Effects of self-monitoring on the classroom preparedness skills of kindergarten students at-risk for developmental disabilities (Free full text available)

Jennifer Boyd Bialas
Richard Boon

University of Georgia

The purpose of this study was to examine the effects of a self-monitoring procedure on the classroom-preparedness skills of three male kindergarten students at risk for developmental disabilities in an inclusive classroom. A multiple-baseline design across participants (Alberto & Troutman, 2009) was used to evaluate the effects of the self-monitoring procedure to increase the student’s compliance to classroom-preparedness behaviours. Participants were taught to self-monitor and evaluate their classroom-preparedness behaviour using a checklist with picture prompts. The data, gathered for baseline, intervention, and maintenance phases, was measured as a percentage of compliance to the targeted classroom-preparedness skills.

Results indicated that the use of the self-monitoring intervention significantly increased the student’s compliance with the classroom-preparedness skills for all three participants. In addition, the skills generalised across different content areas and were maintained without the use of the checklist. Limitations of the study, implications for the classroom, and future research questions are discussed.


Self-monitoring is a self-management procedure whereby a person systematically observes his or her own behaviour and then records the occurrence or non-occurrence of a target behaviour (Ganz, 2008; Gulchak, 2008). It can include an evaluation component where the student actively obtains feedback and records progress towards a standard (Cooper, Heron & Heward, 2007). The procedures for self-monitoring are most effective when they are simple, efficient and acceptable to the student, minimally obtrusive or laborious and relevant to the student’s needs and goals (Harris, Friedlander, Saddler, Frizzelle & Graham, 2005). Self-monitoring can be a valuable component of an intervention package that might also include consequence-based contingencies such as reinforcement. Self-monitoring is important as a student-directed strategy that can promote independence, motivation, engagement, self-reliance and self-determination to increase learning (Agran et al., 2005).

Self-monitoring is practical since it encourages more self-regulation and less teacher-directed support for behaviours that interfere with learning. A potential classroom benefit of using a self-monitoring procedure is that teachers can spend more time on instruction and less time managing students’ off-task and inappropriate behaviours (Ganz & Sigafoos, 2005). Self-monitoring interventions have been used in a variety of settings including resource, inclusion and general education classrooms for students with and without disabilities (Hughes & Boyle, 1991; Hughes et al., 2002) and have shown positive outcomes for students with a wide variety of disabilities, such as learning disabilities, speech and language impairments, mild-to-moderate intellectual disabilities, emotional and/or behavioural disorders and attention-deficit hyperactivity disorder (ADHD) (for reviews, see Reid, 1996; Reid, Trout & Schartz, 2005; Webber, Scheuermann, McCall & Coleman, 1993).

In addition, self-monitoring has been studied across a variety of diverse behaviours. For instance, it has been shown to be an effective intervention to address a wide range of adaptive behavioural deficits including distractibility, impulsivity, non-compliance and aggression (Levendoski & Cartledge, 2000), as well as for organisational and academic problems with classroom preparedness (Creel, Fore, Boon & Bender, 2006; Gureasko-Moore, DuPaul & White, 2006; Gureasko-Moore, DuPaul & White, 2007), engagement (Amato-Zech, Hoff & Doepke, 2006; Brooks, Todd, Tofflemoyer & Horner, 2003; Crawley, Lynch & Vannest, 2006; Harris et al., 2005; Rock, 2005; Rock & Thead, 2007; Stahr, Cushing, Lane & Fox, 2006), task completion and academic performance (Brooks et al., 2003; Gureasko-Moore et al., 2007; Harris et al., 2005; Rock, 2005; Rock & Thead, 2007). Academic success and self-regulation behaviours are linked. While teachers expect students of all ages to exhibit classroom-preparedness skills, these types of organisational behaviours are seldom directly taught in the classroom. Teachers expect students to self-regulate organisational behaviours; however, many students with disabilities often struggle to perform basic classroom survival skills (Snyder & Bambara, 1997).

It is important to recognise how classroom-preparedness skills include and are closely related to on-task behaviour, academic engagement, academic productivity and performance as these behaviours are described conceptually and measured in the self-monitoring research. Classroom-preparedness skills are organisational behaviours that enable students to meet academic demands which can include preparing materials, listening, following directions, attending to instruction, staying seated, completing tasks, and finishing work on time (Gureasko-Moore et al., 2006). On-task behaviour can be viewed as a component of classroom preparedness and is often defined as focusing eyes on the material or teacher, holding a pencil, active execution of any step in the academic task, asking for help and remaining seated to complete assigned work (Crawley et al., 2006; Harris et al., 2005; Stahr et al., 2006). Similarly, academic engagement is used to measure if the student is attending and on task (Brooks et al., 2003). The end result of academic engagement or on-task behaviour can be academic productivity or work completion, as well as academic performance which can include accuracy measures. Self-monitoring of classroom preparedness can therefore be related to self-monitoring of attention, on-task behaviours, engagement, academic productivity, work completion, performance and accuracy.

