How the Use of Self-Monitoring App Improves the Task Behavior

A High-School Pilot Study on the Use of a Self-Monitoring App to Improve On-Task Behavior


In secondary, inclusive classrooms, technological advancements hold potential for improved intervention implementation. A withdrawal design was used with two high-school students to examine the effectiveness of a technologically delivered, self-monitoring intervention in enhancing on-task behavior in a science classroom. Due to issues with on-task behavior despite long-term administration of psychostimulant medication, two adolescents ages 14 and 15 with diagnoses of particular learning disability (Student 1) and attention deficit hyperactivity disorder (ADHD; Student 2) were chosen by the case manager’s recommendation. After collecting baseline data, both students were instructed on how to utilize an I-Connect self-monitoring application given via a handheld tablet. In an ABAB withdrawal design, on-task cues were supplied at 5-minute intervals. For all adolescents, the intervention resulted in positive, sustained changes in the primary dependent variable of on-task conduct but less so in the generalization variable of disruptive behavior.



Self-management refers to abilities and tactics that people use to evaluate and control their behavior. While many tactics are included in the broader category of self-management, self-monitoring (SM) is the most extensively researched component in the school-based intervention literature (Fantuzzo and Polite 1990). Self-monitoring is a multi-step process in which a learner observes the occurrence or absence of behavior and records characteristics of the observed behavior (Mace et al. 2001). Increased on-task behavior (Amato-Zech et al. 2006; DiGangi et al. 1991; Holifeld et al. 2010; Legge et al. 2010), increased social behaviors (Parker and Kamps 2011), and improved academic achievement have all been effectively implemented using self-monitoring methods (Paquin 1978; Varni and Henker 1979). Self-management and self-monitoring intervention studies, on the other hand, are rarely done in general education settings. Mooney et al. (2005) discovered that 73 percent of self-management intervention studies with 78 participants with emotional and behavioral disorders (EBD) were done in public schools. In contrast, zero research that satisfied inclusion criteria was conducted in general education classrooms. McDougall (1998), in a previous review, emphasized the importance of research undertaken in available education settings, stating that just 14 of 240 self-management studies, including primarily self-monitoring interventions, had targeted students with disabilities in general education settings. Given that most high-school children with EBD, ADHD, and specific learning disabilities are put in general education classrooms, especially for science teaching (Vannest et al. 2009), more study into the efficacy of SM therapies in this setting is needed.

Review of Literature

Although self-monitoring procedures have shown promise in adolescents (Young 1991), most intervention research supporting self-monitoring treatments in schools has focused on younger pupils. Fantuzzo and Polite (1990) conducted a meta-analysis of 42 self-management intervention trials. None of the studies that satisfied the inclusion criteria had an average participant age of more than 12, and more than two-thirds had an average age of less than 10. Only 5 of 14 research in general education settings, according to McDougall (1998), involved individuals aged 13 to 18. None of the 51 participants in a review of self-regulatory methods (self-monitoring being the main component) for children with ADHD (Reid et al. 2005) were in high school. While early intervention is crucial in enhancing academic and behavioral performance for students with and without disabilities, interventions for teenagers, particularly those at most significant risk of school failure, must be designed and validated. Technology, such as tablets or mobile devices, could be used in general education classrooms to help adolescents with self-monitoring. Self-management and self-monitoring literature has just recently begun to mention the use of such technology. Gulchak (2008), for example, taught self-monitoring to a young student with EBD using an electronic self-monitoring form given via a mobile and handheld computer (i.e., Palm Zire 72). The third grader’s fidelity to the system was 100 percent, and on-task conduct improved from 64 percent at baseline to 90 percent during self-monitoring. Mobile computing devices and handhelds, according to Gulchak, are incredibly stimulating to kids, making them more likely to be employed for behavioral progress tracking.

