Content area
When an instructional strategy lacks evidence of its effectiveness with certain learner groups, educators can conduct action research in their classroom to determine strategy effectiveness. In the present study, seven preservice teachers of the deaf (TODs) implemented self-graphing interventions with deaf/hard of hearing (DHH) learners to change target on- and off-task behaviors. Preservice teachers were master 's-level candidates in a university teacher preparation program. DHH learners were 5-16 years old, attended general education classrooms at their local schools or schools for the deaf, and used multiple forms of communication/language. Candidates successfully completed ABAB design studies, and three candidates collected maintenance data after the interventions were withdrawn.They conducted their studies with high interobserver agreement. All DHH learners changed their target behavior when self-graphing was introduced. This study expands self-graphing evidence from high-incidence disability groups to a diverse group of DHH students.
Abstract
When an instructional strategy lacks evidence of its effectiveness with certain learner groups, educators can conduct action research in their classroom to determine strategy effectiveness. In the present study, seven preservice teachers of the deaf (TODs) implemented self-graphing interventions with deaf/hard of hearing (DHH) learners to change target on- and off-task behaviors. Preservice teachers were master 's-level candidates in a university teacher preparation program. DHH learners were 5-16 years old, attended general education classrooms at their local schools or schools for the deaf, and used multiple forms of communication/language. Candidates successfully completed ABAB design studies, and three candidates collected maintenance data after the interventions were withdrawn.They conducted their studies with high interobserver agreement. All DHH learners changed their target behavior when self-graphing was introduced. This study expands self-graphing evidence from high-incidence disability groups to a diverse group of DHH students.
Keywords: self-graphing, self-monitoring, action research, preservice teachers, deaf/hard of hearing
Beal is a professor at Valdosta State University. She teaches Deaf education and interpreting courses and researches ASL assessment and acquisition in deaf children and second language learners and evidence-based instructional strategies. She was previously a teacher of the deaf within mainstream schools and schools for the Deaf.
The Individuals With Disabilities Education Improvement Act (IDEIA, 2004) and Every Student Succeeds Act (2015) require educators to use assessment data paired with evidence-based instructional strategies for all learners, including those who are deaf/hard of hearing (DHH). Therefore, in-service and preservice teachers of the deaf (TODs), and those that prepare them in university teacher preparation programs, must be well-versed in evidence-based instructional strategies to provide effective instruction for their students.
Instructional strategies are learning techniques or methods that contain a series of actions with a definable student learning outcome (Knoors & Hermans, 2010; Marzano, n.d.). Student learning outcomes are goals that define a specific skill and level of proficiency that students must achieve (Brown University, 2024). Instructional strategies are considered evidence-based if an accumulation of studies that include quality indicators document that use of a strategy is responsible for a change in specific learner performance (Horner et al., 2005; Kratoch-will et al., 2013). Small and diverse target populations, such as students who are DHH, present a challenge to the creation of an accumulation of studies focused on specific intervention strategies (Antia et al., 2017; Mitchell & Karchmer, 2011). When this accumulation of studies is not available, educators might conduct action research with their students in the classroom to determine the effectiveness of instructional strategies. This process allows educators to document evidence of the effectiveness of instructional strategies and allows learners to make progress at an adequate rate (Alberto & Troutman, 1986; Bloom et al, 1992; Figarola et al., 2008).
One method for documenting individual learners' progress is learner graphing of their performance data (Bloom et al., 1992). Fuchs and Fuchs (1987) noted that graphing "leads to empirically derived and validated individualized education programs" (p. 5). First, I review self-monitoring, self-graphing, and published studies that included self-graphing as an instructional strategy. Then, I present an overview of action research conducted with diverse learners.
