Abstract
Adapting interventions based on learner progress is paramount to the effectiveness of interventions in special education and applied behavior analysis. Although there is some research on effective methods for training practitioners to make general instructional decisions (e.g., modify an intervention) based on graphed performance data, research on training individuals to make specific decisions (e.g., how to modify an intervention) is more limited. Our purpose in this study was to evaluate the effects of a training package, consisting of a brief online training and a visual decision-making model, for increasing preservice teachers’ and behavior analysts’ accuracy in making specific instructional decisions based on graphed performance data. In a multiple baseline across participants design, all participants increased their decision-making accuracy on novel graphs during assessment sessions and maintained accuracy at 1-month follow-up. The implications of these findings for training and future research on data-based decision-making are discussed.
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References
Alonzo, J., Tindal, G., Ulmer, K., & Glasgow, A. (2006). easyCBM® online progress monitoring assessment system. http://easycbm.com. University of Oregon, Eugene.
Articulate (2019). Articulate [E-learning software]. Retrieved from https://articulate.com.
Behavior Analyst Certification Board. (2017). BCBA/BCaBA task list. (5th ed.). Author.
Belfiore, P. J., & Browder, D. M. (1992). The effects of self-monitoring on teacher’s data-based decisions and on the progress of adults with severe mental retardation. Education & Training in Mental Retardation, 27(1), 60–67.
Brawley, S., & Stormont, M. A. (2014). Investigating reported data practices in early childhood: An exploratory study. Journal of Positive Behavior Interventions, 16(2), 102–111. https://doi.org/10.1177/1098300713480838.
Brodhead, M. T., & Truckenmiller, A. (2021). Proof-of-concept evaluation of a decision-making algorithm. Behavior Analysis: Research and Practice, 21(1), 84–89. https://doi.org/10.1037/bar0000207.
Browder, D. M., Liberty, K., Heller, M., & D’Huyvetters, K. K. (1986). Self-management by teachers: Improving instructional decision making. Professional School Psychology, 1(3), 165–175. https://doi.org/10.1037/h0090506.
Browder, D. M., Spooner, F., & Jimenez, B. (2011). Standards-based individualized education plans and progress monitoring. In D. M. Browder & F. Spooner (Eds.), Teaching students with moderate and severe disabilities. (pp. 42–91). The Guilford Press.
Browder, D., Karvonen, M., Davis, S., Fallin, K., & Courtade-Little, G. (2005). The impact of teacher training on state alternate assessment scores. Exceptional Children, 71, 267–282. https://doi.org/10.1177/001440290507100304.
Bruhn, A. L., Wehby, J. H., & Hasselbring, T. S. (2020). Data-based decision making for social behavior: Setting a research agenda. Journal of Positive Behavior Interventions, 22(2), 116–126. https://doi.org/10.1177/1098300719876098.
Council for Exceptional Children. (2015). What every special educator must know: Professional ethics and standards. . CEC.
Demchak, M., & Sutter, C. (2019). Teachers’ perception of use and actual use of a data-based decision-making process. Education and Training in Autism and Developmental Disabilities, 54(2), 175–185.
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, 174–187. https://doi.org/10.1177/0022466918790001.
Fuchs, L. S., Deno, S. L., & Mirkin, P. K. (1984). The effects of frequent curriculum-based measurement and evaluation on pedagogy, student achievement, and student awareness of learning. American Educational Research Journal, 21(2), 449–460. https://doi.org/10.3102/00028312021002449.
Grigg, N. C., Snell, M. E., & Loyd, B. (1989). Visual analysis of student evaluation data: A qualitative analysis of teacher decision making. Journal of the Association for Persons with Severe Handicaps, 14(1), 23–32. https://doi.org/10.1177/154079698901400104.
Haring, N., Liberty, K., & White, O. (1980). Rules for data- based strategy decisions in instructional programs: Current research and instructional implications. In W. Sailor, B. Wilcox, & L. Brown (Eds.), Methods of instruction for sevverely handicapped students. (pp. 159–192). Paul H. Brookes.