Review of the literature

Recently, five studies have shown that self-monitoring procedures have the potential to increase elementary and middle school students’ on-task behaviours, academic skills and compliance to classroom-preparedness skills. In the first study, Rock (2005) examined the effects of a self-monitoring procedure on the academic engagement and problem behaviours of elementary students with and without disabilities. Participants included nine elementary school students from inclusion classrooms. All students were referred by teachers for active or passive disengagement in the classroom on a daily basis. A multiple-baseline-across-subjects design with an embedded reversal was used to evaluate the effects of the intervention, which included monitoring engagement and problem behaviours during independent mathematics and reading seatwork. Productivity and accuracy were assessed during independent mathematics seatwork on individualised new and previously learned material. Dependent variables included academic engagement, productivity and accuracy on the students’ seatwork, while the independent variable employed a combined self-monitoring of attention (SMA) and self-monitoring of performance (SMP) intervention (ACT–REACT strategy) using six-steps: (1) articulate your goal, (2) create a work plan, (3) take pictures (self-modelling), (4) reflect using self-talk, (5) evaluate your progress, and (6) ACT again. Results indicated the strategy was effective for increasing academic engagement and decreasing problem behaviours. However, there were some inconsistencies with students’ productivity and accuracy, as in most cases productivity increased but accuracy rates were variable.

In the second study, Rock and Thead (2007) replicated a previous study (Rock, 2005) to explore the effects of a self-monitoring intervention on the academic engagement, productivity and accuracy of elementary students with and without disabilities. Participants included five elementary school students with problematic behaviours and disengagement during independent seatwork. Similar to the previous study, a multiple-treatment reversal design (ABABC) was used to examine the effects of the intervention and use of gradual fading in a multi-age inclusion classroom on the students’ independent mathematics seatwork on new and previously learned material. Dependent and independent variables were identical to those employed in the Rock (2005) study. Results revealed that students’ academic engagement and productivity increased from baseline to the intervention phases. However, as in the previous study, engagement, productivity and accuracy levels varied during the second intervention phase and within the fading procedures.

In the third study, Harris et al. (2005) examined the effects of SMA and SMP on the on-task and spelling behaviour of elementary students with ADHD. Participants included six third-, fourth- and fifth-grade students with ADHD who had difficulty sustaining attention and performance in the inclusion classroom, even with their medication. A counterbalanced, multiple-baseline-across-subjects design was implemented to assess students’ self-monitoring of attention and academic performance in spelling during language arts instruction in an inclusion classroom. Dependent variables included students’ on-task behaviour and academic performance in spelling instruction, while the independent variable employed was the SMA where students self-recorded ‘yes’ or ‘no’ to the question, ‘Was I paying attention and on task?’, and the SMP in which the student counted and graphed the number of correctly spelled practice words at the end of each spelling period. Results indicated that the SMA and SMP procedures had a positive effect on the student’s on-task behaviour and on their academic performance, as the SMA, for example, resulted in more correct practices for each student.

In the fourth study, Amato-Zech et al. (2006) investigated the effects of a tactile self-monitoring prompt (MotivAider) to increase the on-task behaviour of elementary students with disabilities. Participants included three fifth-grade students with low levels of on-task behaviours. A reversal design (ABAB) was used for each participant with an extended baseline for the third participant. Intervention data was collected in a self-contained special-education classroom during writing instruction. Generalisation probes were conducted in a second academic setting (mathematics) without the use of the self-monitoring procedures. In addition, a classroom token economy system was in place for personal behavioural goals throughout all conditions, including the baseline phase of the project. Dependent variables included the on-task behaviour defined as sit up, look at the person talking, activate thinking, note key information and track the talker (SLANT), while the independent variable was the self-monitoring with the MotivAider using the SLANT strategy to identify on-task behaviours followed by recording behaviour as, ‘Yes, I was paying attention,’ or ‘No, I was not paying attention’. The MotivAider was an electronic beeper attached to the student’s waistband or belt that vibrated on a programmed schedule to provide a tactile cue to self-monitor. Results indicated the self-monitoring procedure increased levels of on-task behaviour. Generalisation data was limited but did suggest that the intervention benefited students in other classroom settings without the use of prompts.

Lastly, in the fifth study, Creel and colleagues (2006) investigated the effects of a self-monitoring procedure on the classroom-preparedness skills of middle school students with ADHD. Participants included four sixth-grade students with ADHD who exhibited a lack of classroom-preparedness skills. A multiple-baseline-across-participants design was used to examine baseline, intervention and maintenance data in a language arts resource classroom. After mastery was achieved for all participants, two maintenance probes were taken for each student once a week for two weeks. During this time, baseline procedures were followed without the use of checklists or prompts. Dependent variables included the classroom-preparedness skills, such as entering the classroom appropriately, going to their seat immediately, beginning classroom procedures without prompting, having classroom materials and completing work without reminders, while the independent variable was the use of the self-monitoring checklist to measure compliance (‘yes’ or ‘no’) for seven targeted classroom-preparedness behaviours. Results indicated that the use of the self-monitoring procedures was effective for increasing classroom-preparedness skills for all four students in the classroom. In addition, students maintained the targeted skills for two weeks after completion of the intervention.

In summary, the use of the self-monitoring procedures improved students’ classroom behaviours related to academic preparedness and is supported in the literature for students with mild disabilities who struggle with self-regulation behaviours. The research represents self-monitoring as a flexible, adaptable intervention that can assist students in successfully managing their own behaviour, organisation and academic learning (Maag, 2004). The most recent studies have revealed the value of self-monitoring to improve classroom preparedness (Creel et al., 2006; Gureasko-Moore et al., 2006; Gureasko-Moore et al., 2007), on-task behaviour or engagement (Amato-Zech et al., 2006; Brooks et al., 2003; Crawley et al., 2006; Harris et al., 2005; Rock, 2005; Rock & Thead, 2007; Stahr et al., 2006) and academic productivity and performance (Brooks et al., 2003; Gureasko-Moore et al., 2007; Harris et al., 2005; Rock, 2005; Rock & Thead, 2007). Self-monitoring has also been shown to generalise to other content-area settings. There is some interest as to whether SMA or SMP is more effective; however, both types of self-monitoring have shown positive results (Harris, Graham, Reid, McElroy & Hamby, 1994; Harris et al., 2005). There is also some discussion as to whether student accuracy in self-recording has any effect on the outcome—however, most studies suggest it does not. Social validity data suggests that the interventions are acceptable, practical for teachers and can be individualised to accommodate student preferences.

