ABSTRACT:
This study investigated the efficacy of sound-field amplification (SFA) for improving the speed with which students with emotional and behavioral disorders (EBD) follow teacher directions. We used a multiple baseline design across six students in general education classrooms. Latency data were collected under nonamplified and amplified conditions for two types of directions: (a) task demand and (b) high interest. Results indicated that SFA substantially increased the speed with which students complied with task demand directions but had minimal effect on compliance with high interest directions. Implications for practice and directions for future research are discussed.
Students with emotional and behavioral disorders (EBD) frequently display academic deficits that place them one or more grades below their nondisabled peers in most academic areas (e.g., Mattison, Spitznagel, & Felix, 1998; Meadows, Neel, Scott, & Parker, 1994; Scruggs & Mastropieri, 1986). Improving their academic skills represents a daunting task because of the externalizing behaviors associated with academic instruction (McEvoy & Welker, 2000; Nelson, Benner, Lane, & Smith, 2004). These behaviors interfere with teachers getting students with EBD to following directions (Walker, Ramsey, & Gresham, 2004). Noncompliant behaviors may sometimes function as a way for students with EBD to escape undesirable academic tasks (negative reinforcement) or obtain attention from others (positive reinforcement). A variety of applied behavior analysis (ABA) strategies have been used during functional analysis to address behaviors characteristic of noncompliance (Maag, 2005).
A quite different variable that may contribute to noncompliance is the attentional problems students with EBD display (Barriga et al., 2002). These students are often hindered in their ability to behave appropriately if classrooms are noisy (Reid, 1999). Crandell and Smaldino (1999) concluded that inappropriate levels of classroom noise may have detrimental effects on all students' behaviors.
Several researchers have concluded that in order for students to hear effectively, the noise level of a classroom should not exceed 35-40 decibels (dB) and not exceed 40-50 dB for an occupied classroom (Eriks-Brophy & Ayukawa, 2000; Flexer, Millin, & Brown, 1990; McSporran, Butterworth, & Rowson, 1997; Palmer, 1998). At certain times during the school day, an occupied classroom can climb into the 75-85 dB range, which would create a difficult environment for students to listen and follow directions (Berg, Blair, & Benson, 1996). This situation creates an unfavorable signal to noise ratio, or how loud the voice (signal) is as compared to background noise in a classroom. According to the American Speech-Language-Hearing Association (ASHA, 1995), a teacher's voice should be at least 15 dB above background noise in a classroom. Typical classroom signal to noise ratios range from +5 to -7 dB (Palmer, 1998). Speech recognition is decreased, making the instruction from the teacher less effective (McSporran et al., 1997).
Sound-field amplification (SFA) is a way to optimize the signal to noise ratio in a classroom. It simply involves mounting two or three loudspeakers on the wall in the back of the classroom or in the ceiling in the middle of the classroom and the teacher wearing a wireless FM microphone. The teacher talks in a normal conversational tone, yet the voice is evenly amplified throughout the room no matter where he or she or students are located. The idea is that optimizing the signal to noise ratio will increase students' ability to hear directions and, consequently, follow them. Several researchers have conducted studies that, to varying degrees, corroborate the efficacy of SFA to improve students' social and academic behaviors (Flexer et al., 1990; McSporran et al., 1997; Filmer, 1998; Zabel & Taylor, 1993). Some of these studies, however, suffered from methodological flaws, used standardized tests as dependent measures rather than overt behaviors, or were conducted in self-contained special education classrooms. Furthermore, no research to date has focused on students with EBD who display severe noncompliant behaviors. Consequently, the purpose of this study was to extend the research on the effectiveness of SFA for use with students with EBD in general education classrooms.
Method
Participants
Participating students were selected from nine elementary schools in a midwestern school district that served approximately 3,000 students. In these schools, at least 20% of the student body qualified for free or reduced lunch. The decision to select elementary students was made because only these classrooms were equipped with SFA systems.
