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Abstract

Individuals with intellectual disabilities face additional risks in digital environments compared with their typically developing peers. Therefore, special attention should be paid to digital safety skills development. This study analyzed the effectiveness of video modeling in teaching individuals with intellectual disabilities how to make their Instagram profiles private. The study used a multiple-probe design across participants from single-subject research models and involved three middle school students (aged 12–15 years) with intellectual disabilities. Video modeling was effective in teaching the participants about making Instagram accounts private. While two students successfully generalized their skills to other devices, one did not. The participants maintained their acquired skills for one, three, and five weeks following the study. Parents were satisfied with the study and had favorable opinions regarding the learned skills. Video modeling can effectively teach digital safety skills to individuals with intellectual disabilities, thereby indicating its adaptability to various digital safety scenarios.

Keywords

intellectual disability digital safety skills video modeling Instagram privacy digital awareness

Digital safety involves technical, organizational, and educational processes to protect digital assets and information (Zulqadri et al., 2022). In today’s digital landscape, safeguarding individuals and organizations from cyber threats, which have increased with the widespread use of the Internet and digital platforms, is vital (Verizon, 2023). Such skills include information security, privacy protection, safe online behavior, and suitable digital threat response (Rodríguez-de-Dios & González-Vázquez, 2016; Shelyugina et al., 2019; van Deursen et al., 2015). Individuals must possess these skills to effectively shield themselves from cyber risks.

The rapid evolution of technology has significantly improved living standards; technology has thus become essential in daily life and influences work, entertainment, education, and health (Larsson-Lund & Nyman, 2019). However, misuse or overuse of technology can have serious consequences (Lee & Chae, 2012; Ólafsson et al., 2013). It is essential to manage interactions with technology to mitigate risks while enjoying its benefits. Acquiring digital safety skills can help protect individuals from various virtual threats (Hafeez et al., 2020; Silaule et al., 2022; Tvaronavičienė et al., 2020; Wang et al., 2021).

The online safety of individuals with intellectual disabilities (ID) is a critical issue, particularly with the increasing prevalence of social media. Chadwick et al. (2013) highlighted Internet access inequalities for individuals with ID, emphasizing barriers in the digital world and the Internet’s role in social relationships. Plichta (2018) explored digital inequality and its impact on young individuals with ID, underlining the need for educational programs to develop digital skills and promote social participation. Sheehan and Hassiotis (2017) examined digital mental health applications, acknowledging their potential and noting limitations in addressing ID-specific needs. Shpigelman and Gill (2014) focused on Facebook’s impact on societal participation for individuals with ID, though they caution against overlooking other platforms, stressing the need for comprehensive research and understanding. Chadwick (2022) investigated online safety risks and protective behaviors among individuals with ID. Chiner et al. (2022) revealed teachers’ perspectives on students with ID using digital tools, though limited student-focused insights are noted. Finally, Safari et al. (2023) demonstrated how digital technology design activities enhance participatory skills among young individuals with ID, emphasizing the need for more robust data on their effectiveness.

A review of the literature highlights the need for online safety skills training for individuals with ID who face risks like cyberbullying and privacy breaches due to limited digital literacy (Chadwick, 2022; Shpigelman & Gill, 2014). While participatory design can enhance digital skills (Safari et al., 2023), specific interventions for online safety are lacking. This underscores the need for targeted programs to help individuals with ID safely navigate the digital space. Video modeling is an evidence-based tool used to teach individuals with ID skills, including social and daily living skills (Neff et al., 2017; Ågren et al., 2019). Few studies have examined its effectiveness in teaching online safety skills. This study addresses this gap by utilizing video modeling to help individuals with ID make their Instagram profiles private, highlighting its potential for improving online safety skills.

Digital technologies play a crucial role in enabling social participation for individuals with ID. However, significant systemic barriers still hinder their access and effective use. Key issues include limited digital literacy, lack of access to technological devices and Internet connectivity, and insufficient support from caregivers and service providers (Devitt et al., 2024; Heitplatz et al., 2021). Contextual factors such as inadequate Internet infrastructure and design flaws in technology further restrict accessibility, particularly in essential areas such as e-health services (Oudshoorn et al., 2020; Puyaltó et al., 2022). These digital inequities limit online participation and increase the risk of cyberbullying and privacy breaches, making online safety training essential to mitigate these risks and promote digital inclusion (Abroshan et al., 2021; Islam et al., 2023; Pehlivanoglu et al., 2024).

In addition to external access barriers, individuals with ID may experience internal challenges due to cognitive limitations that affect their ability to navigate and interact safely in digital environments. Difficulties in understanding privacy settings, recognizing online risks, and retaining safety strategies contribute to increased vulnerability (Chadwick, 2022; Shpigelman & Gill, 2014). These individual-level barriers highlight the importance of accessible instructional methods and tailored training programs designed to support the specific learning needs of this population.

The minimum age for creating a Google account is 13 years, though it varies based on local regulations (Google Account Help, nd). In Turkey, the age is set at 13 for platforms like Google, Instagram, and Facebook (Güvenli Web [Safe Web], 2017). This highlights the need for children, adolescents, and individuals with special needs to learn Internet safety skills for responsible social media management. Shpigelman and Gill (2014) noted that individuals with ID frequently face challenges in understanding and navigating social media privacy settings, making them vulnerable to privacy breaches, cyberbullying, or online exploitation. For instance, they found that many users were unaware of the risks associated with sharing real personal information or accepting friend requests from strangers. They also pointed out that interface changes on platforms like Facebook increased the likelihood of accidental oversharing due to confusion around new privacy options. In a related example, Chadwick (2022) reported that individuals with ID expressed a desire for more support in understanding online safety, with some participants proactively taking steps, such as regularly changing their passwords after a breach. Similarly, Safari et al. (2023) found that engaging individuals with ID in participatory technology design enhanced their understanding of application features, indirectly supporting better management of digital privacy. These findings highlight the importance of experiential, individualized, and accessible training that enables users to understand how privacy settings can reduce exposure to online threats. Individuals with ID must adopt safety behaviors for their protection and to contribute to society’s overall digital security. Practices like protecting personal data and using strong passwords can prevent cyberattacks targeting individuals and broader communities (Betlej & Ohotina, 2022; Glencross et al., 2021); a comprehensive educational approach is crucial to improving digital safety skills.

