Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills

Abstract

This study evaluated the effects of behavior skills training (BST) as a means to acquire and generalize self-instruction behaviors using video activity schedules (VidAS) loaded on an iPod Touch for four U.S. high school students with intellectual disabilities. Behavior skills training procedures were implemented in two different vocational training environments and evaluated using a multiple probe across participants design. Following the mastery criterion in the training environments, the generalization of self-instruction behaviors to two different vocational training environments was assessed. All participants acquired self-instruction behaviors with BST instruction. However, only three of four participants independently generalized responding to two additional vocational environments. Results and directions for future research regarding training loosely and using multiple exemplars to program for generalization are discussed.

Keywords

self-instruction video activity schedules video modeling behavior skills training

The ultimate goal of special education is to increase the quality of life for individuals with disabilities by ensuring they have the skills needed to be as independent as possible. Turnbull and Turnbull (1985) defined independence as “. . . freedom of choice, self-determination, and autonomy from outside interference. Independence is the converse of being obliged to live one’s life as others want that life to be lived” (p. 108). Of the eight quality-of-life indicators outlined by Schalock and colleagues (2002), self-determination is a key indicator leading to an increase in overall independence. Self-determination occurs when an individual makes decisions regarding their quality of life without others unnecessarily influencing those decisions (Wehmeyer, 1996). Individuals with disabilities who have self-determination skills see greater outcomes in employment and independent living (Wehmeyer & Palmer, 2003). However, individuals with disabilities do not experience the same postsecondary outcomes compared with their typically developing peers. According to the U.S. Department of Labor, 34.9% of employment-age (i.e., 16–64 years old) individuals with disabilities were employed, whereas 74.8% of those without disabilities were employed in 2022 (U.S. Department of Labor, n.d.). Limited engagement within the community and employment opportunities could indicate lower quality of life outcomes for those with disabilities if intervention is not provided (Schalock et al., 2002). Those who teach and advocate for individuals with disabilities should shift their focus to teach self-determined behaviors in hopes of improving postsecondary outcomes.

The Individuals with Disabilities Education Improvement Act (IDEA, 2004) mandates that, prior to graduation, transition planning must be provided for all individuals with an identified disability to increase postsecondary outcomes such as employment and independent living. To gain and maintain employment, individuals who possess skills valued by employers will have greater outcomes. A survey from various employers found that basic skills (in general and specific to the job), integrity in work, following directions, social skills, and punctuality were some of the more valued skills of all employees (Ju et al., 2012). In addition, it was noted that employers reported having lower expectations for individuals with disabilities than those without; this has implications for positions within a company, assigned tasks, and pay (Ju et al., 2012). Educators therefore have the task of targeting skills necessary for post high school success, such as basic employment skills and job-specific skills. The selection of these vocationally-focused skills, which ultimately lead to employment goals, should be based on age-appropriate transition assessments designed to evaluate individual preferences and interests related to future work environments (Sitlington et al., 1997). It is an educator’s job to expose individuals with intellectual disability with or without autism (ID) to a variety of vocational domains throughout their transition years while assessing their aptitude and motivation to complete the presented work. Yet, it is unrealistic for educators to assess, plan for, and teach every necessary skill in all possible employment domains prior to their students leaving school (Shepley, 2017). Compounding this issue, individuals with ID typically receive caregiver or instructor prompts to complete tasks to increase their success within the classroom environment. Many students relying on prompts from their caregivers or instructors could become prompt-dependent when asked to transition to new tasks, follow multi-step tasks, or extend known tasks to new stimuli (MacDuff et al., 2001). As those supports disappear once students leave the classroom environment and enter postsecondary environments, it is paramount that educators find ways for students to learn new skills without the need for adult assistance.

Self-instruction, a self-management strategy, provides an avenue through which individuals with ID could complete tasks without caregiver support. Self-instruction involves the self-mediated application of support to complete a desired outcome, often in the moment and environment in which a task needs to be completed (Cooper et al., 2020). The use of visual activity schedules (VAS) and video-based instruction offer two directions through which educators can facilitate self-instruction with their students. VAS are created using static images or symbols of desired task steps or skills that are arranged sequentially to guide the individual to complete the task independently (e.g., a recipe that has pictures of each step; Spriggs et al., 2017). Video-based instruction provides a more salient demonstration of task completion; for example, a video model (VM) of the desired task allows individuals to watch a task being completed in its entirety or in small clips (Clinton et al., 2016).

Recent research has explored combining these strategies into video activity schedules (VidAS) as a self-instruction tool (e.g., Johnson et al., 2019; Shepley et al., 2018; Spriggs et al., 2014). VidAS can be created using a scheduling application (e.g., ChoiceWorks; Bee Visual LLC, 2011) on a portable device such as an iPod Touch or a phone, where VMs can be embedded into VAS. Teachers or caregivers can create VidAS using a scheduling application by organizing pictures to represent activities to be completed; for any activity that might need additional support, a VM can be linked to the picture. Bereznak et al. (2012) described several benefits offered by providing video-based instruction via portable devices including (a) multiple opportunities to review the video if needed, (b) ability to transport videos to new environments, (c) flexibility of device through which video can be viewed (e.g., smartphone, tablet, iPod), (d) reinforcing quality of engaging with electronics, and (e) increased implementation fidelity by instructors. Furthermore, the use of technology to present support for individuals with ID provides a discrete form of instruction that is also used by those without disabilities. As people gain access to more portable devices like tablets and smartphones, they become more reliant on the prompts embedded in applications on the device (e.g., reminders, alarms, and searching the internet).