However, a noticeable gap in the research literature provided a new direction for this study. To date, there is limited published research on self-monitoring interventions for very young students with developmental delays, who may be having difficulty with self-regulation of behaviours as is expected in the primary school classroom environment (Reinecke, Newman & Meinberg, 1999; Strain, Kohler, Storey & Danko, 1994). Teachers strive to provide opportunities for independent behaviours to develop and a self-monitoring intervention can provide students increased responsibility for their own behaviour regulation and achievements. This type of intervention can be individualised to teach young students how to recruit appropriate attention and help while also providing the opportunity for self-directed feedback and greater independence.

Self-monitoring and students at risk

Developmental delays in young children can affect their ability to achieve success when they first enter the school environment in pre-kindergarten and kindergarten classrooms. Significant developmental delay (SDD) is a disability category with eligibility that is currently determined on a state level in the United States of America (USA). According to the Georgia Department of Education (2010) in the USA, a significant developmental delay is defined as a ‘delay in a child’s development in adaptive behaviour, cognition, communication, motor development or social development to the extent that, if not provided with special intervention, it may adversely affect his/her educational performance in age-appropriate activities’. It is important to note that students eligible for SDD are often later identified with a mild disability such as a learning disability, mild intellectual disability, emotional and/or behavioural disorder or ADHD and many are identified with a secondary disability of speech and language impairment (SI).

The intervention procedures and experimental design in this study replicated a recent self-monitoring intervention in the aforementioned literature review (Creel et al., 2006). The intervention was expected, similar to the previous study, to increase students’ classroom-preparedness skills by using a simple classroom behavioural checklist. However, the self-monitoring intervention extended previous research by Creel et al. (2006) in several significant ways. First, the participants were kindergarten students and not middle school students with ADHD. Also, the participants received interventions in an inclusive classroom compared to a resource classroom setting, which is a placement option for students with disabilities to receive direct, specialised instruction in an individualised and/or small group environment. A few elements to strengthen the findings were similar to the study by Gureasko-Moore et al. (2006). For example, procedural integrity checklists were used to make sure all condition procedures were followed. Also, the only reinforcements provided were related to the students seeing their progress and receiving some assistance from the researcher as they charted their progress for the self-evaluation component.

The purpose of the present study was to examine the effects of a self-monitoring procedure to increase the classroom-preparedness skills of kindergarten students at risk for developmental disabilities who were identified by teachers to have difficulties in self-regulation of behaviour as it applies to academic preparedness. The self-monitoring intervention required each participant to systematically observe their own behaviour and then record the occurrence or non-occurrence of the target behaviour. It included an evaluation component where the student actively obtained visual feedback of progress towards a standard. The following research questions were addressed:

  1. Will a self-monitoring intervention increase the percentage of compliance behaviours of kindergarten students at risk for developmental disabilities?
  2. Will the compliance behaviours be maintained when the intervention has faded?
  3. Will the compliance skills generalise across different content areas?



The participants in this study included three male kindergarten students with deficits in academic-preparedness behaviours. The first participant, Jake, was a 5.7-year-old Caucasian student identified with SDD and SI. He was receiving one hour per week of speech and language therapy and 11.25 hours per week of inclusion services for reading and social skills. Jake also had difficulty attending to whole group and small group activities. He rarely focused on the teacher during instruction and he almost always seemed distracted and detached from the lesson. In addition, Jake also had mild behavioural tics (pressing on his hands or objects) which interfered with his ability to focus and complete tasks. Jake’s articulation was in the mild severity range and he had difficulty with social interactions and was very shy. Jake was on grade level academically, but required constant teacher support for listening to directions and remaining on task during the teacher presentation.

The second participant, Jonas, was a 5.5-year-old Caucasian student identified as at risk for learning and behavioural problems. Jonas displayed behavioural symptoms of ADHD and had difficulty remaining seated, quiet and attentive. He was receiving academic support via the early intervention program and was below grade level in reading.

The third participant, Kareem, was a 5.7-year-old African–American student who had been diagnosed with SI for mild-to-moderate difficulties in articulation and language. He was receiving two hours per week of speech and language therapy and 22.5 hours per week of inclusion services in reading, language arts and mathematics. Kareem had difficulty listening and responding to teacher-directed questioning. Kareem needed instructions repeated two or three times and would always observe other students to see what they were doing before he started an activity. He had difficulty focusing and completing his work. He needed intensive teacher support to be successful with written work as he was weak in motor-planning skills and handwriting. At the time of the study, he was in the referral process for additional testing to rule out a diagnosis of SDD or a learning disability. He was below grade level in reading and mathematics. No students were receiving medication at the time of this study. All three participants were in the same inclusion classroom in a rural school district. Characteristics of the students are summarised in Table 1.

