UNDERSTANDING RESPONSE TO INTERVENTION

School districts are in the process of adopting the Response to Intervention (RTI) approach to identify and remediate academic and behavioral deficits. As an integral member of the school behavior team, school counselors must use data on individual interventions to contribute to the data-based decision making process in RTI. This article presents a method and rationale to use behavioral observations to determine the effica- cy of focused responsive services. It includes implica- tions for school counseling practice.

I n the years since the reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA; U.S. Department of

Education, 2004), many school districts have adopt- ed the Response to Intervention (RTI) approach to addressing academic and behavioral difficulties as an alternative to the traditional special education assess- ment model (Shores, 2009). The passage of IDEA 2004 was noteworthy because it brought about a fun- damental change in how students may be qualified for special education services (Buffum, Mattos, & Weber, 2009). Under IDEA 2004, states are no longer required to pursue the lengthy and controversial process of identifying a severe discrepancy between achievement and intellectual ability (Fletcher & Vaughn, 2009). Instead, educators may use an RTI process to identify and address learning and behavior problems as quickly as possible in a child’s education. Broadly defined, RTI is a school-wide, multi-

tiered approach requiring teachers and support per- sonnel to implement school-wide, research-based practices and frequently assess student progress in two domains, academics and behavior. When a stu- dent fails to respond to system-wide interventions, small group or individual interventions are applied with greater intensity. As members of school inter-

vention and student support teams, school coun- selors have long contributed to the group of educa- tors who hear concerns and formulate plans to sup- port students at risk of school failure. Under IDEA 2004, school counselors, like other team members, are now required to utilize data to drive this inter- vention planning process for individual students. Fortunately, the recent focus on accountability in

the counseling literature has equipped school practi- tioners with the mindset and skills to collect and ana- lyze data effectively (Astramovich, Coker, & Hoskins, 2005; Dahir & Stone, 2009; Dimmitt, 2010; Dimmitt, Carey & Hatch, 2007; Loesch & Ritchie, 2009). In fact, the methods for analyzing school-wide academic and behavioral indicators and engaging in data-based decision making have been promoted as a “new cornerstone of effective school counseling prac- tice” (Poynton & Carey, 2006, p. 129). However, fruitful participation in an RTI process at the more intensive services level will require that school coun- selors translate these systematic data-based skills to the individual responsive services level. The purpose of this article is to introduce a

method for school counselors to collect and use individual data to contribute to RTI behavioral teams. The article begins by reviewing the current literature on RTI and defining potential ways that a school counselor can contribute to team interven- tion planning and decision making. Next, the authors describe several observational methods for collecting data on classroom environment and stu- dent behavior. Finally, the article demonstrates how observational data were used in three actual cases to assess classroom environment and student behavior and determine the efficacy of school counseling interventions in the behavioral domain.

RESPONSE TO INTERVENTION (RTI) AND RESPONSIVE SERVICES

A detailed description of RTI is beyond the scope of this article but an overview will help to frame the school counselor’s role in the system. For more

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Determining Responsiveness to School Counseling Interventions Using Behavioral Observations

Diana H. Gruman, Ph.D., is an associate professor and director of the School Counseling Program at Western Washington University, Bellingham, WA. E-mail: [email protected] Brian Hoelzen, MA, is a school psychologist with Bellingham Public Schools.

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information on implementation and delivery, school counselors should consult one of the many recent books on RTI and behavior support (see Applebaum, 2009; Buffum, et al., 2009; Horner, Sugai, Todd, & Lewis-Palmer, 2005; Shores, 2009). RTI is represented graphically by a pyramid shape

with three levels or tiers: universal, secondary, and tertiary. A wide base at the bottom, encompassing approximately 80% of the shape, is known as Tier One and includes prevention activities delivered to all students. The two smaller tiers, stacking on top, represent the Tier Two (10-15%) and Tier Three (5%) activities that are delivered to an increasingly select group of students. To represent the two domains of RTI, the pyramid is often split in half to show both the academic and behavioral aspects of the approach. For the purposes of this article, the focus is on the school counselor’s role in the behav- ioral domain of RTI. In a school-wide implementation of RTI, the

