Augmented Reality: Teaching Daily Living Skills to Adults With Intellectual Disabilities

Abstract

Independent living is recognized as a marker of adulthood. For individuals with intellectual and developmental disabilities, however, the need for continued support in completing daily living tasks reduces the likelihood of achieving independence in this domain. Barriers to living independently include increased dependence on family and support staff and deficits in functional life skills. In this study, a multiple-baseline across behaviors design was used to examine the efficacy of an augmented reality intervention for teaching daily living skills to three young adults with disabilities in a residential postsecondary education program. Our results indicate the intervention was effective for increasing independence among all participants. Furthermore, the intervention was found to be a socially acceptable and nonstigmatizing method for supporting young adults in a residential postsecondary education program.

Keywords

augmented reality video modeling independent living transition content/curriculum area intellectual disablity exceptionality

In 2016, The American Association on Intellectual and Developmental Disabilities reported that the majority of the estimated 6.2 million people with intellectual and developmental disability (IDD) in the United States live with either family or in segregated and supported living arrangements. Historically, individuals with IDD have been afforded limited opportunities to make choices regarding their living arrangements (Stancliffe et al., 2011). The shifting paradigm from segregated housing to community living resulted from advocacy efforts by individuals with IDD seeking increased decision-making opportunities regarding their daily lives (Stancliffe, 2001; Wehmeyer & Schwartz, 1998). The right to choose where and with whom to live was further validated in article 19a of the United Nations (2006) Convention on the Rights of Persons with Disabilities and continues to be upheld by organizations such as the National Council on Independent Living (2018) and the World Health Organization (2011).

These organizations emphasize the need for assistance and supports that aim to reduce the number of people living in supported living arrangements and promote independent living to the highest degree possible for those with IDD. Benefits associated with such freedom of choice include better quality of life (Emerson et al., 2001; Howe, Horner, & Newton, 1998), access to community and social activities, and the development of broader social networks (Chang, Coster, & Helfrich, 2013). Progress toward achieving independent living, however, is hindered by barriers that prevent individuals with IDD from attaining independence. Examples of such barriers include increased dependence on family or staff and deficits in functional life skills.

Adaptive Functioning

Newman et al. (2011) report that adults with IDD are less likely to live independently than their same-aged peers. Severe limitations in adaptive functioning are characteristics associated with IDD and are significant barriers for achieving independence regarding community living (Woolf, Woolf, & Oakland, 2010). Many individuals with IDD experience deficits in functional living skills related to self-care, cooking, cleaning, and managing personal finances, which impedes their ability to live without additional supports. These limitations influence outcomes across the life span of the individual, requiring additional support from family and agencies as they progress throughout their educational careers and transition into adulthood. Equally important, previous research has found failure to teach such skills can result in negative outcomes that include low self-esteem, learned helplessness, and lower quality of life (Cannella-Malone et al., 2006; Curtis, 1989; Hayden, 1997; Parmenter, 1994). Therefore, a focal point of transition programs for students with IDD is the development of the functional skills and adaptive behaviors that increase independence in the area of daily living.

Interventions for Teaching Daily Living Skills

Video Modeling

Research has demonstrated the efficacy of video modeling as an instructional strategy for teaching a multitude of behaviors. Furthermore, video modeling has been identified as an evidence-based practice for teaching daily living skills to young adults with IDD (Domire & Wolfe, 2014). Video modeling interventions involve creating a video of an adult, peer, or the individuals themselves performing a task. Learners view videos of a model performing a task or demonstrating a skill and then imitate the behavior seen in the video (Haring, Kennedy, Adams, & Pitts-Conway, 1987). Research has shown video modeling leads to faster skill acquisition and generalization of daily living skills (Domire & Wolfe, 2014) and have been developed to teach participants with IDD shopping skills (Haring et al., 1987), meal preparation (Mousa AL-Salahat, 2016; Taber-Doughty et al., 2011), cleaning skills (Cannella-Malone et al., 2006; Mechling, Ayres, Bryant, & Foster, 2014), safety skills (Mechling, Gast, & Gustafson, 2009), transportation (Mechling & O’Brien, 2010), and general skills related to daily living (Laarhoven, Zurita, Johnson, Grider, & Grider, 2009). Before implementing video modeling interventions, however, it is critical for researchers and practitioners to take certain considerations into account. These considerations include the selection of the model, perspective, and whether additional components are necessary to enhance the success of the intervention.

