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There is a growing interest in ensuring that students are prepared for college and a career when they finish high school. Research identified several indicators that are associated with college preparedness including attendance, grade point average (GPA), and test scores. However, it is difficult to use these indicators to determine the preparedness level of the nation's students in general. This study used results from National Assessment of Educational Progress (NAEP) mathematics assessment, the "gold standard" assessing what students know and can do in grade 12 to establish college preparedness benchmarks. This study linked ninth-grade student background data and school-reported high school and postsecondary transcript data from the National High School Longitudinal Study of 2009 (HSLS:09) to student item responses on the 2013 National Assessment of Educational Progress (NAEP) mathematics assessment to examine the relationship between college preparedness and NAEP mathematics achievement. NAEP scale scores imputed for all HSLS:09 participants via marginal maximum likelihood regression analyses were used to predict college preparedness. This study extended the earlier investigations of college preparedness based on NAEP grade 12 mathematics data from the prediction of college enrollment alone to the prediction of additional postsecondary outcomes including remedial course-taking and first-year college GPA based on postsecondary transcript data. Defining college preparedness as enrolling in a 2- or 4-year college without remedial course-taking and acquiring a first-year college GPA of 2.7 or higher as did National Assessment Governing Board, this study showed that the probability of college preparedness increased from 26 percent for students performing at the NAEP Basic level to 58 percent at the NAEP Proficient level and 87 percent at the NAEP Advanced level. The probabilities at preparedness increased when the definition of college preparedness omitted remedial course-taking. Although similar patterns were observed for various student groups including race/ethnicity, gender, and socioeconomic status, there were some differences in results across groups. Since the report shows the probability of college preparedness (under various definitions) at each NAEP achievement level, it is thereby also providing important validity information for these NAEP achievement levels.

This paper summarises the exploratory quantitative analysis undertaken to investigate how vocational education and training (VET) students cluster and segment in the Australian VET market. This analysis is outlined in three sections. The first section focuses on 'clustering' as a technique for grouping data and the three clustering algorithms used. These are then discussed in more detail to provide some insights into how they operate. Their specific data requirements, along with their strengths and weaknesses, are also considered. In next section the outputs of the clustering approaches are considered. The resultant clusters are examined to better understand them, and meaningfully label and group them into segments. The final section returns to the raw data. This step was necessary to further explore (in this case, only some of) the identified market segments. Three key market segments are explored: students in targeted English programs; students in social inclusion programs; and migrant students.

Science, technology, engineering, and mathematics (STEM) professional societies (ProS) are uniquely positioned to foster national-level diversity, equity, and inclusion (DEI) reform. ProS serve broad memberships, define disciplinary norms and culture, and inform accrediting bodies, thus providing "excellent leverage with which to design and promote change" (National Academy of Sciences et al., 2005). ProS could be instrumental in achieving the DEI culture reform necessary to optimize engagement of all STEM talent by leveraging disciplinary excellence resulting from diverse teams. Inclusive STEM culture reform requires that underlying "mental models'' be examined (Kania et al., 2018). The "Inclusive Professional Framework for Societies (IPF:Societies)" can help ProS change leaders (i.e., "boundary spanners'") and organizations identify and address mental models hindering DEI reform. "IPF:Societies" uses four "I's"--Identity awareness and Intercultural mindfulness (i.e., equity mindset) on which Inclusive relationships and Influential DEI actions are scaffolded. We discuss how "IPF:Societies" complements existing DEI tools. We explain how "IPF:Societies" can be applied to existing ProS policy and practice associated with common ProS functions (e.g., leadership, membership, conferences, awards, and professional development). Next steps are to pilot "IPF:Societies" with a cohort of STEM ProS. Ultimately, "IPF:Societies" has potential to promote more efficient, effective, and lasting DEI organizational transformation, and contribute to inclusive STEM disciplinary excellence.

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as sufficient predictors. This study compares the out-of-sample predictive power of early momentum metrics (EMMs)--13 near-term success measures suggested by the literature--with that of metrics from ML-based models that employ approximately 500 predictors for community college credential completion. Using transcript data from approximately 50,000 students at more than 30 community colleges in two states, I find that the EMMs that were modeled by logistic regression accurately predict completion for approximately 80% of students. This classification performance is comparable to that of the ML-based models. The EMMs even outperform the ML-based models in probability estimation. These findings suggest that EMMs are useful predictors for credential completion and that the marginal gain from using an ML-based model over EMMs is small for credential completion prediction when additional predictors do not have strong rationales to be included in an ML-based model, no matter how large the number of those predictors may be.

School value-added models are increasingly used to measure schools' contributions to student success. At the same time, policymakers and researchers agree that schools should support students' socialemotional learning (SEL) as well as academic development. Yet, the evidence regarding whether schools can influence SEL and whether statistical growth models can appropriately measure this influence is limited. Recent work shows meaningful differences across schools in changes in SEL scores by grade (Loeb, Christian, Hough, Meyer, Rice, & West, 2019), but whether these differences represent the effects of schools is still unclear. The current paper builds upon this earlier work by examining the stability of the estimated school-by-grade effects on SEL across two years, using a large-scale SEL survey administered in California's CORE districts. We find that correlations among school effects in the same grades across different years are positive, but they are lower than those for math and English Language Arts (ELA). Schools in the top or the bottom of the school effect distribution are more persistent in their impacts across years than those in the middle of the distribution. Overall, the results provide evidence that these school effects measure real contributions to SEL. However, the low stability of effects from one year to the next draw into question whether including these school value-added measures of self-reported SEL in school performance frameworks and systems would be beneficial.

Principals are widely seen as a key influence on the educational environment of schools, and nearly all principals have experience as teachers. Yet there is no evidence on whether we can predict the effectiveness of principals (as measured by their value added) based on their value added as teachers, an issue we explore using administrative data from Washington. Several descriptive features of the principal labor market stand out. First, teachers who become principals tend to have higher levels of educational attainment while teaching and are less likely to be female, but we find no significant differences in licensure test scores between those teachers who become principals and those we do not observe in the principalship. Second, principal labor markets appear to be quite localized: about 50 percent of principals previously taught in the same district in which they assumed a principalship. We find positive correlations between teacher and principal value added in reading (ELA) and similarly sized but less precise estimates in math. Teachers who become principals have slightly higher teacher value added, but the difference between the two groups is not statistically significant, suggesting that principals are not systematically selected based on their prior effectiveness when serving as a classroom teacher.