expectation exists that educators will deliver a high- quality program of instruction and behavioral man- agement to all students at Tier One. Typically, Tier One interventions are delivered in the general edu- cation classroom and include behavior management or social skills programs like Positive Behavioral Supports (see the U.S. Office of Special Education Programs Web site, www.pbis.org) and Second Step: A Violence Prevention Curriculum (Committee for Children, 2003). To support student behavior in Tier One, a school counselor might deliver respon- sive services by consulting with teachers on student engagement strategies, implementing transition pro- grams for new students, or training peer mediators to deliver a school-wide conflict resolution program. In Tier One, all students are screened for poten-

tial deficits using a school-wide or “universal screen- ing” process. Students who are identified as needing extra instruction or support move to Tier Two, where they continue to take part in Tier One pro- grams and also receive targeted, research-based interventions to address their specific weaknesses. Tier Two responsive services involving school coun- selors in the behavioral domain might include small group counseling to reinforce social skills (e.g., Student Success Skills; Brigman, Webb, & Campbell, 2007), teacher and parent consultation meetings to align goals, or a peer mentoring initia- tive to support selected at-risk students. At Tier Two, frequent assessments or “progress

monitoring” are utilized to determine how well a student is responding to the chosen interventions. When a child improves to the point that his or her academic skills or behavior more closely approxi- mates peer levels of performance, the student returns to the less intensive level of intervention in Tier One. When a child does not respond favorably

to the interventions, a team of educators meets to plan different small group interventions at Tier Two or more intensive, individualized interventions in Tier Three. In Tier Three, focused responsive services may be

delivered by the school counselor in such forms as individual counseling, behavior improvement plans, or coordination with community resources (e.g., foster care, physical or mental health care) to assist with services beyond the scope of the school (e.g., referral for ADHD evaluation or substance abuse treatment). From a professional school counseling standpoint,

the RTI emphases on prevention, teaming/consulta- tion, and data-based practices represent an excellent philosophical and practical fit. Each of these themes resonates through the ASCA National Model (2005) and is supported by the literature on effective profes- sional counseling practice (Doll & Cummings, 2008; Kampwirth, 2003). Yet the RTI literature infre- quently defines a role for school counselors in the model. The authors suggest that school counselors need a method to efficiently collect behavioral data in order to be viewed as valuable participants of the RTI behavioral team. Guidance on this question may be drawn from a small group of school counseling papers on single-subject research design that demon- strate the power of individual student data collection (Eschenauer & Chen-Hayes, 2005; Foster, Watson, Meeks, & Young, 2002; McDougall & Smith, 2006; Sherrod, Getch, & Ziomek-Daigle, 2009).

DATA COLLECTION ON STUDENT BEHAVIOR

Many different approaches exist for collecting stu- dent behavior data to use in RTI. Procedures range from lengthy behavior coding schemes requiring extensive assessment training (see Volpe, DiPerna, Hintze, & Shapiro, 2005) to simple techniques such as tallying a targeted behavior in a particular setting. This section highlights several common observation methods that a professional school counselor can conduct without a specialized assessment instrument or extensive training.

Office Discipline Referrals Office Discipline Referrals (ODRs) are used often as a universal screening tool to determine which stu- dents are in need of Tier 2 interventions (Sugai, Sprague, Horner, & Walker, 2000). When ODR information is entered into the student information system, a school counselor can readily access the data and determine who is receiving a significant number of infractions. The data can be useful for generating a list for the student support team and tracking sys- tems-wide trends; however, as a tool for progress

184 A S C A | P R O F E S S I O N A L S C H O O L C O U N S E L I N G

States are no longer

required to pursue

the lengthy and

controversial process

of identifying a

severe discrepancy

between

achievement and

intellectual ability.

Instead, educators

may use a Response

to Intervention

process to identify

and address learning

and behavior

problems as quickly

as possible in a child’s

education.

monitoring, ODRs have several limitations. First of all, discipline referral data can be unreliable if teach- ers have different expectations for student behavior and refer students inconsistently (Shores, 2009). Secondly, teacher referrals may be influenced by a number of factors including cultural bias. Finally, ODRs only capture externalizing behavior problems such as aggressive or disruptive behaviors. In review- ing the literature, Walker, Cheney, Stage, and Blum (2005) summarized the limitation, stating, “stu- dents with less disruptive, internalizing behavior problems, such as extreme shyness, withdrawal, and depression, who are equally in need of supports and intervention are often not identified” (p. 195).

