The research behind FPK

The documentation burden in special education and therapy

• Paperwork takes a real share of the week. The US Department of Education’s national Study of Personnel Needs in Special Education found the typical special education teacher spent about 5 hours a week on administrative duties and paperwork, about 2 hours preparing each IEP, and had only around 50 minutes of the school day available for it. 78% said paperwork interfered with teaching to a moderate (32%) or great (46%) extent. (Carlson et al., 2003)
• Paperwork competes with instruction. An observational study of 31 special education teachers in 24 schools found only about 20% of time spent on academic instruction and nearly 17% on paperwork. (Vannest et al., 2011)
• Therapists carry the same load. The joint workload guidance of the occupational therapy, physical therapy and speech-language associations notes that documentation can take as much as one full day per week for a full-time school-based therapist. (AOTA, APTA and ASHA, 2014)
• Behaviour analysis builds data work into the model. Practice guidelines set case supervision at about 2 hours for every 10 hours of direct treatment, of which summarising and analyzing data is one part (BACB, 2014); the current guidelines ask for supervision to be individualised to each client. (CASP, 2024)
• Workload drives people out of the profession. Reviews of special educator attrition identify working conditions and workload as central factors (Billingsley and Bettini, 2019), and teachers who spend significant time on paperwork report that it interferes with their work and contributes to their intent to leave. (Brunsting et al., 2023)
• The workforce is already short. 45 states reported special education teacher shortages for 2024–25 (Learning Policy Institute, 2025). At the start of 2024–25, 74% of elementary and middle schools and 66% of high schools reported difficulty filling special education vacancies with certified teachers. (NCES School Pulse Panel, 2024)

For how long documentation took one school team in FPK-X, see what we’ve measured so far.

Why frequent, structured data improves outcomes

• Progress monitoring works when the data are used. A meta-analysis of 21 controlled studies found systematic formative evaluation raised achievement by an average effect size of 0.70. Effects were larger when data were graphed rather than only recorded (0.70 against 0.26) and when teachers followed explicit decision rules rather than judgement alone. (Fuchs and Fuchs, 1986) A later review concluded that “monitoring student progress alone does not enhance student achievement”: gains came when teachers changed instruction in response to the data. (Stecker, Fuchs and Fuchs, 2005) The underlying studies are older and of mixed quality, but the direction of the finding has held.
• Data-based individualisation. The federally funded National Center on Intensive Intervention defines data-based individualisation as intensifying support “through the systematic use of assessment data, validated interventions, and research-based adaptation strategies” (NCII). A meta-analysis of 14 studies found an effect of g = 0.37 over business as usual. (Jung et al., 2018)
• It is now expected practice. The Council for Exceptional Children’s High-Leverage Practices call on special educators to use multiple sources of information (HLP 4), share assessment information with families and colleagues (HLP 5), and use student data to adjust instruction (HLP 6). (McLeskey et al., 2017)
• Judgment alone is not enough. Across 75 studies, teachers’ judgements of achievement correlated about .63 with test performance, and informed judgements were more accurate than uninformed ones. (Südkamp, Kaiser and Möller, 2012)
• Brief ratings can be reliable. Direct Behavior Rating, a quick rating completed at the end of an observation period, has been found dependable for measuring academic engagement and disruptive behaviour, especially when the same teacher rates consistently (Chafouleas et al., 2010). This is the same pattern as an FPK-X classroom check-in.
• Evidence-based practices for autism rely on good records. The national review of autism intervention research identified 28 evidence-based practices from 972 studies, including functional behaviour assessment, antecedent-based intervention, parent-implemented intervention, self-management, visual supports and technology-aided intervention (Steinbrenner et al., 2020). Interventions based on functional behaviour assessment reduced problem behaviour by an average of 70.5% across 69 single-case studies (Gage, Lewis and Stichter, 2012), and depend on accurate records of what happened before and after a behaviour.
• Home and school working together helps. Across 77 studies, family–school partnership interventions improved academic achievement (effect size 0.25), behaviour (0.30), social-behavioural competence (0.32) and mental health (0.34), with two-way communication among the influential components. (Smith et al., 2020)
• The legal standard asks for evidence of progress. The US Supreme Court held that a school must offer “an IEP reasonably calculated to enable a child to make progress appropriate in light of the child’s circumstances” (Endrew F. v. Douglas County School District RE-1, 2017). Showing that progress depends on consistent, measured records.

