694407 BBXXXX10.1177/1074295617694407Beyond BehaviorCook et al. research-article2017 Article Self-Monitoring Interventions for Students With EBD: Applying UDL to a Research-Based Practice Beyond Behavior 2017, Vol. 26(1) 19–27 © Hammill Institute on Disabilities 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav https://doi.org/10.1177/1074295617694407 DOI: 10.1177/1074295617694407 journals.sagepub.com/home/bbx Sara Cothren Cook, PhD1, Kavita Rao, PhD1, and Lauren Collins, PhD1 Abstract Students with emotional and behavioral disorders (EBD) have unique academic and behavioral needs that require the use of evidence-based practices. One way that teachers can support students with EBD is by individualizing interventions, such as self-monitoring, while maintaining a high level of fidelity. In this article, the authors describe how the Universal Design for Learning framework can be used to design individualized self-monitoring interventions for students with EBD while still maintaining core components of the intervention. Keywords self-monitoring, Universal Design for Learning, UDL, emotional and behavioral disorders Students with emotional and behavioral disorders (EBD) experience low academic achievement, display maladaptive behavior, and have difficulty building and maintaining interpersonal relationships (Kauffman & Landrum, 2013). They fail more classes, experience more disciplinary action (e.g., suspension, expulsion), and drop out of school more than any other category of students with disabilities (Landrum, Tankersley, & Kauffman, 2003; Wagner, Kutash, Duchnowski, Epstein, & Sumi, 2005). Historically, students with EBD have been placed in more restrictive special education settings (Trout, Nordness, Pierce, & Epstein, 2003). However, that trend is changing. National statistics reveal 43% of students with EBD are educated in the general education classroom for 80% or more of their school day (U.S. Department of Education, National Center for Education Statistics, 2015). This is a clear indication of the significant need for general education teachers to be prepared to support both the academic and behavioral needs of this population. Despite the increase in general education placement for students with EBD, teachers often lack the necessary preparation for supporting students with diverse academic and behavioral needs. In fact, teachers often perceive that students with EBD are the most challenging to teach (Kauffman & Landrum, 2013) and often feel unprepared to work with this population of learners (Cook, 2002). Furthermore, teacher survey data have indicated that teachers are not often implementing instructional strategies that research supports as effective for students with EBD (Gable, Tonelson, Sheth, Wilson, & Park, 2012), which likely contributes to the unsatisfactory academic and social outcomes for this population of students. Although special education teachers are often responsible for designing and implementing interventions to support students with EBD, general education teachers can use these practices as well. Cook, Landrum, Tankersley, and Kauffman (2003) suggested that, with training, all teachers can implement effective practices for this group of students. However, to implement effective strategies for students with EBD in general education settings, teachers must (a) identify appropriate strategies that research supports as effective and (b) implement the strategy as it was designed (i.e., with fidelity; Cook et al., 2003) while also considering ways to individualize instruction to meet the needs of the student(s) with EBD. One of the hallmarks of special education for all students with disabilities, including students with EBD, is that instruction and interventions should be individualized (Landrum et al., 2003). Practices that allow some flexibility in implementation may allow teachers to “sustain the implementation of effective practices and positively impact student outcomes” (Harn, Parisi, & Stoolmiller, 2013, p. 190). The purpose of this article is to demonstrate how a general education teacher can effectively plan and implement self-monitoring, a strategy supported as effective by more than two decades of peer reviewed research studies, to improve both academic and behavioral outcomes for two students with EBD in the general education setting. Furthermore, we describe how the teacher can rely on the Universal Design for Learning (UDL; 1 University of Hawaii, Honolulu, USA Corresponding Author: Sara Cothren Cook, Department of Special Education, University of Hawaii, 1776 University Avenue, Wist 120, Honolulu, HI 96822, USA. Email: cothren@hawaii.edu 20 Beyond Behavior 26(1) Figure 1. Universal Design for Learning Guidelines v2.0. Source. Center for Applied Special Technology (CAST; 2011). Meyer, Rose, & Gordon, 2014) framework to make decisions about how to individualize the intervention while still maintaining the fidelity of critical components. Integration of Implementation Fidelity and Adaptations for Research-Based Practices Durlak and DuPre (2008) noted that instructional strategies implemented with high fidelity increase the likelihood of positive outcomes. Therefore, it is essential that practitioners identify the critical components of the practice and understand how to implement them as intended, or with fidelity (Torres, Farley, & Cook, 2014). However, as Cook, Tankersley, and Harjusola-Webb (2008) stated, implementing effective practices requires a combination of both implementation fidelity and use of professional wisdom and judgment in selecting and adapting a strategy to meet a student’s individual needs. Harn and colleagues (2013) suggested that teachers prioritize the use of practices that have clearly identified components but are designed in a way that can meet the various needs of students with disabilities. Teachers can determine goals for the student, consider the critical components of the practice that