Identifying Students for Academic Support

Because resources for academic support are limited in schools, educators need data to guide decisions. The use and collection of data to identify students for academic support is broadly referred to as universal screening in schools.

Our research activities under Identifying Students for Academic Support help us to evaluate ways in which we can make the universal screening process for our programs more accurate—and importantly—more efficient. Even minor improvements in our process for selecting students for academic intervention can translate into a sizeable impact on the educational experiences of students.

Research Spotlight

Does more equal better when it comes to academic screening?

In this study, we worked with our colleagues to evaluate whether we could re-purpose existing information about students (in this case, their performance on the previous years’ state test) for screening purposes. That is, rather than test students again in the fall, do we already have enough information to make a good decision about their need for extra support?

As it turns out, using previous state test performance to predict future state test performance is a pretty accurate way to flag students for extra support! So much so that it may be worth considering whether to collect additional information at all—something that could save schools and students a lot of time and resources.

There are many reasons to collect fall screening data, and results from this work—as well as work by others—simply let us know that it’s worth considering whether extra data actually add value to the screening and intervention process.

Want to learn more? Connect with us to discuss our work in this area.


Van Norman, E.R., Nelson, P.M. & Klingbeil, D.A. (in press). Post-test probabilities: An empirical demonstration of their use in evaluating the performance of universal screening measures across settings. School Psychology Review.

Klingbeil, D.A., Nelson, P.M., Van Norman, E.R., & Birr, C. (2017). Diagnostic accuracy of multivariate universal screening procedures for reading in upper elementary grades. Remedial and Special Education. Advance online publication. doi: 10.1177/0741932517697446

Clarke, G., Parker, D. C. (2016).  Comparing assessment approaches for use with brief experimental analysis. School Psychology Forum, 10, 93-105. 

Nelson, P.M., Van Norman, E.R., & Lackner, S.E. (2016). A comparison of methods to screen middle school students for reading and math difficulties. School Psychology Review, 45, 327-342.

Nelson, P.M., Van Norman, E.R., & VanDerHeyden, A. (2016). Reduce, reuse, recycle: The longitudinal value of local cut scores using state test data. Journal of Psychological Assessment.

Van Norman, E.R., Nelson, P.M., & Klingbeil, D. (2016). An investigation of the incremental benefit of collecting fall data for universal screening decisions. School Psychology Quarterly. Advanced online publication.

Parker, D. C., Zaslofsky, A. F., Burns, M. K., Kanive, R., Hodgson, J., Scholin, S. E., & Klingbeil, D. A. (2015). A brief report of the diagnostic accuracy of oral reading fluency and reading inventory levels for reading failure risk among second-and third-grade students. Reading & Writing Quarterly, 31, 56-67.

Burns, M. K., & Parker, D. C. (2014). Curriculum-Based Assessment for Instructional Design: Using Data to Individualize Instruction. New York: Guilford. 

Parker, D. C., & Burns, M. K. (2014). Using the instructional level as a criterion to target reading interventions. Reading & Writing Quarterly, 30, 79-94.

Christ, T.J., & Nelson, P.M. (2013). Developing and evaluating screening systems: Practical and psychometric considerations. In Kettler, R.J., Gover, T.A., Albers, C.A. & Feeney-Kettler, K.A. (Eds.), Universal screening in educational settings. Washington, DC: American Psychological Association.