Despite the promise mentioned above for technology-enabled self-monitoring, technical availability has had minimal impact on self-monitoring literature to date. Mason and Davis (2013) found that adopting more current technology in self-monitoring therapies has not kept pace with technological availability in their evaluation of 62 single-case, peer-reviewed studies utilizing self-monitoring interventions with cueing components. Only one study (Quillivan et al. 2011) used a cell phone, and none used tablets, smartphones, or other mobile devices. In the Quillivan et al. study, the cell phone was also utilized to prompt paper-based self-monitoring for a kindergarten kid, reducing the device to a notification system. Blood et al. (2011) employed an iPod touch and video modeling to help a fifth-grade student with EBD and ADHD learn to self-monitor his on-task behavior. Every two minutes, the iPod touch reminded the student to write down his on-task and off-task conduct on a piece of paper. Despite the favorable outcomes, the iPod touch served as an alarm clock or a signal to write down on paper. Handheld technological advances have made it possible to conduct unobtrusive self-monitoring interventions in inclusive settings. However, more research is needed to see if technology-based self-monitoring produces comparable or better results than traditional methods. This study tackles the lack of technology-based self-monitoring interventions for older students in general education settings and the restricted number of self-monitoring interventions in available education settings. Students in the current study utilized an app loaded on a mobile device (a Samsung Galaxy Player 5.0, a smartphone-sized tablet) to track their on-task behavior. The self-monitoring component of the I-Connect Intervention Package was used in this research. The I-Connect intervention package comprises a self-monitoring software and a school-based mentor who meets with children regularly to address homework, attendance, on-task, and acceptable behavior monitoring. Without the mentorship and weekly problem-solving meetings that come with the whole treatment package, the current study focused simply on self-monitoring of on-task conduct. This study aimed to see how two high-school students receiving special education services in a general education science classroom used the I-Connect self-monitoring intervention. The following research questions were considered: (a) What effect does the intervention have on the primary dependent variable of on-task conduct among students? (b) What effect does the intervention have on the disruptive behavior generalization variable? (c) Can the intervention be carried out consistently? & (d) How do the pupils and their instructor rate the intervention’s treatment acceptability?


Participants and the Environment

The research was carried out at a suburban high school in the Midwest of the United States. The total ninth- through the twelfth-grade population of the high school comprises 21% minorities and 25% pupils from low socioeconomic backgrounds. The study took place in a general education ninth-grade remedial level science classroom with 14 students from both regular and special education programs. There were about equal numbers of students with and without disabilities in the school, and around a third of students were repeating the class after failing a previous course. The classroom was staffed by a teacher and a co-teacher (primarily served in a disciplinary rather than an educational role). The experiments took place within the regularly planned 55-minute time. Lecture, solo seatwork, and small group assignments made up the class structure, with the first two categories accounting for all but two of the baseline and intervention sessions observed. Participants were recruited with the help of a high-school teacher who worked as a case manager for children receiving special education services. Participants were eligible for the study if they received special education services, displayed off-task behavior and classroom disruptions, were not currently served by any other university study, were struggling academically in a general education classroom, and, in the case manager’s opinion, would be willing to engage in self-monitoring of behavior in a general education classroom. One of the three students recommended was left out due to concurrent participation in another academic study in which the case manager was not involved.

The study was explained to the two remaining pupils and their parents, and they were asked to join. Students and parents were advised of acceptable tablet use standards, including appropriate internet access and classroom use. The science classroom was chosen as the intervention setting for recruitment due to students’ low marks in the class at the time of recruitment. The classroom teacher agreed to enable observations in the classroom and to measure perceived interventional efficacy after the trial was completed. Still, she was not obliged to engage in any direct intervention, measurement, or prompting of pupils. Both adolescents agreed to participate in the study after receiving parental authorization and completing an assent procedure that explained the study’s objective, advantages, and minimal hazards. At the start of the study, Student 1, a male of Native American heritage aged 15 years and seven months, was served in special and general education classrooms. He was classified as having a specific learning disability for particular education purposes, despite receiving elementary and middle school services for emotional disturbance due to a diagnosed mood condition and substantial anxiety. Despite being on ADHD medication for several years before the current study, problems with off-task behavior led to school failure. The classroom instructor and school-based case manager described work initiation as a challenge. Student 1 was told by the classroom teacher as entirely disengaged and “shut down” when unhappy or anxious or distracted, and off-target, so disruptions were not a worry. According to the teacher, student l’s low classroom performance was attributed to disengagement and distraction. Student 1 was retaking the course after failing it the previous year.