Self-Monitoring and Self-Graphing
Self-Monitoring
Self-monitoring and self-graphing are strategies with an evidence base to change an academic or social behavior when used with students with disabilities (DiGangi et al, 1991; Figarola et al, 2008; Gunter et al, 2003; McDougall et al, 2006). Self-monitoring is an executive processing strategy that requires self-regulation skills, including forethought (i.e., development and sharing of the performance objective); performance (engagement in the task and self-monitoring of performance); and self-reflection and evaluation (McConnell, 1999; Sheehey et al., 2017; Zimmerman & Moylan, 2009). Self-monitoring provides immediate feedback on target skill performance (Bartlett, 2013; Bol et al, 2010; McDougall et al., 2006; Shimabukuro et al., 1999). Self-monitoring increases learners' self-efficacy and motivation and is more effective in creating lasting behavioral change than teacher-regulated strategies (Harris et al., 1994; Mooney et al, 2005; Rafferty, 2010; Sheehey et al, 2012). While self-monitoring is frequently used with students with disabilities, self-graphing has been underused (DiGangi et al, 1991; Figarola et al, 2008; McDougall et al., 2006), specifically with DHH students, which were included in only one published study to date (Hyun & Kim, 2002).
Self-Graphing
While graphical display of data increased students' academic performance (i.e., production and accuracy; Fuchs & Fuchs, 1987), student self-graphing of their data allows students to actively participate in the process of evaluating their own performance via immediate visual feedback that compares their present performance to their past performance and to an aim line that explicitly depicts their goal performance (Capizzi & Barton-Arwood, 2009; Codding et al., 2005; Figarola et al., 2008; Gunter et al., 2002; Hirsch et al, 2013; Lindsley, 1990; McDougall et al, 2006; Stotz et al, 2008). Self-graphing empowers students through setting their own performance goals, requesting modifications to procedures when needed, and selecting personalized graphical displays (Beal-Alvarez, 2017; Codding et al, 2005; Dollard et al, 1996; Figarola et al., 2008; Gunter et al., 2002; McDougall et al, 2006; Olympia et al, 1994; Sheehey et al, 2017; Trammel et al, 1994). Self-graphing also increases student motivation and confidence (Bartlett, 2013; Bloom et al., 1992; Figa-rola et al., 2008; Sheehey et al., 2017) and students appear to enjoy the graphing process and visual representation of their performance (Bartlett, 2013; Codding et al., 2005; Gunter et al., 2002; Sheehey et al, 2017; Stotz et al, 2008; Sutherland & Snyder, 2007).
Additionally, self-graphing is easy to implement and protects instructional time as the teacher is not spending time graphing individual student data (Gunter et al., 2002; Harris et al, 1994; Hirsch et al, 2013; McDougall & Brady, 1998; McDou-gall et al, 2006; Shimabukuro et al, 1999; Stotz et al., 2008). Teachers (and students) can easily share graphed performance with stakeholders during Individual Education Program (IEP) meetings (Bryan & Sulli-van-Burstein, 1998; Figarola et al., 2008; Gunter et al., 2002; Hirsch et al, 2013). Self-graphing is a socially valid intervention as well. McDougall et al. (2006) found that 23 of 43 (53%) self-monitoring studies they reviewed included social validity data, and nearly all studies found positive changes in students' performance that applied directly to classroom instruction.
Self-Graphing Process
When pairing self-monitoring with self-graphing, first, the teacher identifies a behavior or area of academic concern and formulates a clear description of the behavior in terms understood by the student (Rafferty, 2010). Then, the teacher collects baseline data on the student's target behavior and conferences with the student to explain the rationale for needed behavior change and obtains student commitment to change the behavior. The teacher (and student) sets a behavioral goal, selects the self-monitoring procedures (i.e., frequency of and method to record student behavior), and teaches the student the self-monitoring procedures (Gunter et al, 2002; Rafferty, 2010). The teacher also sets up a graph that the student can easily access, such as on the student's laptop, tablet, or mobile device, and teaches the student how to enter their performance data, which is automatically graphed and viewed (Figarola et al., 2008; Gunter et al., 2003; Sheehey et al., 2017). Self-graphing procedures have evolved from paper to classroom computers (Figarola et al., 2008; Gunter et al, 2003) to individual technology, such as tablets and smartphones (Mize et al., 2021).