must be retained, and reflect on ways various resources and engaging methods can be used when adapting the practice. Using Universal Design for Learning Guidelines The UDL framework, developed by the Center for Applied Special Technology, is organized around providing multiple means of engagement, representation, and action and expression. Each principle has three guidelines that define ways to build in flexibility, choice, and options to engage students (see Figure 1). The guidelines associated with Representation address the provision of information in varied modalities and an attention to the background information and prerequisite knowledge and skills that students need for comprehension. The guidelines associated with Action and Expression address the use of tools, media, and technologies for expression and demonstration of knowledge and highlight the importance of supporting executive function such as goal setting. The guidelines associated with Engagement focus on ways to increase relevance, foster persistence, and build selfregulation skills. The UDL guidelines focus on increasing access to curricula and instruction by providing these options within instructional environments and processes. The 31 UDL checkpoints were derived from an extensive review of the research base on the most effective educational practices to address each guideline (see http://www.udlcenter.org/ research/researchevidence). Researchers have examined how UDL can be applied to various instructional settings 21 Cook et al. (e.g., general education, special education, inclusive), grade levels (preK–12), and academic outcomes across content areas (e.g., literacy, math, science; Crevecoeur, Sorenson, Mayorga, & Gonzalez, 2014; Rao, Ok, & Bryant, 2014). Universal Design for Learning is based on the premise that we can identify “barriers” in the learning environment and design curricula and instruction in ways that reduce those barriers. The UDL guidelines offer a useful structure to consider when designing instruction and adapting interventions. The guidelines can be used to adapt instructional practices in inclusive settings as well as in more intensive intervention settings, providing a framework that both general education and special education teachers can use. The UDL guidelines delineate a variety of ways to ensure that instructional practices are accessible and engaging for students. Teachers can refer to the UDL guidelines and checkpoints as they design instructional goals, assessments, materials, and methods. The guidelines provide teachers with a menu of options for scaffolds and supports that can be used to develop flexible and engaging instructional plans. Teachers can start by considering the goal of a particular intervention or activity and then build in options within assessments, methods, and/or materials used. By considering UDL guidelines at the outset, teachers can proactively make determinations about options and scaffolds that address academic and behavioral objectives. The UDL guidelines can be applied to whole-class instruction to ensure that flexible options and means for engagement are provided for all students. In addition to addressing the variability of learners at a whole-class level, UDL guidelines can be used to design interventions that address academic and behavioral outcomes for students with EBD. Given the challenging nature of teaching students with EBD, UDL may be particularly useful for teachers who are unsure of how to support the unique needs of this population. Using UDL when designing instruction, teachers can integrate options for engagement and selfregulation that can target some of the challenges of students with EBD. For example, UDL guidelines provide teachers options for addressing engagement by increasing relevance, providing mastery-oriented feedback, fostering collaboration, and facilitating personal coping skills (see Figure 1). These guidelines directly address common EBD characteristics (e.g., lack of engagement, poor interpersonal relationships, self-regulation of overt behavior) that lead to low classroom engagement, off-task behaviors, and poor academic outcomes. Teachers wanting to address these “barriers” to learning can consider the UDL guidelines when designing instruction to increase both “buy in” and engagement. For example, teachers can consider how to make the lesson relevant by considering the UDL checkpoints that relate to recruiting interest (UDL Guideline 7). By designing instruction in alignment with these checkpoints, teachers can increase engagement for students with EBD. A teacher can support students with EBD to persist and sustain effort (UDL Guideline 8) by providing varied and appropriate levels of challenge and specific, mastery-oriented academic and behavioral feedback. Teachers can also address the self-regulation for students with EBD by taking UDL Guideline 9 (provide options for self-regulation) into consideration. More specifically, teachers can address important self-management skills, such as self-assessment (UDL Checkpoint 9.3). When using a specific academic or behavioral intervention, such as self-monitoring, a teacher can consider how these UDL guidelines may be applied to components of the strategy to promote engagement and provide specific support for students to increase academic and/or behavioral outcomes. Self-Monitoring Self-monitoring teaches students to “be cognizant of their own behavior” (Bruhn & Watt, 2015, p. 5) and requires both self-assessment and self-recording (Harris, Friedlander, Saddler, Frizzelle, & Graham, 2005) with the intent of improving a student’s ability to regulate a particular behavior. Two types of self-monitoring interventions are most prevalent in special education research: self-monitoring for attention and self-monitoring for academic performance. The primary difference between these two types of self-monitoring interventions is what is being self-assessed (Harris et