For VidAS to become a self-instruction tool, teachers must consider how to teach their students to use them in a way that leads to independence. For a skill to be useful to the learner, it must be generalized, meaning the behavior occurs under novel, untrained conditions (e.g., settings and materials; Cooper et al., 2020). Teachers can increase the probability that skill generalization will occur by implementing strategies to facilitate generalization when teaching a new skill (i.e., during the acquisition stage of learning). Two examples of these strategies include training sufficient exemplars and training loosely (Stokes & Baer, 1977). Training sufficient exemplars involves using multiple stimuli, such as various instructors, materials in the task, or places in which the skill is needed when teaching a single skill. While most teaching in special education involves salient stimuli (e.g., Collins, 2012) and systematic, controlled strategies (e.g., Burke et al., 2010), training sufficient exemplars and training loosely involve using various stimuli and teaching in a way that allows for flexibility in instruction and student responses (Stokes & Baer, 1977). Training loosely, or using variable conditions to teach, is uncommon in special education published literature for two reasons: (a) errorless learning is the preferred instructional approach which is highly systematic (Collins, 2012) and (b) published literature is technological, allowing for tight experimental control and future replication (Stokes & Baer, 1977). One way to train loosely is by using behavior skills training.

Behavior skills training (BST) is a method of teaching that involves instruction, modeling, rehearsal, and feedback (Miltenberger, 2012). Behavior skills training can be flexible in that the instructions can be provided verbally or written or both; modeling can occur via live performance or VM, with a single example or multiple examples; rehearsal can occur until the individual acquires the skill and can be stopped and started as needed; and performance feedback can be provided during rehearsal, sometime after rehearsal, or a combination, and it can be provided in person or through writing such as via e-mail or text (DiGennaro Reed et al., 2018; Saunders et al., 2021). Whereas the instructional strategies often used in special education (e.g., time delay, system of least prompts) have salient stimuli and responses, BST allows for the stimuli and responses to occur naturally and with flexibility. Behavior skills training literature primarily demonstrates its effectiveness for teaching adults, such as caregivers (Schaefer & Andzik, 2021), medical students (Clay et al., 2021), and paraprofessionals (Andzik et al., 2021). The literature on using BST to teach individuals with disabilities is emerging. Macadangdang and Black (2022) used BST to teach three middle school students with ID to engage in recreational sport skills. Results indicated BST was effective in teaching novel skills to individuals with disabilities. Ethington et al. (2022) used BST to teach self-instruction skills to elementary students with ID to engage in recreational dances. All participants in the study were able to acquire self-instruction skills and generalize them to novel, untrained dances (Ethington et al., 2022).

The purpose of the present study was to determine the efficacy of BST in teaching self-instruction of vocational skills via VidAS to individuals with disabilities preparing for transition from secondary education settings. The primary research questions that guided this study were: Is BST effective for teaching the acquisition of self-instruction behaviors using VidAS? Will the combination of training loosely (i.e., BST) and the use of multiple exemplars lead to the generalization of self-instruction behaviors using VidAS?

Method

Participants

Students

Four high school students between the ages of 16 and 20 years participated in the study. All participants received instruction in a special education classroom for students with moderate to severe disabilities for the majority of their school day and participated in general education classrooms for specials (e.g., art, PE) for 25% of the school day. Participants met the following inclusion criteria according to teacher report, record review, and direct observation: (a) ability to attend to a task for 5 min, (b) fine motor ability to navigate an iPod Touch, (c) ability to imitate a VM with up to 5 steps, and (d) adequate hearing and vision. Table 1 includes participant demographic information. Participants were excluded from the study if they had prior experience self-instructing using VidAS.

Table 1.

Information for Participants in Behavioral Skills Training Study.

Participant	Age	Ethnicity	Eligibility category	Cognitive assessment	Adaptive behavior scale	BST environments	Generalization environments
Brianna	20	Black	ASD	64^a	54^b	Grocery; Office	Cosmetology; Coffee
Isaiah	16	Black	ASD	M = 46 (range 40–58)^c	55^b	Coffee; Grocery	Office; Cosmetology
Cameron	20	White	ID	55^d	79^b	Cosmetology; Coffee	Grocery; Office
Kiara	18	Black	ID	42^d	64^b	Office; Cosmetology	Coffee; Grocery

Note. BST = behavior skills training; ASD = autism spectrum disorder; ID = intellectual disability.

^aStanford-Binet-Fifth Edition (SB-5); ^bVineland Adaptive Behavior Scales-Second Edition (VABS II); ^cKaufman Test of Educational Achievement- Second Edition (KTEA-II); ^dKaufman Assessment Battery for Children- Second Edition (KABC-II).