Throughout the self-graphing process, the teacher provides frequent encouragement and feedback to the student and monitors student progress to determine the effectiveness of the self-monitoring and self-graphing intervention (McConnell, 1999). Refer to the work of McConnell (1999) for examples of self-monitoring data recording forms, ideas for data collection time intervals (e.g., specified times or intervals, during certain activities, predictable or random intervals, etc.) and types of prompting cues for students to self-record their performance (e.g., visual, physical, verbal, etc.). In the following paragraphs, I review published studies that provide evidence of self-graphing effectiveness when used with students with disabilities and review action research case studies with DHH students.
Self-graphing increased academic and social skills for students with high-incidence disabilities, including learning disabilities (LDs), emotional-behavioral disorders (EBD), attention deficit/hyperac-tivity disorder (ADHD), and intellectual disabilities (IDs) in general education settings (see Table 1). Academic skills included math automaticity (Codding et al, 2005; Figarola et al, 2008; McDougall & Brady, 1998; Sheehey et al., 2017), reading comprehension, (Shimabukuro et al, 1999), reading rate (Gunter et al, 2003; Sutherland & Snyder, 2007), writing skills (Stotz et al., 2008), on-task behavior (DiGangi et al., 1991; Shimabukuro et al., 1999), homework completion (Bryan & Sullivan-Burstein, 1998; Trammel et al., 1994), in addition to social skills (Gansle & McMahon, 1997). Self-graphing was implemented with mostly elementary-aged students, from kindergarten to fifth grade, and two studies included middle schoolers. Most students were identified with LDs (N = 47), followed by ADHD/attention deficit disorder (ADD) (N = 6) and EBD (N = 5). Based on the cumulative evidence of the effectiveness of self-graphing to change the academic and social behaviors of learners with diverse abilities, it is possible that self-graphing can be effective with DHH learners. However, self-graphing has scant evidence for DHH students.
Self-Graphing and DHH Students
An extensive review of published literature was conducted to identify studies that implemented self-monitoring and self-graphing with DHH students. The terms self-graphingpaired with deaf, hard of hearing (HH), or hearing impaired were searched within the following journals (presented in alphabetical order) that publish articles related to DHH students: American Annals of the Deaf; Communication Disorders Quarterly; Deafness & Education International; Exceptional Children; Journal of Deaf Studies and Deaf Education; Journal of Language, Speech, & Hearing Research; Journal of Special Education; Sign Language Studies; Teacher Education and Special Education; Teaching Exceptional Children; and Volta Review. Odyssey was unsearchable within an online format. Self-graphingpaired with deaf, HH, or hearing impaired was also searched within Google Scholar. Across all venues, only one article was located that included DHH students and self-graphing (Hyun & Kim, 2002). In this study, three kindergartners at a school for the deaf in Korea increased their spoken language initiations during storybook reading after self-monitoring at 1-minute intervals with vibrating cell phones as cues to self-record their behavior. They placed stickers with their number of initiations on graph paper at their desks.
When published research in support of a specific strategy with a specific population is not available, action research is one avenue to explore the effects of the intervention, such as self-graphing with DHH students. Preservice and in-service educators can implement action research within their classrooms, field experiences, clinical practice, and coursework to determine the effectiveness of instructional strategies, such as self-graphing, to document changes in academic and social skills.
