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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.

In a nationally representative sample, first-year U.S. college students \"somewhat agree,\" on average, that they feel like they belong at their school. However, belonging varies by key institutional and student characteristics; of note, racialethnic minority and first-generation students report lower belonging than peers at 4-year schools, while the opposite is true at 2-year schools. Further, at 4-year schools, belonging predicts better persistence, engagement, and mental health even after extensive covariate adjustment. Although descriptive, these patterns highlight the need to better measure and understand belonging and related psychological factors that may promote college students' success and well-being.

Hispanic-Serving Institutions (HSIs) are colleges and universities that enroll at least 25% Latinx students. Despite being recognized by the federal government since 1992, HSIs lack a historical mission to serve Latinxs. As such the idea of “servingness” has become an elusive concept. An abundance of literature centering HSIs has been published, yet there continues to be a debate about what it means to serve students. We conducted a systematic review of 148 journal articles and book chapters to better understand how researchers conceptualize the idea of servingness at HSIs. We identified four major themes used by researchers to conceptualize servingness: (1) outcomes, (2) experiences, (3) internal organizational dimensions, and (4) external influences. We also found that researchers are often unintentional in their efforts to conceptualize what it means to be an HSI. We offer a multidimensional conceptual framework of servingness to be used in research, policy, and practice.

This study draws upon survey and administrative data on over 9,000 teachers in 336 Miami-Dade County public schools over 2 years to investigate the kinds of collaborations that exist in instructional teams across the district and whether these collaborations predict student achievement. While different kinds of teachers and schools report different collaboration quality, we find average collaboration quality is related to student achievement. Teachers and schools that engage in better quality collaboration have better achievement gains in math and reading. Moreover, teachers improve at greater rates when they work in schools with better collaboration quality. These results support policy efforts to improve student achievement by promoting teacher collaboration about instruction in teams.

High rates of attrition make it challenging for schools to provide qualified special education teachers for students with disabilities, especially given chronic teacher shortages. We synthesize 30 studies from 2002 to 2017, examining factors associated with special educator attrition and retention, including (a) teacher preparation and qualifications, (b) school characteristics, (c) working conditions, and (d) teacher demographic and nonwork factors. Most studies examined working conditions (e.g., demands, administrative and collegial supports, resources, compensation) among special educators who left teaching, moved to other positions, transferred to general education teaching, or indicated that they intended to stay or leave. The majority of researchers used quantitative methods to analyze national, state, or other survey data, while eight used qualitative methods. Our critique identifies both strengths and weaknesses of this literature, suggests research priorities, and outlines specific implications for policy makers and leaders.

In the context of a national conversation about exclusionary discipline, we conducted a multilevel examination of the relative contributions of infraction, student, and school characteristics to rates of and racial disparities in out-of-school suspension and expulsion. Type of infraction; race, gender, and to a certain extent socioeconomic status at the individual level; and, at the school level, mean school achievement, percentage Black enrollment, and principal perspectives all contributed to the probability of out-of-school suspension or expulsion. For racial disparities, however, school-level variables, including principal perspectives on discipline, appear to be among the strongest predictors. Such a pattern suggests that schools and districts looking to reduce racial and ethnic disparities in discipline would do well to focus on school- and classroom-based interventions.

According to the internal/external frame of reference model, academic achievement has a strong impact on people’s self-concept, both within and between subjects. We conducted a series of meta-analyses of k = 505 data sets containing the six bivariate correlations between achievement and self-concept in two subjects. Negative paths from achievement to noncorresponding self-concept, indicating dimensional comparison effects, were strongest when the subjects were dissimilar with regard to the math-verbal continuum, reduced but still significantly negative when both subjects belonged to the verbal domain, and near-zero when both subjects belonged to the math/science domain. Additionally, we found stronger positive paths from achievements to corresponding self-concepts, indicating social comparison effects, and stronger dimensional comparison effects for grades than for standardized test scores, and for older rather than younger students. We extend dimensional comparison theory by discussing these results with particular regard to the nonexistence of assimilation effects, the effects of subject similarity on dimensional comparison effects, and other moderators of dimensional comparison effects.