Others

The classroom teacher, who had 2 years of experience teaching students with disabilities, served as the implementor of this study. She had a bachelor’s in moderate and severe disabilities and was working on her master's in teacher leadership with a focus on autism and applied behavior analysis. Graduate students in special education and applied behavior analysis from a local university served as reliability data collectors. The graduate students and teachers were trained in the VidAS procedures prior to the start of the study.

Setting

The study took place in a high school in an urban city in the Southeastern United States. There were two locations in the students’ school: the special education classroom and a nearby staff room. Sessions that took place in the classroom were conducted at one of two tables (approximately 2 m × 1 m) within the room. The staff room location included a small table with relevant materials on it (e.g., gloves and utensils). Additional materials for the implementor to arrange prior to sessions were stored in a neighboring classroom that had a refrigerator.

Materials

IPod Touch and Application

Sixth-generation iPod Touches were used to deliver VidAS for all schedules depicting various target vocational tasks. All three iPod Touches were in black protection cases, so they all looked alike. Researchers used the Choiceworks (Bee Visual LLC, 2011) application to create the VidAS for each vocational environment (i.e., cosmetology, coffee and food cart, grocery, and office tasks). The VidAS included line drawing thumbnail images of each vocational environment (e.g., image of a brush, comb, and scissors for “cosmetology”). Corresponding schedules were loaded into each environment with photograph thumbnail images that linked to VMs representing the tasks to be completed (e.g., a picture of the coffee pot for “prepare a pot of coffee”); see Figure 1 for an example.

Videos

VMs for each task were filmed using point-of-view perspective so that the participant could view the critical steps of the target behavior from their perspective (e.g., the model’s arms and hands were visible in the videos). The models were graduate students; however, due to the video perspective, no model’s face was included. All videos were filmed in the setting in which sessions occurred with voice narration of each task analytic step and then the verbal instruction, “Now it’s your turn. You do” at the end of each video. There were three schedules per each of the four environments and three tasks per schedule, for a total of 36 VMs. To ensure the participants did not memorize the tasks in each vocational schedule, individual schedules were randomized across sessions.

Vocational Task Materials

Vocational domains were selected based on the types of job training sites available within the school district and the feasibility of replicating those environments and tasks within a classroom setting. Domains included office/clerical, grocery, cosmetology, and coffee/food preparation. The research team identified materials based on the task analysis of each vocational task. Researchers pre-arranged the materials for the target schedule prior to each session. See Table 2 for an example of task materials. A complete list of task-related materials is available upon request.

Table 2.

Example Tasks and Materials for Cosmetology–Schedule 1.

Task	Materials
Setting up a manicure station 1. Get “Nails” basket 2. Place towel open on table 3. Get out nail polish remover 4. Get out 1 cotton ball 5. Get out lotion Removing Nail Polish 1. Open nail polish remover 2. Get cotton balls 3. Put remover on cotton ball 4. Rub cotton ball on all nails until paint is gone Applying lotion 1. Get lotion 2. Flip cap off lotion 3. Squeeze sides of container and dispense pea-size amount of product into your hand 4. Rub hands together 5. Apply product to mannequin’s hand	“Nails” Basket ⃝ Nail polish ⃝ Cotton balls ⃝ Nail polish remover ⃝ Nail file ⃝ Lotion ⃝ Hand towel Mannequin hand

Task

Materials

Setting up a manicure station
1. Get “Nails” basket
2. Place towel open on table
3. Get out nail polish remover
4. Get out 1 cotton ball
5. Get out lotion
Removing Nail Polish
1. Open nail polish remover
2. Get cotton balls
3. Put remover on cotton ball
4. Rub cotton ball on all nails until paint is gone
Applying lotion
1. Get lotion
2. Flip cap off lotion
3. Squeeze sides of container and dispense pea-size amount of product into your hand
4. Rub hands together
5. Apply product to mannequin’s hand

“Nails” Basket
⃝ Nail polish
⃝ Cotton balls
⃝ Nail polish remover
⃝ Nail file
⃝ Lotion
⃝ Hand towel
Mannequin hand

Experimental Design

A single-case multiple probe design across participants was used to evaluate the effectiveness of BST for teaching the acquisition of self-instruction behaviors using VidAS (i.e., history training) and to evaluate if the combination of training loosely (i.e., BST) and the use of multiple exemplars (i.e., vocational domains) would lead to generalization of those self-instruction behaviors using VidAS (Ledford & Gast, 2018). Data were collected on the percentage of independent self-instruction behaviors (see Table 3) for all participants for all four vocational environments. As a secondary measure, data were also collected on the percentage of independent vocational task completion for all 12 vocational schedules. Once data were stable across participants in baseline, BST was introduced to teach self-instruction behaviors to Brianna (i.e., navigation of technology when completing tasks in grocery and office environments); the remaining participants were intermittently probed a minimum of every 8 sessions across vocational environments. When mastery criteria were reached (i.e., 100% self-instruction to navigate technology and 80% task completion), Brianna was introduced to the use of VidAS with novel tasks (i.e., tasks in cosmetology and coffee environments), and the remaining participants were probed; Isaiah was probed for 3 consecutive sessions and then entered BST. This continued until all participants received BST and VidAS with novel schedules. Threats to internal validity were controlled for in this design in several ways. A multiple probe is generally less susceptible to testing threats than a multiple baseline due to the intermittent probe procedures. Instrumentation and procedural infidelity were controlled for by collecting ongoing reliability data. Vocational environments and schedules were counterbalanced across participants and conditions to ensure each participant had a unique pairing of environments in each condition (i.e., BST, VidAS; see Table 1).