Action Research
Action research connects educational theory with educational practice. Through action research, educators implement interventions (i.e., instructional strategies) with their learners in the classroom and identify effects on learners' performance (Beal-Alvarez, 2017; McDougall et al, 2006; Rosen et al, 2015). Dewey (1933) noted that effective educators engage in problem-solving through observation, data collection, and hypothesis generation and testing. This aligns with a recent teachers-as-research-ers framework approach to action research within the classroom (see Rosen et al., 2015; Wang et al., 2010, for reviews). Within this framework, teachers identify the educational needs of their learners based on IEP goals and objectives and select possible interventions or instructional strategies from published research studies (Wang et al., 2010). After teachers identify a student's present level of performance on a given skill through assessment, they create data-based learning objectives and implement the selected intervention, monitoring learner performance across baseline (i.e., without the intervention) and intervention phases. Based on learner performance data, teachers can make adjustments as needed to increase a learner's effectiveness and efficiency in learning the target skill (Beal-Alvarez, 2017). Based on student performance data, teachers can adjust their instructional strategies as needed to make learning more efficient and effective for individual learners.
Teachers who participated in classroom research frequently made data-related changes in their classroom practices, such as tailoring their strategies to learners' performance (Campbell et al., 2004; Jones & Krouse, 1988; Kochendorfer, 1997; Paulsen, 2005). Action research has changed the academic skills of DHH learners as well (Aceti & Wang, 2010; Arenson & Kretschmer, 2010; Brigham & Hartman, 2010; Hoffman & Wang, 2010; Howell & Luckner, 2003; Smith & Wang, 2010; Uzuner, 2007; see review in Beal-Alvarez, 2017). Action research is often successfully completed by preservice teachers who are university students in teacher preparation programs (Rosen et al., 2015; Stotz et al., 2008). Action research can include group or single-case experimental studies. In group studies, learners with similar characteristics are divided among control, comparison, and intervention groups, with the average score for each group compared across time (Gersten et al., 2005). Group averages can conceal the variability within groups, and group designs require large populations of participants (Antia et al., 2017).
With diverse populations, such as DHH students, single-case design (SCD) studies are often used within an action research framework, in which the performance of a student is compared only to themselves (see Antia et al., 2017, for a review). SCD documents and replicates a functional relation between introduction and removal of an intervention, or independent variable, and changes in a target learner skill, or the dependent variable. SCD experimental designs are "tightly controlled time-series designs that allow researchers to examine the change from an individual's preinter-vention performance or behavior without requiring a control group or control case" (Antia et al, 2017, p. 226). SCD also includes measures of interobserver agreement (IOA) (reliability) and fidelity, or evidence that the intervention was delivered as planned. Fidelity is frequently measured by checklists to document the procedures that occurred during an intervention session (Beal-Alvarez & Easterbrooks, 2013; Bergeron et al., 2009).
Strengths of SCD include that each participant acts as their own control, with frequent data collection, continuous monitoring of the intervention's effectiveness, and flexibility to make changes as needed based on learner performance (Antia et al., 2017; Kratochwill et al, 2013). Limitations of both action research and SCD research include a lack of replication across learners and an inability to generalize findings outside of the learners and settings in which the study occurred (Antia et al, 2017; van Manen, 1990). SCD is an authentic research design for implementation within an action research framework in a classroom to address individual learners' IEP goals and objectives.
Many researchers note that delivering evidence-based instructional strategies during preservice teachers' preparation programs is an integral practice for their effectiveness with students with special needs (Mayton et al., 2015; McDougall et al., 2006; Paulsen, 2005; Swanwick & Marschark, 2010). Jones and Krouse (1988) compared 12 preservice teachers (i.e., student teachers) who received training and supervision in data-based instruction for students with special education instruction to nine student teachers in a comparison condition. The teachers in the intervention condition evaluated the effectiveness of their instruction based on their learners' graphed performance data and from supervisor feedback in weekly meetings. Learners in the intervention group outperformed their peers in the comparison condition for words read correctly per minute (WRCPM) and reading comprehension (although there were no differences in math performance). Other researchers have documented the success of learner self-graphing interventions implemented by preservice teachers (Rosen et al., 2015; Stotz et al, 2008). For instance, a graduate student in a special education preparation program with only supervised practicum teaching experience implemented the self-graphing intervention with students with LDs in a study by Stotz et al. (2008). Similarly, preservice teachers in a master's-in-teaching program implemented action research to determine the effectiveness of different instructional approaches within their sign language courses (Rosen et al., 2015).