Observation Methods Anecdotal observations. Anecdotal observations of behavior are conducted by an observer who enters the classroom and notes all the behaviors and interactions that occur during a set period of time. A “time- stamp” is often recorded each time the student’s behavior or environment changes. For example:

0:00 Start of observation: student work-time — Geography map lesson. J is looking for his pencil around his desk, wanders to the back table.

0:48 Teacher instructs J to get to work. J works until teacher moves away.

1:32 J appears to be carving a pencil with his scissors. Drops pencil and makes a loud gasp. Seatmate laughs. Teacher signals from across room to get to work.

2:13 J drops his eraser and looks to see if teacher is watching. J then stares out window.

3:28 J makes several loud gasping noises and teacher comes over to his desk.

Anecdotal observations provide information on the Antecedent-Behavior-Consequence (ABC) links related to a child’s behavior, which could suggest environmental clues to problematic behaviors such as peer or teacher contributions to the problem (Barnett et al., 2006). While very useful in gathering information about the classroom zeitgeist, they have several limitations including the difficulty in quanti- fying and sharing the information, the time-con- suming nature of the observations, hand cramps, and the inability to compare two different snapshots of behavior. Interval observations. Interval observations are

structured observations conducted to assess the fre- quency of specific student behaviors linked to aca- demic success (e.g., disruptions, disorganization, and poor attention). Interval observations are objec- tive, easy to conduct and track over time, and easy for others (parents, teachers, doctors, and students)

to read and understand. They also provide a means for comparing the target student’s behaviors to peers’ behavior in the same setting. To conduct an interval observation, the observer,

in consultation with the teacher, selects times when the class will be engaged in individual or small group tasks. During the observation period, the observer remains in the back of the class, selects a same-gen- der peer, and begins to tally behavior for the target student and peer on a set interval schedule. At each time-point, the observer looks at both students and chooses among a number of categories that best describes the behavior that the observer has just observed (see Appendix). A simple tally mark is put into that category until the next interval. Typically, several observations are taken (using a

different comparison peer each time) and data are compiled to find a behavioral “average.” For exam- ple, if an observer uses a 10-second interval and records data for 10 minutes on three different occa- sions, the data set would include 180 data points for the target student and 180 data points for the com- parison students. The data points can then be graphed and simple percentages can be reported to team members.

INTERVENTION METHOD AND CASE EXAMPLES

This section describes how one professional in the school counseling role participated in data collec- tion, intervention planning, and team decision mak- ing during the 2007-2008 school year. The authors first describe the school context and the typical process used by the counselor and intervention team to respond to a behavioral referral. Next, they pres- ent three case examples chosen from the dozens of cases handled by the team during the school year, not to suggest a quantitative research agenda (e.g., posing a research question, selecting cases, or ana- lyzing data), but simply to illustrate the range of uses of behavioral observations. Finally, they discuss the limitations for school counseling practice.

Context All the students discussed in this section were enrolled in a suburban, Title I elementary school with a population of approximately 329 students in kindergarten through fifth grade. The ethnic com- position of the school was 76% White, 13% Hispanic, 5% Asian/Pacific Islander, 3% African American, 2% American Indian/Alaska Native, and, using Federal criteria, 56.6% of the students quali- fied for free or reduced lunch. The professional in question is a White male with 13 years of experience as a school psychologist and elementary support spe- cialist. Although not called a “school counselor” at

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Fruitful

participation in an

RTI process at the

more intensive

services level will

require that school

counselors translate

these systematic

data-based skills to

the individual

responsive

services level.

the time of these case studies, the professional was implementing a comprehensive counseling program; therefore, for ease of communication, this article refers to him as the school counselor in the follow- ing sections.