al., 2005). Self-monitoring for attention is used to increase awareness of a student’s attention to a specific behavior or task and typically involves cueing a student to whether he or she is engaged in the target behavior (Reid, Trout, & Schartz, 2005). When cued, students self-assess the occurrence of the target behavior and record whether it was present. This approach to self-monitoring, which seems to be the most frequently used approach for students with EBD (see Bruhn et al., 2015), assumes that by targeting and improving student behavior, engagement will improve and, consequently, result in improved academic performance (Harris et al., 2005). For a student who is capable of completing an academic task but who demonstrates behavior that impedes work completion, self-monitoring for attention is intended to improve behavior so that the student is able to appropriately attend to the academic task. Research on self-monitoring for attention indicates its effectiveness in improving outcomes related to both discrete behaviors (e.g., sitting in seat, interactions with others, verbal outbursts) and broadly defined behaviors (e.g., on-task, academic engagement; see Bruhn et al., 2015). Bruhn and colleagues (2015) found that all 41 studies included in their comprehensive review of self-monitoring reported positive effects for students with behavior problems when self-monitoring interventions were implemented. Self-monitoring for academic performance requires students to perform an academic task and monitor “the amount of completion or accuracy of their work either during or following 22 the task” (Reid et al., 2005, p. 362). This approach is based on the assumption that self-monitoring of academic responses will improve not only academic performance but also behavioral outcomes (Harris et al., 2005). When a student is cued to monitor academic performance, the student’s behavior improves by focusing on task completion and consequently decreasing offtask behavior. Although self-monitoring for academic performance has not been as frequently researched for students with EBD as self-monitoring for attention, individual research studies suggest this type of self-monitoring can also improve both academic and behavioral outcomes (e.g., Carr & Punzo, 1993; Rafferty & Raimondi, 2009). Whereas both self-monitoring for academic performance and self-monitoring for attention are intended to improve academic and behavioral outcomes, teachers may wonder whether one type of self-monitoring is more effective than the other. Several researchers have examined the differential effects of the two types of self-monitoring. Lloyd, Bateman, Landrum, and Hallahan (1989) investigated the effects of both self-monitoring for academic performance and selfmonitoring for attention with five students diagnosed with EBD, learning disabilities, or both EBD and learning disabilities. Results indicated that neither treatment was superior to the other. Harris and colleagues (2005) found that self-monitoring for attention produced higher gains across spelling behaviors for students with attention deficit hyperactivity disorder. However, Harris (1986) found that selfmonitoring of academic performance was more effective in improving behavior for students with learning disabilities. For students with EBD, Rafferty and Raimondi (2009) found self-monitoring for academic performance was superior in improving both mathematical performance and social behaviors compared with self-monitoring of attention. Because research generally supports both self-monitoring for attention and self-monitoring for academic performance, we suggest teachers select the type of self-monitoring most appropriate for the individual student. A teacher may use self-monitoring for attention to replace a student’s display of undesirable behaviors (e.g., verbal outbursts, roaming around classroom) with desirable behaviors (e.g., appropriate expression of frustration, staying in seat), which can result in a student being more prepared to engage in academic tasks. Alternatively, a teacher may implement self-monitoring for academic performance after observing a student having difficulty following steps to complete an academic task (e.g., mathematical word problems, writing assignments), which appears to be related to his or her off-task and disruptive behavior. After determining that the student has the prerequisite skills for completing the academic task, the teacher may provide additional instruction, teach the student to use selfmonitoring for academic performance, and require the student to monitor completion of each step of the task. The use of self-monitoring for academic performance increases the likelihood that the student will more accurately complete the academic task, thus improving behavior. Beyond Behavior 26(1) Regardless of the type of self-monitoring intervention, it is important to consider that the implementation of self-monitoring alone may not be sufficient for increasing academic gains for students with EBD. As Graham-Day, Gardner, and Hsin (2010) suggested, teachers need to be cognizant of whether students have the necessary skills to complete the academic task. Teachers may need to consider the use of additional instructional strategies to further support student engagement and utilization of the selected strategy. Finally, students will require direct instruction in the self-monitoring intervention of choice and will likely benefit from the use of reinforcement strategies. By definition, a self-monitoring intervention requires students to monitor and record their own behavior; monitoring and recording are two critical components to the intervention for which implementation fidelity should be maintained (Torres et al., 2014). Furthermore, for students to independently self-monitor, a teacher must do the following: 1. 2. 3. 4. 