Self-Monitoring With Secondary Students: Using a Smartphone App to Increase Attention to Task

Abstract

Despite decades of robust evidence demonstrating the effectiveness of self-monitoring for attention, the strategy is not universally taught to students who struggle with attention, particularly within general education settings. Recent studies have included technology such as tablets or smartphones, adding to the ease and social acceptability of the self-monitoring strategy. In this article, we provide the steps and tips for teachers to use an inexpensive smartphone app to increase on-task attending during instruction.

Keywords

self-monitoring of attention on-task behavior smartphone applications

Ms. Hahn is a special education teacher for high school students in a small rural school system. Students on her caseload have emotional and behavioral disorders (EBD), attention-deficit/hyperactivity disorder (ADHD), learning disabilities (LD), and other high-incidence disabilities and are served in general education settings for most of the school day. Ms. Hahn has personally observed and also frequently received reports from the general education teachers with whom she co-teaches that many of the students on her caseload are off task more than other students. The off-task behaviors have been described as frequently disruptive to instruction and have reportedly affected the students’ abilities to complete their work and engage effectively during class. Ms. Hahn decides to investigate strategies for increasing on-task behavior in general education settings.

The benefits of attending class are numerous. Higher rates of attention and engagement are associated with the timely completion of work, better grades, and overall improved student well-being (Fredricks et al., 2016). Unfortunately, the consequences of inattention are also significant (Barbaresi et al., 2013). For example, students who have difficulty paying attention experience increased negative interactions with their teachers, which adversely affects school engagement (Rogers et al., 2015). Many students with high-incidence disabilities such as EBD, ADHD (who qualify for special education services under Other Health Impaired), and LD struggle to attend in class (Trainor et al., 2016). Students with EBD face other behavioral challenges in addition to attending to classroom instruction and activities, which include difficulties with self-regulation and in forming appropriate relationships (Rogers et al., 2015). Moreover, overall classroom engagement declines for these students as they get older (Wang & Eccles, 2012). Compounding the situation, most general educators who work with students with attentional difficulties report not knowing appropriate support strategies that, if implemented effectively, could help these students engage with the curriculum and increase the likelihood that they succeed in the classroom setting (Poznanski et al., 2018).

Numerous studies have indicated that self-monitoring strategies are effective with students with disabilities—improving on-task behavior, academic performance, and social skills across a range of ages and disabilities in both general and special education settings (Fishley & Bedesem, 2014). In addition, advances in technology now allow for self-monitoring prompts to be delivered from tablets and smartphones that are less intrusive in the classroom than the traditionally used audio-tones (Fishley & Bedesem, 2014). Although relatively fewer studies have focused on secondary students, explicit teaching of self-monitoring of attention is warranted for students with high-incidence disabilities in upper grades (Fishley & Bedesem, 2014). The purpose of this article is to provide teachers with the steps to implement a self-monitoring strategy using an inexpensive smartphone app that is designed to increase on-task attending during instruction, thereby providing an additional support tool for students with or at risk for EBD.

Self-Monitoring

Self-monitoring consists of two basic components, self-observation and self-recording (Briesch & Chafouleas, 2009). Self-monitoring involves the transfer of responsibility for noting off-task behavior from an adult to the student (Cooper et al., 2020b). For example, in a typical classroom such as Ms. Hahn’s, a student who is off task may be reminded by a teacher using a surface management technique (e.g., proximity, eye contact, a flash of the lights, direct request) to return to task. Self-monitoring’s goal is to transfer that responsibility for managing behavior to the student. Increasing students’ awareness of their off-task behavior and noting its frequency are foundational elements of self-management (i.e., the self-application of behavior-change strategies; Cooper et al., 2020b). Thus, self-monitoring is particularly relevant for educators who desire instructional efficiency and want to promote greater self-sufficiency and higher levels of academic engagement in their students with disabilities.

Research on self-monitoring has involved both self-monitoring of performance (i.e., productivity or accuracy) and self-monitoring of attention, with the latter being the most frequently studied (Sheffield & Waller, 2010). With most self-monitoring of attention strategies, a prompt is given to students to periodically check or self-monitor attention along with a method for self-recording responses. Self-monitoring of attention training involves teaching students to engage in a controlling response when prompted (e.g., “Am I on task?”; Cooper et al., 2020b). Use of self-monitoring of attention techniques has resulted in increased student on-task behavior in the classroom (Fishley & Bedesem, 2014).