Referral Process and Tier One Interventions Following the initial referral by the teacher or parent, the school counselor engaged in a consultation process to gather information from the person mak- ing the referral. He also obtained information from other sources: the student’s cumulative file, academ- ic performance data, and interviews with the student and prior teachers in an effort to learn about the soci- ocultural context of the referral. He often conducted anecdotal observations to help define the problem behavior exhibited by the student. Taken together, these informal data-gathering techniques allowed the counselor to identify possible causes for the behavior that might be addressed without applying Tier Two or Tier Three interventions. For example, if a student had poor eyesight or hearing or was not eating break- fast, the counselor could intervene on a less intensive level to obtain necessary resources. The counselor also considered that classroom fac-

tors might be at the root of a behavior problem. For example, if the classroom teacher was not imple- menting the school-wide management plan, result- ing in a chaotic classroom environment, instruction- al support could be delivered to the teacher through a mentor teacher or school administrator. Or, if other factors were present in the classroom that could be modified, the school counselor applied environmental engineering to simply modify the classroom environment to increase the likelihood of a desired behavior occurring or to decrease the like- lihood of an undesired behavior occurring. Typical examples include: moving the student to the front of the classroom, changing the classroom structure from tables to rows of desks, or closing the shades in the room during work time.

Intervention Planning and Data Collection in Tier Two and Tier Three Once the Tier One causes of behavior were ruled out, the school counselor engaged in an interven- tion planning process as part of the Student Support Team. Parents were contacted and baseline interval observation data was collected to inform the team’s decision making. As a rule, the team implemented the simplest and least intrusive interventions first and collected behavioral data on a consistent basis to determine the efficacy of each intervention. One example of a Tier Two intervention, the

school-home report form, was implemented to cre- ate a consistent channel of communication between the teacher and the parents on an identified behav-

ior of concern (for empirical support for this approach see Riley-Tillman et al., 2008). The school counselor created a form for the teacher to fill out each day and send home with the student. The par- ents agreed to reward the student when he or she met a specified target of appropriate behavior. The counselor encouraged parents to develop a menu of possible rewards with emphasis on nontangible rewards such as game time with parents, extra screen time, or choosing the menu for dinner. The purpose was to let the student know that the parents were paying attention to improvements in behavior, not to punish the student for failing to do well. One example of a more time-intensive Tier Two

intervention (or Tier Three, depending on the severity of behavior) is a behavioral improvement plan (BIP). A BIP is a school-based rewards system tailored to reinforce a student for engaging in spe- cific behaviors. Typically, a token economy is devel- oped in which the student is given a token or stick- er when they engage in the desired behavior. The student can turn in the tokens and choose from a menu of reinforcers that can be tangible or nontan- gible. The reinforcement schedule (ratio of behav- iors to rewards) is modified depending on the suc- cess rate of the student and gradually faded as the student engages in the desired behavior with more automaticity. BIPs are quite conspicuous in the classroom and the social costs of being on a behav- ior plan must be weighed against the benefits.

Case Examples Case #1. Susan, a 10-year-old fourth-grader, was referred by her teacher for depressive symptoms and poor school performance. The teacher had been informed that Susan had been recently adopted and was receiving outside counseling. Through consulta- tion with the parents, the school counselor learned that Susan had multiple mental health diagnoses and complex issues surrounding her birth mother. Susan was already under the care of an outside counselor and psychiatrist. At the behavior team meeting, the group decided that a set of interval behavior obser- vations would be done once a month to measure Susan’s progress in school. This data, shown in Figure 1, was then given to the outside health pro- fessionals each month to help them determine the efficacy of their interventions. Figure 1 shows that at the initial baseline observation Susan was much less engaged in classroom work than her peers but she made slow and steady growth over the year. Case #2. Roger, a 7-year-old first-grade student,

was referred by his classroom teacher for disruptive and inattentive behavior in class. The school coun- selor followed Tier One referral, consultation, and data-gathering strategies and notified Roger’s par- ents about the teacher’s concerns. An initial interval

186 A S C A | P R O F E S S I O N A L S C H O O L C O U N S E L I N G

From a professional

school counseling

standpoint, the RTI

emphases on

prevention,

teaming/consultati

on, and data-based

practices represent

an excellent

philosophical and

practical fit. Yet,

the RTI literature

infrequently

defines a role for

school counselors in

the model.