Fifteen general education teachers volunteered not to use the SFA equipment during the school year until the study was implemented. The behavioral program coordinator for the school district identified students classified as behaviorally disordered who received their primary education in one of the 15 general education teachers' classrooms.
Students for possible inclusion in the study met four criteria: they (a) attended a classroom with a SFA system, (b) had an individual education plan (IEP), (c) had hearing in the average range, and (d) verified as behaviorally disordered according to criteria similar to federal criteria for seriously emotionally disturbed. All participants had behavioral goals or a behavior support plan attached to their IEP.
Permission letters were mailed to prospective participants' parents or guardians. Teachers of students who returned permission letters were contacted and asked if they were willing to participate in the study. This process was discontinued when six subjects were obtained-a number deemed sufficient for multiple baseline designs (Hayes, Barlow, & Nelson-Gray, 1999). Contact was made with two additional students and their teachers, who also gave permission as alternates if one or two of the original six participants had to be dropped from the study for some reason (e.g., sickness, suspensions, family relocations).
Participating students. Six students (5 male, 1 female) in kindergarten, second, third, and fourth grades participated in the present study. A list of participating students' ages, IQ scores, standardized achievement test scores, and any diagnoses in addition to behavioral disordered appears in Table 1.
Kyle and Bobby were kindergarteners in different classrooms. They had never been in an SFA environment before the study. Ann was a second grader who had been in an SFA environment the previous year but not during the first 7 months before the study. Kris was a third grader and had never been in an SFA environment before the study. LyIe and Troy were fourth graders in different classrooms. They had been in an SFA environment the previous year but not during the first 7 months before the study. All six students displayed pronounced refusal to follow directions from teachers and paraeducators. Table 2 describes their problematic behaviors and interventions attempted-all of which had minimal effect.
Teacher participants. The students' classroom teachers were contacted to determine their willingness to participate in the study after parent permission was obtained. Training procedures and time commitment were explained to the teachers as well as the need for them to permit a researcher to control their SFA system for the duration of the study. All 6 teachers agreed to participate.
Five of the teachers held a BS in elementary education and one held an MS in elementary education. The teachers' experience ranged from 5 to 32 years. Four of the teachers had taught at least two elementary grade levels and the other two had only taught fourth grade. None of the teachers had any experience teaching special education. Three of the participating teachers had taken an introductory special education class, but none of them had any courses that focused primarily on working with students with EBD. Five of the six teachers had participated in at least one workshop on working with students with EBD.
Setting
Data were collected in six elementary general education classrooms. All classrooms were approximately 20 by 25 feet long.
The kindergarten classrooms of Bobby and Kyle each had 17 students. There were 13 boys and four girls in Bobby's class with six students with disabilities. There were ten boys and seven girls in Kyle's class with four students with disabilities. In each classroom, three to four students sat at rectangular tables about two feet apart from each other. Students were positioned so that no one had their back to the main dry erase board at the front of the classroom used for whole class.
Ann's second grade classroom had 22 students: 11 boys and 11 girls, with four students with disabilities. Students sat in four rows of desks, with two rows of five students and two rows of four facing the dry erase board at the front of the room.
Kris attended a third grade classroom with 20 students arranged in four rows of five desks. There were eight boys and 12 girls, with three students with disabilities.
Lyle's fourth grade classroom had 19 students: nine boys and ten girls, with three students with disabilities. Students sat at desks positioned in a semicircle around the perimeter of the classroom with five additional desks in the middle. The fourth grade classroom that Troy attended had 21 students: 11 boys and ten girls with four students with disabilities. Students sat in desks. There were seven clusters each with three desks: one student facing straight ahead and the other two facing each other perpendicular to the third desk in the grouping.
