respect to smartness and engineering of undergraduate engineering students from different institutionalized pathways into engineering. Each pathway is a trajectory to earning an engineering degree from a large, public, research-intensive university in the Midwest, which enrolls just over 1600 new first-year undergraduates in engineering each year on the main campus alone. Specifically, this project is designed to address the following research questions: 1) What do students from different institutionalized pathways into engineering believe about smartness and engineering? 2) How do these students express their personal identities related to being smart and being an engineer? In order to answer our research questions over the scope of the full, three-year project, we will collect and analyze a series of three interviews with 30 participants across six different first-year institutionalized pathways into engineering: main campus-honors program, main campus-residential cohorts, main campus-standard program, main campus-alternative math starting point, regional campuses, and community college. The first interview is to establish the participants' beliefs and identities related to smartness and engineering. The two follow up interviews, conducted approximately six months and one year after the initial interview, will provide additional insight by looking back at what aspects of the individuals' beliefs and identities have changed or remained the same during their degree progress. During the time span of the data collection, it is expected that participants will move between institutions, pathways, and perhaps even out of engineering. To date, we have completed the pilot phase of this multi-year, qualitative study. During the pilot, we conducted semi-structured, one-on-one interviews with nine first-year engineering students across three different institutionalized pathways into engineering. The main objective of the pilot was to develop and refine the interview protocol. As a constructivist study, we are interested in how each participant assigns meaning through their subjective experiences [6]. As such, the one-on-one interviews are the primary means of data collection, and it is essential that the protocol elicits responses from the participants that allow us to answer our research questions. The methods utilized during the pilot, the interview protocol development and refinement, and future work will be discussed in the following sections of this executive summary. © American Society for Engineering Education 2020.

[No abstract available]

Collaborative problem-solving (CPS) has become an essential component of today's knowledge-based, innovation-centred economy and society. As such, communication and CPS are now considered critical 21st century skills and incorporated into educational practice, policy, and research. Despite general agreement that these are important skills, there is less agreement on how to capture sociocognitive processes automatically during team interactions to gain a better understanding of their relationship with CPS outcomes. The availability of naturally occurring educational discourse data within online CPS platforms presents a golden opportunity to advance understanding about online learner sociocognitive roles and ecologies. In this paper, we explore the relationship between emergent sociocognitive roles, collaborative problem-solving skills, and outcomes. Group Communication Analysis (GCA) - a computational linguistic framework for analyzing the sequential interactions of online team communication - was applied to a large CPS dataset in the domain of science (participant N = 967; team N = 480). The ETS Collaborative Science Assessment Prototype (ECSAP) was used to measure learners' CPS skills, and CPS outcomes. Cluster analyses and linear mixed-effects modelling were used to detect learner roles, and assess the relationship between those roles on CPS skills and outcomes. Implications for future research and practice are discussed regarding sociocognitive roles and collaborative problem-solving skills.

Studies show that historically underserved students are disproportionately assigned to less qualified and effective teachers, leading to a teacher quality gap. Past analyses decompose this gap to determine whether inequitable access is driven by teacher and student sorting across and within schools. These sorting mechanisms have divergent policy implications related to school finance, student desegregation, teacher recruitment, and classroom assignment. I argue that analyses of the teacher quality gap that consider how teachers and students are sorted across labor markets offer additional policy guidance. Using statewide data from Texas, I show that teacher quality gaps are driven by sorting across school districts within the same labor market, but this finding differs depending on how teacher quality is defined.

The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

Past research has examined parental support for math during early childhood using parent-report surveys and observational measures of math talk. However, since most studies only present findings from one of these methods, the construct (parental support for early math) and the method are inextricably linked, and we know little about whether these methods provide similar or unique information about children's exposure to math concepts. This study directly addresses the mono-operation bias operating in past research by collecting and comparing multiple measures of support for number and spatial skills, including math talk during semi-structured observations of parent-child interactions, parent reports on a home math activities questionnaire, and time diaries. Findings from 128 parents of 4-year-old children reveal substantial within-measure variability across all three data sources in the frequency of number and spatial activities and the type and content of parent talk about number and spatial concepts. Convergence in parental math support measures was evident among parent reports from the questionnaire and time diaries, such that scale composites about monthly number activities were related to number activities on the previous work day, and monthly spatial activities were correlated with spatial activities the prior non-work days. However, few parent report measures from the survey or time diary were significantly correlated with observed quantity or type of math talk in the semi-structured observations. Future research implications of these findings are discussed.