Table 3.

Task Analysis for Self-Instruction Using Video Activity Schedules.

Navigating self-instructional materials
1. Get iPod Touch 2. Press home button 3. Press home button again to unlock (If not on home screen, press ‘home’ or slide left to home screen) 4. Select Choiceworks application 5. Select the corresponding environment (according to the task direction) 6. Select the first picture in the schedule to play the first video model WATCH and DO^a 7. Drag the first picture to the ‘all done’ column 8. Select the second picture in the environment to play the second video model WATCH and DO^a 9. Drag the second picture to the ‘all done’ column 10. Select the third picture in the environment to play the second video model WATCH and DO^a 11. Drag the third picture to the ‘all done’ column

Navigating self-instructional materials

1. Get iPod Touch
2. Press home button
3. Press home button again to unlock
(If not on home screen, press ‘home’ or slide left to home screen)
4. Select Choiceworks application
5. Select the corresponding environment (according to the task direction)
6. Select the first picture in the schedule to play the first video model
WATCH and DO^a
7. Drag the first picture to the ‘all done’ column
8. Select the second picture in the environment to play the second video model
WATCH and DO^a
9. Drag the second picture to the ‘all done’ column
10. Select the third picture in the environment to play the second video model
WATCH and DO^a
11. Drag the third picture to the ‘all done’ column

WATCH and DO was not considered a step in the self-instruction task analysis. This is a place holder for the individual vocational tasks loaded within the video activity schedule (i.e., the secondary dependent variable).

Dependent Variable

The primary dependent variable was the percentage of independent self-instruction behaviors (i.e., steps for navigating the mobile technology to self-instruct). The secondary dependent variable was the percentage of vocational task steps completed correctly. A single vocational schedule consisted of three separate vocational tasks (e.g., Grocery Schedule 2 included: prepare grocery bags, place cold items in one bag, and place not-cold items in another bag; see Figure 1). Researchers used BST to teach participants to navigate mobile technology; the task analysis for self-instruction steps is in Table 3. VMs embedded within the VidAS were the only instructions provided for task completion. Each vocational task consisted of three to five steps (e.g., the steps for cosmetology “apply face mask” were: lie mannequin on table and place towel on forehead, press face mask on mannequin’s face so that eyes and mouth are still visible; set timer for 3 min and press “start”). All three vocational tasks performed within a session were summarized together. The percentage of vocational task steps completed correctly was calculated by adding the total number of correct responses and dividing by the total number of steps and multiplying by 100. As task analysis steps consisted of three to five steps, the total number of steps for each schedule varied. An example schedule task analysis is in Table 2; task analyses for all 36 vocational tasks are available upon request.

General Procedures

Sessions were conducted during morning and/or afternoon instructional blocks for each participant for 3 days per week. The grocery, cosmetology, and office vocational settings were physically arranged in distinct areas of the classroom and the coffee setting was arranged in a separate staff room. Each vocational area was prearranged to include all possible task materials within the domain. There were three possible schedules per vocational domain; the presentation order was randomized using an online list randomizer at the beginning of the study. Prior to each session, the teacher implementor placed one of three iPod Touches in the participant’s work area. The teacher delivered the attentional cue, “It’s time to do your vocational work,” and waited for an attentional response (e.g., eye contact, “ok”). The teacher then provided the task direction, “Check your {environment name} schedule.” Participants were given 5 s to initiate the first step of the self-instruction task analysis (i.e., get iPod Touch) and 5 s to complete each additional step. Sessions lasted 1 to 10 min based on the condition and participant performance.

Probe

Probes followed the general procedures described earlier. In the first probe session across all participants, a multiple opportunity probe procedure (Cooper et al., 2020) was used to evaluate the participant’s ability to complete each step in the self-instruction task analysis. If a participant failed to initiate a step or inaccurately completed a step, their view of the iPod Touch screen was blocked, the step was completed by the teacher, the iPod Touch was placed back in front of the participant, and they were instructed to “keep going.” In all subsequent probe sessions, the session ended when the first error in the self-instruction task analysis was made (i.e., single opportunity probe procedures; Cooper et al., 2020). In the event a participant independently completed the first six steps of the task analysis (i.e., getting the iPod Touch and selecting the first picture in the schedule), they would see the VM for that vocational task (in the first probe session, the VM was a 1 s clip of the task since the teacher would be completing that step for the participant). Errors that occurred during task completion did not end the session; if one or more of the task steps were performed incorrectly or omitted, the teacher reminded the student to “keep working.” Participants were allowed to continue performing the vocational tasks as long as they were completing steps in the task analysis or until 30 s had passed with no response or consecutive errors (i.e., fixed-opportunity probe procedures; Alexander et al., 2017). Participant’s performance was praised on an average of every third correct response; if they completed a vocational task in its entirety, praise was also provided at the end of the task.

