Maryland Longitudinal Data System Center

The Institute of Education Sciences (IES) funded State Longitudinal Data System (SLDS) grant team held a half-day in-person convening: Enhancing the Teacher Pipeline in Maryland. This convening brought together over 100 researchers, policymakers, and practitioners from across the state to provide a comprehensive understanding of Maryland’s teacher pipeline, as well as the research questions and policy challenges that can help move us forward.

MLDS Center staff presented on the MLDS data and how it can be used to study the teacher pipeline.

Four research presentations used MLDS data to highlight Maryland's teacher pipeline:

•    ■Teacher Preparation, Localness, and Student Outcomes
•    ■Understanding Racial Disparities in Teaching
•    ■Distribution of Teacher Quality
•    ■Matching English Learner Students with ESL Teachers in K-12 Schools in Maryland

Finally, a policy panel focused on next steps for enhancing Maryland's teacher pipeline.

Poverty during childhood and adolescence has been linked to long-term outcomes in many areas of life, including physical health, academic achievement, social and emotional functioning, education completion, and workforce wages. Long term educational outcomes are also greatly influenced by the schools students attend because the educational experience reflects the resources, curriculum, and composition of the school. The presentations contained here explore the relationships between individual and school concentrated poverty on long-term outcomes in Maryland. By specifically examining Maryland students, schools, school systems, and state-wide systems, and identifying the mechanisms through which poverty influences outcomes, the information gained through these studies can be directly applied by policymakers to support Maryland students and schools.

Educational predictors, such as coursetaking behavior, attendance, or test scores, can be used to predict academic outcomes, such as on-time graduation, postsecondary degree attainment, and participation in the workforce. This group of presentations examine educational predictors and long-term outcomes using data from the MLDS, which collects data from multiple state agencies, allowing for analysis of student outcomes over time and across life stages (e.g., from secondary school to postsecondary to the workforce). Increasing the understanding of the relationship between these predictors and outcomes in the context of Maryland schools can assist policymakers and stakeholders with implementing and supporting programs or policies that can improve outcomes for students

Building a Bridge to Graduation in Maryland (Presentation at Stats DC, August 2021) (opens in new tab)

These presentations explore the MLDS Center data including 1) how the data are maintained and analyzed and 2) how findings are disseminated to stakeholders. The MLDS Center focuses on cross-sector analysis, or what happens to students before and after critical transitions, such as from K-12 to postsecondary education, with the goal of producing relevant information and informing decisions to improve Maryland student and workforce outcomes.

The MLDS is a longitudinal administrative data system which contains linked longitudinal data from state agencies and external sources. The MLDS Center works to apply and expand advanced statistical methods, such as propensity score analysis and multiple membership modeling, which puts the MLDS Center at the forefront of new developments in state-wide longitudinal data analysis. The presentations in this section examine the utilization of administrative data for research as well as the application of advanced statistical methods to research conducted using a statewide longitudinal data system.

Handling Student Mobility in Educational Research (Presentation at Stats DC, August 2021) (opens in new tab)

The Synthetic Data Project (SDP) was proposed and implemented by the Research Branch of the MLDS Center and was funded by the Institute of Education Sciences through the Maryland State Department of Education. Synthetic data statistically mimic the data in the MLDS, but do not contain any information from individuals in the MLDS. The goal of producing synthetic data is to balance confidentiality with data access. These presentations describe the development of the synthetic data sets and the evaluation of synthetic data for research use.

Synthetic Data as a Strategy for Expanding Access to Linked Administrative Data in Prevention Science (SPR, June 2021) (opens in new tab)

This first session provides an overview about the organization, placement and governance of the MLDS, a review of available data in the MLDS, and an understanding of how data are used and secured. The session also provides examples of how the MLDS has been used to inform policy and decision making.

The Career Preparation and Expansion Act requires the MLDS Center to annually produce a report that analyzes the outcomes of high school graduates five years after high school completion. Since that time, the MLDS Center has conducted similar analyses of Associate’s Degree Graduates, GED/NEDP Completers, Apprenticeship Program Completers, and Bachelor’s Degree Graduates. This session reviews the findings of these reports and discusses policy implications.

The MLDS Center is involved in research and reporting on various aspects of teachers, including analyzing the pipeline from college to employment, out-of-school employment trends among teachers, and various reporting on teacher placement, employment and attrition. This session focuses on the findings from these various research and reporting outputs.

The MLDS Research Branch provides a survey of several research reports and analyses conducted using Center data. The researchers present their work and explain the findings, the implications for policymakers, and how the MLDS data allowed them to undertake novel analyses.

The final session provides an overview of how other key stakeholders have used the MLDS to support their research and analytic needs. The session also provides an overview of new data sources being added to the MLDS and how those data will provide important avenues for research and analysis.