How Progress Monitoring Assists Decision Making in a Response-to-Instruction Framework

Celebrating 45 Years of IDEA

Join the Office of Special Education Programs and the U.S. Department of Education as we celebrate the 45th Anniversary of the Individuals with Disabilities Education Act!

Deborah Speece
Department of Special Education
University of Maryland

My colleagues and I studied a response-to-instruction model as a method of identifying children for special education services. To judge responsiveness, we used curriculum-based measures (CBM) of oral reading fluency to monitor progress. In one of the schools we worked in, children were administered these one-minute measures every week. About every 8 weeks we met with the children’s teachers to share graphs of children’s progress, identify children who were falling behind their peers, and design reading interventions that the general educator thought were feasible to implement in the classroom. Children who caught up with their peers were considered responsive and continued with weekly measurement; those who did not make adequate progress continued to receive specially-designed intervention from the general education teacher as well as weekly measurement. This process generated a number of examples of how weekly progress monitoring, which includes systematic data interpretation and teacher action, is central to good decision-making in an RTI framework. Two children are discussed whose profiles illustrate different aspects of the progress monitoring-RTI interface.

Kyle: Don't Forget About Academics

Kyle was in second grade when he entered our study. When we met with his classroom teacher to discuss his lack of reading progress, the discussion was dominated by a focus on his problem maintaining attention and the excellent involvement of his parents with the school and classroom. Kyle’s father volunteered in the classroom one day per week and both parents were aware of Kyle’s impulsivity, difficulties completing assignments and working independently. They declined any involvement with special education assessment or suggestions to evaluate for attention deficit disorder but did work closely with his pediatrician.

Although attention was clearly an issue, it was equally apparent that Kyle was not making progress in reading. Our weekly CBM measures in the fall showed that he was reading about 20 words correctly in one minute. The average number of words read by second graders at that time of year is about 65. The graph below depicts Kyle’s performance on the CBM measure across the year. As indicated, he was reading only 20 words per minute in November and December. Equally problematic was that he was showing no growth. His performance was somewhat better in January but he was still behind his peers.

The teacher may have realized the extent of his reading problems but our sense was that her emphasis was how to keep Kyle focused (a reasonable goal). Reading instruction seemed a secondary concern. In the second half of the year, Kyle’s reading performance showed good improvement following the teacher’s implementation of a reading intervention developed collaboratively. The dotted line on the graph indicates when the teacher began the intervention, the “G” represents the goal we set for him, and the “T” represents the trend line that summarizes his rate of growth. The interpretation is that Kyle exceeded his goal. Because we did not establish experimental control, we cannot say his improvement was due to the teacher’s additional instruction.

There are two important points relevant to progress monitoring and RTI. First, it is conceivable that Kyle’s alarmingly poor reading may not have received proper due given the preoccupation with his attention problems. Thus, frequent monitoring and interpretation of performance seems essential to keep track of children’s academic progress. Second, performance comparisons to both individual progress and group progress is necessary in an RTI framework. Kyle exceeded his goal at the end of second grade but when compared to his second grade peers, he still lagged behind on the number of words he could read and his rate of growth. In planning for the next year, instructional arrangements and practices should be considered that might help Kyle close the gap with his peers.

A CBM graph for Kyle in Reading two. The y-axis represents words correct and ranges from zero to ninety in increments of five. The x-axis is months, September through May. Kyle's progress is charted from November through April. He begins around ten words a minute and ends around forty words a minute, which is twenty words above his goal line.


Janis: When More is Needed

Janis’ profile was quite a bit different from Kyle’s. She was identified by our project in first grade because she was making very little growth in oral reading fluency. Janis was viewed by her teachers as cooperative, hard working, and very quiet. She was consistently described as slow, being the last child to join her group or finish work. Spanish was her first language and teachers believed she had made a lot of progress with her English skills since kindergarten. Janis was in our study for two years and was identified three times by us as being below her classmates on her growth and the number of words she could read.

Janis received several rounds of intervention that we developed with her teachers including individual instruction from a general education teacher in second grade. As far as we could tell, her teachers were devoted to improving her reading skill but despite their efforts, Janis remained behind her classmates. The graphs below show Janis’ first and second grade oral reading fluency scores. Although she made growth, it was minimal and certainly not enough to catch up with her peers. She was growing at a rate of approximately .5 words per week while her peers were more than doubling her growth.

Janis’ progress monitoring data show that she was not responsive to general education efforts and more intensive intervention was needed. Interestingly, the teachers never raised the possibility of special education services. Possibly the fact that she was an English language learner clouded the issue of her reading progress. That is, disability was implicitly ruled out because of her language status. In any event, the progress monitoring graphs are telling and difficult to argue with. Regardless of perceived cause, Janis required something different to make gains in reading.

A CBM graph for Janis in Reading one. The y-axis represnets Words correct and ranges from zero to fifty in increments of five. The x-axis is months, September through May. This graph plots Janis' progress from January through May. She begins around five words a minute and ends around fifteen words a minute. Her trend line is below her goal line.

A CBM graph for Janis in Reading two. The y-axis represnets Words correct and ranges from zero to ninety in increments of five. The x-axis is months, September through May. This graph plots Janis' progress from October through May. She begins around fifteen words a minute, peaks at thirty words a minute, but ends around fifteen words a minute.



Progress monitoring is a method of keeping track of children’s academic development. Progress monitoring requires frequent data collection (i.e., weekly) with technically adequate measures, interpretation of the data at regular intervals, and changes to instruction based on the interpretation of child progress. The two cases presented were meant to illustrate how progress monitoring data could be used to make reasonable decisions about children’s responsiveness. In one example the data shined a light on reading problems that may have been overshadowed by behavioral issues and, in the other, the data indicated that the child needed more than what could be delivered in general education. The approach requires a different way of thinking about children’s learning but is a powerful method of judging responsiveness.


Case, L. P., Speece, D. L., & Molloy, D. E. (2003) The validity of a response-to-instruction paradigm to identify reading disabilities: A longitudinal analysis of individual differences and contextual factors. School Psychology Review, 32, 557-582.

*Has further analysis of Kyle, Janis and other students

Deno, S. L. (1997). Whether thou goest…Perspectives on progress monitoring. In J. W.

Lloyd, E. J. Kameenui, & D. Chard (Eds.), Issues in educating students with disabilities (pp. 77-99). Mahwah, NJ: Lawrence Erlbaum.

*Compares two methods of progress monitoring: Mastery measurement and General Outcome Measurement

Fuchs, L. S., Hamlett, C., & Fuchs, D. Monitoring basic skills progress. Pro-Ed: Austin, TX.

*Software for CBM administration, scoring, graphing on Mac; reading, math concepts and applications, spelling

Speece, D. L., Case, L. P., & Molloy, D. E. (2003). Responsiveness to general education instruction as the first gate to learning disabilities identification. Learning Disabilities Research & Practice, 18, 147-156.

*Entire issue devoted to Response to Instruction

*Has oral reading fluency passages grades 1-4, administration, scoring directions; local norms for a group of elementary school children


This document was developed through Cooperative Agreement (#H326W0003) between the American Institutes for Research and the U.S. Department of Education, Office of Special Education Programs. The contents of this document do not necessarily reflect the views or policies of the Department of Education, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This publication is copyright free. Readers are encouraged to copy and share it, but please credit the National Center on Student Progress Monitoring.