A comprehensive strategy for enhancing digital safety in individuals with ID should encompass digital literacy, social skills training, technology use, and safety education (Caton et al., 2022; Chadwick & Buell, 2023; Glencross et al., 2021; Murphy et al., 2024). Each component addresses a specific aspect of the challenges these individuals face in digital environments. For instance, digital literacy enables users to understand icons and menu structures commonly found in apps like Instagram, allowing them to interact more independently and effectively (Murphy et al., 2024). However, access to technology alone is insufficient; individuals must be empowered with the skills to navigate these platforms (Murphy et al., 2024). Social skills training helps individuals with ID grasp the importance of online boundaries and privacy. This includes understanding when to accept or reject contact requests, how to communicate respectfully online, and the risks of oversharing personal information (Caton et al., 2022; Glencross et al., 2021). Without these skills, interpreting social cues online becomes challenging, leading to increased vulnerability (Caton et al., 2022). Technology training focuses on practicing specific steps such as enabling private account settings, blocking unwanted users, and turning off location sharing features. Programs like Digi-ID have demonstrated the effectiveness of teaching these tasks using inclusive and accessible methods, thereby building confidence and competence in users (Chadwick & Buell, 2023; Murphy et al., 2024). Safety education further reinforces digital resilience by equipping individuals with strategies to recognize and respond to online risks, including cyberbullying, scams, and inappropriate content (Glencross et al., 2021). Teaching users about potential threats ensures they are not passive consumers but informed digital citizens. Finally, video modeling, as an evidence-based instructional method, facilitates learning by visually demonstrating how to perform digital tasks step by step. This method is especially effective for individuals with ID in mastering complex actions like adjusting Instagram’s privacy settings (Neff et al., 2017).

This study evaluates the effectiveness and feasibility of using point-of-view (POV) video modeling to teach individuals with ID how to make their Instagram profiles private, while also considering the views of parents and teachers on this method. Video modeling is widely used in special education to teach skills and behaviors to individuals with developmental disabilities (McCoy & Hermansen, 2007). It provides visual cues and demonstrations, making it especially practical for learners with ID (Smith et al., 2015). Research has demonstrated its effectiveness across various domains, including teaching play skills (D’Ateno et al., 2003), daily living skills (Cannella-Malone et al., 2011), functional tasks (Mechling, 2005), and robotic coding (Wright et al., 2019). Various types of video modeling exist, including adult modeling, peer modeling, self-modeling, mixed modeling, and POV modeling (Bellini & Akullian, 2007; Delano, 2007; Genç-Tosun & Kurt, 2014). The POV model presents the skill from the learner’s visual perspective and is particularly effective in supporting motor skill comprehension and task replication (Genç-Tosun & Kurt, 2014). When combined with narration and structured prompts, POV modeling can enhance attention, motivation, and learning outcomes for individuals with ID (Bellini & Akullian, 2007). In this study, the POV video was created by capturing the learner’s screen activity with a visible cursor navigating the Instagram interface, demonstrating each step to enable privacy settings. The footage includes adult narration to provide explicit, step-by-step verbal instruction, allowing viewers to follow each action as if they were performing the task themselves. The study results can guide educators, experts, and policymakers in developing effective teaching strategies. Thus, this study aims to answer the following sub-questions:

(1) Is video modeling effective in teaching individuals with ID to make their Instagram profiles private, apply this skill to another tool, and retain it after one, three, and five weeks?

(2) What proficiency levels do individuals with ID have in making their Instagram profiles private compared to their same-age typically developing peers after learning this skill?

(3) What are parents’ and teachers’ perspectives on teaching the children to make their Instagram profiles private using video modeling?

Method

Research Model

The study used a multiple-probe design across participants, which is common in single-subject research. This method gathers data at specific intervals, called “probes,” instead of continuously, assessing intervention effectiveness over time while reducing constant data collection demands. The model is well-suited for applied settings, enabling targeted progress assessment and a clear demonstration of intervention impacts in a structured yet flexible manner (Greer et al., 2005; Kratochwill et al., 2012; Tekin-İftar, 2018). It sequentially assesses each participant, linking intervention phases with measurable behavioral changes to establish a functional relation between the program and outcomes. Horner et al. (2005) highlighted the significance of single-subject research designs in uncovering evidence-based practices in special education.

First, baseline data were collected simultaneously from all participants. Stability is defined as minimal variability in data points (Tekin-İftar, 2018c); in this study, the acceptable stability range for the baseline level of the seven-step task analysis, which represents the target behavior, was defined as data points fluctuating between 0%, representing the inability to perform any step correctly, and 14.28%, representing success in performing only the first step. Stability was defined as data points fluctuating between performing no steps correctly and performing only the first step correctly to maintain student interest and prevent accidental learning during the baseline phase. Given the students’ familiarity with digital devices, they might have tapped through the first few steps by chance without fully completing the task. Therefore, stability was determined based on this limited performance, where students were not expected to progress beyond the first step during the baseline phase. Preventing accidental learning was particularly important because a more extended baseline phase could have increased the risk of students unintentionally learning the skill through repeated exposure. We ensured this by defining stability as at least five consecutive baseline data points where performance did not progress beyond the first step. This aimed to enhance the validity of teaching students the skill. Once stability was confirmed for the first participant, the intervention began, while the other participants continued to receive probes. This staggered intervention initiation allowed for comparisons between baseline (Phase A) and intervention (Phase B), demonstrating a functional relation between dependent and independent variables (Greer et al., 2005; Horner et al., 2005; Kratochwill et al., 2012). After the first student began the intervention, periodic probes continued for others, and this process was repeated for each participant.