Research Questions
As noted previously, self-graphing has been implemented successfully with diverse students by in- and preservice teachers; yet, only one research study has been published on the use of self-graphing with DHH learners. The purpose of this study was to investigate preservice teacher implementation of self-graphing with K-12 DHH learners within an action research framework to change academic or social behaviors as part of a graduate-level course in a teacher preparation program. The research question was as follows: Can preservice teachers in a graduate teacher preparation program effectively implement a SCD study, as determined by demonstration and replication of a functional relation between self-graphing and an increase or decrease in learner performance, with K-12 learners?
Methods Participants
Preservice Teachers
Participants were university students in a 2-year master's-level Teacher of the Deaf Preparation Program. In their last semester of this program, candidates complete their clinical practice experience (i.e., student teaching with DHH learners) paired with a capstone research project (described later). The seven candidates in the present study were from a total of 71 candidates who completed their semester-long capstone course across nine different years. Candidates were included in the present study because their capstone research project included both self-monitoring and self-graphing as intervention strategies with DHH K-12 learners. Five candidates were hearing and two were deaf (see Table 2). Four candidates completed their clinical practice and their capstones in a public school in the general education classroom and three did so at schools for the deaf. Candidates accessed the capstone course content via an asynchronous online learning platform and face-to-face or video conferencing calls with the instructor. The author was the instructor for the capstone course.
K-12 Learners
The seven learners in these action research studies ranged from 6 years to 15 years of age, in grades kindergarten through tenth grades (see Table 3). Four were HH, three were deaf, three used sign language alone, two used sign-supported spoken language, and two used spoken language alone. Per their IEPs, three learners were identified with specific language impairment (SLI), one with significant developmental delay (SDD), and one with Down syndrome and mild intellectual disability (ID). Target behaviors for the SCD studies were determined by IEP objectives and included off-task behaviors (N = 5), on-task behavior (N = 1), and writing one's name on papers (N = 1).
Procedures
Candidates completed capstone action research projects, in which they designed and implemented an ABAB SCD intervention study with a DHH K-12 learner, across the 16-week semester. ABAB design refers to an initial baseline phase (Al) in which a stable performance trend is established, followed by an initial intervention phase (Bl) in which a stable performance trend is established, followed by subsequent baseline (A2) and intervention phases (B2) (Kazdin, 2011; Kennedy, 2005). In each phase, a functional relation is required, such that performance for the target behavior changes only when the intervention is present. Additionally, the functional relation between the change in performance and presence of intervention must be replicated in the second intervention phase. Maintenance data are often required a few weeks after the intervention is withdrawn to determine whether changes to the target behavior are maintained.
To begin their research, candidates identified a student with an academic or social behavior in need of change by reviewing their learners' IEPs. Then, they obtained parent or caregiver permission to videorecord their student during the intervention. Candidates defined their learner's behavioral objective in measurable terms, with feedback from the instructor, and reviewed published research to identify an evidence-based intervention (i.e., strategy or combination of strategies) to address the learner's target behavior. Then, they developed their intervention procedures and met with the instructor to incorporate her feedback and fine-tune their procedures. Candidates submitted finalized baseline and intervention procedures and submitted them to the instructor before beginning their ABAB SCD studies.