Considerations for implementation

Selecting the type of model or perspective that best aligns with the viewer’s preferences allows for videos to be individualized according to the viewer’s needs (Mechling, 2005). Most commonly, third person perspective is used in video modeling interventions (Mason, Davis, Boles, & Goodwyn, 2013). Models in this type of video include same-age peers, adults, or the individual performing the intervention. Point-of-view modeling is another perspective used in the development of video modeling interventions and depicts what the learner would see from his or her perspective and is used when participants are easily distracted or overstimulated. This method allows learners to focus only on the most critical elements of the task by removing unnecessary stimuli from the video (Gardner & Wolfe, 2013).

Additional components such as audio features, error correction procedures, and/or reinforcement can be used in conjunction with video modeling to increase the potency of the intervention. A common limitation described in video modeling literature, however, is that additional components make it difficult to decipher whether changes in the behavior were the result of the intervention alone or whether the results were influenced by the addition of other components (Hammond, Whatley, Ayers, & Gast, 2010; Hart & Whalon, 2012; Mechling & Ortega-Hurndon, 2007). Mason, Davis, Boles, and Goodwyn (2013) recommend researchers begin the intervention before implementing additional components to determine the success of the intervention as a stand-alone procedure.

Social learning theory (SLT)

The general theoretical context of SLT is assumed to be fundamental to video modeling. Based on Bandura’s SLT, the effectiveness video modeling is attributed to the premise that all behavior is learned, and people learn through observation of behaviors performed by models who they perceive as competent (Bandura, 1971; Bellini & Akullian, 2007). Learning through observation and modeling is governed by four key principles of the SLT: (a) attention, a person must be able to attend to a model in order to learn; (b) retention, information is retained through two symbolic systems which represent behavior in either visual or verbal form; (c) replication, a person must possess the motor skills necessary to replicate the learned behavior; and (d) reinforcement and motivational processes, imitation occurs as the result of motivation and reinforcement (Bandura, 1971; Ormrod, 1999). Observational learning is effective for shaping a person’s self-esteem, self-concept, and self-efficacy (Bandura, 1977).

Augmented Reality (AR)

More recently, studies have shown that using AR for video modeling increases independence (Akçayır & Akçayır, 2017; Bower, Howe, McCredie, Robinson, & Grover, 2014). Through AR, learners perceive digital information (i.e., video models, picture cues) in real time. This technology differs from virtual reality in that users view virtual information in tandem with authentic environments rather than immersing viewers in alternate realities (Bower et al., 2014). Virtual information is accessed when the user activates the AR system by pointing a device at a trigger. Two types of systems are used for accessing AR: marker-based systems and markerless systems. Marker-based systems use a premade target in the form of an object or visual to activate the virtual overlay. Markerless systems rely on global positioning system to cue the appearance of the digital content (Lee, 2012). AR can be used on most wireless technology devices (Lee, 2012). The increasing accessibility of mobile devices and the advancement of AR technologies have led researchers to explore new ways for incorporating this technology into the lives of people with IDD.

The benefits associated with AR are well-documented. AR has shown it to be an effective tool for increasing student motivation, self-confidence, and learning outcomes (Akçayır & Akçayır, 2017; Bower et al., 2014). When used as an instructional tool, AR reduces dependence on teachers and increases student engagement (Lin et al., 2016). Moreover, using technology (such as an AR app on a cellphone) in the classroom or community reduces the stigma of needing accommodations. The use of AR systems allows students to be in charge of their own learning. Furthermore, the control AR affords users promotes self-regulation and increases self-determination (Cihak et al., 2016; Smith, Cihak, Kim, McMahon, & Wright, 2017). Students can engage in “authentic exploration” when engaging in instruction delivered through AR (Wu, Lee, Chang, & Liang, 2012). AR systems are a socially valid method for providing support in any environmental setting (Cullen & Alber-Morgan, 2015; Smith et al., 2017).