References

1. AOTA, APTA & ASHA (2014). Workload approach: A paradigm shift for positive impact on student outcomes. American Occupational Therapy Association, American Physical Therapy Association, American Speech-Language-Hearing Association.
2. BACB (2014). Applied behavior analysis treatment of autism spectrum disorder: Practice guidelines for healthcare funders and managers (2nd ed.). Behavior Analyst Certification Board.
3. Billingsley, B., & Bettini, E. (2019). Special education teacher attrition and retention: A review of the literature. Review of Educational Research, 89(5), 697–744. doi:10.3102/0034654319862495
4. Brunsting, N. C., Cumming, M. M., Garwood, J. D., & Urquiza, N. (2023). Special education teachers’ wellbeing and burnout. Routledge. doi:10.4324/9781003297093-19
5. Carlson, E., Chen, L., Schroll, K., & Klein, S. (2003). SPeNSE: Study of Personnel Needs in Special Education. Final report of the paperwork substudy. Westat for the US Department of Education, OSEP. ERIC ED479674.
6. CASP (2024). Applied behavior analysis practice guidelines for the treatment of autism spectrum disorder (3rd ed.). Council of Autism Service Providers.
7. Chafouleas, S. M., Briesch, A. M., Riley-Tillman, T. C., Christ, T. J., Black, A. C., & Kilgus, S. P. (2010). An investigation of the generalizability and dependability of Direct Behavior Rating Single Item Scales (DBR-SIS). Journal of School Psychology, 48(3), 219–246. doi:10.1016/j.jsp.2010.02.001
8. Endrew F. v. Douglas County School District RE-1, 580 U.S. 386 (2017).
9. Fuchs, L. S., & Fuchs, D. (1986). Effects of systematic formative evaluation: A meta-analysis. Exceptional Children, 53(3), 199–208. doi:10.1177/001440298605300301
10. Gage, N. A., Lewis, T. J., & Stichter, J. P. (2012). Functional behavioral assessment-based interventions for students with or at risk for emotional and/or behavioral disorders in school. Behavioral Disorders, 37(2), 55–77. doi:10.1177/019874291203700201
11. 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. doi:10.1111/ldrp.12172
12. Learning Policy Institute (2025). Teacher shortages by subjects across states (fact sheet).
13. McLeskey, J., et al. (2017). High-leverage practices in special education. Council for Exceptional Children & CEEDAR Center.
14. National Center on Intensive Intervention. Data-based individualization. American Institutes for Research. intensiveintervention.org (accessed September 2026).
15. NCES (2024). School Pulse Panel: Most US public schools faced hiring challenges for the start of the 2024–25 academic year. Institute of Education Sciences.
16. Smith, T. E., Sheridan, S. M., Kim, E. M., Park, S., & Beretvas, S. N. (2020). The effects of family-school partnership interventions on academic and social-emotional functioning: A meta-analysis. Educational Psychology Review, 32(2), 511–544. doi:10.1007/s10648-019-09509-w
17. Stecker, P. M., Fuchs, L. S., & Fuchs, D. (2005). Using curriculum-based measurement to improve student achievement: Review of research. Psychology in the Schools, 42(8), 795–819. doi:10.1002/pits.20113
18. Steinbrenner, J. R., Hume, K., Odom, S. L., et al. (2020). Evidence-based practices for children, youth, and young adults with autism. FPG Child Development Institute, National Clearinghouse on Autism Evidence and Practice.
19. Südkamp, A., Kaiser, J., & Möller, J. (2012). Accuracy of teachers’ judgments of students’ academic achievement: A meta-analysis. Journal of Educational Psychology, 104(3), 743–762. doi:10.1037/a0027627
20. Vannest, K. J., Hagan-Burke, S., Parker, R. I., & Soares, D. A. (2011). Special education teacher time use in four types of programs. Journal of Educational Research, 104(4), 219–230. doi:10.1080/00220671003709898

Findings were checked against the original publications in September 2026. If you spot an error, email info@futureproofknowledge.com.