BST

Behavior skills training was used to teach self-instruction behaviors (i.e., technology navigation) for two of four vocational environments. Environments were counterbalanced across participants, so each participant had instruction in a unique combination of environments (see Table 1). Behavior skills training procedures consisted of a brief didactic explanation, modeling, rehearsal, and performance feedback. Behavior skills training procedures occurred each session until participants reached mastery (i.e., 100% self-instruction to navigate technology and 80% task completion). Once a participant reached mastery, sessions began with rehearsal and performance feedback. An environment was considered mastered when a participant was able to perform at mastery criteria for three sessions.

Brief Didactic Explanation and Modeling

The teacher used a PowerPoint presentation presented on a classroom iPad to deliver the brief didactic explanation as a visual story. Each slide in the PowerPoint explained the steps in the self-instruction task analysis. The teacher then modeled the steps in vivo using a non-target BST environment and scheduled on an iPod Touch. Modeling included performance and narration of the self-instruction steps as well as completing the vocational tasks for the “WATCH and DO” steps in the schedule.

Rehearsal and Performance Feedback

After the teacher modeled the procedures, she provided the task direction, “Check your {environment name} schedule” for one of the target BST environments (see Table 1). The same procedures for single opportunity probe sessions were used during rehearsal, with the addition of performance feedback. If a rehearsal included an error in the self-instruction task analysis, the teacher stopped the participant, provided feedback on the error (e.g., “You found the app right away. Next time click the picture at the top of the schedule first” while gesturing) and said “We are going to start over. Check your {environment name} schedule.” If an error occurred on a technology navigation step (see Table 3), the entire environment and schedule was reset, and the teacher provided the task direction again. Although data were collected during all rehearsals, the data during the final rehearsal (i.e., the end of the training session) for each session were graphed. Rehearsal sessions continued until a participant reached mastery on a single rehearsal (i.e., 100% self-instruction to navigate technology and 80% task completion) or until 10 min had passed.

BST Modification

After six sessions with little to no vocational task completion, but 100% self-instruction to navigate technology, Brianna received an additional verbal prompt, “Go do it,” to complete the task after watching the VM (Session 17). For the remainder of her BST sessions, she received that prompt if she did not initiate the task within 5 s of the VM ending (she received that prompt for 10 sessions of BST). She also received an additional verbal prompt to “Watch the video” in Session 28 when it was observed she was not continuously watching the VM and her vocational task completion accuracy was variable. Brianna also stayed in BST post mastery due to the variability in vocational task performance and the need for the additional prompt (i.e., she technically mastered 100% self-instruction to navigate technology and 80% task completion in session 32; she had three additional sessions to ensure she could perform at least 80% of the vocational task steps with no additional prompts).

VidAS

To assess the generalization of self-instruction skills to navigate technology, participants were provided the task direction, “Check your {environment name} schedule” for the two environments not targeted during their BST. VidAS environments were counterbalanced across participants like they were in BST (see Table 1). Procedures for VidAS were the same as those used during single opportunity probe sessions. Behavior specific praise was provided at the completion of each task if all steps were completed correctly; general praise was provided at the completion of each session, regardless of performance. Isaiah did need an additional prompt to select the correct environment after 3 sessions of selecting the incorrect environment (Session 54). Mastery criteria for VidAS were the same as BST (i.e., 100% self-instruction to navigate technology and 80% task completion).

Maintenance

Maintenance occurred every five to eight sessions after VidAS mastery. Maintenance procedures were the same as VidAS procedures, and environments from both BST and VidAS were randomly assessed. Isaiah mastered VidAS in the last session of the study (i.e., Session 60), so maintenance data were not assessed for him.

Reliability and Fidelity

Graduate students pursuing degrees in special education or applied behavior analysis were trained on data collection and procedures for all conditions of the study; they collected interobserver agreement (IOA) and procedural fidelity (PF) data for a minimum of 20% of sessions across all conditions and participants. A minimum of 80% reliability and fidelity were required to continue the study.

Using point-by-point agreement, IOA data were calculated separately for self-instruction to navigate technology and for vocational task completion. Percentage IOA was calculated by dividing the number of agreements by the number of agreements plus disagreements and then multiplying that value by 100 (Ledford & Gast, 2018). PF data were collected on the following teacher behaviors: (a) having materials arranged prior to the start of the session; (b) having the iPod and iPad (for BST) in the set location and set up correctly (turned on, charged, not in Choiceworks application, schedule from previous user closed out and reset); (c) delivered attentional cue and waited for attentional response; (d) provided the correct task direction; (e) praised on the average of every third correct response; (f) praised at the completion of a correctly completed task; (g) provided general praise at the end of a session; (h) provided a brief didactic explanation using visual story at the beginning of the sessions (BST only); (i) modeled the procedures following the brief didactic explanation (BST only); and (j) provided feedback if a student error occurred (BST only). Procedural fidelity was calculated by adding the teacher behaviors that occurred and dividing by the total number of expected teacher behaviors.