Independent Variable

The independent variable in this study was a POV video designed to teach students with ID how to set their Instagram profiles to private. The video was created using screen recording software to capture each step of the task from the learner’s perspective, with a visible cursor navigating through the Instagram interface. The video was narrated by an adult who provided clear verbal instructions. Each of the seven steps was demonstrated in sequence, allowing learners to follow along as though performing the task themselves. The POV video was presented individually to each student under the guidance of the implementer prior to the performance trials. This modeling format was selected because it allows students to experience the task visually and cognitively from a first-person perspective, potentially enhancing comprehension and replication (e.g., Neff et al., 2017; Ågren et al., 2019).

Dependent Variable

The dependent variable is the skill of setting one’s Instagram profile to private using a smartphone app. This skill is crucial for maintaining digital safety and privacy, particularly for individuals with ID. The researchers identified the following steps to this skill:

(1) Open the Instagram app on their phone.

(2) Log in to a profile by clicking profile in the bottom right corner.

(3) Click on the three-dash symbol in the upper right corner.

(4) Tap on the “Settings and Privacy” menu.

(5) Tap on the phrase “Account Privacy.”

(6) Activate the “Secret Account” option.

(7) Confirm the “Switch to Secret Account” option in the pop-up warning.

Acquiring this skill helps individuals with ID safely and consciously engage in digital environments, thus contributing to strengthening their social adaptation processes. Success was defined as completing all seven steps (100% accuracy).

Participants and Setting

Three middle-school students of a special education class participated in this study. The following inclusion criteria ensured that they met the skill prerequisites: basic literacy, comprehension, adhering to multistep instructions, and lacking proficiency in customizing Instagram profiles. These criteria ensured effective engagement with the intervention targeting a novel skill. Students with advanced digital skills beyond the intervention’s scope were excluded. The required skills included (1) basic literacy, (2) focusing for at least 5 minutes, (3) following two- or three-step verbal instructions, (4) understanding and responding to a text, and (5) needing improvement in customizing Instagram profiles.

Pseudonyms were used to protect identities, and the students’ parents provided written informed consent. Participant demographic characteristics are shown in Table 1.

Table 1.

Demographic Characteristics of Participants.

Code Name	Age (years)	Gender	Diagnosis	Additional Disability
Yusuf	15	Man	Moderate ID	Down syndrome
Melek	14	Woman	Mild ID	-
Orhan	13	Man	Mild ID	Speech and language difficulty

Although the study involved only three participants, the sample size aligns with the multiple probe model’s design, which often involves limited participants to facilitate detailed, individualized observations and interventions (Edwards et al., 2020). Small sample sizes in single-subject research are scientifically valid and are particularly advantageous for establishing functional relations between the intervention and outcomes rather than generalizing findings to a larger population (Maggin et al., 2018). The multiple-probe design for single-subject research enables the detailed analysis of individual responses to the intervention (Kazdin, 2011; Maggin et al., 2018), providing valuable insights into the effectiveness of the program for students with similar characteristics (Hardy & Hemmeter, 2021). The small sample allows close monitoring and ensures fidelity, providing strong evidence of effectiveness for individuals with ID (Gast & Ledford, 2014; Horner et al., 2005). While the sample size limits generalizability, it facilitates the study’s aim to assess video modeling’s feasibility for teaching digital safety skills. Future research with larger, more diverse samples is needed to validate these findings.

Yusuf, a 15-year-old eighth grader with Trisomy 21 (Down syndrome) and mild ID, as per his medical report, attended a special education class and received 2 hours of weekly supportive education from a rehabilitation center. He spends three to 4 hours online daily, has a Facebook account, and has no other health issues.

The second participant, Melek, a 14-year-old 6th-grade student in a special education class, received 2 hours of weekly support from a from a rehabilitation center. The Guidance and Research Center (GRC) diagnosed her with mild ID and recommended special education due to cognitive development delays. Melek had no other disabilities or health issues. She accessed the Internet via smartphone for 30 minutes on weekdays and for 2 hours on weekends. She was not on social media but watched the YouTube Kids Channel.

Orhan, a 12-year-old boy in 5th grade, attended a special education class, receiving 2 hours of weekly supportive education outside school at a special education and rehabilitation center. The GRC diagnosed him with a mild ID, and a hospital medical evaluation reported speech and language difficulties but no other health issues. His reading skills were limited to recognizing words visually, and he had an articulation disorder. Orhan accessed the Internet for two to 3 hours on weekdays and four to 5 hours on weekends, using a smartphone to watch videos and play games, but did not have a social media account.

In Turkey, disability diagnoses are determined through medical assessments conducted GRCs. The resulting reports classify disabilities as mild, moderate, or severe, focusing on functional classifications for educational support rather than specific IQ scores. Information about the participants is included in the descriptive paragraphs provided above.

None of the participants had received any prior education on the Internet or digital safety. All the participants could follow two- and three-step instructions, had literacy skills, followed school and classroom rules, and were curious about the things around them. They also had self-care skills; good receptive language skills; and social skills such as communicating, recognizing, and expressing emotions.

The study included a comparison group of ten typically developing students, aged 12 and 13, with similarities in age, school, and social media habits (four girls and six boys). Data were collected individually in the classroom with verbal consent from students and parents.

The research took place in a computer lab at the students’ school, chosen for its accessibility and alignment with the study’s requirements for special education services, including support for students with ID. No comparative analysis with other schools was conducted because the focus was on evaluating the intervention’s impact in this context. The classroom, measuring 4.5 m by 4.5 m, contained a smart board, a teacher’s desk, 18 desktop student computers, 18 desks, and one teacher’s computer. The teaching was conducted one-on-one, with the student seated at the teacher’s desk.

Materials

A Lenovo V330 15IKB laptop was used to create a video on how to make an Instagram profile private, recorded with a Samsung Galaxy S10e smartphone. The video, narrated by an adult using the POV model, was confirmed by a Special Education academic as demonstrating the skill effectively. During the intervention, the video was shown to students using the practitioner’s Instagram account on a Samsung Galaxy S10e smartphone. Students did not create an Instagram account or use their accounts; all procedures were conducted using the practitioner’s account. A Lenovo V330 15IKB laptop was used to assess generalization skills. The baseline level, teaching applications, attendance, monitoring, and generalization sessions were recorded using a Samsung A20 smartphone.

