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Like many states, Massachusetts has intensified efforts in recent years to improve literacy outcomes, particularly in the early grades. Initiatives have focused on providing guidance and resources for implementing changes in curriculum, instruction, and additional screening and support for struggling students. In September 2022, the Board of Elementary and Secondary Education in Massachusetts amended state law to require that all elementary schools assess each student's reading abilities and early literacy skills a minimum of twice per year from kindergarten through at least of grade 3. The goal of such screening is to identify students who may be at risk of reading difficulty and proactively intervene to ensure that all students have the foundational skills needed to be successful readers. Beginning with the 2020/21 school year, the Massachusetts Department of Elementary and Secondary Education (DESE) began collecting literacy screening assessment data from schools and districts participating in certain state grants. The goal of collecting the data is to analyze patterns of performance to inform improvement efforts--for example, knowing when students struggle or how they progress over time may help schools, districts, and the state target resources and supports. Using the benchmarks provided within each assessment to identify students at risk of reading difficulty, this issue brief provides a first look at student progress within and across grade levels based on available data.

Beginning with the 2020/21 school year, the Massachusetts Department of Elementary and Secondary Education (DESE) began collecting literacy screening assessment data from schools and districts participating in certain state grants. Screening assessment benchmark scores were available for just over 35,000 unique students across 2020/21 and 2021/22, representing about 10 percent of the state's population of grade K--3 students and including students from 43 districts and 159 schools. The screening assessments in use in the state vary in how they were designed, how they are administered, and how they define risk of reading difficulty. Most, but not all, screening assessments also include several levels of risk. These differences, along with the fact that students take these assessments several times per year, mean that multiple methods are used to calculate numbers of students at risk of reading difficulty across assessments in the state and that the meaning of "risk" can differ somewhat across assessments. The screening assessment data include more economically disadvantaged and English learner students and fewer grade 3 students than the state as a whole, suggesting some caution may be needed in generalizing from results of these analyses to the state. Nonetheless, these literacy screening assessment data provide an initial opportunity to examine student performance in the early grades and assess the extent to which intended policy outcomes in literacy are being achieved. This report provides a first look at the literacy performance of students in grades K-3 in Massachusetts based on available data from both the 2020/21 and 2021/22 school years.

This brief provides information about student literacy performance of students with different background characteristics and learning environments. It draws from data collected and analyzed in the report "A First Look at Early Literacy Performance in Massachusetts: Results of Initial Analysis Based on State Grantee Literacy Screening Assessments," which provides the first-ever large-scale information about early literacy performance and progress of students in grades K-3 in Massachusetts.

Beginning with the 2020-21 school year, the Massachusetts Department of Elementary and Secondary Education began collecting literacy screening assessment data from schools and districts participating in certain state grants in order to analyze patterns of performance to inform improvement efforts. These assessments, which were selected from a list of state-approved screener products, vary in how they were designed, how they are administered, and how they define risk of reading difficulty, making it difficult to compare and summarize performance for students in different schools and districts. However, grade 3 students do take one common assessment: the Massachusetts Comprehensive Assessment System (MCAS) English Language Arts (ELA) assessment. MCAS therefore provides a mechanism to compare literacy benchmarks using a common metric. This issue brief describes what it means to be "at risk" of reading difficulty as defined by each screening assessment and then provides results of a study that links screener benchmark scores to the MCAS grade 3 scale.