5. target and operationally define the behavior, determine how to cue the student to self-monitor, determine the medium for recording the behavior, teach the student how to self-monitor, and implement the intervention. These five components of self-monitoring are flexible; teachers can adapt components by considering student’s preferences and needs, including integrating technology, when appropriate, for the individual student. In addition to the five components, function-based assessments (FBAs), reinforcement, feedback, and technology may be integrated into self-monitoring. In Bruhn and colleagues’ (2015) review, they reported that the majority of studies used reinforcement and feedback within self-monitoring interventions, about half integrated technology, and a minority used function-based assessments to design the intervention. However, because only a small number of studies directly examined the direct impact of reinforcement, feedback, and function-based support, Bruhn and colleagues (2015) suggested that further investigation is needed to determine whether these additional components are actually critical to the intervention’s effectiveness, particularly because self-monitoring studies that did not include additional components still yielded positive effects. We suggest that teachers consider these additional components (e.g., FBA, reinforcement, feedback, technology) when planning the self-monitoring intervention as a way to potentially maximize the intervention’s effectiveness for individual students as some evidence suggests that self-monitoring in combination with additional components (i.e., FBA, reinforcement, feedback) is more effective than self-monitoring alone for students with EBD (see Bruhn et al., 2015; Hansen, Wills, Kamps, & Greenwood, 2014). In the following vignette, we describe how a teacher uses (a) critical components of self-monitoring to implement the practice in her classroom and (b) UDL guidelines as a tool to adapt the 23 Cook et al. intervention to meet the individual needs and interests of two students with EBD. The UDL framework is most often used to design flexible and engaging lessons for all students rather than individual students. In our example, however, we illustrate how a teacher can apply UDL guidelines to a researchbased strategy to ensure that the practice integrates flexible and engaging options for the students with whom it is used. A Classroom Scenario: Adapting and Implementing Self-Monitoring Ms. Taylor’s general education language arts class is composed of 28 students, two of whom have been identified with EBD and are receiving special education services. Ms. Taylor is concerned about how the two students with EBD—Chad and Alex—are currently performing in the area of writing. Although both students display a variety of behaviors during the language arts writing block, they are not completing assigned tasks and are currently at risk of failing language arts. Chad displays verbal outbursts after instruction and refuses to begin assignments; Alex often has his head down on his desk when other students are completing the class assignment. Ms. Taylor reviews FBA data for Chad and Alex; the results indicate that behaviors displayed by Chad are reinforced by teacher attention. Alex, however, is reinforced by escaping task demands. In addition, Individualized Education Program (IEP) data indicate that Chad is academically capable of completing writing tasks; in fact, student work samples indicate that when he is on task, he completes writing assignments that meet grade-level expectations. Alex, however, struggles in writing and has several IEP objectives related to increasing both the quantity and quality of his writing. Ms. Taylor is seeking an intervention that can increase on-task behavior for Chad and support Alex in improving writing performance. After consulting with the special education teacher about possible interventions, Ms. Taylor determines that self-monitoring may be an effective intervention for improving both Chad and Alex’s behavior and academic performance. Using the results of the FBA, she determines that self-monitoring for attention is an appropriate intervention to increase ontask behavior for Chad. Alternatively, the data described in Alex’s FBA indicate that he needs an intervention that addresses his academic needs. Because self-monitoring of academic performance is designed to increase student’s completion of academic work, Ms. Taylor hopes that this self-monitoring intervention will support Alex’s writing performance and, in turn, improve his on-task behavior. Component 1: Identify Target Behavior The first step of planning to implement a self-monitoring intervention is to operationally define the target behavior. In addition, the teacher may collect baseline data to determine the student’s current performance. When defining the target behavior, a teacher should consider the following: 1. 2. Does the target behavior relate to attention or academic performance? What is the operational definition of the behavior? a. For attention: What does the target behavior “look like” (e.g., on-task behavior may be defined as in assigned seat, looking at teacher, completing assigned task)? b. For academic performance: What academic task will be used with self-monitoring (e.g., number of words spelled correctly, number of math problems completed, completions of necessary steps in word problem solving)? c. Does the student have the academic skills necessary to perform this task? d. What kind of supplemental instruction may the student need to perform this academic task? Because Ms. Taylor decides to implement self-monitoring for attention for Chad, she needs to define what ontask behavior looks like during the language arts writing block and collect baseline data for Chad’s attention based on this definition. On-task behavior occurs when Chad (a) begins the writing task once directions are given without verbal outburst, (b) focuses on the writing task during the assigned time (e.g., writing a draft, re-reading