The Implementer

The second author conducted all the study sessions. He graduated from Sakarya University’s Computer Education program in 2013 and is pursuing a master’s at Bolu Abant İzzet Baysal University in Special Education. He has 10 years of experience as a Computer Technology teacher in public schools.

Observers

Two observers gathered implementation and interobserver reliability data. The first observer is a graduate student at Bolu Abant İzzet Baysal University, and the second observer has a bachelor’s and master’s degree in Science Education and a PhD from Hacettepe University, with 22 years of teaching experience. Before data collection, the second observer was briefed on the study’s purpose, variables, methodology, and reliability forms. The data from all phases of the study were then compared.

Instruments

The study instruments included the following forms:

The Demographic Information Form

This form included the students’ gender, age, diagnosis, and grade.

The Prerequisite Skills Observation Form

This form was provided to teachers to help them identify study participants based on their observations of students’ prerequisite skills. Teachers were briefed on the study purpose and asked to indicate which students had the prerequisite skills for the target behavior.

The Performance Recording Table

The Performance Recording Table was designed to collect data during baseline, intervention, follow-up, and generalization sessions based on analyses of the target skill. After consulting two special education experts, the researchers finalized the table to assess participants’ baseline levels and performance across phases. The table was also refined based on insights gained from a pilot study conducted prior to the main implementation, which aimed to evaluate the clarity of instructions and the practicality of the data collection tools. This structured table facilitated data collection for all phases and social comparison. The participants’ skill performance was evaluated using the single-opportunity method with a 100% success criterion for completing all seven target skill steps in each intervention session.

The Treatment Integrity Data Collection Form

This form assessed whether the instructor had followed all the instructional process steps and to evaluate the instruction’s intervention phase considering the video modeling stages specified by Genç-Tosun and Kurt (2014). Two Special Education academics provided expert opinions to ensure the form’s validity. The form consisted of 12 steps the interventionist was required to follow to deliver the intervention with fidelity (see Appendix A for the entire form).

Subjective Evaluation Form

This form initially comprised one multiple-choice and four open-ended questions asking the parents of the students in the study group about their satisfaction with the instructional process, the skills taught, and their opinions on the intervention. Following a revision process, an additional form was created to gather feedback from the intervention teachers. The revised form, including both parent and teacher perspectives, is in Appendix B.

Internal and External Validity

Internal validity ensures that the independent variable is responsible for observed changes in the dependent variable. In single-subject research, factors affecting internal validity include external influences, maturation, repeated testing, measurement changes, participant selection bias, attrition, cyclical events, multiple-treatment interference, and data instability (Çakıroğlu, 2012; Kratochwill et al., 2012; Tekin-İftar, 2018; Zuidersma et al., 2020). To address these factors, we extended the study duration to consider maturation and cyclical events, increased the number of repetitions to mitigate testing effects, employed a consistent measurement tool, collected baseline data to reduce selection bias, included backup participants to prevent attrition, and avoided multiple treatment interference using a single independent variable.

External validity concerns generalizing research findings and is crucial in single-subject research. Factors affecting it include carry-over effects, artificial conditions, testing effects, selection–intervention interaction, and treatment integrity (Kholifah & Dinata, 2023; Tekin-İftar, 2018b). In this study, we implemented several measures to control such factors. We defined participants’ baselines, settings, and characteristics, repeated the experimental application for reliability, maintained consistent control conditions to minimize external impacts, and ensured similarities in age, functional level, and behavioral factors to enhance generalizability across different groups.

Assessment

This study applied the single opportunity method to assess target behaviors at the baseline, follow-up, and generalization sessions. To evaluate the target skill, the implementer gave the mobile phone to the students and instructed them to “go to Instagram and make the profile private.” To collect the data, the implementer assessed the students for each step and calculated the correct response percentages by marking them on the Performance Recording Table. Mastery was defined as correctly completing all seven steps with 100% accuracy in at least five consecutive sessions.

Inter-observer Reliability

Inter-observer reliability data were collected for all study sessions. The observers separately recorded their observations using the Performance Recording Table. The collected data were then evaluated using the formula [Agreement/(Agreement + Disagreement)] × 100 (Erbaş, 2012), and the inter-observer reliability was 100%.

Treatment Integrity

Treatment integrity crucially affects a study’s internal validity. It refers to how consistent and reliable a study’s implementation process is and indicates the standardization degree of the methods and data collection processes used in single-subject studies (Erbaş, 2012; Lane et al., 2004). In this study, the instructional practice components for teaching the target skill were: (1) organizing the instructional setting; (2) introducing the instructional materials; (3) preparing the necessary materials to demonstrate the skill; (3) directing the student to the area where they will watch the video; (4) presenting the prompt for attention (“Now we will watch a video, are you ready?”); (5) directing the student’s attention to the screen; (6) providing appropriate reactions to the student’s reactions while watching the video; (7) verbally reinforcing the student’s actions after watching the video; (8) directing the student to the material where the skill will be performed; (9) presenting the skill instruction; (10) waiting for 5 s; (11) responding appropriately to students’ reactions (verbal reinforcement for correct reactions; re-instruction for unresponsive behaviors and incorrect reactions); and (12) terminating the session.

Treatment integrity data were collected during all study sessions. A second observer assessed the treatment integrity and collected interobserver reliability data using the Treatment Integrity Data Collection Form. Treatment integrity was calculated using the formula (Observed Implementer Behavior/Planned Implementer Behavior) × 100 (Erbaş, 2012), and the result showed that the treatment was implemented exactly as planned, with a 100% treatment integrity rate. A minimum fidelity threshold of 80% was established for acceptable implementation. If fidelity dropped below this level, the session would be invalid, and the interventionist would undergo re-training. However, the interventionist maintained 100% fidelity throughout all sessions, following all 12 steps. Re-training was not required.

Pilot Study

A pilot study was conducted with a 13-year-old male student diagnosed with specific learning disabilities to identify potential issues before implementation. Results indicated that the instructions were clear and sufficient, and the intervention was carried out as intended.