Next, candidates collected baseline data until a stable performance trend emerged, which was defined as three consecutive data points in the same direction (i.e., increasing, decreasing, or no change; Kennedy, 2005). Then, candidates began and continued the intervention phase of their study until a stable performance trend emerged and their students met their learning objective two out of three consecutive times. Candidates repeated these procedures for subsequent baseline and intervention phases. Candidates emailed their graphed data to the instructor every time they added a data point after a baseline or intervention session. Unlike the study by Jones and Krouse (1988), candidates did not have weekly meetings with the instructor; however, they received feedback and guidance as needed after each graphed data point and across each phase of their SCD studies, including visual analysis of their graphed data in each phase to identify when learners met their objectives, when to change phases, and when to collect maintenance data. Candidates were required to collect IOA data for 20% of their data collection sessions. They trained a second on-site observer, usually their mentor teacher, on the expected learner behavior within their intervention study. The candidate and second observer independently collected data and compared their agreement on the measured learner behavior. They calculated IOA by dividing the number of agreements by the sum of agreements and disagreements and multiplied this number by 100%. Candidates presented their SCD studies in both a research paper and a live presentation to a committee of three university faculty members.
Data Coding
To gauge the effectiveness of graphed data, previous researchers used visual analysis (i.e., changes in mean, trends in data, overlap of data points; McDougall et al., 2006) or calculated the percentage of nonoverlapping data (PND) (Bartlett, 2013). PND refers to determination of the number of intervention data points that are higher than the highest baseline point (for increasing behaviors) or lower than the lowest point (for decreasing behaviors) (Bartlett, 2013; Kennedy, 2005). A higher percentage indicates a more effective intervention (Scruggs & Mastropieri, 1998). Above 90% indicates a very effective intervention, 70%-90% indicates an effective intervention, 50%-70% shows a questionable intervention, and <50% indicates an ineffective intervention (Bartlett, 2013).
After candidates completed their ABAB SCD studies, I reviewed their final capstone research papers to document information on their K-12 learners, their dependent (i.e., behavior to change) and independent (i.e., intervention) variables, the results of their intervention, maintenance data, and IOA (see Table 2). Candidates were required to submit one 5-minute video of them delivering the intervention as evidence of implementation of their intervention procedures; however, they were not required to collect fidelity data (i.e., the degree to which an intervention is implemented as intended; Kazdin, 2011; Kennedy, 2005). The lack of fidelity data can be considered a limitation; however, because candidates received consistent oversight from the instructor throughout the action research process and SCD procedures, specific fidelity data were not collected.
I reviewed each of their final graphs and calculated the PND to investigate the effects of self-graphing across learners and target behaviors. For target behaviors that were desired to increase, I divided the number of intervention data points higher than the highest baseline data point by the sum of intervention data points both higher and lower and multiplied by 100%. For target behaviors that were desired to decrease, I divided the number of intervention data points lower than the lowest baseline data point by the sum of intervention data points both higher and lower and multiplied by 100%. Next, I present the results of this study
Results
I asked whether preservice teachers in a graduate teacher preparation program can effectively implement a SCD study, as determined by demonstration and replication of a functional relation between self-graphing and an increase or decrease in learner performance, with K-12 learners. Table 3 presents details about each candidate's study. All candidates completed all four phases (Al, Bl, A2, and B2) of their SCD study, except Thea, whose school closed due to the COVID pandemic during her study (see Figures 1-7). The total number of sessions across studies ranged from 12 to 45. Three candidates collected maintenance data. All candidates reported an IOA of 75%-100% agreement (see Table 3).
To address changes in target behaviors, I calculated the PND for each candidate's final graph of their learner's performance. I present studies by level of effectiveness based on PND. Two studies, Seth's and Jack's, were very effective, with a mean PND of 100% (no range). Seth's student increased on-task behavior from a low of two instances to a high of 13 instances across 31 sessions, although he did not maintain on-task performance 10 sessions after the intervention ended, returning to second baseline performance. Jack's student decreased his off-task behavior (looking away from instruction for more than 3 s) from a high of 37 times to a low of five times across 12 sessions (no maintenance data).