The three students were selected for the study by the general education teacher and the researcher to be functionally similar for having difficulty with classroom-preparedness behaviours that had been taught to the class for two months since the beginning of the school year. A pre-intervention survey was given to the general education teacher to rate the targeted classroom-preparedness behaviours identified as most important to classroom survival and academic preparation in her classroom (see Table 2). The participants in this study had the highest scores on this rating scale and, with agreement from the observations of the researcher, were selected as the most appropriate students to receive the intervention. None of the students had previous experience with a self-monitoring intervention.

Written parental consent and student assent for participation was obtained for each student before the study. Before the study, all students had the physical ability to sit in a seat, write with a pencil, and raise their hand for teacher attention. They were all able to follow one-step directions. All three students were included in the study due to their previously high attendance levels, which was important to prevent inconsistencies of data collection throughout the study.

Table 1. Student demographic information

1. Battelle developmental inventory, Second edn (Newborg, 2004)
2. Hodson assessment of phonological patterns, Third edn (Hodson, 2004)
3. Goldman–Fristoe test of articulation, Second edn (Goldman & Fristoe, 2000)
4. Preschool language scale, Fourth edn (Zimmerman, Steiner & Pond, 2002)

Note. BDI-2 test scores for Jake reflect improvement from initial SDD eligibility testing completed in 2007 before entering special-needs preschool. The current cognitive and adaptive scores are still considered below average and he is still receiving SDD services.

Table 2. Classroom-preparedness rating scale: Pre-intervention survey


The baseline, intervention, and maintenance data was gathered in a kindergarten inclusion classroom, which begins for most students by the age of five years. Data from reading, language arts and mathematics sessions were assessed for generalisation across content areas. The general education inclusion classroom contained a total of 17 students with five receiving special education services. A general education teacher and a special education paraprofessional were present in the classroom during the study. They received specific instructions on how to interact consistently with the participants during all conditions. The special education teacher was the researcher and provided guidelines for the general education teacher. All teachers were responsible for teaching small group lessons. The lessons in reading and language arts involved learning about letters and letter sounds, with activities including writing, tracing, drawing, colouring, matching items, cutting and pasting. The mathematics lessons included learning about shapes and numbers zero to 10, with activities including writing, tracing, colouring, counting, matching items, cutting and pasting.

The classroom was set up with all 17 inclusion students seated at three mixed-ability level tables with approximately four to five students per table. Each student had their own designated seat, chair and materials. Small groups were then ability-grouped and instruction occurred at teacher tables so that reading, language arts and mathematics instruction could be differentiated for students. The participants for this study were in the same group for this type of targeted instruction. The table used for the study was a kidney-shaped table which allowed the researcher to easily observe all the students as they were seated. During the study, participants were separated from each other, with a student who was not participating seated next to them, to minimise the chance for observational learning of the self-monitoring procedures. Non-participants were completing the same tasks as the participants but they were not using the self-monitoring procedures and materials.


The researcher utilised the weekly teacher observation checklist during baseline, intervention and maintenance to mark the classroom-preparedness behaviours she observed for the participating students during the session. The teacher checklist was printed on a 21.6 × 27.9 cm (8 ½ × 11 inch) sheet of paper with one page per week. The teacher checklists were stored in a folder inside the researcher’s filing cabinet so that she could keep them secure and access them each day.

Each participant used a daily student checklist during the intervention. The student checklist was printed on a white, 10.2 × 15.2 cm (4 × 6 inch) piece of paper with coloured-picture prompts taped to the participant’s desk during intervention conditions. The checklist was placed on each participant’s desktop by the researcher and comprised two specific self-monitoring questions: (a) ‘I listened to directions’ and (b) ‘I could repeat the directions’. The students stored their daily checklists in a folder with a page protector to keep the monitoring cards organised. They also kept a 21.6 × 27.9 cm (8 ½ × 11 inch) line graph in their folder to visually record their progress of how many checks they earned each day. The folders were stored in a box behind the special education teacher’s table.

Classroom reinforcers were not specific to this study and were implemented by the teachers as part of their everyday routine and class discipline system. Specifically, the teachers provided all students with the opportunity to earn stickers and other incentives for good behaviour during small group activities. Each student had three Unifix® cubes at the start of each session. If students did not behave appropriately, a cube was taken away and a reward was then unavailable. If they lost all three cubes, a consequence was given according to the classroom discipline system. The classroom discipline system consisted of a multicoloured board with an indicator clip for each student to use to monitor their classroom behaviours during instruction. For example, if a student exhibited an inappropriate and/or off-task behaviour, the teacher asked the student to move their clip on the board from green, which represented the student was on-task and engaged in instruction and the student could receive an incentive to purple (a warning); to yellow (a five-minute time-out in the room); to orange (a five-minute time-out in another room); and finally to red, which resulted in a referral to the office each time they broke a classroom rule. All special education students were provided with rule reminders and redirection in accordance to their individualised education plan so they would not be disciplined unfairly due to their disability.

Dependent variable and recording procedures

The individual percentage of compliance to classroom-preparedness skills for each student served as the dependent variable in the study. The researcher used a teacher observation checklist to measure the behaviours for all participants for each session. The checklist measured two target behaviours that were identified by the general education teacher as being essential to demonstrating classroom preparedness. The behaviours were operationally defined before baseline conditions with input provided from the general education teacher. They included ‘listening to directions’—defined as the student seated with eyes on the teacher and quietly listening (not talking or making noises)—and ‘repeating directions’ to the teacher—defined as the student verbally describing a set of two-step directions. If there was more than one set of two-step directions then a successful target behaviour was calculated if more than 50% of the directions were repeated correctly. Prompting was defined to mean that the teacher should provide no more than two verbal prompts to the student for target behaviours in baseline or intervention procedures. The teacher may use visual prompts or explain a picture on the checklist as needed to ensure that the student was following the self-monitoring procedures correctly.