Social Validity

Two types of social validity data were collected: subjective evaluation and social comparison. Social validity assesses the importance of an intervention’s goals, methods, and outcomes (Schlosser, 1999). In applied research, goals should be relevant, techniques useful, and results beneficial to individuals and society (Fawcett, 1991). Subjective evaluation involves gathering opinions from those with expertise or a close relationship with the individual, while social comparison compares an individual’s performance to a typically developing group (Schlosser, 1999). We collected anonymous feedback from parents and two teachers using the written Subjective Evaluation Form.

The study collected social comparison data to assess students’ ability to set their Instagram profiles to private, comparing them to their peers without instruction. The comparison group, consisting of typically developing students, was assessed once individually during the baseline phase. This measurement served solely as a reference point; their average success rate was calculated and presented graphically alongside the experimental group’s performance. No intervention was applied to the comparison group, as their role was to establish normative proficiency levels for the target skill. Students in the comparison group were asked to “make the profile private” on Instagram, with their responses recorded in the Performance Recording Table. In contrast, the experimental group participants were evaluated multiple times across baseline, intervention, and fading phases. These assessments employed the single-opportunity method using a seven-step skill set, with 100% accuracy required to be considered mastered.

Experimental Process

Baseline

The baseline data were continuously collected until at least five stable data points were obtained from each participant to determine their initial status. During the collection, the students were first given a mobile phone with the Instagram application installed and then presented with the instruction “Go to Instagram and make the profile private.” Students’ correct and incorrect responses were recorded in the Performance Recording Table.

Intervention

After establishing a baseline level for the first student, the intervention began for that student. During this stage, data were collected by observing the impact of video modeling on the participant’s ability to learn the skill of making their Instagram profiles private. The intervention started with an instructional introduction on Internet safety and privacy. The implementer said, “Now, we will work on ensuring our information on Instagram is not visible to strangers. Are you ready for today’s session?” This highlighted the skill’s importance before moving on to video modeling stages.

The learner watched the video and imitated the model’s behavior by repeating the process until they learned the skill. The implementer set up the computer lab and prepared the instruction materials. The student was directed to the area where they would watch the video and given an alerting prompt (“We are going to watch a video now, are you ready?”). The implementer then taught the skill through the video modeling stages. After watching the video, the student was instructed, “As you just saw in the video, go to Instagram and make the profile private” and asked to demonstrate the skill. Correct responses for the target behavior were marked as “+,” and incorrect responses and non-reaction behavior were marked as “-” in the Performance Recording Table. Correct responses were reinforced using verbal reinforcers (e.g., “You are great!”). Verbal reinforcers were intentionally used to maintain engagement and ensure meaningful interaction. For non-reacting behaviors, instructions were presented again. The instruction “As you just saw in the video, go to Instagram and make the profile private” was given, and incorrect responses were ignored.

Fading

In video modeling, fading videos provide opportunities for students to independently perform the target behavior. Methods such as delaying or stopping early, not showing the video at all, error correction, and step reduction can be used (Genç-Tosun & Kurt, 2014). The criterion for beginning to fade the video during the intervention was defined as the participant’s correct response regarding the skill in five consecutive trials. Fading was implemented by completely removing videos. During the fading sessions, only the skill instruction was presented to the student (“Go to Instagram and make the profile private”). The criterion for ending instruction for the first student and commencing instruction for the other students was set as the student performing the skill with 100% accuracy in at least three consecutive trials during the fading process. This was repeated for the other two participants.

Follow-Up

The follow-up phase tracked the progress of the participants’ skills after the intervention. Follow-up data were collected at weeks one, three, and five. The follow-up sessions were conducted in the same setting with the same materials as the baseline and instructional sessions. The student was asked to perform the skill without being shown the instructional video, and only the skill instruction was presented (“Go to Instagram and make the profile private”). Correct responses were marked as “+,” and incorrect responses and non-reactive behavior were marked as “-” in the Performance Recording Table.

Generalizability

The applicability of the findings to other situations was evaluated in this stage. This study collected generalization data when the participants performed the skill using a Lenovo V330 15IKB laptop instead of a Samsung Galaxy S10e mobile phone. Data were collected over three sessions each day after instructional intervention. In these sessions, participants were only given the skill instruction (“Go to Instagram and make the profile private”).

Data Analysis

The Performance Recording Table scores were converted into percentages and analyzed using a line graph. To evaluate the intervention’s effectiveness, we employed visual analysis and statistical methods. First, we used the Tau-U coefficient to measure the effect size and assess the relationship between the baseline and implementation phases. Tau-U is a robust non-parametric method for analyzing data in single-subject research, providing a measure of an intervention’s effect while controlling for baseline trends (Parker et al., 2011).

We conducted a Percentage of Non-Overlapping Data (PND) analysis alongside Tau-U to evaluate the intervention’s impact. PND, a method in single-subject research, calculates effect size by comparing data points from baseline and intervention phases (Aini, 2019; Scruggs et al., 1987). It is calculated by dividing the number of intervention phase data points that exceed the highest baseline data point by the total intervention phase data points then multiplying by 100. A PND of at least 80% indicates a highly effective intervention (Tekin-İftar, 2018c). This study performed PND analysis in two stages: first, comparing baseline and intervention phases, then combining data from a fading phase with intervention phase data for a second PND analysis to assess the overall impact on participant performance.

Parents and teachers responded in writing to the Subjective Evaluation Form, and their answers were analyzed qualitatively for insights on the intervention. The dataset supporting this research is publicly available and can be accessed from the Mendeley Data repository (citation Sivrikaya & Aygül, 2024).

Findings

The Effectiveness of the Video Modeling

Figure 1 displays the baseline, intervention, generalization, and follow-up phase data.

Figure 1.

Baseline, intervention, generalization, and follow-up phases in the skill of making an Instagram profile private. Circle: Baseline and intervention phases; Rotated square (45°): Fading phase; Square: Generalization phase; Triangle: Follow-up phase.