Self-Monitoring Technologies

Traditionally, self-monitoring studies have used audio-tones presented to the whole class using a speaker system or individuals via headphones to prompt students to self-monitor. This procedure typically required students to record their responses with pencil and paper (Fishley & Bedesem, 2014). However, with the increasing use of technology in classrooms, devices such as tablets, handheld computers (e.g., iPods), and smartphones are now being used to prompt students with less distracting cues such as vibrating signals or visual messages. Students can also use personal technology to self-record (Bruhn et al., 2015). The unobtrusiveness of personal technology for self-monitoring is especially attractive for students with high-incidence disabilities (e.g., EBD, ADHD, LD) who are served in special education resource classrooms and general education inclusive classrooms (Fishley & Bedesem, 2014).

Several studies indicate the rise of technology use in self-monitoring of attention research. Personal electronic devices have been used to deliver audio prompts to elementary-grade students in special education self-contained settings (Blood et al., 2011), and an increasingly used option in general education settings has been the more discrete vibrational prompts, which allow students to keep smartphones in pockets and out of sight, in alignment with the smartphone usage policy of many schools (Cook & Sayeski, 2020).

An early example of personal technology for self-monitoring was the MotivAider (Behavioral Dynamics, 2020a), a pager-size electronic device that can be clipped onto a belt for inconspicuous use and programmed to emit pulsing vibrations on random or fixed time schedules. The Virtual MotivAider app (Behavioral Dynamics, 2020b) is now available, which is similar to the original MotivAider in that it emits vibrations on random or fixed time schedules but has fewer features and is less expensive (<US$5 for the app, whereas the MotivAider costs >US$50). A recent study (Cook & Sayeski, 2020) investigated the effects of self-monitoring with smartphones equipped with the MotivAider for Mobile app on the on-task behaviors of high-school students in general education inclusion classrooms and found moderate improvements in on-task behavior for two of four students who self-monitored. Teachers and students also reported that the availability, low cost, and ease of use made the intervention viable and socially acceptable.

In a recent review of self-monitoring research with students with behavioral disorders, Bruhn et al. (2015) recommended considering technology not only for prompting self-monitoring but also for self-recording of responses. Currently, the only app designed for self-monitoring that has features for both prompting and self-recording is I-Connect (The University of Kansas, 2019). The I-Connect’s prompt can be delivered as a vibration and a screen message (e.g., “Are you on task?”). Students self-record their responses by touching the “yes” or “no” that appears on the screen as the self-monitoring prompt fades (The University of Kansas, 2019). Researchers have implemented I-Connect self-monitoring strategies using smartphone-sized tablets (Clemons et al., 2016; Wills & Mason, 2014) and mobile devices (Bunch-Crump & Lo, 2017) with elementary and high-school students with disabilities and disruptive or inattentive behaviors in general education and special education self-contained classrooms. Results of these studies indicated improvements in the behaviors targeted for self-monitoring across participants. In a research-to-practice article, Bedesem and Dieker (2014) described steps to implement a self-monitoring strategy that also allows students to self-record with text-messaging features on smartphones. Students are sent text messages to cue self-monitoring and then self-record via a responding text message.

Few studies have been conducted on self-monitoring of attention at the high-school level, and fewer still have used technology for self-monitoring. However, the limited research indicates that technology is potentially effective and socially valid for high-school classrooms. The one study that used the I-Connect app was conducted with two high-school students with specific LD and ADHD in a general education remedial science classroom during regular instruction and a class size of 14 students (Wills & Mason, 2014). The two students demonstrated increases in on-task behavior and decreases in disruptive behavior when self-monitoring with the app. Similarly, McDougall et al. (2012) found that the use of a MotivAider and self-recording sheet improved the on-task behavior and productivity of a high-school student with ADHD during independent work time in an algebra class. Finally, Cook and Sayeski (2020) examined high-school students’ use of the MotivAider for Mobile app and self-stick notes to self-monitor and self-record during a general education Coordinate Algebra class with 24 students. Two of the four students demonstrated clear increases in on-task behavior that were maintained for 5–7 weeks after the intervention ended; a third student showed increased on-task behavior with feedback and positive reinforcement added to the self-monitoring strategy. All students across the three studies reported that the self-monitoring apps were socially acceptable.