A percentage score was used for the dependent variable. Percentage of compliance provided an appropriate measure to compare classroom-preparedness behaviours. The percentage of compliance was calculated by dividing the total number of displayed behaviours by the total number of possible target behaviours and then multiplying by 100.

All items on the checklist were recorded during small group instruction sessions immediately following a whole group skill instruction in reading, language arts or mathematics instruction. In the small groups, the teacher reviewed a topic, taught and modelled a skill, provided an opportunity for group practice and then assigned an independent practice activity. For every baseline, intervention and maintenance session, the researcher recorded the student’s behaviour by marking a ‘+’ or ‘–’ for each target behaviour and then recorded the percentage of compliance. Generalisation behaviours were recorded during maintenance conditions without student use of the checklist.

Experimental design

A multiple-baseline-across-participants design (Alberto & Troutman, 2009) was used to assess the effectiveness and maintenance of the self-monitoring procedure. The design evaluated experimental control by predicting that selected participants were functionally independent (to avoid co-variation) and functionally similar (to avoid inconsistent effects). This allowed for the demonstration of a replication of effect across the tiers. The multiple-baseline design evaluated threats to internal validity such as history, maturation and testing by staggering the introduction of the independent variable across tiers. Variability of data threats were addressed in this design by incorporating baseline and intervention criteria that must be met before beginning the next condition. Instrumentation threats were addressed by including inter-observer agreement (IOA) data. Adaptation threats were addressed before the study as students were familiar with the researcher who was also an inclusion teacher in the classroom. Procedural integrity was evaluated to increase the confidence of the results.

Internal validity was addressed in two ways. It was first demonstrated through direct inter-subject replication of the effects of the independent variable consistently in the data patterns (level and trend) across the participants in the study. The number of participants provided three demonstrations of effect each across three points in time. External validity was also demonstrated by the extent to which this study was an effective systematic replication of previous studies using the same independent variable to measure similar behaviours but with a different researcher, in a new setting, and with different participants.

All conditions

The number of sessions for all conditions including baseline, intervention and maintenance was determined by stable responding for at least three consecutive data points. There were one to two sessions per day and the researcher was able to observe each session. The sessions occurred after whole group instruction in either reading and language arts or mathematics in the general education classroom. There were some variations in length of the sessions, but each session was timed for a maximum of 30 minutes per student. A procedural integrity checklist was utilised by the researcher to maintain consistency in the steps required for all conditions.

During all conditions the participants were provided with the same prompts for preparedness behaviours as were provided to all other students in the classroom. Individualised education plan accommodations were addressed and any excessive prompting that occurred was required to be recorded by the researcher on the procedural integrity checklist. The standard classroom reinforcers and discipline system were in effect for all conditions.

Generalisation assessment procedures

The researcher used the same teacher checklist to observe the classroom-preparedness behaviours across all content areas (reading, language arts and mathematics instruction).

Baseline procedures

All the students had been taught the expected behaviours for preparing for class work before recording baseline conditions. After operational definitions for target behaviours were agreed upon and participants were selected, baseline for each participant began. The purpose of the baseline phase was to assess the initial level of classroom preparedness that each student demonstrated without excessive prompting from teachers. Stable baseline data was defined as three consecutive data points with a median below 50%. During baseline, the general education teacher, paraprofessional and researcher agreed not to excessively prompt any participant of the study for preparedness behaviours. Excessive prompting was defined as more than two reminders per behaviour. Teachers could intervene in a typical fashion during problematic situations (Gureasko-Moore et al., 2007) according to the standard classroom discipline requirements. Participants did not use the self-monitoring checklist. They were not familiar with it or trained in its use. After stable baseline was assessed for one participant, the intervention began for that participant, while behaviours of other participants in baseline conditions continued to be measured. All participants had to meet stable baseline criteria to begin intervention according to procedures outlined below.

Intervention procedures

During intervention, each student was expected to meet an established criterion for mastery. Mastery was defined as meeting 100% of demonstrated target behaviours for three consecutive days. The first student began intervention after stable baseline data was demonstrated. Intervention for the second student whose pre-intervention data was stable began once the first student met the mastery criterion. Once the mastery criterion was met for student two, intervention began for the third student in the same manner. Intervention continued for all three participants until all met the mastery criterion.

Intervention for each student began with a training session on the first day following baseline. Data from this training session was not recorded. The participant was introduced to the self-monitoring checklist. An explanation was provided for why the student would be allowed to self-monitor his behaviour. The researcher let the student know that he could improve his good behaviour and earn rewards in small group activities. This was according to standard classroom reinforcement procedures and did not go above and beyond in rewarding the participants. The researcher explained that the student would use the checklist so that he could remember to use his good behaviour and keep all his Unifix® cubes. The participant was instructed to keep the checklist taped to his desk and encouraged not to share it with others. The notebook was given to the student and he was told where it would be stored each day. The self-evaluation graph and the purpose of self-evaluation were explained: ‘You can see your progress each day’. The student was taught how to record his success on the graph. All self-monitoring procedures including recording and evaluation were modelled for the student by the researcher. The student then completed a sample checklist and a sample evaluation graph on his own as a practice exercise. The researcher checked for understanding after the session. The student was required to explain what each picture meant and to demonstrate the behaviour again.