Student 2, a Caucasian guy aged 14 years and 11 months, received an education similar to Student 1. Due to an ADHD diagnosis, he was classified as other health impaired (OHI) for particular educational purposes. Student 2 was prescribed ADHD medication before and throughout the research. In both regular and special education settings, Student 2 displayed inattention and impulsivity. The classroom teacher identified off-task behavior and work completion as contributing to school failure. The teacher and the student’s case manager identified classroom disruptions as a common concern. Student 2 was taking the course for the first time, and the teacher noted that while he passed the exams, his off-task conduct had a significant influence on his ability to complete assignments in class.


The I-Connect app was an Android software that provided participants with scheduled prompts to self-evaluate and self-monitor their intended behaviors. The investigation was conducted on a Samsung Galaxy Player 5.0 tablet with a 5-inch screen. This tablet was about the same size as a smartphone. The tablet had no cellular (phone or data plan) connectivity and only had wifi internet access. The tablet was pre-configured to limit internet access, loaded with the I-Connect program, and tested before intervention usage to ensure compatibility with school firewalls. Text prompts such as “Are you on task?” could be displayed on the tablet at user-defined intervals. The “yes” and “no” response options were buttons that students might press to record their responses. When the question “Are you on task?” was given, the software provided three alternatives for notice. The options were a flashing screen, a chime tone, or vibration. After 6 seconds, the application was set to remove the question and start the following interval. After capturing a response, the application sends the data to a secure database through a wireless network connection. A remote server hosted this password-protected database.

Contingent Measures

The primary dependent variable was the proportion of time participants were on task during each 15-minute observation period. The percentage of time students were passively (e.g., paying attention to the teacher during a lecture) or engaged with instructional content via choral response, raising a hand, responding to teacher instruction, writing, reading, or otherwise actively completing an assigned task was defined as on duty (e.g., typing on the computer, engaged with posted materials). For observation periods, duration recording was used, with on-task recording occurring continuously throughout the session. The on-task variable was used to make all phase-change judgments.

Disruptive behavior was defined as a participant engaging in behavior that was counter to the behavioral expectations of the assigned activity and could potentially interrupt instruction. It was recorded as a frequency count during observation periods. This variable was included because the teacher was concerned about Student 2’s frequent disruptions in the classroom. Talking during individual seatwork or roaming around the room during a lecture are examples of disruptive behavior. Disruptive behavior was tallied only once during the period until either (a) the behavior stopped and a new behavior was coded, or (b) the teacher redirected the behavior, but the behavior continued (for example, two ignored redirections to the participant’s desk would result in three total disruptive behaviors). On a Dell Windows-based tablet with a 5 x 8-inch screen, dependent variables such as student on-task and disruptive behaviors were recorded using the multiple option observation system for experimental studies (MOOSES; Tapp et al. 1995). MOOSES enables real-time duration and event recording of teacher and student conduct. The MOOSES application was used to perform 15-minute observation sessions from the back of the room. Before collecting baseline data, the investigators and a study assistant did all observations and were trained to a 90% interobserver agreement rate on each behavior code. Before the start of the study, participants received in-class coaching and feedback as well as independent practice coding videos, coaching and feedback coding videos, and in-class coaching and feedback (2010)

Procedure and Design

The effects of the I-Connect self-monitoring intervention on participants’ off-task activities in a science classroom and the generalization measure of disruptions were evaluated using an ABAB withdrawal design (Kazdin 1982). Baseline, I-Connect self-monitoring intervention, withdrawal, and return to the intervention were the phases of this study. All phase-change decisions, as previously stated, were based on on-task data. Additional data points per condition were gathered if visual analysis revealed trending or variable data.


The baseline data were collected during science class and consisted of one to two 15-minute observations each day, with the number of data collection sessions determined by the instructional activity. It was accomplished if the instructional activity was independent work or lecture, and there was enough time left for the second observation. Only one word was completed if a transition to the video was expected or had occurred before the second observation, as teacher expectations for on-task behavior were modest during the video presentation. Students were not given any requests to monitor during baseline and were not given an I-Connect self-monitoring tablet.

Self-monitoring Intervention I-Connect

I-Connect self-monitoring was applied after a steady baseline of five observations (with IOA). Investigators taught students how to open the I-Connect app on their mobile devices, enter classroom information, and start behavioral tracking in one 20-minute session. The training covered both how to use the software and distinguish between on-task and off-task activities.