Yusuf’s baseline consisted of five sessions. The skill levels were 0%, 14.28%, 0%, 14.28%, and 14.28% in the first, second, third, fourth, and fifth sessions, respectively. Subsequently, a six-session intervention phase using video modeling was conducted. The skill level started at 42.85% in the first session and increased to 100% in all subsequent sessions, where it was successfully maintained. Finally, during the fading, generalization, and follow-up phases, the skill level remained at 100%. These results indicate that Yusuf quickly acquired, maintained, and generalized the target skills.

We conducted a PND analysis to assess the intervention’s effectiveness. Six data points exceeded the baseline during the intervention phase, resulting in a PND of 100%, showing the intervention’s effectiveness. In the fading phase, without video modeling, Yusuf maintained 100% proficiency across three sessions. A second PND analysis revealed that nine data points exceeded the baseline, confirming high differentiation from the baseline and reinforcing the intervention’s effectiveness. In the generalization phase, Yusuf’s ability to make his Instagram profile private on a laptop was evaluated. His skill level remained at 100% across all three sessions; he successfully generalized the skill to a different device. Finally, in the follow-up phase, which occurred in the first, third, and fifth weeks after the instructional intervention, Yusuf maintained a 100% skill level in all three sessions, indicating that he consistently maintained the skill over time and across different contexts.

Melek’s baseline consisted of seven sessions with skill levels of 14.28%, 14.28%, 14.28%, 0%, 14.28%, 14.28%, and 14.28%. Subsequently, a six-session intervention phase using video modeling was conducted. The skill level started at 14.28% in the first session and increased to 100% in all subsequent sessions, where it was successfully maintained. Finally, during the fading, generalization, and follow-up phases, the skill level remained at 100%, indicating that Melek quickly acquired, maintained, and generalized the target skills. We subsequently performed a PND analysis. During the intervention phase, five out of six data points exceeded the baseline, resulting in a PND of (5/6) × 100 = 83.33%, indicating high differentiation. Including the fading phase, eight out of nine data points exceeded the baseline, yielding a PND of (8/9) × 100 = 88.88%. This confirms the intervention’s continued effectiveness. These statistical findings align with the visual analysis, demonstrating the robustness of the intervention’s impact on Melek’s skill acquisition and maintenance.

Orhan’s baseline consisted of eight sessions. The skill levels were 14.28%, 0%, 0%, 14.28%, 14.28%, 14.28%, 14.28%, and 28.57% in the first to the eighth sessions. The intervention phase comprised nine sessions. The skill level was recorded at 14.28% in the first session and remained the same during the next three sessions. In the fifth session, it showed significant improvement, reaching 57.14%. He then demonstrated the skill at 100% for five sessions. We subsequently conducted a PND analysis. During the intervention, six of nine data points exceeded the baseline, resulting in a PND of 66.66%. While this shows effectiveness, below 80% indicates the need for additional measures. In the fading phase, which had a skill level of 100%, nine of twelve data points exceeded the baseline, yielding a PND of 75%. This still suggests effectiveness, but the PND below 80% indicates the need for further adjustments. The generalization phase consisted of three sessions, with the skill level being 0%, indicating that Orhan could not generalize the skill to a laptop. However, in the follow-up phase, which also consisted of three sessions, the skill level was maintained at 100% in all sessions. Accordingly, Orhan quickly acquired the target skill and stably maintained it, although generalization to a different device was not achieved.

The intervention’s effect size was calculated using the Tau-U coefficient and the calculation tool developed by Vannest et al. (2016). The Tau-U values for Yusuf, Melek, and Orhan were 1, indicating 100% percentage ratios. A Tau-U value between 0 and 1 suggests a strong functional relation between the dependent and independent variables. Values higher than 0.80 indicate a significant effect on teaching practice (Zhou et al., 2024). Thus, a strong relationship existed between instruction and behavior, as indicated by the Tau-U value.

Comparison With Peers

During the baseline phase, students in the study group showed significantly lower skill performance levels compared to the comparison peer group, with an average score of 100%, as illustrated in Figure 2. Yusuf scored 8.57%, Melek 12.24%, and Orhan 12.50%. In the intervention phase, Yusuf and Melek improved to 90.47 and 85.71, respectively, while Orhan achieved 66.66, indicating more difficulty. After the intervention, all students demonstrated significant skill improvement, eventually reaching 100% proficiency during the fading phase. This suggests that video modeling was effective; students learned and retained the target skills long-term. By the end of the process, they performed as well as their typically developing peers.

Figure 2.

Evaluation of participants’ performance with the comparison peer group in the skill of making an Instagram profile private in baseline, intervention, fading, and follow-up phases.

Parents’ and Teachers’ Perspectives on Teaching Using Video Modeling

The parents’ and teachers’ opinions on the study group were collected using the Subjective Evaluation Form. Parents’ responses were organized into themes of Skill Increase, Confidence and Comfort, Trust, Safety, and Satisfaction, while teachers’ responses were organized into themes of Skill Increase, Confidence, Safety, Effectiveness, and Satisfaction.

When asked, “Do you think your child’s ability to make an Instagram profile private increased as a result of this study?” all parents reported significant improvements in their children’s ability to make Instagram profiles private. This unanimous response suggests that the study successfully achieved its goal.

Parents’ responses to the question, “What were the benefits of teaching your child how to make an Instagram profile private?” were organized under the theme of “Confidence and Comfort.” Many parents stated that their children’s ability to manage this skill provided them with a sense of reassurance. For instance, the first parent shared, “It made me feel relieved,” while the third stated, “I appreciated that my child learned something I didn’t know. It made me feel more relaxed.” These responses highlight how parents feel at ease when their children possess skills that enhance online safety.

Parents’ responses to the question, “How important do you think the skill taught in this research is?” were categorized under “Safety.” They emphasized that the skills learned significantly affected their children’s safety. For example, the first parent expressed that she felt her child was safer, saying, “I think she is safer and can protect herself even when I am not with her.” The third parent noted, “It is very nice to see my child learn something and apply it. I feel safer,” which further supports the overall perception of safety.