On the second day of intervention, the researcher checked for understanding of the picture prompts on the self-recording card. The participant then began the procedure to monitor and record his behaviours. If the participant had questions about the picture prompts, he could be reminded by the researcher. If he was not actively recording, he could be reminded to record. The student completed his self-recording card and received help to record the evaluation graph after each session. The researcher also monitored the session and recorded each behaviour as it occurred on the teacher checklist. These intervention procedures continued over consecutive sessions until the criterion had been met for the intervention for each student.

Maintenance procedures

After the intervention phase was completed, two maintenance probes were taken for all of the students once a week for two weeks. The teacher followed baseline procedures during the maintenance probes (that is, no checklists were provided to the students).

Social validity

Social validity data was obtained using a survey completed by the general education teacher to assess how the participants compared to their peers before and after intervention for each student. The pre- and post-intervention survey allowed for a side-by-side comparison of rating scores for each participant. This social validity measure helped to assess treatment acceptability by the teachers and if they felt the intervention resulted in significant and important behaviour changes for their students. A second level of social validity was taken by assessing the participants’ attitudes towards self-monitoring of classroom-preparedness skills. After the intervention, the students were given a survey to complete orally which asked how they felt about the self-monitoring intervention.


A special education paraprofessional trained by the researcher on the specific procedures observed the experimental conditions in order to obtain IOA data. The paraprofessional used a copy of the teacher checklist during her observations. She was trained to identify the target behaviours and to understand the operational definitions for each behaviour. IOA data was gathered by the paraprofessional for a minimum of 25% of the sessions and included at least one check during each condition: (a) baseline, (b) intervention and (c) maintenance for each participant. Point-by-point reliability data was collected by dividing the number of agreements by the number of agreements and disagreements combined.

Procedural integrity was measured by the researcher using a task analysis checklist incorporating all the steps of the condition completed for all sessions during baseline, intervention and maintenance. The number of steps that were followed correctly was totalled, and a percentage was calculated by dividing the number of correctly trained steps by the total number of steps and multiplying the result by 100.


Jake scored a mean of 0% on his baseline target behaviours for all three sessions. He experienced some variability in his scores for the first six sessions of intervention with a mean of 58% and a median score of 50% before stabilising at a 100% for each of the last six sessions and scored 100% on both maintenance checks.

Jonas scored a mean of 25% with some variability of scores (three sessions at 0% and three sessions at 50%) during baseline. He scored at 50% for the first two sessions of intervention before he levelled off at 100% for each of the remaining six sessions and scored 100% on both maintenance checks.

Kareem scored a mean of 12.5% and a median score of 0% for 12 sessions of baseline. He had consistent scores of 0% for the first six baseline sessions and then variability in scores (three sessions at 0% and three sessions at 50%) for the next six sessions. Kareem scored a 100% for each of his three intervention sessions and for both maintenance checks. Data from the mathematics sessions 3, 6, and 12 did not appear to vary from the data collected during the reading and language arts sessions. The graphs in Figure 1 demonstrate the percentage of compliance to classroom-preparedness skills for each session and condition for all three participants.

Visual analysis

Similar to the data analysis procedures used by Creel et al. (2006), data was visually analysed according to the guidelines described by Alberto and Troutman (2009) to determine if a functional relationship existed between the independent and dependent variables. Level and trend data was evaluated for this study within conditions (baseline, intervention, maintenance), between conditions (baseline to intervention, intervention to maintenance) and across similar conditions (baseline to maintenance).

Within conditions

Level stability was determined for each participant in baseline before beginning the intervention and then again before ending the intervention condition. The maintenance condition exhibited level stability at 100% for all participants.

Level change was calculated using absolute level change and relative level change methods. Absolute level change within a condition is the difference between the first and last data points of a condition. This provides a gross measure of level change. Relative level change within each condition was calculated by dividing the data path in half to find the difference between the median values. If data patterns were variable, trend direction was estimated within each condition using the split-middle method as referred to in Alberto and Troutman (2009). Level change calculations identified zero-celeration or stability of the data within the baseline condition for Jake. Jonas experienced some variability with his scores as compared to Jake during baseline. Kareem experienced an accelerating trend of data points at the end of his baseline which may have been due to observational learning since he was the last participant to begin intervention. Level change within the intervention condition indicated an accelerating trend for all participants in a therapeutic direction.

Between conditions

The immediacy of the effect from baseline to intervention was important to analysing the functional relationship of the independent variable as it affects the dependent variable. Level change was calculated using absolute and relative level change methods. Absolute level change between conditions was measured as the difference between the last data point of the first condition and the first data point of the adjacent condition. All participants experienced a 50% absolute level change from baseline to intervention. Relative level change was calculated by comparing the median values for the second half of the data in the first condition with the first half of the data in the adjacent condition. An abrupt level change from baseline to intervention is preferable. In this study, we expected we might see a more gradual change as the training period for intervention was short and students may have needed more time to adjust to the procedures. This adjustment period was seen with Jake and Jonas when they began intervention. Jake experienced improvement for three sessions followed by a regression in scores for two sessions and then another improvement before a steady period of success at 100%. Relative level change for Jake was calculated at 58%. Jonas experienced no clear level change for the first two sessions and then a jump to 100% for the remaining sessions. Relative level change for Jonas was calculated at 58%. Kareem experienced a more abrupt and immediate change from baseline to intervention. Relative level change for Kareem was calculated at 75%.