his work), (c) raises a hand before asking a question, and (d) sits in his assigned seat. Ms. Taylor hopes that by teaching Chad to monitor his behavior, on-task behavior will increase, allowing him more time to complete written assignments. Using FBA data, Ms. Taylor realizes that she will need to only reinforce Chad when displays target behaviors (i.e., on-task). Because Ms. Taylor decided to implement self-monitoring for academic performance for Alex, she needs to define what academic performance “looks like” during the writing block and collect baseline data for Alex’s performance according to this definition. Based on the definition used by Harris, Graham, Reid, McElroy, and Hamby (1994), Ms. Taylor describes academic performance as the number of written words in a given assignment, regardless of spelling errors. By giving Alex the opportunity to count and graph the words he generates, Ms. Taylor’s goal is for him to increase the number of words he generates and to turn in assignments that are complete. When using a UDL approach to designing instruction, identifying clear goals is the first step (see Figure 2). Ms. Taylor starts by reviewing FBA data, collecting baseline data, and identifying clear goals for each student based on the outcomes. In the next few steps, she will adapt the self-monitoring intervention based on the target behaviors and goals she has identified for Chad and Alex. Component 2: Determine Cueing Procedures The next critical component in self-monitoring is for the teacher to determine how each student will be “cued” to 24 Beyond Behavior 26(1) Ms. Taylor’s Decisions •• Uses FBA data to determine appropriate self-monitoring intervention •• Collects baseline data to determine student’s current performance prior to intervention •• Sets goals for on-task behavior and academic performance UDL Connections A key component of UDL-based design is to identify clear goals. The FBA and baseline data let the teacher decide on a clear goal for each student. Ms. Taylor’s Decisions •• Chooses an app on a mobile device for one student who benefits from discrete cues; for the other student, chooses to use teacher cue UDL Connections UDL Guideline 4: Options for physical action •• Vary the methods for response and navigation •• Optimize access to tools and assistive technologies Figure 3. Ms. Taylor’s decisions and related UDL connections for Component 2: Cueing procedures. Note. UDL = Universal Design for Learning. Figure 2. Ms. Taylor’s decisions and related UDL connections for Component 1: Identifying and targeting the intervention. Note. UDL = Universal Design for Learning; FBA = function-based assessment. self-record. A teacher may consider the following questions when determining cueing procedures for self-monitoring: 1. 2. For attention: What external cue will be used and how often cue will be presented (e.g., intermittent intervals, regular)? For academic performance: When and how will the student be reminded to monitor his or her academic progress (e.g., during assignment, after completion)? For Chad, Ms. Taylor needs to determine the external cuing prompt (e.g., auditory or visual) to use when signaling the student to the assessment and recording of the target outcome (Briesch & Chafouleas, 2009). Ms. Taylor reads about several methods for cueing students. Technology such as tape-recorded tones, a timer on an iPad, a Motivator©, and cell phones have all been used to prompt a student to monitor his or her attention to the target behavior (Bruhn et al., 2015). Researchers have also used other strategies such as directing students to the classroom clock on fixed intervals (e.g., every 15 min) or having students record their behavior at the end of a class period (see Sheffield & Waller, 2010). Ms. Taylor is concerned that Chad may refuse to participate in self-monitoring because he does not like to be singled out from his peers. To gain “buy in” from Chad, Ms. Taylor decides to download a free app on the student’s cell phone that will allow him to keep the phone in his pocket. The app is designed to have the phone vibrate on a random schedule to cue the student in a more inconspicuous way. For Alex, who will be using self-monitoring for academic performance, Ms. Taylor and her educational assistant decide that they will walk around to provide assistance to all students during the writing block and discretely encourage Alex to generate more words when he has his head down. At the end of the writing block, either Ms. Taylor or the educational assistant will ask Alex to count and self-graph how many words he has written and then discuss his progress, providing praise for increased writing or encouragement if Alex has struggled. Figure 3 shows how Ms. Taylor’s decisions regarding cueing for self-monitoring align with UDL Guideline 4, which focuses on the use of appropriate tools and technologies based on each student’s preferences. Component 3: Determine Medium for Recording Behavior The third component of self-monitoring is to determine the medium students will use to record behavior. A teacher can consider whether students will use traditional paper/pencil to record or integrate technology. Ms. Taylor understands that in the majority of published self-monitoring research studies, students use pencil and paper to self-monitor their behavior (Bruhn et al., 2015), but she wants to adapt the practice and make use of technology to engage the students. Ms. Taylor has even read about a self-monitoring study in which students, in an inclusive language arts classroom, received texts messages asking them to respond whether they were on task (Bedesem, 2012) and she likes the idea of integrating technology that students use daily. Ms. Taylor meets with Chad and Alex individually and briefly reviews the elements of the intervention. Ms. Taylor encourages each student to make a choice of how he would like to record his self-monitoring. Chad, who prefers not