To summarize, findings from research indicate that self-monitoring of attention interventions, including those using smartphone technology, can increase students’ on-task behavior, reduce interruptions and redirections in the classroom, and be socially valid tools in the general education setting. Furthermore, studies have shown that self-monitoring strategies using apps can be successfully implemented by teachers or paraprofessionals (Boswell et al., 2013).

Implementing the Self-Monitoring of Attention Strategy

Implementing a self-monitoring strategy with students begins with clearly defining and operationalizing the behaviors of interest. From there, teachers can collect baseline data, which informs the student training and support process. These steps are detailed in the following paragraphs.

Step 1: Define On-Task Behavior

The first step, developing an operational definition of on-task behavior relevant to the teacher’s expectations and instructional activities, promotes consistent and reliable measurements of on-task behavior. In Behavioral Observation of Students in Schools (Shapiro, 2011), Shapiro operationalizes on-task and off-task behaviors by delineating specific behaviors that would fall under these categories (see Table 1). Using Shapiro’s (2011) examples as a foundation, classroom teachers can adapt the list of on-task examples and non-examples to suit their classroom expectations.

Table 1.

Definitions of On-Task and Off-Task Behaviors.

Label	Description
On-task active	Writing when instructed Raised hand and waiting quietly before being called on by teacher to ask or answer question Getting out materials or moving when assigned Asking teacher an on-topic question Answering teacher’s question
On-task passive	Looking at teacher when teacher is talking to whole class or to self Looking at book or notebook at desk Looking at board if instruction-related information is on board
Off-task motor	Out of seat without asking teacher for permission Combing or playing with hair Playing with pencil or other items Typing on cell phone Rummaging through purse-book bag-binder when not assigned Passing a note Throwing an object Eating—unless also looking at teacher, board, or materials
Off-task verbal	Talking to a classmate when prohibited (e.g., during lecture or test) Talking to a classmate outside of group during group work
Off-task passive	Head down—even if eyes are open and toward teacher or board Eyes shifted downward Hands covering eyes Head back; eyes toward ceiling Yawning Looking at hands/fingers Looking at cell phone Looking around—not at teacher, instructional materials, or board as appropriate at that moment Looking out window or at neighbor who is talking or disruptive Looking at person who enters class

Note. Adapted from Academic Skills Problems Fourth Edition Workbook (pp. 42–45), by E. S. Shapiro, 2011. Copyright Guilford Press. Adapted with permission of The Guilford Press.

Two purposes are served in defining a list of classroom behaviors that meet on-task expectations. First, teachers can consistently measure students’ levels of on-task behaviors before, during, and after implementing the strategy. Second, teachers can explicitly teach students how to identify classroom engagement expectations. Ideally, teachers should consider not only their personal expectations for on-task behavior but also the different cultural expectations for student engagement, according to the diversity of the students in the classroom.

Step 2: Collect Baseline Data

Direct observations are necessary to determine the percentage of on-task behaviors that the student demonstrated during the designated class time. When collecting baseline data prior to the intervention, regular classroom procedures and activities should remain in place. Thus, the data provide evidence that an intervention is needed in that setting (Alberto & Troutman, 2017). The type of classroom activity (e.g., direct instruction, small group work, independent assignment) can affect on-task behavior (Cook & Sayeski, 2020). Therefore, collecting baseline data across various activities is important as students’ behaviors may change under different instructional conditions. Of course, no student will remain completely engaged throughout class. Students with on-task percentages of <60% or consideration of other factors, such as the level of disruption, can be good benchmarks for student selection for self-monitoring instruction.

The duration of data collection can typically be 10–30 min, depending on the projected duration of class activities and the teacher’s schedule. Smartphone data collection apps can facilitate the ease of recording behavioral observations. For example, Cook and Sayeski (2020) used a momentary time sampling method for measuring behavior an interval data collection tool (Romanczyk & Gillis, n.d.) that provided a signal at the end of each interval, prompting the observer to record the observed behavior. Teachers may find it easier to collect data during independent work activities rather than direct instruction.

