Response to Intervention
Monitoring Student
Progress at the Secondary
Level
Jim Wright
www.interventioncentral.org
www.interventioncentral.org
Response to Intervention
“Few agree on an appropriate curriculum
for secondary students…; thus it is difficult
to determine in what areas student
[academic] progress should be
measured.”
-- Espin & Tindal (1998)
Source: Espin, C. A., & Tindal, G. (1998). Curriculum-based measurement for secondary students. In M. R. Shinn (Ed.) Advanced
applications of curriculum-based measurement. New York: Guilford Press.
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Response to Intervention
RTI Literacy: Assessment & Progress-Monitoring
•
•
•
To measure student ‘response to instruction/intervention’ effectively,
the RTI model measures students’ academic performance and
progress on schedules matched to each student’s risk profile and
intervention Tier membership.
Benchmarking/Universal Screening. All children in a grade level are
assessed at least 3 times per year on a common collection of
academic assessments.
Strategic Monitoring. Students placed in Tier 2 (supplemental)
reading groups are assessed 1-2 times per month to gauge their
progress with this intervention.
Intensive Monitoring. Students who participate in an intensive,
individualized Tier 3 intervention are assessed at least once per week.
Source: Burns, M. K., & Gibbons, K. A. (2008). Implementing response-to-intervention in elementary and secondary schools:
Procedures to assure scientific-based practices. New York: Routledge.
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Response to Intervention
Measuring General vs. Specific Academic
Outcomes
• General Outcome Measures: Track the student’s
increasing proficiency on general curriculum goals such
as reading fluency. An example is CBM-Oral Reading
Fluency (Hintz et al., 2006).
• Specific Sub-Skill Mastery Measures: Track short-term
student academic progress with clear criteria for
mastery. An example is CBA-Math Computation
Fluency (Burns & Gibbons, 2008).
Sources: Burns, M. K., & Gibbons, K. A. (2008). Implementing response-to-intervention in elementary and secondary schools:
Procedures to assure scientific-based practices. New York: Routledge.
Hintz, J. M., Christ, T. J., & Methe, S. A. (2006). Curriculum-based assessment. Psychology in the Schools, 43, 45-56.
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Response to Intervention
Local Norms: Screening All Students (Stewart & Silberglit,
2008)
Local norm data in basic academic skills are collected at least
3 times per year (fall, winter, spring).
• Schools should consider using ‘curriculum-linked’ measures
such as Curriculum-Based Measurement that will show
generalized student growth in response to learning.
• If possible, schools should consider avoiding ‘curriculumlocked’ measures that are tied to a single commercial
instructional program.
Source: Stewart, L. H. & Silberglit, B. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 225-242). Bethesda, MD: National Association of School Psychologists.
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Response to Intervention
Local Norms: Using a Wide Variety of Data
(Stewart & Silberglit, 2008)
Local norms can be compiled using:
• Fluency measures such as Curriculum-Based
Measurement.
• Existing data, such as office disciplinary referrals.
• Computer-delivered assessments, e.g., Measures of
Academic Progress (MAP) from www.nwea.org
Source: Stewart, L. H. & Silberglit, B. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 225-242). Bethesda, MD: National Association of School Psychologists.
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Response to Intervention
Measures of
Academic Progress
(MAP)
www.nwea.org
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Response to Intervention
Applications of Local Norm Data (Stewart & Silberglit, 2008)
Local norm data can be used to:
• Evaluate and improve the current core instructional
program.
• Allocate resources to classrooms, grades, and buildings
where student academic needs are greatest.
• Guide the creation of targeted Tier 2 (supplemental
intervention) groups
• Set academic goals for improvement for students on
Tier 2 and Tier 3 interventions.
• Move students across levels of intervention, based on
performance relative to that of peers (local norms).
Source: Stewart, L. H. & Silberglit, B. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 225-242). Bethesda, MD: National Association of School Psychologists.