to have the intervention noticed by his peers, decides to use a pencil and paper to record his progress. Ms. Taylor suggests he can secure the recording sheet in his language arts notebook so that it is not obvious to other students. Alex prefers to use a classroom iPad to graph the number of words he generates. Ms. Taylor realizes that this will allow Alex to create charts of his progress, which she hopes may further motivate him to write. Figure 4 shows how Ms. Taylor’s decisions for recording behavior align with UDL Guideline 5, which addresses the provision of varied formats for expressing and recording information and UDL Guideline 7, which focuses on taking students’ interests into consideration. Component 4: Teach Student(s) to Self-Monitor For students to know how to self-monitor their attention and/ or academic performance, the teacher needs to consider how he or she will ensure that the student(s) know how to selfmonitor and record appropriately. 25 Cook et al. Ms. Taylor’s Decisions •• Students choose how they will record their selfmonitoring •• Varied the ways to represent data collected (paper, technology) UDL Connections UDL Guideline 7: Options for recruiting interest •• Optimize individual choice and autonomy •• Minimize threats and distractions Ms. Taylor’s Decisions •• Discusses goals and procedures with each student •• Giving students incentives that they like (i.e., earning free time, Taylor cash) UDL Guideline 5: Provide options for expression and communication •• Use multiple media for communication UDL Guideline 9: Options for self-regulation •• Develop self-assessment and reflection Figure 4. Ms. Taylor’s decisions and related UDL guidelines for Component 3: Determining a medium for recording behavior. UDL Guideline 7: Options for recruiting interest •• Optimize relevance, value, and authenticity Note. UDL = Universal Design for Learning. Ms. Taylor meets with each individual student to (a) review the purpose of the intervention, (b) review baseline data, (c) and describe the specific steps for each individual student. Using direct instruction, which includes modeling, guided practice, and independent practice, Ms. Taylor is confident that the procedures of the self-monitoring intervention are clear to each student. During the meeting, Ms. Taylor addresses the students’ interest by discussing how they will undertake the self-monitoring, letting them make choices, and allowing them to pick incentives that motivate them. While meeting with Chad, Ms. Taylor first operationally defines what on-task behavior looks like during the language arts writing block and involves Chad with providing examples and non-examples of on-task behavior. Ms. Taylor shows Chad the app she downloaded on his cell phone and explains that each time he feels the cue (he will keep the cell phone in his pocket on silent vibrate mode), he will mark on his paper chart whether or not he was on task (i.e., focusing on the writing task, remaining quiet and not speaking/shouting at peers or teacher, raising hands before asking a question, in his assigned seat). Knowing that listening to a song on the classroom iPad is a powerful incentive for Chad, Ms. Taylor tells him that if he can accurately record his on-task behavior, he will receive 5 min of “free time” on the classroom iPad each day. Ms. Taylor plans to have the educational assistant observe and monitor Chad’s on-task behavior. At the end of each writing session, the educational assistant will compare her observational data with Chad’s self-monitoring data. The educational assistant will occasionally observe Chad’s self-monitoring to verify he is accurately responding to the cues for at least 80% of the intervals. In addition, if Chad completes the daily writing assignments, he can earn an additional 5 min of his preferred activity. Ms. Taylor meets with Alex during the writing period. Ms. Taylor explains to Alex that she has seen him independently complete a graphic organizer and now wants him to focus on transferring the information to an actual written assignment. Ms. Taylor explains that Alex will count the number of words he completes each day in the writing block UDL Connections UDL Guideline 6: Options for executive functions •• Guide appropriate goal-setting •• Support planning and strategy development •• Enhance capacity for monitoring progress Figure 5. Ms. Taylor’s decisions and related UDL guidelines for Component 4: Teach students to self-monitor. Note. UDL = Universal Design for Learning. and enter the data in a graphing app on the iPad. She knows that there is a very likely chance that Alex will be motivated by seeing this visual depiction of progress on a technology device. Ms. Taylor uses a token economy system in her class and students enjoy earning “Taylor Cash.” Ms. Taylor has noticed that Alex is highly motivated by the “Taylor Cash” and carefully saves this money to buy items in the classroom store. During their meeting, Ms. Taylor explains that she will help him set weekly goals for progress on his self-monitoring graph. If he meets his weekly goal, he will earn an additional $10 in “Taylor Cash” every Friday. The educational assistant in the classroom will check Alex’s writing assignments and graph to ensure Alex has monitored his academic performance accurately. Ms. Taylor models how to record and graph data on the classroom iPad and provides Alex with an opportunity to practice. Figure 5 shows how Ms. Taylor’s instructional decisions for teaching self-monitoring align with three UDL guidelines. Ms. Taylor addresses UDL Guidelines 6 and 9 by providing means for students to develop and monitor goals and self-assess. She also includes elements that are of value and relevance to the students, addressing UDL Guideline 7. Component 5: Implement Self-Monitoring Before implementing the self-monitoring intervention for either attention or academic performance, a teacher may consider the following: 1. 