Select a method for observation

Teachers rarely have dedicated time for continuous observations of student behavior. As such, using a discontinuous measurement system like momentary time sampling with a smartphone interval data collection app is an efficient method for data collection. Momentary time sampling is an interval recording method that requires the teacher to attend to the student only at the end of the specified intervals of time (e.g., 2 min) and record the occurrence or nonoccurrence of the target behavior (Cooper et al., 2020a). In addition, some data collection apps (see Table 2) allow teachers to record interval observations with a tap on the smartphone screen (i.e., yes, no). Thus, the teacher can hold the smartphone in one hand and continue with regular classroom duties, pausing only when the smartphone’s vibration signals the teacher to observe the target student and record the observation of on-task behavior. At the end of the observation session, the teacher can obtain an estimate of the student’s on-task behavior by calculating the percentage of intervals in which the student was recorded as on-task, dividing the number of on-task intervals by the total number of intervals, and multiplying by 100 (Barton et al., 2018).

Table 2.

Mobile Apps for Self-Monitoring and Data Collection.

Name of App	Device Type	URL	Cost	Notes
Self-monitoring applications
I-Connect (The University of Kansas, 2019)	iOS, Android	https://iconnect.ku.edu/get-started/	Free	Three self-monitoring prompts, including on-task; fixed or variable intervals; with vibration and screen message
Virtual MotivAider (Behavioral Dynamics, Inc., 2020b)	iOS	https://habitchange.com/virtual_motivaider.php	US$3.99	Regular or random intervals; with vibration, sound, and text messaging
Data collection applications
Behavior Snap (Mittelman, & Gahn, n.d.)	iOS	http://behaviorsnapapp.com/	US$9.99 initial fee	Multi-use data collection tool, including interval data; subscription required after first 12 observations
Behavior Tracker Pro (Marz Consulting, Inc., n.d.)	iOS	https://www.behaviortrackerpro.com/	US$29.99	Multi-use data collection tool, including interval data
Catalyst client (Data Finch Technologies, n.d.)	iOS, Android	https://datafinch.com/aba-data-collection/	US$24.99/month basic	Multi-use data collection tool, including interval data; monthly price reduction with more than 30 clients or students
Intervals ABA (elocinSoft, n.d.)	iOS	https://www.elocinsoft.com/intervals/	US$3.99	Prompts (vibration and/or sound) for observations can be set at regular or variable intervals; results can be electronically exported

Select an appropriate interval for observation

It is important to note that data collected through momentary time sampling provide only a snapshot of observed behaviors at the end of each interval of time. As such, when using a momentary time sampling system, shorter intervals provide more accurate estimates of the target behaviors (Fiske & Delmolino, 2011). Recent research on interval measurement systems used in applications of behavioral strategies found that momentary time sampling was generally accurate when intervals of up to 2 min were used (LeBlanc et al., 2020). Therefore, we recommend that teachers set the intervals for momentary time sampling at 2 min or less.

In addition, should a teacher have more than one student to observe, data can be collected through momentary time sampling by rotating observations between students, as first shown in a seminal study by Thomson et al. (1974). For example, if using an observation interval of 2 min per student, the teacher could collect data on two students by observing one student every 60 s. Note that the interval used by teachers for data collection will be different than the interval students will use for self-monitoring (see Step 3: Train the Student).

Select an app for data collection

Teachers might find that data collection is easier when using an app on their smartphones. Several data collection apps are commercially available for behavioral observations. Most of the apps have capacity for several types of data collection (e.g., interval, duration, frequency). One example of an app specifically designed for interval observations is Intervals (Elocinsoft, n.d.), which allows the user to set an interval length and an overall duration of time for the data collection session. The end of the interval can be set to signal as a vibration or an audible tone. In addition, when signaled, the user can tap the smartphone screen to indicate “yes” or “no.” The app tracks the user’s responses until the end of the pre-set session. The total count of responses can be saved on the device and transferred through text messages or email (Elocinsoft, n.d.).

After reviewing the research literature, Ms. Hahn developed a systematic plan for implementing a self-monitoring of attention intervention. First, she met with her general education co-teachers to develop a definition of on-task behavior and compile a list of examples and non-examples of on-task behaviors in their classrooms. Then, using her list of defined on-task behaviors, she collected observational data on the students of concern in their general education classrooms. She collected baseline data in 10 min sessions during various classroom activities (e.g., independent work time, lecture, group work) on three to five occasions per activity type. Her graphed data indicated which students showed low enough levels of on-task behavior (below 60%) to warrant a behavior-change strategy. The data also pinpointed the specific classroom activities during which to apply the strategy. Next, Ms. Hahn reviewed the data with her co-teachers, and together, they decided which students to teach the self-monitoring strategy.