Studies on the use of AR technology have applied this method to an array of skills. AR interventions have been empirically validated for improving the performance of independent living skills related to personal hygiene and meal preparation (Cihak et al., 2016; Johnson, Blood, Freeman, & Simmons, 2013). One study used AR to teach participants with ID to check food labels for possible food allergens (McMahon, Cihak, Gibbons, Fussell, & Mathison, 2013). Studies have found AR technology effective for teaching students with disabilities to navigate to unfamiliar locations (McMahon, Cihak, & Wright, 2015; Smith et al., 2017). In addition, research on the use of AR for employment training has shown it to be successful for increasing independence in completing vocational tasks (Chang, Kang, & Huang, 2013).

AR for Daily Living

We aim to expand existing literature by employing an AR intervention for teaching daily living skills to adults with IDD in a postsecondary education setting. The purpose of this study is to examine the effects of using AR to improve independence in completing daily living skills: ironing, bed making, and setting an alarm clock. In the effort to determine the efficacy of the AR intervention for achieving this purpose, we propose the following research question: Does the use of AR increase the percentage of steps completed independently for individuals with IDDs in completing daily living tasks?

Method

Participants

Participants were selected from a group of 15 adults with moderate to severe IDD attending a 9-week postsecondary education program. During the first 2 weeks of the program, the researchers observed and collected data on all students performing the targeted daily living tasks (i.e., ironing, setting an alarm, and making a bed). Individuals were selected based on the following criteria: (a) demonstration of the need for intensive intervention for completing daily living tasks based on observational data, (b) proficiency in performing basic iPad functions (i.e., turning on the device, independently opening apps, and logging into programs), and (c) personal goals to increase independence in the area of daily living. Two males and two females were recruited to participate in this study. However, one participant temporarily left the program and was unable to complete the intervention.

The participants and their parents/guardians were required to attend a 2-day orientation before beginning the program. During orientation, participants and their families were given consent forms agreeing to participate in research projects that took place during the 9 weeks. Parents/guardians were informed of the specifics of this particular study and were asked to sign consent forms allowing their child to participate in the study. The purpose of the study was explained to the participants and assent was obtained giving their permission and stating their willingness to participate in the study.

Brady

Brady was a 20-year-old student diagnosed with Williams syndrome. He worked at a local grocery store once a week stocking groceries with the assistance of a job coach. This was Brady’s first year in a postsecondary program. He had many strengths in the area of daily living such as tending to his personal needs and preparing simple meals and snacks. Based on observational data, Brady used his smartphone to make calls, play music, and watch entertaining videos. Brady did not know how to use the alarm function on his cell phone and relied on staff to tell him when it was time to prepare for class or work. Brady’s goal was to attend a certified transition program and find a job so that he could one day live with roommates. Based on the results of the Reynolds Intellectual Assessment Scales (RIAS), his IQ was 63. His average adaptive score was 60.5. He received a score of 68 from the parent portion of the assessment and 58 from his teacher on the Adaptive Behavior Assessment System-2nd Edition (ABAS-II). Results from the RIAS and ABAS-II indicate Brady had limited ability for performing daily living tasks and operating mobile devices.

Michelle

Michelle was a 19-year-old student diagnosed with Down syndrome. Before attending the program, Michelle lived at home with her family. This was Michelle’s second summer in the postsecondary transition program. Michelle’s goal was to be a physical therapy assistant and to live independently or with roommates. Michelle independently tended to her personal needs but needed support for cooking, laundry skills, cleaning, and managing her money. Michelle independently used her smartphone to make calls, send text messages, access social media, and listen to music. Her most recent assessment results indicate Michelle had an IQ of 52, based on results from the Wechsler Adult Intelligence Scale-4th Edition (WAIS-IV). Results from the WAIS-IV and ABAS-II indicate Michelle had limited ability for daily living tasks and controlling a mobile device. She received a score of 83 on the parental assessment and a 63 from her teacher. Her average adaptive score was 73.

Robert

Robert was a 36-year-old student diagnosed with an attention deficit hyperactivity disorder and atypical behaviors of pervasive developmental disorder. Because of his age, his family was unable to produce current assessments of his intellectual and adaptive functioning. Before attending the postsecondary program, Robert worked independently as a bagger at a local grocery store for 12 years. Robert lived at home with family but was independent with preparing simple snacks and tending to his personal needs. Robert only used his phone to make calls. Robert’s goal in attending the program was to gain more independence in order to live with a roommate. This was Robert’s first summer in the postsecondary education program. Direct observations indicated that Robert struggled with operating his cell phone and iPad. Table 1 lists the characteristics for each participant.

