Introduction

The growing attention to developmental disorders, particularly Autism Spectrum Disorder (ASD), has led to significant advancements in early diagnosis and interventions to support individuals with ASD. These efforts aim to enhance the competence of professionals in the field, enabling them to overcome challenges and provide high-quality services (Al-Beltagi, 2024; Nasrallah et al., 2025).

Disabilities, including ASD, are a global concern that significantly impacts developmental outcomes. A nation’s commitment to supporting individuals with disabilities reflects its overall progress and inclusivity (Chumo et al., 2023; Degener & de Castro, 2022; Gardner et al., 2022). Without appropriate support, children with disabilities may face extensive challenges, placing additional strain on their families and the communities in which they live.

ASD in particular has gained significant interest from researchers due to the condition’s profound impact on a child’s cognitive, social, linguistic, and emotional development, as well as its impact on their families. Effective integration into academic, family, and community environments requires individualized guidance and therapeutic interventions tailored to the abilities of each child (Saulnier et al., 2022).

The rising number of students being identified with ASD, especially those children with intellectual disabilities, reinforces the need for specialized educational programs. Developing such programs requires collaborative efforts across multiple disciplines to ensure that children with ASD receive an inclusive and quality education comparable to that of their neurotypical peers (Gardner et al., 2022; Sánchez Amate & Luque de la Rosa, 2024; Trevarthen & Delafield-Butt, 2013).

Supporting children with ASD presents challenges, requiring specialized skills in behavior modification and social interaction enhancement (Aftab et al., 2024; I.-J. Lee & Yang, 2024). This further highlights the importance of special education programs that provide comprehensive educational and rehabilitative services, including academic instruction, social skills training, vocational training, and psychological support, delivered by a team of dedicated, trained personnel(Bowman et al., 2021; Cheong & Yahya, 2013).

While several studies have evaluated the effectiveness of intervention programs for ASD, most have focused on clinical outcomes or teacher evaluations. The perspectives of families—as primary caregivers and long-term support systems—remain underexplored in the literature. Parental perceptions regarding intervention are essential for understanding how educational programming helps to support them in their day-to-day experiences. Exploring the ease of implementation, sustainability, and emotional impact of intervention programming is fundamental in determining the program’s worth. Addressing the deficit in the research is essential for improving educational program design and ensuring that interventions meet the comprehensive needs of both children and their families.

This study explores the experiences of families whose children have participated in targeted training programs, with a focus on the programs’ effects on self-regulation and social interaction. It also examines how parents perceive these changes and investigates their views on the overall benefits and challenges of the interventions. Understanding family perspectives offers valuable insight into the effectiveness of ASD intervention programs and can help shape future improvements (Brookman-Frazee et al., 2006; Pickard & Ingersoll, 2015).

The current study attempted to answer the following main research questions:

To what extent do educational programs for children with ASD improve their self-regulation behaviors from the perspective of their families?

To what extent do educational programs for children with ASD improve their social interaction behaviors from the perspective of their families?

Are there any statistically significant differences at the level of (a=0.05) in the improvement of behaviors of self-regulation among children with (ASD) based on the child’s gender, age, and severity of the disorder from the perspective of their families??

Are there any statistically significant differences at the level of (a=0.05) in the improvement of social interaction behaviors among children with (ASD) based on the child’s gender, age, and severity of the disorder from the perspective of their families?

Previous studies have typically explored the impact of specific interventions and teaching strategies, this study differs by focusing on the perception of parents regarding the effectiveness of special education interventions in enhancing self-regulation and social interaction behaviors in children with ASD.

Literature Review

Special Education Programs

Special education programs play a crucial role in ensuring the success of students with disabilities by providing them with individualized educational environments tailored to their unique academic, social, and emotional needs (Khaleel et al., 2021; Khasawneh, 2024). These programs employ innovative teaching strategies to enhance students’ learning, communication, social interaction and daily life skills(Barnard & Henn, 2023; Iskakova, 2023). Through personalized support, students are empowered to reach their full potential in both academic and life domains, gaining improvement in achievement, self-confidence, and emotional well-being(Hurwitz et al., 2022; Weiler & Murad, 2022).

Autism Spectrum Disorders, characterized by a wide range of developmental challenges affecting their social interaction, communication, and behavior, necessitate carefully designed educational approaches delivered by well-trained professionals (Bhat, 2020; Kim et al., 2024). For children with ASD, individualized programs are essential to address their developmental needs and promote meaningful progress across functional areas (Kim et al., 2024). For children with ASD, educational programming should be specifically designed to address their unique needs and promote their comprehensive development.

