|
Session Title: Uses of a Statewide Longitudinal Data System to Evaluate and Inform Programs, Policies, and Resource Allocations
|
|
Panel Session 142 to be held in Ventura on Wednesday, Nov 2, 4:30 PM to 6:00 PM
|
|
Sponsored by the Pre-K - 12 Educational Evaluation TIG
|
| Chair(s): |
| Deborah Carran, Johns Hopkins University, dtcarran@jhu.edu
|
| Abstract:
The state of Maryland has been actively working toward the implementation of their Statewide Longitudinal Data System (SLDS) for 15 years. Within the past year, the SLDS has begun to be used by the state to evaluate policy and other issues critical to inform educational reform. This panel proposes to present four papers (1) describing the SLDS, (2) using the SLDS to evaluate the impact of early childhood intervention services (birth to 3) on fall kindergarten assessment scores, (3) using the SLDS to track student performance on fall kindergarten assessment to evaluate grade 3 academic performance, and (4) using SLDS to identify students with disabilities at age 3 (exiting Part C early intervention services) and prospectively track students to grade 3 to evaluate the impact of early childhood (Part C) services.
|
|
The Maryland Longitudinal Data System
|
| Tamara Otto, Johns Hopkins University, tamaraotto@jhu.edu
|
| Jacqueline Nunn, Johns Hopkins University, jnunn@jhu.edu
|
|
The Maryland State Department of Education (MSDE) Division of Special Education/Early Intervention Services in partnership with the Johns Hopkins University Center for Technology in Education (CTE) has positioned itself among the leaders in the Nation in the race to not only develop a high-quality longitudinal data system, but to develop the user-friendly predictive analysis tools to enable the effective use of data by all key stakeholders.
In 2005, MSDE, in partnership with CTE, was awarded a three-year grant from the Department of Education's Institute of Education Sciences to design and implement a statewide longitudinal data system to help strengthen student achievement. Initially, the MD Scorecard was launched at the State and district level to provide a powerful tool in the analysis and use of the longitudinal data for decision making regarding programs, policies, and resource allocations.
The MD IDEA Scorecard has used qualitative and quantitative data to evaluate the effectiveness.
|
|
|
Evaluating the Impact of Early Intervention on Kindergarten Readiness
|
| Helena Mawdsley, Johns Hopkins University, hmawdsley@jhu.edu
|
| Jacqueline Nunn, Johns Hopkins University, jnunn@jhu.edu
|
| Deborah Carran, Johns Hopkins University, dtcarran@jhu.edu
|
| Tamara Otto, Johns Hopkins University, tamaraotto@jhu.edu
|
|
The Maryland Longitudinal Data System was used to evaluate impact of service provided to children (ages 0 to 3) enrolled in Part C early intervention services, on later kindergarten readiness. A total of 5,942 were linked with their subsequent fall kindergarten readiness test (MMSR) scores. Because Part C EIS delivers a wide range and types of services, only high incidence services were examined for this study. Six service types were found to have been provided to more than 15% of the matched participants, with speech/language therapy representing 74% of matched participants.
Evaluation findings support the benefit of Part C early intervention, that Part C EIS has a positive impact on children's readiness to enter elementary school. Regression analysis indicated that the greater the intensity of Part C early intervention services (as measured by maximum minutes per session and age of starting services), the better prepared children are for kindergarten.
| |
|
Data Mining Electronically Linked Grade Three Standardized Assessment Scores from Kindergarten Assessments to Identify Performance Patterns
|
| Deborah Carran, Johns Hopkins University, dtcarran@jhu.edu
|
| Jacqueline Nunn, Johns Hopkins University, jnunn@jhu.edu
|
| Tamara Otto, Johns Hopkins University, tamaraotto@jhu.edu
|
|
Linkage of unique student identifiers across grade levels has generated renewed interest in predicting high stakes test scores at early ages. Data mining, an iterative process using large extant data warehouses to discover meaningful patterns in data, examined the relationship between kindergarten assessments and grade 3 high stakes reading and math assessments. 152,105 kindergarten students were identified as receiving a kindergarten assessment between 2002 and 2005. Of these students, 100,957 were matched with their Grade 3 standardized math score and 100,978 with their Grade 3 Reading score, representing a 66% match rate. Using Classification and Regression Tree modeling analysis results are presented in tree-like figures with branches representing the splitting of cases based on values of predictor attributes. Results indicated that the kindergarten assessment is a moderately successful predictor of later high stakes testing performance; math performance was predicted better than reading
| |
|
Risk of Special Education Services at Age Eight for Children Receiving Early Intervention Services By Age Three
|
| Stacey Dammann, York College of Pennsylvania, sdammann@ycp.edu
|
| Deborah Carran, Johns Hopkins University, dtcarran@jhu.edu
|
|
A prospective risk analysis was used to evaluate whether the provision of early intervention services by age 3 would reduce the need for (intensity) of special education services in early elementary school. Children receiving Part C Special Education services at age 3 were prospectively tracked to determine educational placement in grade 1 (age 6).
Two cohorts of children who had historically received Part C early intervention services were identified; cohorts exited Part C programs in 2006 and 2007. Children were then tracked forward in the SLDS data base to determine their educational placement in first grade. Children were stratified by qualification categories in Maryland: High Probability condition, 25% Delay, and significant atypical development. A risk analysis was used to determine if children had lower risk of special education services if they had received early intervention services.
Results will be presented describing the estimated benefit of Part C services on children's later educational status.
| |