Table 1.

Participants’ Characteristics.

Participant	Age	IQ	ABAS-II	Disability
Brady	20	RIAS: 63	Parent: 68	Williams syndrome
Brady	20	RIAS: 63	Teacher: 58	Williams syndrome
Michelle	19	WAIS-IV: 52	Parent: 83	Down syndrome
Michelle	19	WAIS-IV: 52	Teacher: 63	Down syndrome
Robert	36	N/A	N/A	Attention deficit hyperactivity disorder, atypical behaviors of the pervasive developmental disorder type

Note. ABAS-II = The Adaptive Behavior Assessment System-2nd Edition (Harrison & Oakland, 2003); RIAS = Reynolds Intellectual Assessment Scales (Reynolds & Kamphaus, 2003); WAIS-IV = Wechsler Adult Intelligence Scale-4th Edition (Wechsler, 2008).

Setting

The study was conducted in a dormitory on a large university campus located in the Southern United States. The transition program occupied a dormitory hallway alongside other students without disabilities. Participants were taught to use HP Reveal (2018) in the setting where the daily living task would normally take place. Instructional sessions for making the bed and setting an alarm occurred in the participants’ dorm rooms, and instructional sessions for ironing took place in space adjacent to a hallway where the communal ironing board and iron were located.

Instrumentation

Video models

Videos for the intervention were recorded using a Canon Vixia HD Camcorder and edited on iMovie (2016, Version 10.1.1). Videos were taped at a researcher’s apartment in settings similar to where the skills would take place during the study (e.g., the bedroom for setting the alarm and making the bed). Before taping, the research team developed task analyses for each skill and used them as a guide for the sequence of the video models. The task analyses were created by the principal investigator. Before beginning the study, each target skill was performed using the same materials and in the same environments where the observations would take place. The task analyses were then shown to an expert in the field of transition and were validated to measure the target behaviors. The model in each video was an adult female who provided step-by-step verbal instructions while simultaneously modeling each step of the task. Figure 1 shows a screenshot of the video model that appeared in the video for ironing clothes. Video clips were edited to show the entire sequence from beginning to end of the task analysis, and subtitles were included for each step verbalized by the model. The duration of the videos ranged from 1 min 28 s to 1 min 57 s and were viewed on iPads provided to the participants to use during the program.

Figure 1.

Screenshot of video model.

HP reveal

The mobile app HP Reveal (2018) was downloaded to each iPad prior to the start of the study. HP Reveal is an AR app for smartphones and mobile devices that allows users to create and upload videos that can be accessed by a marker or target. Targets can be items in the environment or user-made labels. For participants to access the video models, targets were created in Microsoft Word (2018, Version 16.14.1). The targets consisted of a 4 × 3 in. laminated card with the title of the task typed in large, black font and a small icon of the HP Reveal logo pasted beside it (see Figure 2). During training and intervention conditions, participants pointed the rear-facing camera on their iPads at the laminated targets to activate or “trigger” the video models. The video models appeared as an overlay across the target. The size and position of the videos could be adjusted by moving closer or farther away from the target or adjusting the angle of the iPad.

Figure 2.

HP Reveal target for making bed.

Procedures

A multiple-baseline across participants and behaviors design was used to determine the efficacy of the AR intervention for improving daily living skills for young adults with IDD (Gast, Lloyd, & Ledford, 2014). Multiple-baseline designs are used to determine the efficacy of an intervention when the participants are unlikely to return to baseline levels after being introduced to the intervention. In a multiple-baseline design, the introduction of the independent variable (AR intervention) is staggered across the participants. One participant receives intervention while the others remain in the baseline condition. A functional relationship is determined when the data reflect a positive change for the participant receiving intervention and the remaining participants maintain stable baseline data (Gast et al., 2014).

Baseline

During the baseline condition, the researchers collected data on each participant’s ability to perform the targeted daily living task without the use of HP Reveal. The researchers instructed the participants to go to the location where the skill would be performed and provided the task direction, “It’s time to _____.” A task analysis was used to assess the steps each student was able to perform independently. For baseline observations, each step of the task analysis was scored as either independent or not observed. No errors were corrected, and no feedback was given on the participants’ performance, and verbal praise was given upon completion of the task. A minimum of three data points were collected for each participant during the baseline condition (Kazdin, 2010). Before moving on to the intervention, the baseline data for each participant had to demonstrate a lack of trend or improvement (Byiers, Reichle, & Symons, 2012).