observation set revealed that Roger was on task dur- ing 41% of the observation intervals and his peers were on task during 73% of the observation intervals (see Figure 2). In consultation with parents and the teacher, the school counselor decided that environ- mental engineering should be attempted first to modify the problematic behavior. After modifica- tions were made in the classroom, a second interval observation set was taken. Roger showed a modest improvement in the amount of disruptive behavior (talking out and out of seat) but had not made real gains in the amount of on-task behavior. The next step was to create a school-home report

with three reporting periods (before and after recess and after lunch) to track how well Roger could “get his work done quietly” with either a frown face, flat lined face or a smiley face. The school counselor asked Roger’s parents to talk with Roger about the form before it started coming home and to reward him when a “good” report came home. Roger prac- ticed working quietly for five minutes with his teacher and was able to do so with her actively mon- itoring his behavior. The parents were enthusiastic and developed several rewards for Roger to choose should his behavior improve. The school-home report form had a dramatic

effect the first week after implementation. At the first intervention collection point (see Figure 2, school-home report 1), behavioral data showed that Roger was on-task during 68% of the observation period compared to his peers at 67%. Over time, Roger’s parents were instructed to taper the reward structure to require increasing amounts of appropri- ate behavior to earn a reward and they did so. The school-home report form remained in place for a total of four months with observation data collected once a month (see Figure 2). Although the effect became somewhat weaker, a positive effect in Roger’s classroom behaviors was seen over the initial observation. Eventually, Roger’s parents asked that the form be discontinued because they felt it was no longer needed. In subsequent weeks, the teacher reported anecdotally that Roger’s problematic behaviors were significantly reduced even after the report form was discontinued. Case #3. Austin, a third-grade boy, was referred

to the school counselor by his parents and his teacher for inattentive behavior. His parents noted that they had received feedback from previous teach- ers about Austin’s inattention but were not overly concerned because he was doing well academically. In third grade, however, his grades were beginning to be adversely impacted and they asked the school for help. In the initial meeting, Austin’s parents expressed concern about the possibility of medica- tion for their son and wanted to try “everything else” first. The team planned a series of interven-

tions, starting with small scale interventions and building in intensity if the data indicated a need. Baseline behavioral data revealed that Austin was

on task during 37% of the observation period and interval observations were then collected following each planned intervention (see Figure 3). Results indicated that environmental engineering was unsuccessful (Austin on task: 36%). The school-

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Susan’s Behavioral Obsevation Data

Susan on task Peer on task

80% 70% 60% 50% 40% 30% 20%

0% 10%

Ba se

lin e

M on

th 1

M on

th 2

M on

th 3

M on

th 4

Figure 1. Interval observation data comparing Susan’s behavior to a composite of her peers’ behavior collected at different points in the intervention process. Each data point represents 180 intervals of data taken in 30 minutes of classroom observations.

Roger’s Behavioral Obsevation Data

Roger on task Peer on task

80% 70% 60% 50% 40% 30% 20%

0% 10%

Ba se

lin e

En v E

ng in

ee r

S/ H

re po

rt 1

S/ H

re po

rt 2

S/ H

re po

rt 3

S/ H

re po

rt 4

Figure 2. Interval observation data comparing Roger’s behavior to a composite of his peers’ behavior collected at different points in the intervention process. Each data point represents 180 intervals of data taken in 30 minutes of classroom observations. Env Engineer = classroom environmental change intervention; S/H Report = school-home report intervention.