The Effect of Training Programs on Self-Regulation

One key development area supported by special education programs is self-regulation that refers to the individual’s ability to manage thoughts, emotions, and behaviors in pursuit of goals (Paethrangsi et al., 2024; Sáez-Delgado et al., 2022). Self-regulation includes planning, goal-setting, attention control, emotional regulation, and behavior monitoring. Research has shown that all individuals, regardless of age or ability, can learn self-regulation behaviors through structured training (Alberto & Troutman, 2008; Wang et al., 2023).

In an academic context, strong self-regulation skills help students manage their time effectively, set clear priorities, break down complex tasks into manageable steps, and use strategies like summarization, mind mapping, and structured note-taking to enhance comprehension and retention. These skills improve organizational habits, reduce procrastination and stress, and enhance productivity in academic performance (Payan-Carreira et al., 2022; Tanksale et al., 2020). Developing self- regulation also involves techniques such as verbal self-direction during task execution; and goal setting, which involves setting clear objectives and setting a timeline for their completion (Khasawneh, 2024).

Several studies have demonstrated the effectiveness of training programs in enhancing self-regulation among children with ASD. Bahrawi's (2022) study examined the impact of using visual activity schedules on self-regulation among students in a school designed for children with ASD in Al-Ehsa cityin Altafelah, Jordan. Bahrawi (2022) found that visual activity schedules help to promote independence, reduce anxiety and support transition particularly among female students and those enrolled in the training program. Similarly, Rychalski et al. (2024) evaluated the effectiveness of using robots to enhance self-regulation skills in children with ASD in typical school settings.The study showed that the teachers' acceptance of the robots is a crucial factor in their successful integration into their school setting. Teacher acceptance is influenced by several factors, including technical knowledge, workload, and preconceptions about technology use in education.

Tanksale et al. (2020)evaluated a yoga-based training program, integrated with elements of third-generation cognitive behavioral therapy (CBT), on improving self-regulation skills in children with ASD. The study found significant improvements in executive functioning, sleep quality, anxiety, and emotional awareness among children with ASD. Although the results are promising, the researchers recommend interpreting them with caution, given that this is a preliminary study, emphasizing the importance of future studies to enhance the effectiveness of this type of intervention. Likewise, Y.-F. Lee et al. (2025) conducted a study to improve the quality of students' reflections and self-regulated learning skills (SRL) by using chatbots to provide immediate, personalized feedback. This intervention was applied in a university "Website Design" course, comparing an experimental group using chatbot-assisted learning with a control group using traditional instruction. Results showed that the experimental group outperformed the control group in academic performance, motivation, SRL skills, reflection, and metacognitive tendencies.

Together, these findings underscore the importance of integrating individualized educational programming and targeted training intervention to strengthen self-regulation and academic success among students with ASD. In this study, self-regulation is operationally defined as the degree to which families perceive their child with ASD to be able to organize thoughts and emotions, increase self-confidence, control their reactions, and develop life skills. Responses are measured through the self-regulation scale using a 4-point Likert scale assessing the frequency of self-regulatory behaviors.

Social Interaction in Children with ASD

Social interaction is defined as the ability to initiate and sustain reciprocal communication and engagement with others. This includes verbal and nonverbal communication, understanding social norms, interpreting emotions, anticipating others ' reactions, and responding appropriately in various situations (Shih et al., 2021; Williams et al., 2022). Effective social interaction requires individuals to attend to social cues, interpret situations correctly, and solve social problems in a contextually appropriate manner (Williams et al., 2022).These skills are essential for building positive relationships, forming friendships, and developing personal interests. Social interaction develops through shared play, taking turns, and engaging in early childhood, while adults, on the other hand, need to understand emotional cues, initiate conversations, and choose appropriate topics to maintain relationships (Shih et al., 2021).

Children with ASD face difficulties in social communication, interpreting emotions, understanding nonverbal cues, and showing interest in interaction, all of which may hinder their ability to form friendships (Ghanouni & Jarus, 2021; Hus & Segal, 2021; Schiavo, 2024; Tathgur & Kang, 2021). These challenges include delayed social language development and difficulty navigating complex social situations. Therefore, it is essential to provide adequate support for these children through training programs designed to enhance social interaction and foster effective communication (Hou et al., 2024; Kumar & Jaiswal, 2025).