Training for HP reveal

Training sessions took place one-on-one with each participant. During the training, the researchers modeled opening the app and pointing the device at designated targets to activate the video model. Then, participants practiced using the app on their own devices on a set of HP Reveal targets unrelated to the skill in which they were being assessed. The researchers collected data on the participants’ ability to operate HP Reveal independently using the task analysis seen in Table 2. Least-to-most prompts with a 5-s wait time were used during the training phase to assist the participants in opening the application, pointing their iPads at the designated targets, and watching the video model. Mastery criterion during training was 100% accuracy over three consecutive trials.

Table 2.

Task Analysis for Using HP Reveal.

HP Reveal
1. Locate iPad
2. Open iPad by pushing the home button and sliding your finger over the home screen
3. Find and click on the HP Reveal App
4. Locate HP Reveal target
5. Push the blue button on the bottom/center of the app home screen
6. Hold iPad up to the HP Reveal target with camera facing target
7. If needed, turn sound on using the button on the side of the iPad
8. Watch video
9. Put iPad down

Intervention

Data collected during the intervention conditions used separate task analyses to assess two measures: (a) the participants’ ability to use the HP Reveal app and targets accurately (as seen in Table 2) and (b) the number of steps the participant performed independently after viewing the HP Reveal video model (see Table 3). To begin the session, the researcher provided the task direction included in the baseline phase to cue the participant to perform the task. If the participant did not pick up the iPad to view the video model after 5 s, the researcher prompted the participant to pick up the iPad and use the HP Reveal target. The subsequent steps were scored according to the level of prompts the participant needed while performing the task.

Table 3.

Task Analyses for Target Skills.

Ironing (Michelle)	Making a Bed (Robert)	Setting an Alarm (Brady)
1. Locate iron and ironing board	1. Take pillows off bed	1. Locate iPhone
2. Set up ironing board	2. Pull comforter back	2. Open iPhone by using finger print recognition or typing in password
3. Plug iron into the wall	3. Pull up sheets	3. Find and click on Clock app
4. Set iron to the correct temperature	4. Tuck sheets under the mattress	4. Press “Alarm” icon at the bottom of the app
5. Slide shirt/pants onto the board and smooth out clothing	5. Pull comforter up	5. Scroll to the correct time to wake up in the morning
6. Rotate clothing on board	6. Straighten out any wrinkles	6. Scroll to AM
7. When completed, take clothing off the board and review work	7. Put pillows back on bed	7. Push “Save” in the top right corner
8. Put up ironing board and iron	8. Review work	8. Push the home button and close the phone

Reinforcement and accommodations

If the participant incorrectly completed a step in the sequence, the observer scored the step as prompted and told the participant to watch the video model using the HP Reveal app again by stating, “Watch the video again.” Once the participant watched the video a second time, the observer provided the prompt, “Now you do it.” Verbal praise was given to the participant for correcting the mistake after viewing the video for a second time. If the second viewing of the video failed to elicit the correct response, a system of least-to-most prompts was implemented with a 5-s wait time between prompts. After the second viewing of the video model, the researcher scored each step of the task analysis according to the level of prompt given. Verbal praise was given upon completion of the task. The intervention ended once the participants demonstrated three consecutive data points at or above 80%.

Accommodations were provided for participants who had difficulty using the app accurately. One participant required a visual marker taped to the ground to indicate the appropriate distance for triggering the video model. Similarly, modifications were made to the size of a participant’s target to improve consistency of trigger the video model. The original targets were 4 × 6 in. in length, but the individual required a larger target (8 × 11 in.) with increased font in order to trigger the video model consistently.

Fading

Once the participants achieved mastery criterion in the intervention condition, the use of the video models via HP Reveal faded. Participants were told the targets were available for them to access in the instance of forgetting a step for completing the task, but they did not have to use HP Reveal to watch the video model prior to initiating the task.

