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School Psychology Professional Development

Using Student Achievement Data to Support Instructional Decision Making: A Practical Guide for Psychologists

Learn how to move from student achievement data to meaningful instructional decisions. This course focuses on practical strategies for collecting, interpreting, and applying student data within consultation, intervention, progress monitoring, and schoolwide decision-making systems.

  • 9 CPD hours
  • 8 lessons
  • 40-question assessment
  • Online self-paced
Enroll — $60Preview Lesson 1 free

Course Overview

School psychologists frequently collect, review, and interpret student data, but the greatest value of that information comes from what happens next. Throughout this course, you will examine how student achievement data can support instructional planning, intervention decisions, student goal setting, progress monitoring, and collaborative problem solving.

The course moves from foundational data practices to applied decision-making cases and implementation in psychological practice, following the cycle of collect → interpret → act.

Learning Objectives

After completing this course, participants will be able to:

  1. Describe the instructional improvement cycle of collecting, interpreting, and acting on student achievement data.
  2. Identify methods for collecting and preparing student achievement data from multiple sources.
  3. Interpret student achievement data to form hypotheses and guide instructional decisions.
  4. Explain strategies for teaching students to examine their own data and set learning goals.
  5. Describe practices that build a schoolwide vision and culture for data use.
  6. Identify features of effective data systems and appropriate practices for protecting student data.
  7. Apply data-based decision making to instructional cases and to psychological practice.

The Five Recommendations

The course is built on the five recommendations in the What Works Clearinghouse practice guide:

  1. Make data part of an ongoing cycle of instructional improvement
  2. Teach students to examine their own data and set learning goals
  3. Establish a clear vision for schoolwide data use
  4. Provide supports that foster a data-driven culture within the school
  5. Develop and maintain a districtwide data system

Course lessons

Every lesson is open as a free preview. Each one moves through short sections and interactive practice (data triage, matching, sequencing, and branching cases) and ends with an applied case that gives AI feedback on your response.

1The Instructional Improvement CycleThis lesson introduces the conceptual framework used throughout the course: student achievement data become useful when they are interpreted in relation to an instructional question, translated into an action, and followed by new evidence about whether the action worked.Free preview2Collecting and Preparing Student Achievement DataThis lesson focuses on the first action step in the instructional improvement cycle: collecting a variety of relevant data and preparing those data in ways that make meaningful patterns visible without overstating what any single measure can tell us.Free preview3Interpreting Data to Guide Instructional DecisionsThis lesson focuses on the second and third steps of the instructional improvement cycle: interpreting patterns, developing testable hypotheses, selecting instructional changes that follow logically from those hypotheses, and evaluating whether the changes improve student learning.Free preview4Helping Students Use Their Own Data and Set Learning GoalsThis lesson examines Recommendation 2 of the practice guide: students should be taught explicitly to understand expectations, interpret feedback, examine their own performance data, establish meaningful learning goals, and use those goals as part of an ongoing learning process.Free preview5Building a Schoolwide Vision and Culture for Data UseThis lesson combines Recommendations 3 and 4 of the practice guide.Free preview6Using Data Systems EffectivelyThis lesson focuses on Recommendation 5 of the practice guide: developing and maintaining a districtwide data system that gives educators timely, usable access to relevant information while reducing fragmentation, duplication, and data overload.Free preview7From Student Data to Action: Applied Decision-Making CasesThis lesson applies the full data-based decision-making framework to complex school cases.Free preview8Implementing Data-Based Decision Making in Psychological PracticeThis capstone lesson integrates the full practice-guide framework and applies it to psychological consultation, intervention planning, assessment, and systems-level problem solving.Free preview★Final assessment40 multiple-choice questions covering all eight lessons and the assigned reading. Enroll to take the assessment and earn your certificate.Enroll to take

Required Reading

Free practice guide
Using Student Achievement Data to Support Instructional Decision Making

Hamilton, L., et al. (2009). IES Practice Guide (NCEE 2009-4067). National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Lessons assign pages 1–49 and Appendix D (pages 55–65).