Dependent Measure
The dependent measure was the speed with which participants complied with teachers' directions, which was obtained using latency recording (Maag, 2004). The movement cycle that was used to judge latency began with the last word of a teacher's direction and ended either when the student began the behavior specified in the direction or when 61 seconds had elapsed. The reason for creating a ceiling effect was to avoid an endless amount of time passing without a student complying and to avoid teachers having to repeat the direction multiple times and, thereby, inadvertently reinforce student noncompliance through their attention.
The ceiling time of 61 seconds was obtained by observing a randomly selected student without disabilities in three elementary classrooms. A list of all 15 elementary school teachers who agreed not to use SFA during the school year was generated. Three groups were formed: (a) kindergarten and first grade, (b) second and third grades, and (c) fourth and fifth grades. These groupings reflected the way teachers were assigned to educational teams within the school district. A teacher's name was randomly drawn from each list. The classroom teacher whose name was drawn helped determine their most average student for observation by rank ordering all students based on their academic achievement and classroom behavior. The median student was selected for observation. If the class had an even number of students, the student just below the median was selected for observation.
A total of three 20-minute observations took place during whole group math instruction in each of the three nominated students' classrooms. The reason for observing only the first 20 minutes of the lesson was because that was the length of observations during baseline and intervention phases of the study. The number of latency observations varied per student depending on how many directions a given student received during the 20-minute lesson. Nine latency observations were collected for task demand instructions: 45, 10, 18, 40, 7, 60, 11, 42, and 50 seconds. Mean latency time was 31.4 seconds. Thirty seconds was added to this duration, creating a ceiling of 61 seconds. The reason for adding the 30 additional seconds was to permit participants an adequate length of time to perform the behavior specified in an instruction before a new observation period started. If participants did not comply within this amount of time, the observation was terminated and a latency of 61 seconds was recorded. The next direction given began the next latency observation period.
Procedures
Teacher training. The six teachers received three training sessions. Each training session lasted approximately one hour and 15 minutes.
The first training session began with each teacher generating their own list of five to ten common task demand (i.e., something students probably would not want to do) and high interest (i.e., something students would most likely want to do) directions. The reason to include both types of directions was to determine if students were more likely to comply quickly with high interest versus low interest and whether SFA had an impact on them, respectively. The teachers and one of the researchers then met to narrow down the list until consensus was reached. Table 3 contains a list of these directions.
Next, teachers learned the cueing system. They were told an observer would hold up a green piece of paper if it was appropriate for them to repeat a direction or give a new direction. Teachers were instructed to use their own judgment as to whether a direction was followed; consequently, a new direction could be given to the student when the learning environment dictated it or the direction could be repeated if the student did not follow it after the 61 second ceiling had been reached. A white sheet of paper with a capital "T" printed on it indicated teachers should ask more task demand directions. A white sheet with a capital "H" cued teachers to give more high interest directions.
During the second and third training sessions, teachers practiced giving a balanced number of high interest versus task demand directions during role-playing conducting a 20-minute lesson. Teachers were divided into two groups: three teachers role played being the teacher and the other three assumed the role of students in the classroom. Role-playing began when a teacher would give the students (i.e., other teachers) directions. The teachers who were role-playing as students were instructed to follow some, but not all of the directions. The third training session was identical to the second session except that the roles were reversed to ensure all six teachers had an opportunity to practice giving directions.
The cueing system was implemented by two observers during all role-playing. Only one observer used the cueing procedure during the actual study; however, two observers were trained in the cueing procedure in case one was sick during one of the days the study was in effect.
Functional assessment. Functional assessment information was gathered from the general education classroom teachers on each participating student before collecting data to ensure that students' noncompliance was not a result of wanting to escape a task, obtaining attention, or lacking the necessary skills to perform the task specified in the direction. This information was obtained using the Functional Assessment Hypothesis Formulation Protocol (FAHFP) developed by Larson & Maag (1998). Combining elements of other checklists, interviews, and observation forms, the FAHFP guided participating teachers through the process of (a) operationally defining a behavior, (b) identifying setting events and functions associated with the occurrence of the behavior, and (c) conducting a systematic observation of the behavior. Using the FAHFP culminates in the development of hypotheses statements and the formulation of a functional analysis plan. This measure had previously been used successfully by general education teachers (Maag & Larson, 2004).