Interobserver agreement and PF data were collected for a mean of 34% of probe sessions (range, 23%–40%), 43.3% of BST sessions (range 33–67%), 35.3% of VidAS sessions (range 20–67%), and 50% of all maintenance sessions. The mean IOA across all participants for navigation of mobile technology to self-instruct was 100% for probe sessions, 99.8% for BST sessions, 100% for VidAS sessions, and 100% for maintenance sessions. The mean IOA across all participants for vocational task completion was 100% for probe sessions, 98.3% for BST, 99% for VidAS, and 100% for maintenance sessions. Mean PF across participants was 99.5% during probe sessions, 98.4% during BST sessions, 98.8% during VidAS sessions, and 96.7% during maintenance sessions. Maintenance data were calculated for Brianna, Cameron, and Kiara. All but one PF error made was regarding praise (e.g., proving general praise at the end of the session, praise provided if all steps were performed correctly). During one session of the study, materials were not arranged prior to the start of the session.

Social Validity

In an effort to evaluate student preference for the various vocational domains, a series of five multiple stimulus without replacement (MSWO; DeLeon & Iwata, 1996) preference assessments were conducted with each participant before each experimental condition (i.e., before baseline probes, between baseline and BST, between BST and VidAS, and after VidAS). MSWO sessions were implemented by the classroom teacher, and the 5 sessions were dispersed across 4 to 5 days. Materials used during the MSWO sessions were four 7 cm × 7 cm pictures depicting the same thumbnail images of the four different vocational domains used in the VidAS application (see Figure 1). The teacher presented the four cards in a row in front of the participant, pointed to each card one at a time while stating the name of the vocational environment, and then gave the task direction to “Choose your favorite job.” Following the participant’s selection, the teacher commented and conversed with the participant about jobs related to that domain, removed the card from the array, shuffled the remaining thumbnail images, and re-presented the remaining cards. These steps repeated until no pictures remained. Rank order data were averaged across five sessions and graphed over time.

While this social validity measure was not a specific research question within the study, the inclusion of preference data for the vocational domains introduced in the study yields important information about self-determination and student voice. Specifically, individuals cannot indicate a preference for one job over another until they know what the job entails, either by direct or by indirect exposure. This study directly exposed participants to four different vocational domains and then indirectly assessed their preference for the type of work throughout the exposure. Preference of vocational domains remained variable throughout the study for Brianna and Kiara; however, Isaiah and Cameron’s preference assessment data yielded a clear preference hierarchy during the latter half of the study (i.e., before and after the VidAS condition). Individual participant MSWO graphs are included as supplemental material.

Results

Data were visually analyzed regarding level, trend, stability, immediacy of effect, consistency of effect, and overlap to determine the effects of BST on the navigation of mobile technology to self-instruct (Ledford & Gast, 2018; see Figure 2, closed data points in line graph) as well as generalization of those behaviors during VidAS (see Figure 2, open datapoints in line graph). The percentage of vocational task steps completed during each session, which was analyzed across conditions, was also considered as a secondary measure of effectiveness (see Figure 2, bar graphs). Given that all threats to internal validity were reasonably controlled for, and therapeutic changes in behavior occurred when and only when intervention was introduced, a functional relation between BST and an increase in the navigation of mobile technology to self-instruct was demonstrated in this study. During the first baseline probe session, two participants were able to complete Steps 7, 9, and 11 of the task analysis (i.e., drag tasks to the “all done” column) using the multiple opportunity probe procedure. Subsequently, each participant met mastery criteria in BST (i.e., 100% self-instruction to navigate technology and 80% task completion). Brianna technically met mastery criteria in 23 sessions; she did stay in BST for 3 additional sessions post mastery due to the variability in vocational task performance to ensure she could perform at least 80% of the vocational task steps reliably without additional prompts. Isaiah met mastery criteria in 25 sessions, Cameron in 5 sessions, and Kiara in 3 sessions. As BST served as a training condition, a functional relation was also demonstrated in the generalization of the navigation behaviors to novel environments and tasks. Participants also met mastery criteria in their respective generalization environments: Brianna in 7 sessions, Isaiah in 10 sessions, Cameron in 4 sessions, and Kiara in 3 sessions.

Effectiveness of BST

Behavior skills training was effective in increasing the navigation of mobile technology to self-instruct in the two targeted vocational environments for all four participants. Results are illustrated with the closed data points in the line graph in Figure 2; data point shapes reflect the vocational environments (e.g., circle for “grocery”). During probe sessions, participants had stable data paths with little variability. Cameron’s first session of BST was conducted using multiple opportunity probe procedures; he was able to complete getting his iPod from the start location, press home button, press home button again to unlock, and select the VM after the previous video had been dragged to “all done.” During all other probe sessions, participants were able to complete getting their iPods from the start location, pressing the home button, and/or pressing the home button again to unlock the iPod. Upon introduction of BST, Brianna and Isaiah had one overlapping data point and then an abrupt change in level and trend, reaching 100% navigation steps in three sessions for Brianna and six sessions for Isaiah. Cameron and Kiara had an immediate and abrupt change in level to 100% in their first session of VidAS. Cameron’s data had a countertherapeutic change in level for two sessions and then returned to 100% for two sessions. Kiara performance was 100% for navigation steps for all BST sessions.