Social validity procedures

A 6-item questionnaire was developed by the researchers to examine the social validity of the intervention (see Table 4). After completing the intervention, the participants selected yes, no, or I don’t know in response to questions about the ease of use HP Reveal and the helpfulness of the intervention for learning new skills. In addition, the social validity questionnaire was used to determine whether the participants thought the intervention would be helpful and nonstigmatizing for learning new skills in other environment (i.e., work and the community).

Table 4.

Social Validity.

Survey of Social Validity
1. HP Reveal was easy to use
2. Using HP Reveal targets was hard
3. HP Reveal helped me learn to skill quickly
4. It was easy for me to remember to skill after I watched the video
5. HP Reveal would be helpful for learning other skills at home, in the community, and at work
6. I felt embarrassed to use HP Reveal

Measures

Variables

The independent variable was the use of the AR app (i.e., HP Reveal) as a video modeling tool for performing daily living tasks. The dependent variable was the percentage of independent steps completed based on a task analysis for performing the target skills. A step was scored independent if the participant completed the step as stated on the task analysis without an additional prompting from the researcher. If a step was not observed during baseline data collected, it was scored as not observed. During the intervention condition, an error correction procedure using least-to-most prompts was implemented. When a participant made an error, the researcher scored the step according to the level of prompt used. The system of prompts included:

indirect verbal prompt (“What do you need to do?”)

direct verbal prompt (i.e., “You need to ___.”)

model “Watch me _____. Now you try.”

partial physical prompt

hand-over-hand assistance

The percentage of independent steps was calculated by counting the number of steps performed independently, dividing it by the total number of steps and multiplying by 100 to produce a percentage of independent steps.

Interobserver agreement (IOA) and procedural fidelity

Prior to data collection, training videos were created with a student not participating in the study. The lead investigator used the videos to teach the coinvestigators the procedures for collecting and scoring observations. The training videos included a student using HP Reveal to access the video models and the researcher demonstrating delivery of the error correction procedures as the coinvestigators would during the training and intervention conditions. Instructions for how to complete and store the data after each observation were also discussed during training.

To determine IOA, each session was video recorded. Point-by-point IOA data were collected across a minimum of 25% of the sessions for each participant across each condition with a minimum of 85% agreement between observers. The overall IOA was 91% (range = 88–94%).

A second observer collected procedural fidelity data using a task analysis of instructional procedures for each condition to ensure the primary researcher provided the task direction, prompted correctly when errors occurred (during intervention condition), and provided verbal praise upon the completion of the task. Procedural reliability was calculated by dividing the number of correct observed during the session by the total number of steps in the task analysis and multiplying by 100. Brady’s procedural fidelity ranged from 67% to 100% (M = 95%). The mean procedural fidelity for Robert was 100%, and procedural fidelity for Michelle ranged from 83% to 100% (M = 97%).

Results

Baseline results for all of the participants indicated they were unable to independently complete the target skill. During baseline, participants completed a mean of 32% (range = 0–50%) of the necessary steps independently. Overall, participants improved their independence on the targeted daily living skill immediately upon the introduction of the HP Reveal intervention. During intervention, the participants completed a mean of 79% (range = 44–100%) of steps independently. Two of the participants reached fading procedures and completed a mean of 100% of the steps independently without using HP Reveal. Visual analysis of the data for all participants indicated the HP Reveal intervention was effective in teaching each participant the target independent living skill. Across participants, the percentage of nonoverlapping data (PND) was 100% during baseline and intervention phases. In addition, the omnibus improvement rate difference (IRD) for all participants was also 100% or 1.00 between baseline to intervention phases. The findings from IRD as well as the PND indicate that once the participants were trained on using HP Reveal, their performance immediately improved and was constantly above baseline performance (Parker, Vannest, & Brown, 2009; Scruggs & Mastropieri, 2001).

Brady

During baseline, Brady performed a mean of 40% (range = 33–44%) of the steps independently for setting his alarm. Following implementation of HP Reveal, Brady’s performance immediately increased with 100% IRD and 100% of nonoverlapping data, which indicates HP Reveal was a highly effective intervention. Brady reached criterion level of 80% or higher for three consecutive sessions after nine intervention sessions. Brady stayed in the intervention phase for an additional five sessions because he kept trying to set his alarm on his iPad instead of his iPhone. This distinction was missing on the task analysis data sheet but was an important aspect of the intervention since the video emphasized the use of setting an alarm on an iPhone. Brady’s mean during intervention for completing the steps independently was 78% (range = 78–100%). He chose not to use HP Reveal in all three of his fading sessions and was able to set his alarm completely independently. Therefore, Brady’s mean of completed steps independently during three fading sessions was 100%. Results for Brady are displayed in Figure 3.