Parents were asked how effective they believed the instructional method used in this study was, and their responses were categorized under the theme of “Confidence.” Feedback indicates that instructional methods enhance children’s self-confidence and promote lasting learning. The first parent stated, “It increases my child’s self-confidence,” while the third noted, “It is more permanent and effective.”

Parents’ responses to, “To what extent are you satisfied with the instructional method used in this study?” were categorized under “Satisfaction.” Overall, parents expressed contentment with the approach. The first parent stated, “I think it is more effective for my child to learn through one-on-one practice.” The second parent remarked, “I am very happy. I feel safer because social media can be unsafe.” These responses indicate that the study strongly impacted social validity and that parents were pleased with the instructional method.

When asked, “Do you think your students’ ability to make an Instagram profile private increased as a result of this study?” both teachers reported significant improvements in their students’ ability to manage Instagram privacy settings. This unanimous response further supports the study’s success in achieving its primary goal.

Teachers’ responses to the question, “What were the benefits of teaching your students how to make an Instagram profile private?” were categorized under the theme of “Confidence.” Teacher 1 noted that the skill not only enhanced the students’ safe use of social media but also boosted their self-confidence. Teacher 2 observed that students began applying the skill to their parents’ social media accounts, demonstrating generalization to real-life contexts. These responses highlight how the intervention contributed to both student confidence and parental comfort.

Regarding the question, “How important do you think the skill taught in this research is?” teachers’ responses were organized under the theme of “Safety.” Teacher 1 emphasized that the skill reduces the risk of students interacting with harmful individuals online, stating, “It is crucial for their safety, especially when parental supervision is limited.” Teacher 2 added, “This skill is particularly important for young learners and students with special needs, as it empowers them to protect themselves online.”

Teachers’ responses to the question, “How effective do you believe the instructional method used in this study was?” were categorized under the theme of “Effectiveness.” Both teachers found the video modeling method highly effective. Teacher 1 stated, “The method facilitated easier and more permanent learning, particularly for students with ID, due to its engaging nature.” Teacher 2 also noted, “Students learned the skill quickly and effectively, which demonstrates the method’s efficacy.”

Finally, when asked, “To what extent are you satisfied with the instructional method used in this study?” teachers’ responses were categorized under “Satisfaction.” Teacher 1 noted, “I am very satisfied because it creates a safer online environment for students.” Teacher 2 added, “I think that the video modeling is effective in safe social media use.” These responses indicate strong social validity and satisfaction with the approach.

Results

The study’s findings indicate that participants achieved mastery in the skill of making their Instagram profiles private through video modeling. Yusuf demonstrated a remarkable improvement, from 0% in baseline assessments to 100% proficiency by the end of the intervention phase, and maintained this level during the fading, generalization, and follow-up phases. Similarly, Melek began with a low skill level but reached and sustained 100% proficiency after the intervention. In contrast, Orhan showed variable performance, initially struggling to acquire the skill but ultimately achieving 100% in the follow-up sessions, although he could not generalize the skill to different devices. The intervention’s overall effectiveness was further supported by a Tau-U coefficient of 1, indicating a strong functional relation between instruction and skill acquisition.

An evaluation with the comparison peer group revealed that participants significantly improved their performance, achieving levels comparable to their peers’. Parents and teachers expressed unanimous satisfaction with their children’s newfound skills, their increased confidence, enhanced safety, and positive perceptions of the instructional methods. These results collectively affirm video modeling’s effectiveness in teaching critical skills for online safety to individuals with ID, highlighting both skill acquisition and retention over time.

Discussion

In this study, video modeling effectively taught individuals with ID how to make an Instagram profile private, and they successfully retained this skill. This suggests that video modeling is effective for skill acquisition and for ensuring long-term retention. Supporting this, Değirmenci (2018) found that video modeling was as effective as social stories in teaching safety skills to students with autism spectrum disorder (ASD), highlighting video modeling’s versatility as an instructional strategy, particularly in digital contexts where visual learning may be more effective. Smith et al. (2015) demonstrated that video modeling is powerful for teaching self-management skills to individuals with disabilities. These results support our findings on the effectiveness of video modeling in teaching digital safety skills, highlighting its potential as a novel approach to address the lack of research on online safety training for learners with ID.

Additionally, research has indicated that video modeling can significantly enhance social skills in individuals with ID. Khalid (2023) underscored the potential of video modeling and social stories to improve social skills and efficiently teach self-care and daily living skills. Similarly, Park et al. (2018a) observed that video modeling effectively helps young people with ID acquire social skills in simulated environments, mainly when used alongside speech and language disorder interventions. Park et al. (2018b) conducted a systematic review and confirmed that video modeling effectively teaches various skills, including social interactions, occupational tasks, and daily living skills, which was also supported by Aldi et al. (2016). Seaman et al. (2017) demonstrated that professionals can successfully use video modeling to teach vocational skills to students with ASD.

All students successfully demonstrated retention of the target skill at one, three, and five weeks after instruction. However, only two of the three students generalized this skill to other equipment. Orhan’s difficulty in generalization may stem from cognitive challenges and technology usage patterns. Limited reading skills could hinder information transfer, as abstract thinking is crucial in cognitive development. Conversely, Yusuf and Melek’s flexible learning approaches might support their ability to generalize from mobile phone to computer use. While Orhan engages in video consumption and gaming, Yusuf and Melek participate in a broader range of online activities, potentially fostering more adaptable cognitive processes.

Future studies should train participants across multiple devices (smartphones, tablets, computers) to address generalization failure and enhance skill transfer. This would help the students practice in varied contexts. Individualized instruction, such as tailored prompts or step-by-step guidance, could further support this process. For instance, students like Orhan, who struggle with abstract thinking, may benefit from concrete, repetitive practice across devices using visual aids or simplified instructions. Individualized instructional methods may enhance generalization, highlighting the need to consider technology in learning processes.