Three studies were effective. Joy's mean PND was 90% (range: 80%-100%). Joy's student increased writing his name on his schoolwork from a low of zero in baseline to 100% consistently when the intervention was withdrawn. Ron's mean PND was 83% (range: 67%-100%). To meet the needs of his student, Ron adjusted his intervention from the student copying problems from the white board to copying from paper. His student decreased her off-task behavior from a high of 15 instances to a low of five during intervention across 45 sessions (no maintenance data). May's mean PND was 75% (range: 50%-100%). May's student decreased yelling out from five to two occurrences across 34 sessions. He pla-teaued at three occurrences during the second baseline phase and the second intervention phase and maintained this performance 11 sessions after the intervention ended.
Finally, two studies were of questionable effectiveness based on PND. Kate's mean PND was 62% (range: 40%-83%). Kate's student reduced off-task behaviors from a high of six to two during instruction across 33 sessions and maintained this performance 10 sessions after the intervention was withdrawn. Thea's mean PND was 50% (range: 0%-100%, due to only one data point in the second intervention phase). Her student reduced his yelling out from a high of 15 instances to a low of three instances across 18 sessions. Based on these results, five of the seven preser-vice teacher candidates had very effective or effective intervention studies with DHH students using self-graphing and demonstrated a change in their students' target behaviors through ABAB SCD action research studies.
Discussion
Based on the results of this study preser-vice TODs can effectively implement self-graphing interventions with diverse K-12 DHH students, some with additional conditions, in various educational settings (i.e., general education classrooms and schools for the deaf) through action research. All but one candidate completed the ABAB phases of their studies and three (43%) collected maintenance data 10-11 sessions after their interventions were withdrawn. The 16-week timeframe of the graduate course, individual school schedules, and available instructional time that candidates had with their students created barriers in the present studies. Candidates began their data collection in Week 4 of the semester, after they obtained student caregiver permission, identified a target behavior to change, reviewed evidence-based strategies related to that target behavior, and verified the specific steps of their interventions with the instructor. This left about 12 weeks to complete all phases of the ABAB design. School schedules also had various days dedicated to testing and school events, which prevented data collection because of the changed schedule of instruction. Candidates also had varied instructional time with their students, such as daily or a few times a week, based on their caseloads and individual student IEP services, and several mentioned this as a limitation in their final capstone papers. This reduced time resulted in a lack of maintenance data for four candidates. McDougall et al. (2006) reported that 28 of 43 (65%) self-monitoring studies they reviewed, 22 of which included students with disabilities, failed to include maintenance data because the school year ended. In the present study, candidates' IOA ranged from 75% to 100%, demonstrating their abilities to reliably agree with others on the presence or absence of a target behavior. Also, based on the results of the seven action research studies presented here, diverse DHH K-12 learners, 5-16 years of age, including those with SLI and mild ID, can change target on-task and off-task behaviors through self-graphing their performance data, in large- and small-group instructional settings in general education classrooms and at schools for the deaf. Based on PND calculations, self-graphing was effective in most of the studies. There were some instances in which PND overshadowed student performance. For instance, Kate's student had a stable baseline of 3 for three sessions in the second baseline phase. In the second intervention phase, the student varied from 2 to 4 with a mean of 3, presenting a low PND of 40%, even though he had two sessions of 2 at the end of the second intervention phase and maintained this performance 10 sessions later, which shows that the intervention was effective across time. In contrast, Seth's PND was 100%, but his student's on-task behavior decreased from a minimum of 9 in both intervention phases to 5 and 6 in the maintenance phase. It appears that his student would benefit from ongoing self-graphing to monitor his on-task behavior. This is one example of how self-graphing within an action research framework is tailored to the individual needs and performance of a student (Beal-Alvarez, 2017; Codding et al., 2005; Dollard et al, 1996; Figarola et al, 2008; Gunter et al, 2002; McDougall et al, 2006; Olympia et al, 1994; Sheehey et al, 2017; Trammel et al, 1994).