Over the past 20 years, international student mobility has experienced a three-fold increase, as planned and emerging education hubs have attracted increasing numbers of students. The appeal of alternative destinations is strengthened by their cultural, linguistic, and geographic proximity, as well as a growing number of internationally ranked universities. This article quantifies shifts in international student mobility and world university rankings over a consequential 20-year period (1999/2000–2018/2019) at the beginning of the twenty-first century. It examines shifts in the number of county-to-country connections (density), relative country importance in the network (centrality), and network structure (multipolarity). The results indicate the overall network density steadily increased year-to-year, with a three-fold increase in the number of country-to-country connections, as influence was more widely and evenly distributed among a larger number of core countries within the network. As the number of universities in planned and emerging destinations listed in the rankings doubled, the network structure indicated a movement toward multipolarity, where a more diverse set of countries exerted greater relative influence in the overall network. The results suggest that while core-periphery dynamics in international student mobility persist, they also have begun to shift, as a larger and more diverse subset of planned and emerging educational hubs in Asia, South America, Africa, and the Middle East exert increasing influence in the overall network.

In recent decades, K-12 school discipline policies and practices have garnered increasing attention among researchers, policymakers, and educators. Disproportionalities in school discipline raise serious questions about educational equity. This study provides a comprehensive review of the extant literature on the contributors to racial, gender, and income disparities in disciplinary outcomes, and the effectiveness of emerging alternatives to exclusionary disciplinary approaches. Our findings indicate that the causes of the disparities are numerous and multifaceted. Although low-income and minority students experience suspensions and expulsions at higher rates than their peers, these differences cannot be solely attributed to socioeconomic status or increased misbehavior. Instead, school and classroom occurrences that result from the policies, practices, and perspectives of teachers and principals appear to play an important role in explaining the disparities. There are conceptual and open empirical questions on whether and how some of the various alternatives are working to counter the discipline disparities.

Surveys play an important role in understanding the higher education landscape. About 60 percent of the published research in major higher education journals utilized survey data (Pike, 2007). Institutions also commonly use surveys to assess student outcomes and evaluate programs, instructors, and even cafeteria food. However, declining survey participation rates threaten this source of vital information and its perceived utility. Survey researchers across a number of social science disciplines in America and abroad have witnessed a gradual decrease in survey participation over time (Brick & Williams, 2013; National Research Council, 2013). Higher education researchers have not been immune from this trend; Dey (1997) long ago highlighted the steep decline in response rates in the American Council on Education and Cooperative Institutional Research Program (CIRP) senior follow-up surveys from 60 percent in the 1960s to 21 percent in 1991. Survey researchers have long assumed that the best way to obtain unbiased estimates is to achieve a high response rate. For this reason, the literature on survey methods is rife with best practices and suggestions to improve survey response rates (e.g., American Association for Public Opinion Research, n.d.; Dillman, 2000; Heberlein & Baumgartner, 1978). These methods can be costly or require significant time or effort by survey researchers and may be unfeasible for postsecondary institutions due to the increasing fiscal pressures placed upon them. However, many survey researchers have begun to question the widely held assumption that low response rates provide biased results (Curtin, Presser, & Singer, 2000; Groves, 2006; Keeter, Miler, Kohut, Groves, & Presser, 2000; Massey & Tourangeau, 2013; Peytchev, 2013). This study investigates this assumption with college student assessment data. It utilizes data from hundreds of samples of first-year and senior students with relatively high response rates using a common assessment instrument with a standardized administration protocol. It investigates how population estimates would have changed if researchers put forth less effort when collecting data and achieved lower response rates and respondent counts. Due to the prevalence of survey data in higher education research and assessment efforts, it is imperative to better understand the relationship between response rates and data quality.