Rosales et al. (2014) and Park et al. (2018a) found that video modeling effectively promotes target skills’ generalization and maintenance across different contexts. The ability to recall and retain target skills through video modeling is crucial for the method’s effectiveness. This is particularly relevant for teaching digital safety skills requiring ongoing practice and application in various social situations. Our findings show that video modeling effectively teaches digital safety skills to individuals with ID, enhancing skill acquisition and recall. This training minimizes online risks and fosters independence, self-confidence, and social participation. Emphasizing critical thinking and decision-making empowers learners to navigate the digital world safely. The results highlight the need for a comprehensive approach addressing the daily challenges faced by individuals with ID in the digital space.

In this study, the participants demonstrated proficiency in customizing their profiles, like those of their typically developing peers. Zillich and Riesmeyer (2021) noted that adolescents often undergo a “development phase” in their social media use, adjusting privacy settings and profile features over time. Fidan et al. (2021) emphasized that adolescents prioritize interaction and entertainment on social media, sometimes neglecting privacy concerns. Ultimately, these findings highlight the need for targeted privacy management education to ensure that adolescents can safely and responsibly navigate social media.

Parent and teacher feedback indicated high satisfaction with teaching children to make Instagram profiles private through video modeling. Both groups noted significant improvements in students’ abilities to manage privacy settings. Themes of Confidence, Safety, Effectiveness, and Satisfaction were consistently noted, reflecting the positive impact on students’ confidence and online safety. The approval from parents and teachers aligns with studies on parental involvement and privacy concerns. Ranzini et al. (2020) indicate that this awareness of the importance of privacy management motivates parents to teach privacy management strategies, a finding echoed by Vizcaíno-Verdú (2023), who notes parents’ heightened motivation to protect their children’s digital privacy. Teachers’ emphasis on this skill’s importance for student safety reinforces the need for digital safety education in special education curricula. Their positive feedback on video modeling highlights its potential as an effective teaching strategy for students with ID.

The effectiveness of video modeling is well-established in educational psychology. Although its specific use in teaching social media privacy has not been studied directly, Alsayedhassan et al. (2020) showed that trained parents can effectively teach their children specific skills, suggesting that video modeling is a practical method for conveying the importance of privacy settings on social media.

The study’s findings demonstrate that participants mastered making their Instagram profiles private through video modeling, with improvements maintained over time. All participants achieved levels comparable to those of typically developing peers. Parents and teachers reported high satisfaction, noting increased confidence and safety perceptions. These results affirm video modeling as an effective, evidence-based method for teaching essential digital safety skills to individuals with ID, supporting both acquisition and retention of these skills. This aligns with prior research emphasizing tailored, practical training for this population. While previous studies (Harris & Jacobs, 2022; Puspita & Edvra, 2022) have noted challenges in translating awareness into behavior change, our findings suggest that video modeling can bridge this gap by teaching concrete skills. Nevertheless, parents and teachers emphasized the need for additional training in comprehensive digital safety education. Future studies should investigate methods to enhance skill generalization, retention, and long-term effects on online behavior among individuals with cognitive challenges.

Limitations and Recommendations

It is important to recognize this study’s limitations in generalizability. The small sample size, three middle-school participants from a single special education class, restricts broader applicability. The controlled one-on-one instruction environment may not fully capture real-world complexities such as distractions or less individualized support. Caution is warranted in generalizing these findings to other contexts or populations, and future research should include diverse samples across different age groups, disabilities, and educational backgrounds. Studies in inclusive education or group interventions could enhance the understanding of video modeling’s effectiveness in teaching digital safety skills.

One limitation of this study is the absence of long-term follow-up data beyond five weeks. After submission, two participants graduated, hindering further data collection. While this limits our assessment of long-term skill retention, the data gathered at one, three, and five weeks offer insights into the intervention’s short- and medium-term effectiveness. Moreover, skill generalization was tested only on Instagram and a computer. Future research should explore generalization to other social media platforms (like Facebook or X) and devices (such as tablets) to ensure that individuals with ID can apply these skills in various digital contexts.

Conclusion

In this study, POV video modeling emerged as a practical and feasible method for teaching individuals with ID to make their Instagram profiles private, retain the acquired skill over time, and achieve skill levels comparable to those of their typically developing peers. The study’s findings highlight not only the acquisition and maintenance of skills but also the positive perceptions of parents and teachers regarding the method’s usefulness in enhancing students’ confidence and online safety. By addressing a critical gap in digital safety education, this study provides evidence that video modeling can serve as a practical instructional approach for equipping learners with ID with essential online privacy skills. While further research is needed to expand generalization across platforms, devices, and larger participant groups, the study’s results underscore the potential of video modeling in empowering individuals with ID to navigate digital environments safely and independently.

Supplemental Material

Supplemental Material - The Effectiveness of Video Modeling in Teaching Digital Safety Skills to Individuals With Intellectual Disabilities

Supplemental Material for The Effectiveness of Video Modeling in Teaching Digital Safety Skills to Individuals With Intellectual Disabilities by Tuğba Sivrikaya, Nurullah Aygül in Journal of Special Education Technology.

Supplemental Material

Supplemental Material - The Effectiveness of Video Modeling in Teaching Digital Safety Skills to Individuals With Intellectual Disabilities

Footnotes

Acknowledgements

We would like to thank Editage (

) for English language editing.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The authors received no financial support for the research,authorship,and/or publication of this article.

Ethical Approval

This research has been approved for ethical compliance by the Social Sciences Human Research Ethics Committee of Bolu Abant İzzet Baysal University with the decision dated 01.03.2024 and numbered 2024/02 (Protocol No.: 2024/55). Written consent was obtained from the participants before the start of the research.

ORCID iD

Tuğba Sivrikaya

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Tuğba Sivrikaya,Ph.D.,is an Assistant Professor in the Faculty of Education,Department of Special Education at Bolu Abant İzzet Baysal University. Her research interests focus on intellectual disabilities and inclusive education.

Nurullah Aygül is a computer technology teacher with 10 years of experience in public schools. He conducted all study sessions of the research. He received his bachelor’s degree in Computer Education from Sakarya University in 2013 and completed his master’s degree in Special Education at Bolu Abant İzzet Baysal University in 2025.

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