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Shudong Wang, NWEA Liru Zhang, Delaware DOE G. Gage Kingsbury, NWEA

Predicting Student Success in Grade 8 Using Longitudinal Data on Summative and Formative Assessments. Shudong Wang, NWEA Liru Zhang, Delaware DOE G. Gage Kingsbury, NWEA. Purpose of Study.

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Shudong Wang, NWEA Liru Zhang, Delaware DOE G. Gage Kingsbury, NWEA

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  1. Predicting Student Success in Grade 8 Using Longitudinal Data on Summative and Formative Assessments Shudong Wang, NWEA Liru Zhang, Delaware DOE G. Gage Kingsbury, NWEA

  2. Purpose of Study The study is an investigation of using longitudinal data to estimate student success in reading and mathematics. Both summative and formative test scores are used to predict student performance at grade 8. The research questions are: 1. Is formative assessment a valid predictor to estimate student success on state summative assessment? 2. To what extent does the formative assessment contribute to estimate student success at grade 8? 3. Is there any different in patterns of prediction between reading and mathematics?

  3. Evidence of Predictive and Concurrent Validity • Predictive validity is the extent to which a score on a scale or test predicts scores on some criterion measure • Concurrent validity is the degree to which results from a test agree with the results from other measures of the same or similar constructs. • Predictive validity shares similarities with concurrent validity in that both are generally measured as correlations between a test and some criterion measure.

  4. Methods of Study – Data The longitudinal data was collected from a state summative assessments and a district-wide, computerized adaptive formative assessment in reading and mathematics. • Students must have a valid score on the state assessment in 2006 at grade 6, 2007 at grade 7, and 2008 at grade 8; • Students must have a valid score on the formative assessment in the same year at the same grade; and • Students who were tested under non-standard accommodation(s) were excluded.

  5. Methods of Study – Data (Continued) The summative assessment data was collected from a statewide assessments. The variables are called • State 2008; State 2007; State 2006 The formative assessment data was collected from 5 large school districts of the state. The variables are called: • Formative 2008; Formative 2007; Formative 2006 The reading data set contains 1,170 students; the mathematics data set contains 1,454 students.

  6. Methods of Study – Data Analysis 1. Multiple linear regression (MLR) was used for the analysis: • Dependent variable - State 2008 • Two sets of independent variables: - State summative assessments - State 2007, State 2006 - District-wide formative assessments – Formative 2008, Formative 2007, Formative 2006 • Stepwise method 2. MLR with selected independent variables from each set as well as combined variables from both sets

  7. Descriptive Statistics

  8. Correlation Matrix - Reading

  9. Correlation Matrix - Mathematics

  10. Multiple Linear Regressionby Stepwise Analysis One

  11. Model Summary – Reading Dependent variable – State 2008 reading score Independent variables: • Model 1: State 2007 • Model 2: State 2007, Formative 2008 • Model 3: State 2007, Formative 2008, State 2006 • Model 4: State 2007, Formative 2008, State 2006. Formative 2007 • Model 5: State 2007, Formative 2008, State 2006. Formative 2007, Formative 2006

  12. Model Summary – Reading

  13. Model Summary – Mathematics Dependent variable – State 2008 mathematics score Independent variables: • Model 1: State 2007 • Model 2: State 2007, Formative 2008 • Model 3: State 2007, Formative 2008, State 2006 • Model 4: State 2007, Formative 2008, State 2006. Formative 2007

  14. Model Summary – Mathematics

  15. Coefficients – Reading (selected models)

  16. Coefficients – Mathematics (selected models)

  17. Coefficient Correlations – Reading

  18. Coefficient Correlations – Mathematics

  19. Residual Summary – Reading

  20. Residual Summary – Mathematics

  21. Multiple Linear Regressionby Designed Entry Analysis Two

  22. Model Summary

  23. Coefficients – Reading (1)

  24. Coefficients – Reading (2)

  25. Coefficients –Mathematics (1)

  26. Coefficients –Mathematics (2)

  27. Summary of Findings • The results of the study indicate that student scores on the formative assessment can be a valid predictor, by itself or combined with student scores on the state summative assessment, to estimate student success at grade 8 in reading and mathematics. • The analysis results suggest that formative assessment scores make significant contribution to improve the accuracy of predicting student success in both reading and mathematics. • It is not surprise that the latest test score is often the strongest predictor in a longitudinal study. The data seem to imply that mathematics is more sensitive to the year of the assessment score in mathematics than in reading, which perhaps is due to the natural of the content area. • The limitations of using matched student records in longitudinal analyses must be taken into consideration in case of significant changes of student populations over time (e.g., mobility).

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