İnci and Sağlam(2025) conducted a study exploring the effect of an interactive art activity program on repetitive behaviors, social skills, and maternal emotions in children with ASD. The study was conducted in Batman, Turkey, with a sample of 28 children, aged 5-17 (13 in the experimental group, 15 in the control group), along with their mothers. Multiple tools were used to collect data, and the results were analyzed using appropriate statistical tests. The results showed significant improvements in social skills, a decrease in repetitive behaviors, and an improvement in the quality of life of the mothers in the experimental group, while no significant changes were recorded in the control group. The study concluded that incorporating interactive art activities into educational programs may effectively contribute to improving the behavioral, social, and emotional aspects of children with ASD and their families. Mirzakhani et al. (2022) investigated the effect of the “Son-Rise” and “Floortime” programs on improving social interaction skills and reducing stereotypical behaviors in children with ASD. The study sample included 60 children aged 6 to 8, years, selected from rehabilitation centers in Alborz Province, and randomly assigned to three groups: the Son-Rise group, the Floortime group, and a control group who received routine occupational therapy. Data was collected using standardized instruments, and results were analyzed using analysis of variance and repeated measures. The results showed that both programs contributed to improved social interaction and reduced stereotypical behaviors, with the Floortime program being more effective than the Son-Rise program at post-test.In this study, social interaction is operationally defined as the degree to which families perceive their child with ASD to be able to improve interaction skills with peers, develop social communication skills, initiate conversations, and regulate responses. Responses are measured through the social interaction scale using a 5-point Likert scale assessing the frequency of self-regulatory behaviors.

Family Perception / Satisfaction

Current literature is beginning to explore family experiences and outcomes of ASD interventions. The Early Start Denver Model (ESDM) focused on the feasibility, acceptability, and social validity of the ESDM. This study found that parents perceived it as empowering, feasible, and meaningful for daily life, valuing their increased knowledge of ASD and ESDM strategies and stronger parent-therapist relationships formed through the program (Carlsson et al., 2024). A mixed-methods study (N = 88) conducted in Switzerland further illustrated that parents value strong social support and access to robust services. Feeling adequately supported leads to increased feelings of resilience and self-efficacy (Mutschler Collins et al., 2025). The World Health Organization’s Caregiver Skills Training program, implemented in Hong Kong, demonstrated measurable improvements in family well-being, emphasizing how structured parent involvement can yield broader family benefits (Wong et al., 2025). A systematic review and meta-analysis, synthesizing 21 studies, yielded that interventions focusing on parents were more effective in reducing parental stress, distress, and depressive symptoms in parents and alleviating problem behaviors in children (S. N. Li et al., 2024).

Family plays a crucial role in supporting children with ASD by providing a nurturing environment that helps them develop social interaction skills (D. Li et al., 2025). Parental guidance fosters a sense of comfort and confidence, enabling children with ASD to express themselves and engage socially with greater ease (Wilson et al., 2025). These concepts are consistent with family systems theory, developed by Bowen (1994), which emphasizes that the family is an interconnected emotional unit, where the behavior, development, and challenges of any individual impact the entire family. This perspective is particularly important when studying children with ASD, as the success of interventions often depends on the family's involvement, support, and adaptability.

The findings in the previously noted literature indicate the need to take into consideration family-centered measures, including parental mental health, self-efficacy, and family functioning, as essential components in assessing the success of an intervention.

Methodology

Research Design

This study adopted a quantitative approach and a descriptive-analytical design to evaluate the impact of the programs provided to children with ASD on their self-regulation skills and social interaction from the perspective of their families.Descriptive components included calculating means and standard deviations to summarize participants' responses, while analytical components used inferential statistical techniques such as one-way analysis of variance (ANOVA) to examine differences based on ASD severity, and Pearson's correlation coefficient to assess relationships between variables. These methods are suitable for identifying patterns and determining the significance and strength of relationships within data.

Sample and Data Collection

The study sample included 150 families of children with ASD (80% male and 20% female) who were enrolled in special education centers in Amman, Jordan. The sample was selected purposively to fit the nature of the measurement tools, as the family serves as a direct source of observation and assessment.The purposive sampling method was chosen because the study specifically aimed to explore families' perspectives on the effectiveness of programs provided to children with ASD in improving self-regulation and social interaction skills. Families who are actively involved in the daily care and educational experiences of their children with ASD are best able to provide accurate and comprehensive insights, making a purposive sample the most appropriate for achieving the study's objectives.

Table 1 presents the distribution of the study sample members by variables, including the demographics of children with ASD in terms of gender, disability severity, parental education levels, and age.

Table 1. Participated Demographic Characteristics

Variable	Category	N	Percentage
Child’s Gender	Male	120	80
	Female	30	20
	Total	150	100
Disability Severity	Mild	70	46.7
	Moderate	30	20
	Severe	50	33.3
	Total	150	100
Mother's Education Level	High School	7	4.7
	Bachelor's	115	76.7
	Master's	13	8.7
	Doctorate	15	10
	Total	150	100
Father's Education Level	High School	2	1.3
	Bachelor's	105	70
	Master's	34	22.7
	Doctorate	9	6
	Total	150	100
Child’s Age	3-7 years	67	44.7
	8-13 years	45	30
	14-21 years	38	25.3
	Total	150	100

Data was collected through an online survey during the first semester of the 2023–2024 academic year. Ethical approval for the study was obtained from the Institutional Review Board (No. 3/10/20779) at the Ministry of Education. Participants received an informed consent form outlining the research objectives. Participation was entirely voluntary, and confidentiality was guaranteed. Participants were informed that they could withdraw from the study at any time without repercussions.