Generalization Across Vocational Environments

Participants were able to generalize the navigation behaviors learned in BST to two novel vocational settings during the VidAS condition (i.e., tasks not trained using BST). Results are illustrated with open data points in the line graph in Figure 2. During probe sessions, participants had stable data paths, with little to no variability. Brianna, Isaiah, and Kiara all had their first session conducted using multiple opportunity probe procedures; they were able to complete getting their iPods from the start location, pressing the home button, pressing the home button again to unlock the iPod, dragging the pictures to “all done,” and/or selecting the VM after a picture had been drug to “all done.” During all other probe sessions, participants were able to get their iPods from the start location, press the home button, and/or press the home button again to unlock the iPod. Brianna, Cameron, and Kiara’s results indicate the generalization of navigation behaviors immediately following mastery during BST. Isaiah had three sessions at 36% immediately following mastery in BST. After a verbal prompt to select the correct environment, there was an immediate change in level to 100%. When compared to initial probe levels, Brianna, Cameron, and Kiara’s navigation accuracy increased from a level below 30% to 100% during VidAS. Isaiah’s probe levels ranged from 18% to 45%; his first three sessions in VidAS were 36% and then his level increased to above 90% until he reached mastery.

Vocational Task Completion

Data were collected on vocational task completion during each session as a secondary measure. Results are shown in the bar graph in Figure 2. Probe levels of accuracy were 0% for all participants. Brianna had variable responding during BST and VidAS but was consistently above baseline levels once she was reminded to “go do it” starting in Session 17, and her last three sessions of BST did not require additional prompts. Isaiah’s level of task completion remained at baseline levels for four sessions in BST, followed by an accelerating trend and then a variable trend; his data had a similar trend during VidAS (i.e., 0% for three sessions, followed by a variable trend). Other than the first sessions of BST and VidAS, Isaiah’s task completion data were consistently above baseline levels. Cameron performed 100% vocational steps correctly during his first session of BST, followed by two sessions at 0% and then two sessions above 90%. His task completion data correlated with his self-instruction data (i.e., the higher the level of self-instruction behaviors, the higher the level of task completion). Cameron’s task completion data were variable during VidAS, ranging from 60% to 100%.

Maintenance

Maintenance data were collected for Brianna, Cameron, and Kiara. Vocational environments and tasks were randomly selected for each participant. The environments randomly selected for Brianna during maintenance were the two environments that had been targeted during VidAS. Brianna was able to complete 100% of the navigation steps and 73% to 82% of the vocational task steps accurately during maintenance. The environments randomly selected for Cameron and Kiara were one environment that had been targeted during BST and one that had been targeted during VidAS. Cameron was able to complete 27% to 100% of the navigation steps and 0% o 64% of the vocational task completion steps accurately during maintenance. Kiara was able to complete 18% to 100% of the navigation steps and 0% to 100% of the vocational task steps accurately during maintenance. For both Cameron and Kiara, the vocational environment that had the lower level (i.e., 27% navigation and 0% task completion for Cameron; 18% navigation and 0% task completion for Kiara) was an environment targeted during the VidAS condition; the vocational environment that had the higher level of maintenance was targeted during BST.

Discussion

This study investigated whether BST was an effective intervention for teaching the acquisition of self-instruction behaviors using VidAS. It also examined the combination of training loosely (i.e., BST) and the use of multiple exemplars to program for the generalization of self-instruction behaviors using VidAS. The results of this study indicate BST was effective in teaching self-instruction skills to individuals with disabilities to increase their performance of vocational tasks. Additionally, this study provides a practical framework for educators that could be implemented in the classrooms of individuals who will be transitioning from secondary education settings to employment settings, as it is critical to program for and assess generalization throughout the acquisition phase of learning. Once students can self-instruct, they can perform new skills without the assistance of another adult or peer. This independence from others is similar to the effects demonstrated by past researchers studying self-instruction behaviors (e.g., Johnson et al., 2019; Shepley et al., 2018; Spriggs et al., 2014).

This study addresses two important concepts in education literature: proactively programming for generalization and selecting socially significant behaviors to increase (i.e., vocational tasks). Often, programming for generalization is not prioritized, and generalization is only assessed and analyzed following the completion of a study. Behavior skills training is an intervention that, by nature, incorporates generalization strategies (i.e., training loosely), which is crucial when teaching pivotal skills. Similar to the present study, Ethington et al. (2022) implemented a BST intervention package targeting self-instruction behaviors that used generalization strategies including training loosely and multiple exemplars. Three out of four participants were able to generalize skills to novel behaviors. Macadangdang and Black (2022) yielded similar results when using BST as an intervention to target socially significant behaviors but did not formally program for generalization. Although BST can be used to promote generalization, it may not be appropriate for all learners.

Literature on BST as a teaching strategy for learners with ID is emerging; therefore, this study can contribute to that base. There were some notable differences between the participants in this study that impacted the external validity of the results. Participants varied in their diagnoses, ages, cognition, and adaptive behavior; perhaps these individual differences need to be considered when deciding to use BST as a teaching intervention in classrooms. Brianna and Isaiah required longer to reach mastery using BST and did not display consistent task performance. On the contrary, Cameron and Kiara, who had higher adaptive behavior scores and were not diagnosed with autism, were quick to master both navigation and task performance. Potentially, loose training, such as BST, is more efficient for individuals who can adjust to their ever-changing environments. Educators should always prioritize individualization of procedures and interventions to ensure the most efficient means of instruction for each individual learner.