Percentage of overlap was an additional measure to analyse data between conditions to provide a sense of the magnitude of the change. Percentage of overlap was calculated by determining the range of the data in the first condition, counting the number of data points in the adjacent condition that fall within the range of the first condition, and dividing that number by the total number of data points in the adjacent condition and multiplying by 100 (Alberto & Troutman, 2009). The percentage of overlap between conditions for the participants in the current study was low (8% for Jake, 25% for Jonas, 0% for Kareem) which indicates a high magnitude of change.

Across similar conditions

The data in baseline and maintenance conditions were analysed for changes in level and trend. While the conditions were similar (no checklist), the magnitude of the level change was clearly high as the percentage of overlap was 0% for all three participants. All participants improved from 0–50% levels to 100% levels.

Social validity

Table 3 illustrates the scores for each student as well as teacher comments from the pre- and post-intervention survey. The data shows a decrease in scores for all students, which indicates an increase in classroom-preparedness skills. All three students indicated they liked the intervention and that the self-monitoring helped them improve their skills in listening as reported on Table 4.


A total of eight reliability checks out of 17 sessions (47%) were recorded for Jake; seven reliability checks out of 16 sessions (44%) were recorded for Jonas; and seven reliability checks out of 17 sessions (41%) were recorded for Kareem. IOA for all sessions for all participants averaged 98% with only one behaviour disagreement recorded for the seventh session with Jake. Procedural integrity during the baseline, intervention and maintenance phases was very high (100% for all conditions).


This study replicates and extends previous research that demonstrates the positive effects of self-monitoring for various behaviours exhibited by students with mild disabilities. Similar to previous research by Creel et al. (2006), the students showed an increase in their classroom-preparedness skills once the self-monitoring procedure was implemented in the classroom. The visual analysis of the data suggests that the self-monitoring intervention was effective for all three of the students at risk for developmental disabilities and a functional relationship can therefore be hypothesised between the use of self-monitoring and students’ compliance with the classroom-preparedness skills. The data analysis also revealed students were able to generalise the skill to more than one content area and maintain these skills after the self-monitoring intervention was removed.

The present study also bolsters the external validity for the self-monitoring procedure by systematically replicating the Creel et al. (2006) study and by demonstrating that the intervention can be successful for kindergarten students at risk for developmental disabilities. Social validity was confirmed by the teachers and students which added to the social and applied significance of the intervention. Procedural integrity data strengthened the level of experimental control exhibited in this study. Internal validity was further established with high IOA scores. While not a focus of this investigation, the accuracy of student recording could have easily been monitored and recorded, however student productivity was not measured in this study.


A limitation of this study was the lack of generalisation data which leads to some questions for future research. Did response generalisation occur for having materials ready, remaining on task and completing work? Can the skills be generalised to different classroom settings such as art, physical education and music? Can kindergarten students use self-monitoring to help them stay focused during whole group lessons? Can teachers provide, and will students benefit from, checklists with picture prompts to learn the behaviours involved in learning to read? Also, the study did not assess more than two classroom-preparedness behaviours. The two behaviours chosen to monitor were considered the starting point for student preparedness. The researcher felt that due to their young age the students may have been overwhelmed with more than two behaviours to track. Perhaps future research should monitor more behaviours at one time. Another limitation to the generality of this study is that it does not provide data for maintenance over long periods of time. Due to time constraints, maintenance checks were completed once a week for two weeks following intervention.

Future research

Future studies should assess for maintenance by collecting data over a longer period of time. Also, the students’ behaviours may have been reinforced by the teacher assisting them during the evaluation component. While the study does not intend to provide reinforcement for the self-monitoring activity, some students may find the attention from a teacher to be reinforcing when they complete the checklist or fill out the progress monitoring sheet. As a result, future research should explore if students can remember how they did without the progress sheet.

Finally, future research is warranted on the various types of learning styles of these students. It may be that self-monitoring interventions help students who are visual or kinesthetic learners more than students who are auditory learners. Future research should address this issue of learning-style assessments to select students for participation in future studies.


In sum, this study supports that the use of a self-monitoring procedure can be an effective intervention to implement in the classroom with very young students at risk for developmental disabilities that are first learning to regulate their own behaviours. Self-monitoring is an important skill to encourage as it can lead to greater levels of motivation and engagement in student learning. Classroom-preparedness skills such as listening, repeating directions, being organised and staying on task during instruction are necessary to succeed in any academic environment including primary school classrooms. With the use of a self-monitoring procedure, teachers can focus more on the academic lessons without spending as much time prompting students who are unsure of how to follow directions that have been explained but not memorised for completion. Students who use this type of intervention may benefit by experiencing a greater level of independence and pride in their own academic accomplishments.


Agran, M., Sinclair, T., Alper, S., Cavin, M., Wehmeyer, M. & Hughes, C. (2005). Using self-monitoring to increase following direction skills of students with moderate to severe disabilities in general education. Education and Training in Developmental Disabilities, 40, 3–13.

Alberto, P. A., & Troutman, A. C. (2009). Applied behavior analysis for teachers (Eighth edn). Upper Saddle River, NJ: Merrill Prentice Hall.

Amato-Zech, N. A., Hoff, K. E., & Doepke, K. J. (2006). Increasing on-task behavior in the classroom: Extension of self-monitoring strategies. Psychology in the Schools, 43(2), 211–221.

Brooks, A., Todd, A. W., Tofflemoyer, S., & Horner, R. H. (2003). Use of functional assessment and a self-management system to increase academic engagement and work completion. Journal of Positive Behavior Interventions, 5(3), 144–152.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (Second edn). Columbus, OH: Pearson/Merrill/Prentice Hall.