Robert

Robert completed a mean of 33% (range = 25–50%) of the task analysis steps for making his bed independently during baseline. During intervention, Robert reached criterion level for three consecutive sessions in six sessions. His mean of steps completed independently during intervention was 79% (range = 63–87%) with 100% nonoverlapping data and IRD. Because of time constraints, Robert was able to complete only one fading session; however, he completed making his bed independently with 100% accuracy.Figure 4 displays a visual analysis of Robert learning to make his bed using HP Reveal.

Michelle

During baseline, Michelle performed a mean of 24% (range = 0–33%) of steps in ironing independently. Once Michelle was taught how to use HP Reveal in helping her to iron her clothes, she completed all task analyze steps independently for three consecutive sessions within five sessions. During intervention, Michelle performed a mean of 80% (range = 44–100%) of steps for ironing independently. Figure 5 shows a visual analysis of Michelle learning to iron using HP Reveal. Her visual analysis confirms that Michelle’s PND and IRD was 100%. As a result of time constraints, Michelle was unable to move into fading procedures.

Figure 3.

Brady’s percentage of independent steps for setting alarm at baseline, intervention, and fading.

Figure 4.

Robert’s percentage of independent steps for making bed at baseline, intervention, and fading.

Figure 5.

Michelle’s percentage of independent steps for ironing at baseline and intervention.

Social Validity

The social validity questionnaire was completed after each participant concluded intervention. All three participants indicated that using HP Reveal was easy. Although, one of the participants did note that HP Reveal could be difficult to use because of having to hold the iPad for so long to watch the video. All the participants agreed that the app helped them learn the skill quickly and helped them remember how to do the skill after watching the video. Two of the participants indicated that HP Reveal would be helpful for them to continue to use in the community in learning skills related to independent living and employment. Lastly, all the participants indicated that they did not feel embarrassed using the app to help them learn new skills in the dorm.

Discussion

The purpose of this study was to examine the effects of using AR for teaching daily living tasks to young adults with IDD in a postsecondary education program. Results of this investigation show that all participants increased the percentage of steps they were able to complete independently with the use of video models delivered through HP Reveal. Additionally, the three participants achieved criterion for mastery of three consecutive trials at or above 80%. The PND (= 100% for all three participants) suggests AR is an effective and powerful tool for increasing independence among adults with IDD living in a college dormitory.

Our results appear to agree with prior findings and further validate AR as an effective instructional tool for improving the performance of functional skills among adults with IDD (Cihak et al., 2016; Johnson et al., 2013; McMahon et al., 2015). Furthermore, our participants indicated the use of HP Reveal for accessing video models was a socially valid method for providing support to students living in a college dormitory. The use of handheld devices provided participants the opportunity to access video models discretely in authentic settings where they were expected to perform the functional tasks. For students with disabilities desiring to attend postsecondary education and live independently, the use of AR can provide supports in a less stigmatizing manner as it is fairly common to witness young adults interacting with handheld technology on a college campus. The portability of AR interventions accessed via handheld devices allows students to view video models as often as necessary and may reduce their dependence on peers and family for completing daily living tasks. Future studies should employ AR interventions to other independent living skills to further corroborate its efficacy for improving functional life skills among this population. In a similar vein, future studies should examine whether AR can increase accessibility for students with disabilities living on college campuses who are learning to perform other functional life skills.

Our findings add to current AR literature by applying AR interventions to skills and settings that have yet to be investigated. Minimal AR studies have focused on functional skills for daily living. Similarly, no studies have investigated the effectiveness of AR interventions for students with IDD living independently on a college campus. Daily living skills are an essential part of independent living. For adults desiring to live on their own or with roommates, such skills are critical as they will be expected to share household responsibilities with others or complete daily living tasks independently.