The application required a “Yes” response to the question “Do you wish to monitor your citizenship goals?” and a selection of the program on the device screen. Participants completed the process twice after receiving training on using the I-Connect self-monitoring program. Participants successfully identified two examples and two non-examples of on-task behavior during discrimination training. Participants were advised to begin monitoring every day in the science classroom with the device placed on the upper right corner of their desk, following the instruction. All science classroom activities were included in self-monitoring periods, which consisted of prompted inquiries such as “Are you on task?” with a binary yes/no choice as a response provided at automated 5-min fixed intervals. Prompts were visual (flashing screen), and tones/vibrating notifications were disabled during the trial to reduce potential peer distraction. If no response was entered within 6 seconds, the response option was disabled, and the application began at a 5-minute interval. The mobile devices were then given to the pupils to use in class. When research professionals were not present, participants were allowed to keep the devices for self-monitoring as long as online content viewing remained suitable and within school norms. Students began each class hour during the intervention by placing the device in the corner of their desk and launching the program. If they didn’t start the application right away, the researcher prompted them to do so. Student 1 was prompted once throughout the study, whereas Student 2 was prompted four times. Students did not give any feedback or conditions on the correctness of their self-monitoring data during the intervention. Furthermore, pupils did not have access to their recorded data for evaluation.


The withdrawal phase was implemented after the targeted behavior, and data stability improved. The withdrawal phase followed the same procedures as the baseline phase. Participants were notified that the I-Connect program would no longer be used to monitor them and that the device would be removed during the sessions. Participants were not given any specific instructions about how to behave during these sessions. During withdrawal, students were not banned from using alternative self-monitoring techniques, such as paper and pencil, although neither student-initiated visible forms of monitoring.

I-Connect Self-monitoring Intervention is being reintroduced.

The intervention was restarted after collecting a minimum of five data points with a return to baseline values and proven stability. The student was given a mobile device and directed to resume monitoring using the I-Connect application. Students did not request assistance, and no additional training or guidance on utilizing the application or mobile device was provided. All procedures remained consistent with the initial implementation protocols without retraining when reintroduced intervention.


The findings look at:
1. The functional relationship between the intervention’s introduction and students’ on-task behavior changes.
2. The I-Connect self-monitoring intervention’s implementation fidelity.
3. Students and their teachers report the intervention’s social validity.
As a supplemental data comparison, disruptive behaviors are included. The Impact of I-Connect Self-Monitoring on Task Performance In Fig. 1, Student L’s percentage of time spent on task across baseline and intervention circumstances is graphically shown. Although there was significant variability in the baseline data, Student 1 was on duty an average of 51 percent of the time, ranging from 71 to 41 percent. His on-task average increased quickly after the I-Connect intervention was deployed, to an average of 95%. (range 77-100 percent ). Student l’s proportion of on-task conduct declined to below baseline levels when the intervention was stopped, averaging 41%. (range 32-51 percent ). The restoration of I-Connect resulted in an instant increase in on-task behavior of 94 percent (range 84-100 percent), which was commensurate with levels seen when I-CONNECT was first introduced.

Graph 2 depicts Student 2’s percentage of time on task per observation session across baseline and intervention circumstances. For Student 2, visual analysis shows a gradual fall in the proportion of time spent on work, with an average of 18% of time spent on the task (range 80-0 percent ). The inclusion of I-Connect resulted in a noticeable positive mean shift, as seen in Fig. 2. Student 2’s on-task behavior percentage stayed above 88 percent for seven of the eight sessions, with one session at 59 percent (averaging 91 percent ranging from 100-to 59 percent ). A noticeable negative mean shift is observed when the I-Connect self-monitoring system is removed (see Fig. 2, withdrawal phase), with an immediate drop of approximately 30%. A continuous downward trend ensued similar to baseline performance levels (average 42 percent, range 71-16 percent ). Student 2’s on-task behavior returned to pre-intervention levels when I-Connect was reintroduced, averaging 91 percent (range 97-81 percent ). During baseline, Student l’s disruptions per observation averaged 2.2 (range 1-4), with intervention disruptions reduced to an average of 1 (see Fig. 3). (range 0-5). Student l’s disorders increased slightly to an average of 2 when the intervention was withdrawn (ranging from 1-3). His disruptions averaged.4 when I-Connect was reintroduced (range 0-2).