Background/Context: Public two-year and technical colleges have experienced declines in enrollment since the COVID-19 pandemic. While new data from the National Student Clearinghouse show that enrollment is beginning to increase, it is still below pre-pandemic levels and not all areas are experiencing the same levels of rising enrollment. At the same time, the labor market is vastly different than it was before 2020. An increase in demand in many sectors, combined with high hiring rates and quit rates, has left many high-growth, high-wage jobs unfilled (Ferguson, 2023). There's little research that has examined if and how sub-baccalaureate students are making decisions about their postsecondary pathways based on labor market data and how that's changed pre- and post-pandemic. This study uses geography of opportunity (López Turley, 2009) to understand how a student's residential location impacts their educational opportunities. This is especially true for students who attend community and technical colleges, as they are more likely than their four-year counterparts to live close to home and have competing work-life demands. Our study uses this framework to add to the limited studies on this topic (Acton, 2020; Baker et al., 2017; Foote & Grosz, 2019; Kienzl et al., 2007) by examining changes specific to CTE career clusters, with a specific focus on pre- and post-pandemic trends. Purpose/Objective/Research Question: In response to the many challenges community colleges face in their pandemic recovery efforts, IES established the ARCC research network to learn how community colleges can and support student learning and outcomes in a post-pandemic "normal." One research team in this network is conducting research to understand the impact of COVID-19 on postsecondary career and technical education enrollment. The research will address the following research questions: 1. To what extent can course-taking, concentration, major declaration and award completion within each CTE Career Cluster be predicted by labor market demand for related Career Cluster occupations? 2. To what extent are there observable differences pre- and post-pandemic? Research Design: To address the research questions, the team developed an exploratory research grant which was awarded by the Institute of Education Sciences. The design incorporates analysis of student-level data from the Florida Statewide Longitudinal Data System by coding course-level and student-level data in order to identify CTE enrollment, concentration, and completion. The labor market data design includes a descriptive analysis of labor market changes. The study combines these two sources of data by developing a predictive model to ascertain the extent to which CTE student uptake and success is predicted by changes in labor market demand. Data Collection and Analysis: Student-level data was collected by the Florida Department of Education. The study team requested and received the data in 2022. The labor market data was collected through Lightcast and includes location quotient, shift share, and job posting data. The research team is conducting a descriptive analysis of student-level data by analyzing and visualizing CTE student course enrollment, concentration, major declaration, and completion at the Florida two-year and district colleges over time. The team is separately analyzing the labor market data in the same way. To combine the data, the team translated the Standard Occupational Codes (SOC) employment and wage data to the Career Cluster framework in Florida and merged the data by Florida county. Our next step is to estimate the probabilities on the county-level demand measures. County-by-Career Cluster location quotients and annual Career Cluster wage figures are introduced on a one-year lag on the assumption that student i's CTE engagement or completion in year t would be in response to the regional labor market demand from year Y_(M,isct)= ?+??_1 X?_i+?_2 Q_((t-1))+?_3 W_((t-1))+?_4 ?Q*W?_(s(t-1))+ [superscript 1]_c+?_isct, M={1,2,3,É,17} where Y represents one of the RQ1A-D family of outcomes for Career Cluster M for student i in community or technical college s in cohort c in year t. On the right side of the equation, the vector X_i contains the student-level demographic and academic measures detailed in the previous section; [superscript 1]_c represents fixed effects for cohort. Q_((t-1)) and W_((t-1)) which represent county-level, Career Cluster-specific location quotient of employment and median annual wages, respectively; ?Q*W?_(s(t-1)) is the interaction of location quotient and median wages. The associated parameter for this term, ?_4, thus represents the change in the location quotient slope, ?_3, for every one-unit increase in W. Practically speaking, ?_4 tests whether the relationship between a Career Cluster-specific location quotient of employment and the CTE student outcomes as a function of the median annual wage for the same Career Cluster. The error term, ?_isc, represents all other factors explaining RQ1A-D not captured by? X?_i and [superscript 1]_c and will be clustered at the school level to account for correlated observations among students within the same community or technical college (Abadie et al., 2017) Findings/Results: Our preliminary descriptive analyses show that there are changes in CTE course enrollment over time, with the largest changes in 2019-2020 and 2020-2021 (average 8% decrease overall in district technical colleges and two-year colleges). Further analysis will include changes in CTE concentration and completion as well as changes in labor market demand based in career cluster. The final paper will also include results from the predictive model.