The participants included in the study were selected from a group of students attending a 9-week residential postsecondary education program. The goal of the program is to provide students with IDD the opportunity to live independently while exploring academic and recreational activities that are typical of college students. Unlike other similar programs, there are no age restrictions for students to apply to participate in this program. Michelle, 19, and Brady, 23, are close in age to traditional college students. Robert’s age of 35 is important to note as this age discrepancy between the participants did not interfere with the success of the intervention. Robert was not accustomed to using technology as an instructional tool. As part of the intervention, Robert was able to expand his knowledge for using technology in addition to learning new daily living skills. Our findings suggest AR is an effective intervention for teaching daily living skills regardless of age.

Experts recommend learners’ preferences and capabilities must be considered when designing video modeling interventions (Laarhoven et al., 2009; Mason et al., 2013). It was evident after beginning the intervention that certain accommodations were necessary to ensure all participants would be successful in using HP Reveal. When the intervention was introduced, and Robert was taught to use HP Reveal to access the video models, he struggled with activating the video models using the original 4 × 3-in. target. The target was too small for Robert to center his iPad over long enough for the video model to appear. Therefore, we created an 8 × 11-in. target with larger font specifically for Robert. In addition, Robert had difficulty standing an appropriate distance from the target. He would stand either too close or too far from the target, preventing the app from activating the video models. An X was taped to the floor as a visual for showing Robert where he should stand while viewing the video. These accommodations were effective for improving Robert’s ability to trigger the video models.

Brady expressed difficulty in holding his iPad up for the duration of the video. Originally, Brady’s target was placed on his wall beside his desk. This location was appropriate because Brady charged his phone on his desk at night. The target was placed at chest level for Brady’s first two observations but was moved to lay flat on his desk when he continued to state that he was uncomfortable holding his arms up while watching the video. For his final observations, Brady viewed the videos by holding his iPad over the target placed on his desk rather than holding it in front of his body.

Brady also struggled with wanting to set the alarm on his iPad after watching the video instead of using his iPhone. Although the functions of an iPhone and iPad alarm were similar, the researchers had to remind him to use his phone as he would not be able to take his iPad home at the end of the program. Brady was able to set alarms on both devices, which indicate the skills learned through the AR intervention may be generalizable between similar devices and applications. Future studies should explore whether the skills learned through AR interventions are generalizable across multiple settings and whether students can apply the skills to similar tasks or devices.

Limitations

As with all single-subject research designs, a small sample size (n = 3) was used in this study to explore the effectiveness of the intervention. A small sample size could be perceived as a limitation in this study due to difficulty generalizing the results to other individuals and/or environments. In addition, generalizability is challenged in this study due to specific procedural and environmental conditions of the 9-week postsecondary education program.

Another limitation of this study is the failure to time-lag the introduction of the intervention. Due to the time constraints of the 9-week program, the ability to stagger the introduction of the intervention was not an option. Therefore, we were unable to demonstrate a functional relationship between the dependent and independent variables. The two additional replications across participants and behaviors, however, indicate the intervention is promising.

Conclusion

Independence among individual with IDD is often limited, causing a negative impact on their quality of life. The development of daily living skills contributes to an increased sense of autonomy for these individuals. Therefore, explicit teaching of such skills across domains and environments is critical. The coupling of video modeling and AR, as demonstrated in this study, can increase the acquisition of daily living skills for individuals with IDD and contribute to an overall better quality of life.

The results of this study support the use of AR as an effective tool for teaching daily living skills to adults with IDD. Using AR as an intervention is not only effective but practical for use in natural environments. The ability to access AR is now, more than ever before, at the fingertips of young adults through the use of smartphones and tablets. Additionally, the use of such devices is prominent among college-aged students, and access to these technologies makes AR an ideal intervention for fostering independence among individuals with IDD on college campuses.

Future Research

Although positive outcomes were seen in this study, future research is needed to further explore the advantages and disadvantages of using AR among young adults with IDD. In this study, we were unable to determine a functional relationship between the intervention and the dependent variable. In future research, staggering the introduction of the intervention will allow researchers to determine whether an increase in the percentage of steps completed independently can be attributed to the intervention.

Expansion and replication of this study should include various forms of technology and exploration of AR interventions across other domains. Further exploration would allow for a broader understanding of how AR impacts learning and retention of new skills and support the generalizability of this intervention across environments.

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.

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