Step 3: Train the Student

Teaching students how to use a self-monitoring strategy involves preparing and gathering materials and following a checklist for training. The use of a training checklist will facilitate standardized instruction and support.

Gather materials

Prior to training students, teachers need to gather the following materials: (a) a sheet printed with the on-task definition (see Table 1), (b) a smartphone with self-monitoring app (e.g., Virtual MotivAider; see Table 2), (c) a self-recording sheet (unless the app allows students to record their responses directly onto the smartphone), and (d) the teacher self-monitoring training checklist (see Figure 1). For apps that do not include a self-recording feature, a 3- by 5-inch self-stick note (see Figure 2) works well as it is can be unobtrusively set on the student’s desk or work area and stay in place while needed.

Figure 1.

Teacher checklist for self-monitoring training.

Figure 2.

Self-stick note for self-recording.

In addition, the use of the self-stick note might provide a less distracting method for self-recording than the smartphone. A vertical line can be drawn to divide the note in half, with “Am I on task?” written at the top and one side marked for on-task (“yes”) and the other side marked for off-task (“no”) responses. To be more time efficient during training, practice can be conducted using shorter intervals (e.g., 10 s or 30 s). However, during in-class self-monitoring, we recommend setting the prompt to vibrate at 3 min intervals, similar to Cook and Sayeski (2020), with the goal of prompting frequently enough to help the student remain on task but not so often that the student is distracted by self-recording. Finally, setting the smartphone to airplane mode to temporarily disconnect from cellular and internet service ensures that the smartphone’s other (potentially distracting) functions are limited.

Use a checklist

Next, using the training checklist (see Figure 1), the teacher trains the students in the self-monitoring strategy. The first steps of training are to share graphs of students’ baseline data and discuss the definition of on-task behavior along with enacting examples and non-examples. Acting out the various behaviors that would be considered on- or off-task ensures the student understands the teacher’s expectations for classroom engagement behaviors.

The then teacher demonstrates the use of the smartphone app for self-monitoring and the self-stick note for self-recording. When the smartphone vibrates, the teacher models how students should mark a tally on the appropriate side of the note (on task, off task). The small, self-stick note should be placed on the student’s desk where it can be easily accessed during self-monitoring sessions. Practicing the procedure until the student demonstrates proficiency (e.g., 100% correct self-monitoring responses on five consecutive opportunities) ensures that the student is comfortable with the procedure prior to in-class application. Practice, modeling, and feedback are part of the explicit teaching of classroom-specific on-task behaviors. Although self-monitoring increases students’ awareness of their behaviors (Alberto & Troutman, 2017), students with disabilities who inherently have difficulty with self-awareness might require additional training in identifying when their behaviors are on- or off-task.

Ms. Hahn was now ready to gather materials and train the selected students to self-monitor. In preparation for training students, she checked the school policies for smartphone use by students, collected old smartphones from friends and colleagues, downloaded the smartphone self-monitoring apps, and collected other training materials (i.e., printed definitions of on- and off-task behaviors, baseline graphs, students’ self-recording form, the training checklist). Ms. Hahn scheduled the training during the study hall period, and she trained students individually or in pairs. In the training session, after learning about on-task behaviors and how to use the self-monitoring of attention strategy, students pretended they were working on a class activity and practiced using the tools until they demonstrated proficiency.

Step 4: Implement the Self-Monitoring Strategy

Following the daily checklist (see Figure 3) ensures procedural fidelity. The daily steps are as follows: (a) check the student’s smartphone to ensure that airplane mode is engaged and the app is set to vibrate at the correct interval (i.e., 3 min) and (b) provide the student with the smartphone and the self-stick note for self-recording. Next, the teacher prepares their personal smartphone to ensure that the data collection app is set at the correct interval (i.e., 2 min) and ready to record on-task behavior using a momentary time sampling method. After the self-monitoring session is complete, the teacher records the percentage of on-task intervals on the checklist and retrieves the self-recording note from the student. Depending on the teacher’s and school’s policies, the student’s smartphone can be stored until the end of class or left in the student’s possession. Ideally, the self-monitoring session occurs during the same class period and under similar teaching conditions or learning activities each school day.

Figure 3.

Daily checklist and datasheet.

Step 5: Monitor Progress and Make Data Driven Decisions

The final step in the process is to monitor students’ progress and make decisions such as providing additional training, adding reinforcement, or fading procedures. Data collection continues throughout this phase of the intervention.