188 A S C A | P R O F E S S I O N A L S C H O O L C O U N S E L I N G

home report forms (Austin on task: 58%, 52%, 43%) and the behavior plan (Austin on task: 55% and 49%) had initial positive effects but the effect quickly faded. Over all of these observation points, Austin’s peers averaged 67.4% on task in the classroom com- pared to Austin’s average of 47.1%. After the team had tried all the interventions ini-

tially agreed upon, the parents felt ready to take the intervention data to their family physician to explore a medical diagnosis. Using the intervention data as part of the analysis, the doctor gave Austin a diag- nosis of Attention Deficit Hyperactivity Disorder and prescribed medication. He asked the family’s permission to conduct similar behavior observations to determine the efficacy of the medication. The school counselor consulted with the physician and an ABAB trial was arranged. On four predetermined days, the family randomly gave Austin one of his new unmarked medications—two were active and two were placebos. His teacher and the school coun- selor did not know which day he received the active medication. At the end of the four-day trial, data indicated that the medications made a difference in Austin’s behavior. Specifically, on the days he received the active medication, he was on task 68% and 65% of the time, compared to 45% and 42% when he received the placebo. Once treated for ADHD, Austin returned to the Tier One level of intervention and his classroom behavior improved steadily over the rest of the year with modest aca- demic gains recorded by the teacher.

Limitations The case examples reported here represent just a small number of the total cases worked on by the school behavior team during the 2007-2008 school year. The authors chose three successful cases to demonstrate how behavioral observation data can be used in a variety of circumstances to inform the intervention process in an elementary school. While not every student on the caseload in this school had the same positive outcomes, every student did receive attention from the team and a consistent measurement of behavior. In this way, even an inter- vention in which the student did not make the expected progress informed the team’s decision- making process in subsequent years. In many of these cases, school personnel exhausted their resources in the building and sought out intensive resources outside the school and family ecosystems (e.g., referral for parental drug treatment, McKinney-Vento homeless assistance, or mental health services) to support the student’s emotional and educational progress.

SUMMARY AND IMPLICATIONS

The call for greater accountability has profoundly shaped the education profession in the United States over the past two decades. The widespread practice of mandated testing, consequences for low perform- ing schools, and higher standards for educator preparation programs give clear evidence of change. Like other educators, school counseling profession- als have had to adapt to educational reform by assessing and reporting the contributions they make to student progress. In this era of school reform and downsizing, school counselors must convince policy makers and school leaders that their efforts have a measurable impact on student achievement (Dahir & Stone, 2009). One way to ensure that school counselors are seen

as valuable members of the school staff is to define a role within the special education reforms associated with the revised IDEA (U.S. Department of Education, 2004). In many ways, school counselors have never been better prepared to answer the call of accountability and team-based decision making required by IDEA and RTI. However, school coun- selors must show how their skills in school-based consultation (Crothers, Hughes, & Morine, 2008) and data-driven practices can support both school- wide and individualized intervention efforts within RTI. This article has presented a method and rationale

for using behavioral observations in the area of pro- fessional school counseling responsive services. As the case examples illustrate, data from anecdotal and interval observations may be used for many purpos-

Austin’s Behavioral Obsevation Data

Austin on task Peer on task

80% 70% 60% 50% 40% 30% 20%

0% 10%

Ba se

lin e

En v E

ng in

ee r

S/ H

re po

rt 1

S/ H

re po

rt 2

S/ H

re po

rt 3

Be th

P lan

1

Be th

P lan

2

Figure 3. Interval observation data comparing Austin’s behavior to a composite of his peers’ behavior collected at different points in the intervention process. Each data point represents 180 intervals of data taken in 30 minutes of classroom observations. Env. Engineer = classroom environmental change intervention; S/H = school- home report intervention; Beh Plan = behavior improvement plan.

Informal

data-gathering

techniques allowed

the counselor to

identify possible

causes for the

behavior that

might be addressed

without applying

Tier Two or

Tier Three

interventions.

es: to determine the extent of the child’s problem, to use as evidence of small changes in behavior, and to consult with team members or medical personnel to coordinate care—all in an effort to determine the efficacy of interventions. The decision-making steps and case studies reported here illustrate how a school counselor can play an active role when they are skilled in behavioral observation techniques. ■

References Astramovich, R. L., Coker, J. K., & Hoskins, W. J. (2005). Training

school counselors in program evaluation. Professional School Counseling, 9, 49-55.

Barnett, D. W., Elliott, N., Wolsing, L., Bunger, C. A., Haski, H., McLissick, C., & Vander Meer, C. D. (2006). Response to intervention for young children with extremely challenging behaviors: What it might look like. School Psychology Review, 35, 568-582.