Teachers met with one of the researchers after completing the FAHFP to discuss the findings and ensure noncompliance did not serve escape or attention functions. Teachers then demonstrated to the researcher that participating students possessed the skills to follow task demand directions by indicating they had either observed the student performing requested behaviors or presented a permanent product showing the student's ability to perform the direction (e.g., successfully completed a math worksheet when instructed to complete it).
Recording and Interobserver Reliability
Each participant was observed in his or her general education classroom for three academic classes for the duration of the study: mathematics, language arts, and science. The average amount of time allotted for content-area instruction varied between 30 to 40 minutes. Therefore, to ensure uniform recording sessions, latency data were collected during the first 20 minutes of each lesson. Each direction given to the participating students constituted one data point.
Observer training. The primary observers were two substitute teachers employed by the school district. One implemented the cueing system and served as the primary data collector. The other's sole responsibility was to collect interobserver reliability. They were trained during a one-hour session. Training included teaching the operational definition for latency, using the stopwatch, and properly using the cueing system. The observers then practiced collecting data in a general education classroom during 30-minute sessions and received feedback from one of the researchers until they reached 80% or higher agreement for three consecutive sessions.
Interobserver reliability. Interobserver reliability was obtained for approximately 30% of recording sessions across all phases. Percentages were obtained by dividing the shorter duration for each direction by the longer duration and multiplying by 100. Average interobserver reliability for task demand directions across participants and conditions was 88.2% (range = 79.9%-95.8%) during baseline and 89.1% (range = 80.3%-95.4%) during intervention. Average interobserver reliability for high interest directions across participants and conditions was 86.7% (range = 78.9%-92.1%) during baseline and 87.2% (range = 79.0%-93.2%) during intervention.
Experimental Design
A multiple baseline across subject content (science, math, reading) was used to assess the effects of SFA in elementary classrooms on latency of following directions for the 6 participating students with EBD (Maag, 2004). This design was selected to rule out extraneous causes of students' improvement in latency of direction-following behavior for each class period and help determine if a functional relation existed between the target behavior and intervention.
Data on both task demand and high interest directions were collected only during the first 20 minutes of each academic lesson for two reasons. First, this practice ensured uniform recording sessions across students because lesson length varied from teacher to teacher but was never shorter than 20 minutes. Second, shorter observation periods ensured that data were collected over multiple days to account for any natural variability in students' behaviors. In effect, each direction constituted one latency data point. For example, if the teacher asked a participating student three directions during the 20 minute observation period, three data points would be plotted. Consequently, different numbers of data points could be collected for each participating student depending on how many directions their teachers gave him or her per lesson. No phase changes, however, occurred only after one day but no longer than three days.
Baseline. This phase lasted at least five data points or until a stable trend emerged. Data points of 61 seconds indicated a ceiling effect was reached rather than compliance to the direction in that amount of time. If baseline data were completed before the end of a recording session, the amplification system remained off until the following day. Components of the SFA system (i.e., microphone, amplifier, speakers) were present, although inactive, during baseline phases to avoid cueing students whether amplification was occurring. Whatever verbal reinforcement teachers used before this study continued through baseline and intervention phases.
Intervention. The intervention phase consisted of adding activating SFA for an academic subject once a stable trend for baseline was established and then discontinued. The SFA system employed was The Easy Listener Sound Field System by Phonic Ear. This system included a 35 watt public address amplified and two loudspeakers mounted in the middle of each classroom on the ceiling in each classroom approximately ten feet apart from each other. The goal was to have a +15 dB signal to noise ratio which was recommended to ensure optimal classroom learning conditions by ASHA (1995). A B & K 2209 Precision Sound Level Meter was used to maintain a +15 dB during the study. The decibel level could only be changed by one of the researchers to maintain this ratio. Once amplification was added for an academic subject, it remained on for that subject for the duration of the study. It was added for successive academic subjects after obtaining a stable trend during baseline.