Limitations

Methodologically, utilizing the single opportunity probe procedure during baseline could have suppressed actual performance ability. A multiple opportunity probe was used for the first baseline session, but only in one environment. However, in their meta-analysis, Alexander et al. (2015) concluded there to be no superior probe procedure for chained tasks. Additionally, one should consider the efficiency of BST as an intervention when interpreting the results of this study. While BST was ultimately successful, some participants required extensive time in this condition before reaching mastery criteria. However, all participants were able to maintain skills, and three of four participants generalized the pivotal skill of self-instructing to new environments, which may justify a longer acquisition period.

Selection of domains and skills is another limitation of this study. The participants in the study had little or no pre-exposure to vocational training, limiting their ability to provide meaningful input on preferred vocational environments. With the passage of the Workforce Innovation and Opportunity Act (WIOA) in 2014, students with disabilities who qualify have access to pre-employment transition services (Pre-ETS) through state vocational rehabilitation agencies. Students have the ability to explore jobs of interest, work-based experiences, counseling following postsecondary education, workplace readiness and training, and instruction in self-advocacy (Carlson, 2022). Participants were administered preference assessments throughout the duration of the study in an effort to determine their interest in specific vocational settings, but the results were negligible. That being said, procedures outlined in this study could be integrated for students receiving Pre-ETS to further support their success in natural environments.

Another limitation of this study is the setting in which sessions took place. Rather than conducting sessions in contrived settings, it would be preferable to conduct sessions in the natural environment while incorporating generalization strategies such as programming multiple exemplars and common stimuli. Researchers should also program for naturally maintaining contingencies to promote generalization (e.g., seeing happy customers and getting paid by the hour). Two of the participants, Cameron and Kiara, did not maintain technology navigation skills in their generalization settings. If time allowed, future research could program a re-teaching procedure (if necessary) for participants and measure their long-term maintenance.

Lastly, data in the BST sessions were based on the participant’s performance during the final training trial in the session, which was either the trial during which the participant met the criterion for that session or the last trial conducted after a duration of 10 min had elapsed. The decision to have a maximum duration of 10 min was based on teacher feasibility. Although the decision to graph the final training trial does positively skew the participants' performance for that session, this decision was based on published BST literature; even though published BST research does vaguely describe criteria for rehearsal and feedback sessions (e.g., 100% correct for three consecutive training trials; Morgan & Wine, 2018), this literature does not include the BST rehearsal data in the graphs. Rather, these data are typically represented as condition lines during which training occurred. Thus, it is often unclear how long BST training sessions occurred or how many sessions to criterion were needed. Future BST literature should consider conducting a probe session, similar to baseline procedures, prior to every BST session to get an accurate depiction of an individual’s performance without being influenced by an immediate training session (Roberts et al., 2021).

Future Research

Future research should examine participants that are more homogenous (e.g., autism only, autism with or without ID, ID within a certain IQ range, etc.). This will help piece together the specific populations for which BST will likely be most effective. It will also increase the generalizability of BST procedures if they are shown to be effective across multiple populations. Studies should also be implemented in more authentic environments across different setting locations (e.g., grocery store, hair salon, and coffee shop) rather than those set up around the classroom only. Researchers should also strive to incorporate vocational tasks that are of interest to the participants of the study to increase their motivation to participate and add to the social significance of the study.

Conclusion

It is important to consider programming skills that teach individuals to self-instruct using their technology. Educators have historically relied on direct instruction to teach discrete and chained tasks. However, the issue with this approach is that the method of teaching does not always result in pivotal behaviors that generalize to untrained environments, people, and materials. Yet, these should be top priorities for educators of students who are transitioning from secondary school settings to community, vocational, or college settings. One way teachers can program for pivotal behaviors that generalize to untrained environments is through use of BST and multiple exemplars. This would allow individuals to learn to self-instruct, which could generalize and enable them to access unlimited instruction based on desires and needs. Self-instruction is a component of self-determination that increases the quality of life for the individual with disabilities, including their engagement in employment and post-secondary environments.

Supplemental Material

sj-docx-1-cde-10.1177_21651434231211258 – Supplemental material for Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills

Supplemental material, sj-docx-1-cde-10.1177_21651434231211258 for Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills by Amy D. Spriggs, Sally B. Shepley, Mark D. Samudre, Hannah E. Keene, Kai O’Neill and Shealynn Hall in Career Development and Transition for Exceptional Individuals

Supplemental Material

sj-docx-2-cde-10.1177_21651434231211258 – Supplemental material for Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills

Supplemental material, sj-docx-2-cde-10.1177_21651434231211258 for Behavioral Skills Training to Teach Self-Instruction of Video Activity Schedules for Vocational Skills by Amy D. Spriggs, Sally B. Shepley, Mark D. Samudre, Hannah E. Keene, Kai O’Neill and Shealynn Hall in Career Development and Transition for Exceptional Individuals

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

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available at

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