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Response to Intervention
Apply the ‘80-15-5’ Rule to Determine if the Focus of the Intervention
Should Be the Core Curriculum, Subgroups of Underperforming
Learners, or Individual Struggling Students (T. Christ, 2008)
–
–
–
If less than 80% of students are successfully meeting academic or
behavioral goals, the intervention focus is on the core curriculum
and general student population.
If no more than 15% of students are not successful in meeting
academic or behavioral goals, the intervention focus is on smallgroup ‘treatments’ or interventions.
If no more than 5% of students are not successful in meeting
academic or behavioral goals, the intervention focus is on the
individual student.
Source: Christ, T. (2008). Best practices in problem analysis. In A. Thomas & J. Grimes (Eds.), Best practices in school
psychology V (pp. 159-176).
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Response to Intervention
Local Norms: Supplement With Additional
Academic Testing as Needed (Stewart & Silberglit, 2008)
“At the individual student level, local norm data are just the first
step toward determining why a student may be experiencing
academic difficulty. Because local norms are collected on brief
indicators of core academic skills, other sources of information
and additional testing using the local norm measures or other
tests are needed to validate the problem and determine why the
student is having difficulty. … Percentage correct and rate
information provide clues regarding automaticity and accuracy of
skills. Error types, error patterns, and qualitative data provide
clues about how a student approached the task. Patterns of
strengths and weaknesses on subtests of an assessment can
provide information about the concepts in which a student or
group of students may need greater instructional support,
provided these subtests are equated and reliable for these
purposes.” p. 237
Source: Stewart, L. H. & Silberglit, B. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 225-242). Bethesda, MD: National Association of School Psychologists.
www.interventioncentral.org
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Response to Intervention
How Does a Secondary School Determine a
Student’s Math Competencies?
“Tests [to assess secondary students’ math knowledge] should
be used or if necessary developed that measure students’
procedural fluency as well as their conceptual understanding.
Items should range in difficulty from simple applications of the
algorithm to more complex. A variety of problem types can be
used across assessments to tap students’ conceptual
knowledge.”
p. 469
Source: Ketterlin-Geller, L. R., Baker, S. K., & Chard, D. J. (2008). Best practices in mathematics instruction and assessment
in secondary settings. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology V (pp.465-475).
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Response to Intervention
Making Use of Existing (‘Extant’) Data
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Response to Intervention
Extant (Existing) Data (Chafouleas et al., 2007)
• Definition: Information that is collected by schools as a
matter of course.
• Extant data comes in two forms:
– Performance summaries (e.g., class grades, teacher
summary comments on report cards, state test
scores).
– Student work products (e.g., research papers, math
homework, PowerPoint presentation).
Source: Chafouleas, S., Riley-Tillman, T.C., & Sugai, G. (2007). School-based behavioral assessment: Informing intervention
and instruction. New York: Guilford Press.
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Response to Intervention
Advantages of Using Extant Data (Chafouleas et al., 2007)
• Information is already existing and easy to access.
• Students are less likely to show ‘reactive’ effects when
data is collected, as the information collected is part of
the normal routine of schools.
• Extant data is ‘relevant’ to school data consumers (such
as classroom teachers, administrators, and members of
problem-solving teams).
Source: Chafouleas, S., Riley-Tillman, T.C., & Sugai, G. (2007). School-based behavioral assessment: Informing intervention
and instruction. New York: Guilford Press.
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Response to Intervention
Drawbacks of Using Extant Data (Chafouleas et al., 2007)
• Time is required to collate and summarize the data (e.g., summarizing
a week’s worth of disciplinary office referrals).
• The data may be limited and not reveal the full dimension of the
student’s presenting problem(s).
• There is no guarantee that school staff are consistent and accurate in
how they collect the data (e.g., grading policies can vary across
classrooms; instructors may have differing expectations regarding
what types of assignments are given a formal grade; standards may
fluctuate across teachers for filling out disciplinary referrals).
• Little research has been done on the ‘psychometric adequacy’ of
extant data sources.