Crawley, S. H., Lynch, P., & Vannest, K. (2006). The use of self-monitoring to reduce off-task behavior and cross-correlation examination of weekends and absences as an antecedent to off-task behavior. Child & Family Behavior Therapy, 28(2), 29–48.

Creel, C., Fore, C., Boon, R., & Bender, W. (2006). Effects of self-monitoring on classroom preparedness skills of middle school students with attention deficit hyperactivity disorder. Learning Disabilities: A Multidisciplinary Journal, 14(2), 105–114.

Ganz, J. (2008). Self-monitoring across age and ability levels: Teaching students to implement their own positive behavioral interventions. Preventing School Failure, 53(1), 39–48.

Ganz, J. B., & Sigafoos, J. (2005). Self-monitoring: Are young adults with MR and autism able to utilize cognitive strategies independently? Education and Training in Developmental Disabilities, 40(1), 24–33.

Georgia Department of Education (2010). Young children with disabilities—significant developmental delay (SDD). Retrieved 10 January 2010, from http://doe.k12.ga.us/ci_exceptional.aspx?PageReq=CIEXCYoung.

Goldman, R., & Fristoe, M. (2000). Goldman–Fristoe test of articulation (Second edn) (GFTA-2). Bloomington, MN: Pearson.

Gulchak, D. (2008). Using a mobile handheld computer to teach a student with an emotional and behavioral disorder to self-monitor attention. Education and Treatment of Children, 31(4), 567–581.

Gureasko-Moore, S., DuPaul, G. J. & White, G. P. (2006). The effects of self-management in general education classrooms on the organizational skills of adolescents with ADHD. Behavior Modification, 30(2), 159–183.

Gureasko-Moore, S., DuPaul, G. J. & White, G. P. (2007). Self-management of classroom preparedness and homework: Effects on school functioning of adolescents with attention deficit hyperactivity disorder. School Psychology Review, 36(4), 647–664.

Harris, K. R., Friedlander, B. D., Saddler, B., Frizzelle, R., & Graham, S. (2005). Self-monitoring of attention versus self-monitoring of academic performance: Effects among students with ADHD in the general education classroom. Journal of Special Education, 39(3), 145–156.

Harris, K. R., Graham, S., Reid, R., McElroy, K., & Hamby, R. S. (1994). Self-monitoring of attention versus self-monitor of performance: Replication and cross-task comparison studies. Learning Disability Quarterly, 17(2), 121–139.

Hodson, B. W. (2004). Hodson assessment of phonological patterns (Third edn) (HAPP-3). Austin, TX: PRO-ED.

Hughes, C. A., & Boyle, J. R. (1991). Effects of self-monitoring for on-task behavior and task productivity on elementary students with moderate mental retardation. Education and Treatment of Children, 14(2), 96–111.

Hughes, C., Copeland, S., Agran, M., Wehmeyer, M., Rodi, M. S., & Presley, J. A. (2002). Using self-monitoring to improve performance in general education high school classes. Education and Training in Mental Retardation and Developmental Disabilities, 37, 262–272.

Levendoski, L. S., & Cartledge, G. (2000). Self-monitoring for elementary school children with serious emotional disturbances: Classroom applications for increased academic responding. Behavioral Disorders, 25(3), 211–224.

Maag, J. W. (2004). Behavior management: From theoretical implications to practical applications (Second edn). Belmont, CA: Wadsworth/Thomson Learning.

Newborg, J. (2004). Battelle developmental inventory (Second edn) (BDI-2). Rolling Meadows, IL: Riverside Publishing.

Reid, R. (1996). Research in self-monitoring with students with learning disabilities: The present, the prospects, the pitfalls. Journal of Learning Disabilities, 29, 317–331.

Reid, R., Trout, A. L., & Schartz, M. (2005). Self-regulation interventions for children with attention deficit/hyperactivity disorder. Exceptional Children, 71(4), 361–377.

Reinecke, D. R., Newman, B., & Meinberg, D. L. (1999). Self-management of sharing in three preschoolers with autism. Education and Training in Mental Retardation and Developmental Disabilities, 34, 312–317.

Rock, M. L. (2005). Use of strategic self-monitoring to enhance academic engagement, productivity, and accuracy of students with and without exceptionalities. Journal of Positive Behavior Interventions, 7(1), 3–17.

Rock, M. L., & Thead, B. K. (2007). The effects of fading a strategic self-monitoring intervention on students’ academic engagement, accuracy and productivity. Journal of Behavioral Education, 16, 389–412.

Snyder, M. C., & Bambara, L. M. (1997). Teaching secondary students with learning disabilities to self-manage classroom survival skills. Journal of Learning Disabilities, 30, 534–543.

Stahr, B., Cushing, D., Lane, K., & Fox, J. (2006). Efficacy of a function-based intervention in decreasing off-task behavior exhibited by a student with ADHD. Journal of Positive Behavior Interventions, 8(4), 201–211.

Strain, P. S., Kohler, F. W., Storey, K., & Danko, C. D. (1994). Teaching preschoolers with autism to self-monitor their social interactions: An analysis of results in home and school settings. Journal of Emotional and Behavioral Disorders, 2(2), 78–88.

Webber, J., Scheuermann, B., McCall, C., & Coleman, M. (1993). Research on self-monitoring as a behavior management technique in special education classrooms: A descriptive review. Remedial and Special Education, 14(2), 38–56.

Australasian Journal of Early Childhood – Volume 35 No 4 December 2010

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Vol. 35 No 4 December 2010
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