Thea's study was truncated by COVID. Her student's initial intervention performance for yelling out was at least four times fewer than the initial baseline phase across three sessions (PND of 100%). However, the initial data point of the second intervention phase was 12, which was the same as the previous baseline data point. It is unclear how her student would have progressed with the target behavior given more intervention time. May's student's yelling out ranged from two times to four times in both intervention phases and plateaued at 3 in subsequent baseline and maintenance phases. At three occurrences, this behavior likely still interrupts instruction. It is possible that her student needs an additional intervention component to further reduce his behavior. Joy's student showed a marked difference in writing his name on his papers with self-graphing and scored at 100% four consecutive times in the second intervention phase. Her results would be strengthened with collection of maintenance data to determine whether her student maintained this performance without self-graphing. Ron modified his intervention after nine sessions in the initial phase. Adapting from copying problems from the whiteboard to copying from a paper on the student's desk better matched her individual needs. This is a second demonstration of the flexibility within action research SCD studies to meet individual student needs. Due to the length of his study, Ron could not collect maintenance data for his student.
While candidates met all components of SCD studies, including replication of a functional relation and collection of IOA from an independent rater, one limitation in the present study is the lack of fidelity data of their interventions. McDougall et al. (2006) found that 27 of 43 (63%) self-monitoring studies did not include fidelity of intervention measures. However, when it was reported, levels of intervention fidelity were high. Similar results were found for the five studies (out of 11 studies; 45%) that collected fidelity measures in Table 1. In the present study, candidates worked closely with the instructor across the semester to outline, implement, and record data for their intervention studies. They also submitted a video of themselves delivering the intervention to their students; however, it was not rated for fidelity by the instructor. Future investigations might have candidates videorecord their performance across sessions and conduct self- and interrater fidelity scoring based on a task-analyzed list of steps that are required during implementation of their individual interventions, similar to previous studies (Beal-Alvarez & Easter-brooks, 2013; Bergeron et al., 2009). Another limitation is that social validity data were not collected in the present studies, although all students readily participated in their preservice teachers' interventions. Also, all studies but one included student self-monitoring with a tally or sticky note system on the student's desk to visually reinforce their performance, paired with self-graphing. Thea's study included visual support through pictures of token reinforcements paired with self-graphing. It is unclear how much change in student behavior was related to both visual support and self-graphing or to each component individually. Future research might investigate any separate effects of student self-monitoring and student self-graphing of their performance data.
Conclusion
The present study provides evidence that preservice TODs can effectively implement SCD self-graphing interventions with their DHH students through action research in their graduate-level teacher preparation program. It provides preliminary evidence that DHH students can use self-graphing to change their academic and social behaviors. Faculty in teacher preparation programs can embed SCD action research studies within the content of their courses. Antia et al. (2017) provide an overview of SCD elements and application of SCD studies to DHH students. TODs should include action research within their classrooms to determine the effectiveness of the instructional strategies that they use with individual learners, including self-monitoring and self-graphing. These two strategies engage DHH students in executive processing, self-regulation, and reflection; empower them to evaluate and change their own behaviors (McConnell, 1999; Sheehey et al., 2017; Zimmerman & Moylan, 2009); and increase their self-efficacy and motivation (Harris et al., 1994; Mooney et al, 2005; Rafferty, 2010; Sheehey et al., 2017). TODs can use the self-graphing steps presented above and classroom or mobile technology to implement self-graphing and meet the needs of their individual students. As demonstrated in previous studies and the present study, novice teachers can effectively use self-graphing across varied educational settings and students to change target behaviors.
Author Note
Address for Correspondence: Professor Jennifer S. Beal, Teacher Education Department, Valdosta State University,
1500 N. Patterson St., Valdosta, GA 31698, Email: [email protected]; ORCID ID: https://orcid.org/0000-0003-1240-9834
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