Source: Chafouleas, S., Riley-Tillman, T.C., & Sugai, G. (2007). School-based behavioral assessment: Informing intervention
and instruction. New York: Guilford Press.
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Response to Intervention
Universal Screening at Secondary Schools: Using Existing
Data Proactively to Flag ‘Signs of Disengagement’
“Across interventions…, a key component to promoting
school completion is the systematic monitoring of all
students for signs of disengagement, such as attendance
and behavior problems, failing courses, off track in terms of
credits earned toward graduation, problematic or few close
relationships with peers and/or teachers, and then following
up with those who are at risk.”
Source: Jimerson, S. R., Reschly, A. L., & Hess, R. S. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 1085-1097). Bethesda, MD: National Association of School Psychologists. p.1090
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Response to Intervention
Mining Archival Data: What Are the ‘Early Warning Flags’
of Student Drop-Out?
•
•
•
•
A sample of 13,000 students in Philadelphia were tracked for 8
years. These early warning indicators were found to predict
student drop-out in the sixth-grade year:
Failure in English
Failure in math
Missing at least 20% of school days
Receiving an ‘unsatisfactory’ behavior rating from at least one
teacher
Source: Balfanz, R., Herzog, L., MacIver, D. J. (2007). Preventing student disengagement and keeping students on the graduation
path in urban middle grades schools: Early identification and effective interventions. Educational Psychologist,42, 223–235. .
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Response to Intervention
What is the Predictive Power of These Early
Warning Flags?
Number of ‘Early Warning Flags’ in
Student Record
Probability That Student Would
Graduate
None
56%
1
36%
2
21%
3
13%
4
7%
Source: Balfanz, R., Herzog, L., MacIver, D. J. (2007). Preventing student disengagement and keeping students on the graduation
path in urban middle grades schools: Early identification and effective interventions. Educational Psychologist,42, 223–235. .
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Response to Intervention
‘Elbow Group’ Activity: What Extant/Archival Data
Should Your RTI Team Review Regularly?
•
Discuss the essential extant/archival data
that your RTI Team should review as
‘early warning indicators’ of students who
are struggling.
•
What process should your school adopt to
ensure that these data are reviewed
regularly (e.g., every five weeks) to
guarantee timely identification of students
who need intervention assistance?
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Response to Intervention
Grades as a Classroom-Based ‘Pulse’ Measure
of Academic Performance
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Response to Intervention
Grades & Other Teacher Performance Summary
Data (Chafouleas et al., 2007)
• Teacher test and quiz grades can be useful as a
supplemental method for monitoring the impact of
student behavioral interventions.
• Other data about student academic performance (e.g.,
homework completion, homework grades, etc.) can also
be tracked and graphed to judge intervention
effectiveness.
Source: Chafouleas, S., Riley-Tillman, T.C., & Sugai, G. (2007). School-based behavioral assessment: Informing intervention
and instruction. New York: Guilford Press.
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Response to Intervention
Marc Ripley
2-Wk
9/23/07
4-Wk
10/07/07
6-Wk
10/21/07
(From Chafouleas et al., 2007)
8-Wk
11/03/07
10-Wk
11/20/07
12-Wk
12/05/07
Source: Chafouleas, S., Riley-Tillman, T.C., & Sugai, G. (2007). School-based behavioral assessment: Informing intervention
and instruction. New York: Guilford Press.
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Response to Intervention
Online Grading Systems
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Response to Intervention
Assessing Basic Academic Skills: CurriculumBased Measurement
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Response to Intervention
Curriculum-Based Measurement: Advantages as a Set of Tools to
Monitor RTI/Academic Cases
•
•
•
•
•
•
•
•
Aligns with curriculum-goals and materials
Is reliable and valid (has ‘technical adequacy’)
Is criterion-referenced: sets specific performance levels for specific tasks
Uses standard procedures to prepare materials, administer, and score
Samples student performance to give objective, observable ‘low-inference’
information about student performance
Has decision rules to help educators to interpret student data and make appropriate
instructional decisions
Is efficient to implement in schools (e.g., training can be done quickly; the measures
are brief and feasible for classrooms, etc.)