Brigman, G., Webb, L., & Campbell, C. (2007). Building skills for school success: Improving academic and social competence. Professional School Counseling, 10, 279-288.

Buffum, A., Mattos, M., & Weber, C. (2009). Pyramid response to intervention. Bloomington, IN: Solution Tree Press.

Committee for Children. (2003). Second Step: A Violence Prevention Curriculum. Seattle: Author.

Crothers, L. M., Hughes, T. L., & Morine, K. A. (2008). Theory and cases in school-based consultation. New York: Routledge.

Dahir, C. A., & Stone, C. B. (2009). School counselor accountability: The path to social justice and systematic change. Journal of Counseling & Development, 87, 12-20.

Dimmitt, C. (2010). Evaluation in school counseling: Current practices and future possibilities. Counseling Outcome Research and Evaluation, 1(1), 44-56.

Dimmitt, C., Carey, J. C., & Hatch, T. (2007). Evidence-based school counseling practice: Making a difference with data-driven practices. Thousand Oaks, CA: Corwin Press.

Doll, B., & Cummings, J. A. (2008). Transforming school mental health services: Population-based approaches to promoting competence and wellness of children. Thousand Oaks, CA: Corwin Press.

Fairbanks, S, Sugai, G., Guardino, D., & Lathrop, M. (2007). Response to intervention: Examining classroom behavior support in second grade. Exceptional Children. 73, 288-310.

Fletcher, J. M., & Vaughn, S. (2009). Response to intervention: Preventing and remediating academic difficulties. Child Development Perspectives, 3(1), 30–37.

Foster, L. H., Watson, T., Meeks, C., & Young, J. (2002). Single- Subject Research Design for School Counselors: Becoming an Applied Researcher. Professional School Counseling, 6(2), 146.

Horner, R. H., Sugai, G., Todd, A. W., & Lewis-Palmer, T. (2005). Individualized supports for students with problem behaviors: Designing positive behavior plans. New York: Guilford Press.

Kampwirth, T. J. (2003). Collaborative consultation in the schools (2nd ed). Upper Saddle River, NJ: Merrill Prentice Hall.

Loesch, L. C., & Ritchie, M. H. (2009). The accountable school counselor. Austin, TX: Caps Press.

Poynton, T. A., & Carey, J. C. (2006). An integrated model of data-based decision-making for school counseling. Professional School Counseling, 11, 81-89.

Riley-Tillman, T. C., Chafouleas, S. M., Sassu, K. A., Chanese, J. A. M., & Glazer, A. D. (2008). Examining the agreement of direct behavior ratings and systematic direct observation data for on-task and disruptive behavior. Journal of Positive Behavior Intervention, 10, 136-143.

Shores, C. (2009). A comprehensive RTI model: Integrating behavioral and academic interventions. Thousand Oaks, CA: Corwin.

Sugai, G., & Horner, R. H. (2008). What we know and need to know about preventing problem behavior in schools. Exceptionality, 16, 67–77.

Sugai, G., Sprague, J., Horner, R., & Walker, H. (2000). Preventing school violence: The use of office referrals to assess and monitor school-wide discipline interventions. Journal of Emotional and Behavioral Disorders, 8, 94-101.

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Volpe, R. J., DiPerna, J. C., Hintze, J. M., & Shapiro, E. S. (2005). Observing students in classroom settings: A review of seven coding schemes. School Psychology Review, 34, 454- 474.

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The counselor also

considered that

classroom factors

might be at the

root of a behavior

problem.

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APPENDIX Interval Observation

STUDENT: ________________________________________________

DATE/TIME: _____________________________________________

CLASSROOM ACTIVITY: ROOM ACTIVITY: ________________________________________________________________________________________________________________________________________

________________________________________________________________________________________________________________________________________________________________________________________

Note. On Task = Student engaged in the work as defined by the teacher expectations; Off Task = Student not engaged in work as defined by the teacher expectations; OOS = Student out of seat for any reason; Talk Out = Student produces an off-topic comment or talks when not allowed; Teacher = Teacher is talking with student or a small group that includes the student.

On-Task

Subject

Peer

Off Task OOS Talk out Teacher

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.