Fade procedures

After observing an increase in the student’s on-task behaviors while using the self-monitoring materials over 3–5 days, the self-monitoring intervention can be faded by systematically extending the duration of the intervals at which the student is prompted to self-monitor (Cook & Sayeski, 2020). For example, the smartphone app’s vibrational intervals can be increased every few days from 3 to 5 to 10 min, so the student is prompted less often. Then the student can discontinue use of the smartphone app but have the option of continuing to place the sticky note on the desktop as a reminder to remain on task. Collecting momentary time sampling data during this time is important in case there is a pattern of decline in on-task behavior to pre-intervention levels, in which case, the student can return to full implementation of the intervention.

Collect maintenance data

Following fading procedures, the teacher removes all self-monitoring materials from the student. However, continued collection of weekly maintenance data provides evidence to determine if increased on-task behaviors have been maintained after the intervention period has ended (Cook & Sayeski, 2020). Similar to decision-making during fading procedures, the teacher guides the student to re-implement the strategy if evidence shows the student’s on-task behavior has returned to baseline levels.

Another option, instead of fading the use of the smartphone app, is to allow the student to continue the use of the app while the teacher moves into a maintenance phase for data collection. Continued use of the app without additional support from the teacher would indicate the social validity of the tool for the student and potentially help the student maintain higher levels of attention.

Tips for Implementing the Self-Monitoring Strategy

As with any behavioral intervention, data collection of observed behaviors is integral to successful implementation. An important first tip is to focus on the intervention components that will lead to accurate information for making data-driven decisions: (a) a clear operational definition, (b) an easy-to-use measurement system, and (c) a plan for frequent data collection. The strong definition and easy measurement system will allow multiple teachers and paraprofessionals to take part in the data collection process, if needed. The benefit of reliable, frequently collected data is that the teacher can make effective data-driven decisions, including determining which students would benefit from the self-monitoring of attention strategy, how long to continue implementing the strategy, and when to fade the strategy and remove self-monitoring materials. Continued collection of maintenance data may indicate a need to briefly re-implement the intervention program from time to time to promote the long-term maintenance of skills.

Another tip is for teachers to work within school policies for smartphone and technology usage. Many schools have policies that already allow the use of smartphones in the classroom. However, if school policies prohibit personal smartphones in the classroom, teachers might be able to secure the necessary permissions to use a smartphone for intervention. Also, the strategy might fit better with school policy if students keep the smartphones in their pockets or lap while in use rather than within sight.

Even though the app the student uses may be inexpensive, the teacher should consider who will be responsible for purchasing the app. Assistive technology under the Individuals with Disabilities Education Improvement Act (IDEA, 2004) encompasses smartphones with apps that support the functional ability of students with disabilities (Qahmash, 2018), and thus, the team that develops the student’s Individualized Education Program might determine that the app should be funded by the school. Otherwise, if students use their own phones, the students’ parents (or the students themselves) would likely be willing to purchase an inexpensive app. If students use their own phones, students may occasionally arrive to class without the phone or with an undercharged phone. To remain consistent in implementing the strategy on a daily basis, teachers could consider having charger cords available or using a donated (outdated) smartphone.

When planning training sessions, there are a couple of things for the teacher to contemplate. Scheduling training during uninterrupted non-instructional times of the school day such as study periods would be better than during shorter breaks between classes. This would allow sufficient time to follow all training steps to completion. In addition, at the end of the training session, the teacher can ask students their views on the intervention. If warranted, other strategies could be introduced (e.g., goal setting and reinforcement for meeting on-task goals) or the self-monitoring of attention strategy could be modified at this time.

The self-monitoring of attention strategy can be modified in many ways for the individual student, as needed. Depending on the features of the selected app, teachers can opt to make simple changes to the prompt intervals (increase or decrease the interval duration between prompts), the type of prompt signal (vibration, text message, or audible tone), and the self-recorded response (tally marks on a self-stick note or at the top of a worksheet or an electronic response directly onto the app). In addition, components can be added to the strategy, including: (a) providing reinforcement (e.g., praise) or feedback for accuracy in self-monitoring or increased on-task behaviors, (b) goal setting with reinforcement, and (c) self-graphing (Cook & Sayeski, 2020).