Provides data that can be converted into visual displays for ease of communication
Source: Hosp, M.K., Hosp, J. L., & Howell, K. W. (2007). The ABCs of CBM. New York: Guilford.
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Response to Intervention
Assessing Basic Academic Skills:
Curriculum-Based Measurement
Reading: These 3 measures all proved ‘adequate
predictors’ of student performance on reading content
tasks:
– Reading aloud (Oral Reading Fluency): Passages
from content-area tests: 1 minute.
– Maze task (every 7th item replaced with multiple
choice/answer plus 2 distracters): Passages from
content-area texts: 2 minutes.
– Vocabulary matching: 10 vocabulary items and 12
definitions (including 2 distracters): 10 minutes.
Source: Espin, C. A., & Tindal, G. (1998). Curriculum-based measurement for secondary students. In M. R. Shinn (Ed.) Advanced
applications of curriculum-based measurement. New York: Guilford Press.
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Response to Intervention
Oral Reading Fluency Probe Sample
http://www.rti2.org/rti2/oralReadings
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Response to Intervention
Maze Probe Sample
http://www.rti2.org/rti2/mazes
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Response to Intervention
Curriculum-Based Evaluation: Math Vocabulary
Format Option 1
• 20 vocabulary terms appear
alphabetically in the right column.
Items are drawn randomly from a
‘vocabulary pool’
• Randomly arranged definitions
appear in the left column.
• The student writes the letter of the
correct term next to each
matching definition.
• The student receives 1 point for
each correct response.
• Each probe lasts 5 minutes.
• 2-3 probes are given in a session.
Source: Howell, K. W. (2008). Best practices in curriculum-based evaluation and advanced reading. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 397-418).
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Response to Intervention
Curriculum-Based Evaluation: Math Vocabulary
Format Option 2
• 20 randomly arranged vocabulary
definitions appear in the right
column. Items are drawn
randomly from a ‘vocabulary pool’
• The student writes the name of
the correct term next to each
matching definition.
• The student is given 0.5 point for
each correct term and another 0.5
point if the term is spelled
correctly.
• Each probe lasts 5 minutes.
• 2-3 probes are given in a session.
Source: Howell, K. W. (2008). Best practices in curriculum-based evaluation and advanced reading. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 397-418).
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Response to Intervention
Assessing Basic Academic Skills:
Curriculum-Based Measurement
Mathematics: Single-skill basic arithmetic
combinations an ‘adequate measure of performance’
for low-achieving middle school students.
Websites to create CBM math computation probes:
•www.interventioncentral.org
•www.superkids.com
Source: Espin, C. A., & Tindal, G. (1998). Curriculum-based measurement for secondary students. In M. R. Shinn (Ed.) Advanced
applications of curriculum-based measurement. New York: Guilford Press.
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Response to Intervention
Assessing Basic Academic Skills:
Curriculum-Based Measurement
Writing: CBM/ Word Sequence is a ‘valid
indicator of general writing proficiency’. It
evaluates units of writing and their relation to
one another. Successive pairs of ‘writing units’
make up each word sequence. The mechanics
and conventions of each word sequence must
be correct for the student to receive credit for
that sequence. CBM/ Word Sequence is the
most comprehensive CBM writing measure.
Source: Espin, C. A., & Tindal, G. (1998). Curriculum-based measurement for secondary students. In M. R. Shinn (Ed.) Advanced
applications of curriculum-based measurement. New York: Guilford Press.
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Response to Intervention
Breaking Down Complex Academic Goals
into Simpler Sub-Tasks: Discrete
Categorization
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Response to Intervention
Identifying and Measuring Complex Academic
Problems at the Middle and High School Level
• Students at the secondary level can present with a
range of concerns that interfere with academic success.
• One frequent challenge for these students is the need
to reduce complex global academic goals into discrete
sub-skills that can be individually measured and tracked
over time.