The teacher was more concerned about Student 2’s disturbances during baseline, which averaged 4.3 per observation (range 0-9). His interruptions dropped to zero when I-Connect was implemented; only two out of eight observations had disruptions greater than one, compared to five out of nine during baseline. The average intervention was 1.5. (range 0-7). When the intervention was removed, his disruptions initially remained low (2 observations with no interruptions) before returning to baseline. During withdrawal, the average number of disturbances per observation was 3.8. (range 0-9). The reintroduction of

Implementation Integrity

100% of baseline and intervention sessions were evaluated for implementation fidelity. During the intervention, 100 percent of the three commitment and implementation items received positive responses from both students. During the intervention, there were no instances where the application’s use was distracting to peers or the teacher. During the baseline and withdrawal periods, no alternative self-monitoring system was detected.


This study aimed to see how a technologically based self-monitoring intervention affected two secondary students with disabilities’ on-task and disruptive behaviors. The perceptions of intervention effects by participants and teachers were also investigated in this study. Despite no additional teacher workload and no specific reinforcement for on-task behavior by the classroom teacher or project staff, the intervention resulted in clear improvements in on-task behavior and improved but more variable results for disruptive behavior. Students and the classroom teacher both reported gains in on-task behavior, with the classroom teacher rating the intervention positively in all categories related to interventional benefits.

The results of the self-monitoring intervention back up previous research (Harris et al. 2005; Reid et al. 2005) and add to the body of knowledge in five ways. First, secondary students are rarely included as participants in self-monitoring studies. This research contributes to the interventional knowledge base for secondary students getting special education services. Second, other than using the self-monitoring software and the mobile device, no reinforcement mechanisms were included in the design. Third, the study used longer intervals than are usual for self-monitoring interventions in general, with 5-min gaps extending the face validity of an intervention that has traditionally used intervals of 1 minute or less. Fourth, delivering the self-monitoring intervention using wireless internet-ready technology was a novel application of a well-researched intervention. Finally, the study was conducted in a high school general education classroom, a unique setting for self-monitoring research. Although the intervention improved students’ on-task behavior, the generalization measure of disruptive behavior was not significantly improved. Increased disruptive conduct and lower on-task behavior (especially for Student 2) were seen during an unusual observation (session 11 for Student 1; session 14 for Student 2). Three snakes kept in the classroom became loudly agitated and active in their terrariums. Both students became more disruptive and less focused during this event. Improved classroom disruptions are immediately more visually convincing when these data points are removed.

Several constraints may have influenced the study’s conclusions and interpretation. For starters, direct observation and self-monitoring evaluation took place in only one class, so it’s unclear whether the findings applied to other classrooms or if monitoring in different contexts would have improved on-task conduct in the observed classroom. Second, while withdrawal caused a noticeable mean shift across phases, fading the intervention did not result in any programming for generalization. Third, because interval length was not varied throughout the study, it is unclear whether the 5-min intervals chosen were optimal or suboptimal for the behaviors assessed; however, because interval frequency was not a focus of the study, no conclusion about interval frequency can be drawn. Fourth, no academic data was gathered to assess achievement levels. Finally, while WWC single-case research requirements were followed for intervention sessions (i.e., three demonstrations of effect, staggered intervention commencement to control for classroom effects), future studies of the I-Connect intervention should include more participants.

Future technologically based self-monitoring research will likely be advantageous if it analyzes the same gaps in the literature as self-monitoring and secondary school-based analysis. Generalization to non-intervention classes and evidence of generalizability to those classes within a tiered preventative strategy is much-required studies. Because the use of technological applications to prompt self-monitoring is still in its early stages, more research in general and particular education settings would benefit from a deeper look into key aspects of self-monitoring research. The interval frequency and format (e.g., tones, visual) of cues/prompts are two elements. Practitioners may enhance adequate class time while lowering self-monitoring task demands if appropriate interval duration could be determined through repeated investigations of student groups. Finally, in future studies of technology-based self-monitoring, achievement metrics should be included in the intervention design. While meeting WWC requirements for single-case research, achievement measures should incorporate proximal (i.e., assignment completion) and distal (i.e., course grades) effects. Answering these critical questions will aid researchers and practitioners in making informed technological adoption decisions.

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