2. How will you monitor student’s ability to follow selfmonitoring protocol? What types of progress monitoring could you include to determine the effectiveness of the intervention? 26 Ms. Taylor’s Decisions •• Offers immediate feedback •• Reviews data with student Beyond Behavior 26(1) UDL Connections UDL Guideline 8: Options for sustaining effort and persistence •• Heighten salience of goals and objectives •• Increase mastery-oriented feedback UDL Guideline 9: Options for selfregulation •• Promote expectations and beliefs that optimize motivation •• Facilitate personal coping skills and strategies •• Develop self-assessment and reflection Figure 6. Ms. Taylor’s decisions and related UDL guidelines for Component 5: Implement self-monitoring. Note. UDL = Universal Design for Learning. Ms. Taylor begins the self-monitoring intervention for Chad and Alex. During the writing block, Ms. Taylor talks to Alex and provides him a prompt and reminder to begin his assignment as soon as directions are given. Ms. Taylor observes Chad to make sure he is monitoring and recording his behavior. She offers him teacher attention in the form of specific praise, or immediate and corrective feedback if necessary, directly related to his use of the self-monitoring intervention. Ms. Taylor observes both students successfully engaging in the self-monitoring intervention. To determine whether the intervention is effective for Chad and Alex, Ms. Taylor monitors each student’s progress and meets with each student on a weekly basis to (a) review the accuracy of the student’s data collection, (b) compare changes in behavior and/or work completion to baseline data, and (c) discuss progress toward goals. For Chad, Ms. Taylor uses the data collected from the educational assistant to monitor whether the self-monitoring intervention is effective for increasing Chad’s on-task behavior. For both Chad and Alex, Ms. Taylor examines their writing assignments and determines whether self-monitoring increases the amount of work that they complete successfully each class period. Ms. Taylor plans to review these data weekly. If the intervention does not show positive gains for either student, she plans to revisit the procedures and make adaptations as needed. Figure 6 shows how Ms. Taylor’s adaptations when implementing self-monitoring address UDL Guidelines 8 and 9. By providing immediate feedback and reviewing outcomes with students, she addresses UDL Guideline 8 by providing options to sustain effort and persistence during self-monitoring. Students get the opportunity to make decisions about how they can succeed with their objectives, which aligns with UDL Guideline 9, which focuses on options for self-regulation. Conclusion To implement self-monitoring for students with EBD, teachers need to (a) identify operationally define the target behavior, (b) determine how to cue students to self-monitor, (c) determine the medium for recording the behavior, (d) teach students how to self-monitor, and (e) implement the self-monitoring intervention. In the vignette described, Ms. Taylor integrated these essential components of selfmonitoring while also using UDL guidelines to adapt the intervention to meet the individual needs of two students with EBD. Adaptations included not only elements such as FBAs and reinforcement that are integrated into many selfmonitoring interventions (see Bruhn et al., 2015) but also adaptations that Ms. Taylor made to the critical components (e.g., varying the types of cues and recording methods). Whereas previous research indicates that self-monitoring is effective (Bruhn et al., 2015) and easy for teachers to implement (Boswell, Knight, & Spriggs, 2013; Shimabukuro, Prater, Jenkins, & Edelen-Smith, 1999), this vignette demonstrates how the UDL guidelines can be used to help support the professional decisions teachers must make to adapt any research-based practice for students with EBD. The UDL guidelines for engagement (Guidelines 7, 8, and 9), in particular, are highly relevant to behavioral interventions. The engagement guidelines emphasize the provision of choices and options that can make learning more relevant, give students opportunities to persist and reach mastery of their goals, and teach students how to be self-reflective to improve both behavior and academic performance. Given that an increase in engagement is critical for improving the academic outcomes of students with EBD, we suggest that teachers pay special attention to these guidelines. Self-monitoring for attention and self-monitoring for academic performance are appropriate interventions for students with EBD in the general education classroom. When choosing which self-monitoring intervention to use, it is important to consider the needs and interests of individual students, including whether the student has the necessary skills to engage in and/or complete the academic task. By following the UDL guidelines, teachers are able to include flexible and engaging options that support the implementation of selfmonitoring strategies for students with EBD. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. References Bedesem, P. L. (2012). Using cell phone technology for self-monitoring procedures in inclusive settings. Journal of Special Education Technology, 27, 33–46. Boswell, M. A., Knight, V., & Spriggs, A. D. (2013). Selfmonitoring of on-task behaviors using the MotivAider® by a Cook et al. middle school student with a moderate intellectual disability. Rural Special Education Quarterly, 32, 23–30. Briesch, A. M., & Chafouleas, S. M. (2009). Review and analysis of literature on self-management interventions to promote appropriate classroom behaviors (1988-2008). School Psychology Quarterly, 24, 106–118. Bruhn, A., McDaniel, S., & Kreigh, C. (2015). Self-Monitoring interventions for students with behavior problems: A systematic review of current research. Behavioral Disorders, 40, 102–121. Bruhn, A., & Watt, S. (2015). Improving behavior by using multicomponent self-monitoring within a targeted reading intervention. Behavioral Disorders, 38, 3–17. Carr, S. C., & Punzo, R. P. (1993). The effects of self-monitoring of academic accuracy and productivity on the performance of students with behavioral disorders. Behavioral Disorders, 18, 241–250. Center for Applied Special Technology. (2011). Universal Design for Learning Guidelines version 2.0. Wakefield, MA: Author. Retrieved from http://www.udlcenter.org/aboutudl/udlguidelines/downloads Cook, B. G. (2002). Inclusive attitudes, strengths, and weaknesses of pre-service general educators enrolled in a curriculum infusion teacher preparation program. Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children, 25, 262–277. Cook, B. G., Landrum, T. J., Tankersley, M., & Kauffman, J. M. (2003). Bringing research to bear on practice: Effecting evidence-based instruction for students with emotional or behavioral disorders. Education and Treatment of Children, 26, 345–361. Cook, B. G., Tankersley, M., & Harjusola-Webb, S. (2008). Evidence-based special education and professional wisdom: Putting it all together. Intervention in School and Clinic, 44, 105–111. Crevecoeur, Y. C., Sorenson, S. E., Mayorga, V., & Gonzalez, A. P. (2014). Universal Design for Learning in k-12 educational settings: A review of group comparison and singlesubject intervention studies. The Journal of Special Education Apprenticeship, 3, 1–23. Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology, 41, 327–350. Gable, R. A., Tonelson, S. W., Sheth, M., Wilson, C., & Park, K. L. (2012). Importance, usage, and preparedness to implement evidence-based practices for students with emotional disabilities: A comparison of knowledge and skills of special education and general education teachers. Education and Treatment of Children, 35, 499–520. Graham-Day, K. J., Gardner, R., III, & Hsin, Y. W. (2010). Increasing on-task behaviors of high school students with attention deficit hyperactivity disorder: Is it enough? Education and Treatment of Children, 33, 205–221. Hansen, B. D., Wills, H. P., Kamps, D. M., & Greenwood, C. R. (2014). The effects of function-based self-management interventions on student behavior. Journal of Emotional and Behavioral Disorders, 22, 149–159. Harn, B., Parisi, D., & Stoolmiller, M. (2013). Balancing fidelity with flexibility and fit: What do we really know about fidelity of implementation in schools? Exceptional Children, 79, 181–193. 27 Harris, K. R. (1986). Self-monitoring of attentional behavior versus self-monitoring of productivity: Effects on on-task behavior and academic response rate among learning disabled children. Journal of Applied Behavior Analysis, 19, 417–423. Harris, K. R., Friedlander, B. D., Saddler, B., Frizzelle, R., & Graham, S. (2005). Self-monitoring of attention versus selfmonitoring of academic performance: Effects among students with ADHD in the general education classroom. The Journal of Special Education, 39, 145–157. Harris, K. R., Graham, S., Reid, R., McElroy, K., & Hamby, R. S. (1994). Self-monitoring of attention versus self-monitoring of performance: Replication and cross-task comparison studies. Learning Disability Quarterly, 17, 121–139. Kauffman, J. M., & Landrum, T. J. (2013). Characteristics of emotional and behavioral disorders of children and youth (10th ed.). Upper Saddle River, NJ: Pearson Education. Landrum, T. J., Tankersley, M., & Kauffman, J. M. (2003). What is special about special education for students with emotional or behavioral disorders? The Journal of Special Education, 37, 148–156. Lloyd, J. W., Bateman, D. F., Landrum, T. J., & Hallahan, D. P. (1989). Self-recording of attention versus productivity. Journal of Applied Behavior Analysis, 22, 315–323. Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal Design for Learning: Theory and practice. Wakefield, MA: Center for Applied Special Technology. Retrieved from http:// udltheorypractice.cast.org Rafferty, L. A., & Raimondi, S. L. (2009). Self-monitoring of attention versus self-monitoring of performance: Examining the differential effects among students with emotional disturbance engaged in independent math practice. Journal of Behavioral Education, 18, 279–299. Rao, K., Ok, M. W., & Bryant, B. R. (2014). A review of research on universal design educational models. Remedial and Special Education, 35, 153–166. doi:10.1177/0741932513518980 Reid, R., Trout, A. L., & Schartz, M. (2005). Self-regulation interventions for children with attention deficit/hyperactivity disorder. Exceptional Children, 71, 361–377. Sheffield, K., & Waller, R. J. (2010). A review of single-case studies utilizing self-monitoring interventions to reduce problem classroom behaviors. Beyond Behavior, 19, 7–13. Shimabukuro, S. M., Prater, M. A., Jenkins, A., & Edelen-Smith, P. (1999). The effects of self-monitoring of academic performance on students with learning disabilities and ADD/ADHD. Education and Treatment of Children, 22, 397–414. Torres, C., Farley, C. A., & Cook, B. G. (2014). A special educator’s guide to successfully implementing evidence-based practices. Teaching Exceptional Children, 45, 64–73. Trout, A. L., Nordness, P. D., Pierce, C. D., & Epstein, M. H. (2003). Research on the academic status of children with emotional and behavioral disorders: A review of the literature from 1961 to 2000. Journal of Emotional and Behavioral Disorders, 11, 198–210. U.S. Department of Education, National Center for Education Statistics. (2015). Digest of education statistics, 2013 (NCES 2015-011, Table 204.60). Retrieved from https://nces.ed.gov/ programs/digest/d15/tables/dt15_204.60.asp Wagner, M., Kutash, K., Duchnowski, A. J., Epstein, M. H., & Sumi, W. C. (2005). The children and youth we serve: A national picture of the characteristics of students with emotional disturbances receiving special education. Journal of Emotional and Behavioral Disorders, 13, 79–96.