Open the practice guide (PDF)

Course Format and Requirements

This independent home-study course lets you work through the assigned reading, instructional content, and applied learning activities at your own pace.

To earn credit, complete all eight lessons and pass the final assessment. Credit hours are documented with the Mergener formula in the credit-hour determination (PDF).

Instructor

JF
Jaime Flowers, Ph.D., NCSP, LEP, BCBA

Child & School Psychology CE is a NASP-approved provider of continuing professional development for school psychologists.

CE Program Information & Disclosures

  • Instructional level: Intermediate
  • Target audience: Psychologists, school psychologists, and other professionals who work with children, adolescents, families, and schools.
  • Format and schedule: Asynchronous, self-paced online home study. No live sessions; complete the course at any time during your access period.
  • Access period: 1 year from enrollment.
  • Cost and refunds: Full refund within 30 days of purchase if the course has not been started. See our refund policy.
  • Instructor / content developer: Jaime Flowers, Ph.D., NCSP, LEP, BCBA
  • Conflict of interest: Dr. Flowers is the owner of Child & School Psychology CE and receives revenue from course sales. No other relevant financial or nonfinancial relationships are reported.
  • Commercial support: This program receives no commercial support.
  • Accessibility: Need an accommodation? See our Accessibility & Accommodations page.
  • Questions or complaints: See our CE Complaint & Grievance Procedure.

Limitations and Risks

Limitations of the content. The core practice guide was published in 2009, and its panel rated most recommendations as supported by low levels of causal evidence; its guidance on data systems predates current assessment platforms and data-privacy expectations. More recent meta-analyses show that data-based decision making and data-based individualization can improve student outcomes and teacher skills, but effects vary with the type of measure, intervention intensity, and quality of training, and many studies involve small samples of students with intensive needs. Achievement data carry measurement error, and their validity for instructional decisions depends on the purpose for which each assessment was designed.

Risks. The most common risks come from over-interpreting data, such as making high-stakes decisions from a single score, treating small score changes as meaningful without considering measurement error, or using accountability data to make individual instructional or eligibility decisions. Achievement data can also be misused to label or track students or to evaluate teachers punitively, which undermines a collaborative data culture. Student data must be stored, shared, and displayed in line with FERPA and district privacy policies, particularly when data walls are used or students review their own data.

References

Primary reading

  • Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. https://ies.ed.gov/ncee/wwc/publications/practiceguides/

Supporting references

  • Espin, C. A., van den Bosch, R. M., van der Liende, M., Rippe, R. C. A., Beutick, M., Langa, A., & Mol, S. E. (2021). A systematic review of CBM professional development materials: Are teachers receiving sufficient instruction in data-based decision-making? Journal of Learning Disabilities, 54(4), 256–268. https://doi.org/10.1177/0022219421997103
  • Filderman, M. J., Toste, J. R., Didion, L. A., Peng, P., & Clemens, N. H. (2018). Data-based decision making in reading interventions: A synthesis and meta-analysis of the effects for struggling readers. The Journal of Special Education, 52(3), 174–187. https://doi.org/10.1177/0022466918790001
  • Gesel, S. A., LeJeune, L. M., Chow, J. C., Sinclair, A. C., & Lemons, C. J. (2021). A meta-analysis of the impact of professional development on teachers' knowledge, skill, and self-efficacy in data-based decision-making. Journal of Learning Disabilities, 54(4), 269–283. https://doi.org/10.1177/0022219420970196
  • Jung, P.-G., McMaster, K. L., Kunkel, A. K., Shin, J., & Stecker, P. M. (2018). Effects of data-based individualization for students with intensive learning needs: A meta-analysis. Learning Disabilities Research & Practice, 33(3), 144–155. https://doi.org/10.1111/ldrp.12172

Course Content

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