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Response to Intervention
Discrete Categorization: A Strategy for Assessing
Complex, Multi-Step Student Academic Tasks
Definition of Discrete Categorization: ‘Listing a number of
behaviors and checking off whether they were performed.’
(Kazdin, 1989, p. 59).
• Approach allows educators to define a larger ‘behavioral’ goal for
a student and to break that goal down into sub-tasks. (Each subtask should be defined in such a way that it can be scored as
‘successfully accomplished’ or ‘not accomplished’.)
• The constituent behaviors that make up the larger behavioral
goal need not be directly related to each other. For example,
‘completed homework’ may include as sub-tasks ‘wrote down
homework assignment correctly’ and ‘created a work plan before
starting homework’
Source: Kazdin, A. E. (1989). Behavior modification in applied settings (4th ed.). Pacific Gove, CA: Brooks/Cole..
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Response to Intervention
Discrete Categorization Example: Math Study Skills
General Academic Goal: Improve Tina’s Math Study Skills
Tina was struggling in her mathematics course because of poor study skills. The RTI
Team and math teacher analyzed Tina’s math study skills and decided that, to study
effectively, she needed to:
Check her math notes daily for completeness.
Review her math notes daily.
Start her math homework in a structured school setting.
Use a highlighter and ‘margin notes’ to mark questions or areas of confusion in her
notes or on the daily assignment.
 Spend sufficient ‘seat time’ at home each day completing homework.
 Regularly ask math questions of her teacher.




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Response to Intervention
Discrete Categorization Example: Math Study Skills
General Academic Goal: Improve Tina’s Math Study Skills
The RTI Team—with teacher and student input—created the following
intervention plan. The student Tina will:
 Approach the teacher at the end of class for a copy of class note.
 Check her daily math notes for completeness against a set of teacher
notes in 5th period study hall.
 Review her math notes in 5th period study hall.
 Start her math homework in 5th period study hall.
 Use a highlighter and ‘margin notes’ to mark questions or areas of
confusion in her notes or on the daily assignment.
 Enter into her ‘homework log’ the amount of time spent that evening
doing homework and noted any questions or areas of confusion.
 Stop by the math teacher’s classroom during help periods (T & Th only)
to ask highlighted questions (or to verify that Tina understood that
week’s instructional content) and to review the homework log.
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Response to Intervention
Discrete Categorization Example: Math Study Skills
Academic Goal: Improve Tina’s Math Study Skills
General measures of the success of this intervention include (1) rate
of homework completion and (2) quiz & test grades.
To measure treatment fidelity (Tina’s follow-through with sub-tasks of the
checklist), the following strategies are used :
 Approached the teacher for copy of class notes. Teacher observation.
 Checked her daily math notes for completeness; reviewed math notes, started math
homework in 5th period study hall. Student work products; random spot check by study
hall supervisor.
 Used a highlighter and ‘margin notes’ to mark questions or areas of confusion in her notes
or on the daily assignment. Review of notes by teacher during T/Th drop-in period.
 Entered into her ‘homework log’ the amount of time spent that evening doing homework and
noted any questions or areas of confusion. Log reviewed by teacher during T/Th drop-in
period.
 Stopped by the math teacher’s classroom during help periods (T & Th only) to ask
highlighted questions (or to verify that Tina understood that week’s instructional content).
Teacher observation; student sign-in.
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Response to Intervention
RTI Teams: Recommendations for Data
Collection
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Response to Intervention
RTI Teams: Recommendations for Data Collection
•
Collect a standard set of background information on each student referred to
the RTI Team.
RTI Teams should develop a standard package of background (archival)
information to be collected prior to the initial problem-solving meeting. For
each referred student, a Team might elect to gather attendance data, office
disciplinary referrals for the current year, and the most recent state
assessment results.
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Response to Intervention
RTI Teams: Recommendations for Data Collection
•
For each area of concern, select at least two progress-monitoring measures.