Results
Means, ranges, and standard deviations for each type of direction appear in Appendixes A through C. Figures 1 through 6 are graphical representations of results for task demand directions and high interest directions across academic subjects.
Results indicated that all students complied with task demand directions at a slower rate than high interest directions in both baseline and intervention phases. All students did experience an increase in the speed with which they followed task demand directions after implementation of SFA. In addition, the baseline conditions for math and reading, when intervention was applied in science, remained stable, indicating that intervention rather than some extraneous variables was responsible for the positive results obtained. Similarly, baseline for reading remained stable when intervention was implemented for math.
Kyle. Figure 7 is a graphic display of Kyle's performance across subjects. He demonstrated a 14.7 second decrease in the time it took him to follow task demand directions after SFA was implemented during science. Improvements in the speed with which he followed directions after intervention was implemented were also noted in math and reading with a 29.2 second and a 21.3 second increase in speed, respectively. In terms of high interest directions, he demonstrated a 15.7 second decrease in the time it took him to follow these directions during science. Similar decreases were noted during intervention for math (27.9 seconds) and reading (13.0 seconds).
Bobby. Figure 2 is a graphic display of Bobby's performance across subjects. He demonstrated a 21.2 second decrease in the time it took him to follow task demand directions after SFA was implemented during science. Improvements in the speed with which he followed directions after intervention was implemented were also noted in math and reading, with a 33.1 second and a 13.2 second increase in speed, respectively. In terms of high interest directions, he demonstrated a 30.3 second decrease in the time it took him to follow these directions during science. Similar decreases were noted during intervention for math (19.4 seconds) and reading (13.3 seconds).
Ann. Figure 3 is a graphic display of Ann's performance across subjects. She demonstrated a 24.4 second decrease in the time it took her to follow task demand directions after SFA was implemented during science. Improvements in the speed with which she followed directions after intervention was implemented were also noted in math and reading, with a 34.0 second and a 29.9 second increase in speed, respectively. In terms of high interest directions, she demonstrated a 10.0 second decrease in the time it took her to follow these directions during science. Similar decreases were noted during intervention for math (8.1 seconds) and reading (16.2 seconds).
Kris. Figure 4 is a graphic display of Kris's performance across subjects. He demonstrated a 20.1 second decrease in the time it took him to follow task demand directions after SFA was implemented during science. Improvements in the speed with which he followed directions after intervention was implemented were also noted in math and reading, with a 17.9 second and a 18 second increase in speed, respectively. In terms of high interest directions, he demonstrated a 29.7 second decrease in the time it took him to follow these directions during science. Similar decreases were noted during intervention for math (9.6 seconds) and reading (24.8 seconds).
Lyle. Figure 5 is a graphic display of Lyle's performance across subjects. He demonstrated a 14.5 second decrease in the time it took him to follow task demand directions after SFA was implemented during science. Improvements in the speed with which he followed directions after intervention was implemented were also noted in math and reading, with a 21.1 second and a 14.1 second increase in speed, respectively. In terms of high interest directions, he demonstrated a 12.4 second decrease in the time it took him to follow these directions during science. Similar decreases were noted during intervention for math (17.3 seconds) and reading (18.6 seconds).
Troy. Figure 6 is a graphic display of Troy's performance across subjects. He demonstrated a 38.5 second decrease in the time it took him to follow task demand directions after SFA was implemented during science. Improvements in the speed with which he followed directions after intervention was implemented were also noted in math and reading, with a 33.2 second and a 35 second increase in speed, respectively. In terms of high interest directions, he demonstrated a 29.3 second decrease in the time it took him to follow these directions during science. Similar decreases were noted during intervention for math (33.0 seconds) and reading (34.8 seconds).