When modifying the prompt intervals, the teacher should keep in mind that the prompts for the teacher and the student serve different purposes. The student is prompted to self-monitor to increase awareness of attention to the task, whereas the teacher is prompted to record student behavior to gain an estimated percentage of time-on-task. It is possible for the teacher and the student to synchronize their smartphones to signal simultaneously, which would provide an accuracy check on the student’s self-recording (thus providing an opportunity for the teacher to reinforce the student’s accurate responding and self-awareness). However, the estimation of targeted behaviors observed with momentary time sampling methods is more accurate (i.e., closer to the data from continuous methods of observation) when shorter intervals are used (Fiske & Delmolino, 2011)—and the 2 min interval that is recommended for the teacher’s data collection might be too frequent for the student’s self-monitoring, potentially serving as a distraction. If the teacher desires an accuracy check on the student’s self-recording, we recommend synchronizing the onset of the two apps while maintaining the 3 min prompt for the student and using a 1 min prompt for the teacher.

There are many factors that could affect an individual student’s off-task behavior, and some variability from day-to-day is not uncommon (Cook & Sayeski, 2020). For example, lack of sleep, hunger, or conflicts with peers might affect a high-school student’s behavior. Nonetheless, if the teacher has confidence that the steps of the intervention have been implemented with fidelity and the student has not demonstrated a clear increase in on-task behavior over a period of days with self-monitoring alone, the teacher should systematically change or add to the strategy and continue monitoring progress to determine the most efficient yet effective intervention for the individual student. Barton et al. (2018) recommended a minimum of three to five data points before changing intervention conditions, and Kratochwill et al. (2013) observed that, generally, collecting more data points will increase the evidence of a pattern of responding. Therefore, analysis of graphed data is the best way for the teacher to determine when to make a change to the intervention. On the other hand, as students show consistent increases in on-task behavior, the teacher can encourage them and their other teachers to try the same strategies to promote generalization in other settings (Bruhn et al., 2015).

With the increasing use of remote instruction, the final tip is for teachers to explore ways to modify the self-monitoring of attention strategy during online learning. It may be viable to implement the self-monitoring strategy for the whole class initially, using an audible signal (from an online timer) for the class to check themselves and self-record, while the teacher observes a selected or random student for data collection. Under these circumstances, the teacher would still provide explicit instruction on the on-task definitions and online classroom expectations as well as self-monitoring procedures. Specific students can be identified for separate training for self-monitoring of attention using an app for a smartphone or tablet. Individualized or group goal setting and reinforcement for goal achievement would likely be good fits for remote learning situations.

Ms. Hahn was pleased to find that her students enjoyed using the smartphone app to self-monitor their on-task behaviors. Most students showed an immediate increase in on-task behavior. Other students were more successful when modifications were made such as setting a daily goal for on-task behavior or praise was provided as a reinforcer for achieving higher levels of on-task behavior. Furthermore, the general education teachers were interested in both the self-monitoring strategy and the smartphone app that Ms. Hahn used for collecting data. She was delighted that her colleagues began using the same strategies and smartphone apps in their classrooms.

Conclusion

Self-monitoring strategies have been shown to help students with high-incidence disabilities, including EBD, maintain attention at higher levels in their general education classrooms (Fishley & Bedesem, 2014). This article presented steps that incorporated inexpensive smartphone apps, a method for self-recording (e.g., a self-stick note), and teacher checklists of procedures to teach and promote self-monitoring of attention (Cook & Sayeski, 2020). In addition, technology was shown to be useful for students to self-monitor and teachers to collect data.

This self-monitoring of attention strategy provides a support tool for teachers to increase student engagement and independence while reducing the amount of time the teacher spends on redirecting students (Bruhn et al., 2015). In addition, the ease of using data collection apps can potentially result in the teacher collecting data more frequently or enlisting a paraprofessional to assist. The self-monitoring strategy is especially attractive when smartphones are already available to the student and the school’s policy permits their use.

Given the relationship between attention and learning, providing students who struggle to monitor and manage their attention with the tools they need is of particular importance to school success. Furthermore, shifting the onus of responsibility from teachers to students increases the likelihood that students will be able to take ownership for their behaviors (Cooper et al., 2020b). Students who learn to use self-monitoring of attention strategies may have more insight into their behavior, resulting in stronger self-awareness and advocacy. Teachers can take advantage of low-cost, socially viable tools to help students gain this independence.

Footnotes

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

This paper was supported by the 2019 Augustana Research-Artist Fund.

ORCID iDs

Kathleen B. Cook

Kristin L. Sayeski

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