RTI Teams can place greater confidence in their progress-monitoring data
when they select at least two measures to track any area of student concern
(Gresham, 1983)-ideally from at least two different sources (e.g., Campbell &
Fiske, 1959).
With a minimum of two methods in place to monitor a student concern, each
measure serves as a check on the other. If the results are in agreement, the
Team has greater assurance that it can trust the data. If the measures do not
agree with one another, however, the Team can investigate further to
determine the reason(s) for the apparent discrepancy.
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Response to Intervention
RTI Teams: Recommendations for Data Collection
•
Monitor student progress frequently.
Progress-monitoring data should reveal in weeks--not months--whether an
intervention is working because no teacher wants to waste time implementing
an intervention that is not successful. When progress monitoring is done
frequently (e.g., weekly), the data can be charted to reveal more quickly
whether the student’s current intervention plan is effective.
Curriculum-based measurement, Daily Behavior Report Cards, and classroom
observations of student behavior are several assessment methods that can be
carried out frequently.
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Response to Intervention
Monitoring Student Academic Behaviors:
Daily Behavior Report Cards
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Response to Intervention
Daily Behavior Report Cards (DBRCs) Are…
brief forms containing student behavior-rating
items. The teacher typically rates the student daily
(or even more frequently) on the DBRC. The
results can be graphed to document student
response to an intervention.
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Response to Intervention
Daily Behavior Report Cards Can Monitor…
•
•
•
•
•
•
Hyperactivity
On-Task Behavior (Attention)
Work Completion
Organization Skills
Compliance With Adult Requests
Ability to Interact Appropriately With Peers
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Response to Intervention
Daily
Behavior
Report
Card:
Daily
Version
Jim Blalock
Mrs. Williams
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May 5
Rm 108
Response to Intervention
Daily
Behavior
Report
Card:
Weekly
Version
Jim Blalock
Mrs. Williams
Rm 108
05 05 07 05 06 07 05 07 07 05 08 07 05 09 07
40
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0
60 60 50
Response to Intervention
Daily Behavior Report Card: Chart
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Response to Intervention
Student Case Scenario: Jim
•
•
•
•
Jim is a 10th-grade student who is failing his math course and in danger of
failing English and science courses. Jim has been identified with ADHD. His
instructional team meets with the RTI Team and list the following academic
and behavioral concerns for Jim.
Does not bring work materials to class
Fails to write down homework assignments
Sometimes does not turn in homework, even when completed
Can be non-compliant with teacher requests at times.
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Response to Intervention
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Response to Intervention
Closing Thoughts About Local Norms & RTI
Assessment at the Secondary Level…
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Response to Intervention
Local Norms: Set a Realistic Timeline for ‘Phase-In’
(Stewart & Silberglit, 2008)
“If local norms are not already being collected, it may be
helpful to develop a 3-5 year planned rollout of local
norm data collection, reporting, and use in line with
other professional development and assessment goals
for the school. This phased-in process of developing
local norms could start with certain grade levels and
expand to others.” p. 229
Source: Stewart, L. H. & Silberglit, B. (2008). Best practices in developing academic local norms. In A. Thomas & J. Grimes
(Eds.), Best practices in school psychology V (pp. 225-242). Bethesda, MD: National Association of School Psychologists.
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Response to Intervention
Activity: How ‘RTI Ready’ is Your Middle or High School in
Measuring Academic Skills?
In your ‘elbow groups’:
•
Discuss the range of assessments that
your school can access to measure the
current performance and progress of
students in basic academic skill areas.
•
How ‘RTI ready’ is your school in
measuring these academic skills?
•
Be prepared to share your discussion
points with the larger group.
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Response to Intervention
End
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Response to Intervention
RTI Team Initial
Meeting Form:
Secondary
Student ProgressMonitoring
Page 15
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Response to Intervention
RTI Team Initial
Meeting Form:
Secondary
Student ProgressMonitoring
Page 16
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Response to Intervention
RTI Team Initial
Meeting Form:
Secondary
Student ProgressMonitoring
Page 17
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Response to Intervention
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