Individuals with Disabilities Education Act of 2004. 20 U. S. C. §1400 et seq. (2004).
Jimenez, B. A., Mims, P. J., & Baker, J. (2016). The effects of an online data-based decisions professional development for in-service teachers of students with significant disability. Rural Special Education Quarterly, 35(3), 30–40. https://doi.org/10.1177/875687051603500305.
Jimenez, B. A., Mims, P. J., & Browder, D. M. (2012). Data-based decisions guidelines for teachers of students with severe intellectual and developmental disabilities. Education and Training in Autism and Developmental Disabilities, 47(4), 407–413.
Kipfmiller, K. J., Brodhead, M. T., Wolfe, K., LaLonde, K., Sipila, E. S., Bak, M. S., & Fisher, M. H. (2019). Training front-line employees to conduct visual analysis using a clinical decision-making model. Journal of Behavioral Education, 28(3), 301–322. https://doi.org/10.1007/s10864-018-09318-1.
Lemons, C. J., Kearns, D. M., & Davidson, K. A. (2014). Data-based individualization in reading: Intensifying interventions for students with significant reading disabilities. TEACHING Exceptional Children, 46(4), 20–29. https://doi.org/10.1177/0040059914522978.
Maffei-Almodovar, L., Feliciano, G., Fienup, D. M., & Sturmey, P. (2017). The use of behavioral skills training to teach graph analysis to community based teachers. Behavior Analysis in Practice, 10(4), 355–362. https://doi.org/10.1007/s40617-017-0199-3.
National Center on Intensive Intervention (n.d.). Retrieved from https://intensiveintervention.org/intensive-intervention.
O’Grady, A. C., Reeve, S. A., Reeve, K. F., Vladescu, J. C., & Lake, C. M. J. (2018). Evaluation of computer-based training to teach adults visual analysis skills of baseline-treatment graphs. Behavior Analysis in Practice, 11(3), 254–266. https://doi.org/10.1007/s40617-018-0266-4.
Parsons, M. B., Rollyson, J. H., & Reid, D. H. (2012). Evidence-based staff training: A guide for practitioners. Behavior Analysis in Practice, 5, 2–11. https://doi.org/10.1007/BF03391819.
Ruble, L. A., McGrew, J. H., Wong, W. H., & Missall, K. N. (2018). Special education teachers’ perceptions and intentions toward data collection. Journal of Early Intervention, 40(2), 177–191. https://doi.org/10.1177/1053815118771391.
Sandall, S. R., Schwartz, I. S., & Lacroix, B. (2004). Interventionists’ perspectives about data collection in integrated early childhood classrooms. Journal of Early Intervention, 26(3), 161–174. https://doi.org/10.1177/105381510402600301.
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. https://doi.org/10.1002/pits.20113.
Stecker, P. M., & Fuchs, L. S. (2000). Effecting superior achievement using curriculum-based measurement: The importance of individual progress monitoring. Learning Disabilities Research & Practice, 15(3), 128–134.
Stormont, M., Reinke, W., & Herman, K. (2011). Teachers’ knowledge of evidence-based interventions and available school resources for children with emotional and behavioral problems. Journal of Behavioral Education, 20(2), 138. https://doi.org/10.1007/s10864-011-9122-0.
The IRIS Center. (2015). Intensive intervention (Part 2): Collecting and analyzing data for data-based individualization. Retrieved from https://iris.peabody.vanderbilt.edu/dbi2/
Wolfe, K., & Slocum, T. A. (2015). A comparison of two approaches to training visual analysis of AB graphs. Journal of Applied Behavior Analysis, 48(2), 472–477. https://doi.org/10.1002/jaba.212.
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Wolfe, K., McCammon, M.N., LeJeune, L.M. et al. Training Preservice Practitioners to Make Data-Based Instructional Decisions. J Behav Educ 32, 1–20 (2023). https://doi.org/10.1007/s10864-021-09439-0
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DOI: https://doi.org/10.1007/s10864-021-09439-0