Discussion
The results of the present study can be summarized as follows. First, all participating students demonstrated increases in the speed with which they followed task demand directions. Second, there were no substantial differences in the speed with which students who had and had not previously been in an SFA classroom followed task demand directions both during baseline and intervention phases. Third, there were some increases in the speed with which students followed high interest directions. Their improvements, however, were not as substantial as those obtained for task demand directions. These results are first compared with those obtained from previous researchers. Possible explanations for more substantial gains occurring for task demand versus high interest directions are explored. Finally, methodological issues are described in terms of areas for future research and implications for practice.
The current findings are consistent with the research conducted by Palmer (1998), who demonstrated that SFA resulted in increased task management and decreased inappropriate behaviors was seen with the introduction of SFA in a general education elementary classroom. General education teachers are looking for ways to increase compliance that are effective and easy to manage (e.g., Simpson, Myles, Simpson, & Ganz, 2005). In both studies, SFA was demonstrated as meeting both criteria. Generalizations should be drawn cautiously, however, because other SFA studies either lacked empirical rigor (e.g., Flexer et al., 1990) or focused on academic functioning (e.g., McSporran et al., 1997; Zabel & Taylor, 1993).
In the present study, all participants demonstrated the largest gains from SFA for task demand compared to high interest directions. Of course, baseline levels for noncompliance to task demand directions were higher; therefore changes appeared more dramatic. One possible explanation was that more alpha commands-those containing specific information-were given in the task demand condition in which information pertaining to precise academic tasks was conveyed. This supposition may be plausible, but is only speculative and requires additional study. Nevertheless, gains were observed in all participants in the high interest condition. Kauffman (2005) noted that students with EBD often refuse to follow even simple directions such as turning in homework or fixing a mistake. The fact that participants in this study also followed high interest directions more quickly lends credence to the hypothesis that amplification made an impact on overall student compliance and, consequently, negates a problem (i.e., noncompliance) that hinders students with EBD from learning (Walker et al., 2004).
The main implication of this study for general educators is the ease with which SFA can be implemented. Teachers are typically more receptive to trying interventions they perceive as easy versus difficult (Kauffman, 2005). They often believe that it is not feasible to spend large amounts of time managing behavior and planning special lessons to accommodate diverse learners (Bos & Vaughn, 2002). In some cases teachers outright reject approaches involving intense effort, extended time periods, or expertise not normally available (Fuchs, Fuchs, & Bahr, 1990). Because of its ease of implementation, SFA may be viewed by general educators as a socially valid intervention. Relatedly, teachers have indicated that compliance with directions is the greatest type of preferred student behavior (Hersh & Walker, 1983).
There are some limitations of the present study. First, only one female participated; this omission, though, may have had a negligible impact because boys are typically more noncompliant than females (Kendall, 2000). Second, a more important limitation is that no ethnic minority students participated in the present study. A host of important factors need to be considered when evaluating and managing the behavior of students from culturally diverse backgrounds (e.g., Fad & Ryser, 1993; Reimers, Wacker, Derby, & Cooper, 1995). Consequently, students who are not English learners should be considered in future research. Third, data were collected over a relatively short period of time (i.e., 1 to 3 days). This practice may have been insensitive to normal variation in behavior typically of students with EBD. Fourth, there was no formal attempt to categorize teacher directions as being alpha or beta commands. The type of command given may substantially affect students following directions regardless of whether directions were of a task demand or high interest variety in a SFA condition.
[Reference]
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[Author Affiliation]
John W. Maag & Jean M. Anderson
University of Nebraska-Lincoln
[Author Affiliation]
AUTHORS' NOTES
Please direct all correspondence to John Maag, Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, 202D Barkley Memorial Center, Lincoln, NE 68583-0732. Phone: (402) 472-5477; Fax: (402) 472-7697; E-mail: jmaag1@unl.edu.
MANUSCRIPT
Initial Acceptance: 9/1/05
Final Acceptance: 6/16/06