Instruments

The study tool consisted of three sections: demographic information, self-regulation questionnaires, and social interaction questionnaires. It was developed based on a review of literature in the areas of self-regulation and social interaction, as well as practical applications from the intervention programs, including social stories and behavior modification strategies. (Chen & Jahromi, 2025; Koegel et al., 2010; Nuske et al., 2021; Zelazo & Carlson, 2012). Each questionnaire included 19 items. The internal validity and reliability of both the self-regulation and social interaction questionnaires were analyzed to ensure the quality of the questionnaires.

Study Tool Validation

Content Validity

To ensure the content validity of the instrument, a panel of five experts in the field of special education was consulted. Three of the five experts specialize in Autism interventions and have more than ten years of experience, and the remaining two specialize in communication disorders. Each expert reviewed the instrument items for clarity, relevance, and alignment with the constructs of self-regulation and social interaction. The validation process involved evaluating the appropriateness of the language used, the cultural and developmental suitability of the items for children with ASD, and how well the items reflected the core areas being assessed. Based on their feedback, minor modifications were made to improve item wording and ensure that the instrument accurately measured the targeted skills. This expert review contributed to the improvement of the instrument’s face and content validity and helped to ensure its alignment with special education theory and practice.

Construct Validity

Constructive validity was verified through a preliminary pilot of the questionnaire among 25 families outside the study sample, with the aim of determining the strength and direction of the relationship between each item and the domain to which it belongs. Pearson's correlation coefficients (r) were calculated between each item and the total score for that domain, and statistical significance was set at a level less than .05.

Cronbach's AlphaTest

Cronbach's alpha coefficient was calculated to verify the internal consistency (stability) of the study tool, after applying it to a preliminary sample of 25 participants who were not included in the main research sample.

Self-Regulation Questionnaires: Internal Consistency Validity

In analyzing the self-regulation questionnaires, Pearson's correlation coefficients were calculated between each item and the dimension to which it belongs, with values ​​ranging between .227 and .661, with the highest value being for item (7) and the lowest for item (12), indicating that internal validity was achieved for all items. The scale was based on a four-point Likert scale (always = 4, often = 3, sometimes = 2, rarely = 1), and the averages were classified into three levels: low (1.00–2.00), medium (2.01–3.00), and high (3.01–4.00).

Table 2. Correlation Coefficients of Self-Regulation Scale Items with Their Dimension

Item	Correlation Coefficient	Item	Correlation Coefficient
1	.507	11	.561
2	.537	12	.227
3	.409	13	.596
4	.622	14	.575
5	.273	15	.523
6	.508	16	.476
7	.661	17	.621
8	.587	18	.520
9	.448	19	.550
10	.482

To ensure the reliability of the self-regulation scale, Cronbach’s alpha coefficient was calculated, as shown in Table 2.

Table 3. Cronbach’s Alpha Reliability Coefficient for Self-Regulation Scale Items

Scale	Number of Items	Cronbach’s Alpha
Self-Regulation	19	.86

Table 3 shows that the Cronbach’s Alpha coefficient for the total scale was .86, indicating an acceptable level of reliability for field application, based on Nunnally’s criterion, which considers .70 as the minimum acceptable reliability level (Nunnally & Bernstein, 1994).

Social Interaction Questionnaires: Internal Consistency Validity

In analyzing the social interaction questionnaires, Pearson's correlation coefficients were calculated between each item and its corresponding dimension, with values ​​ranging between (.217 and.690), with the highest correlation for item (17) and the lowest for item (5). All correlation coefficients were above 0.20, indicating internal consistency of the scale. As a result, the final social interaction scale consisted of (19) items.

Table 4. Correlation Coefficients of Social Interaction Scale Items with Their Dimension

Item	Correlation Coefficient	Item	Correlation Coefficient
1	.342	11	.655
2	.388	12	.512
3	.450	13	.492
4	.415	14	.677
5	.217	15	.293
6	.626	16	.472
7	.482	17	.690
8	.410	18	.332
9	.395	19	.392
10	.410

To ensure the reliability of the social interaction scale, Cronbach’s alpha coefficient was calculated, as shown in Table 4.

Table 5. Cronbach’s Alpha Reliability Coefficient for Social Interaction Scale Items

Scale	Number of Items	Cronbach’s Alpha
Social Interaction	19	.82

Table 5. shows that the Cronbach’s Alpha coefficient for the total scale was .82, indicating an acceptable level of reliability for field application, based on Nunnally’s criterion, which considers .70 as the minimum acceptable reliability level (Nunnally & Bernstein, 1994).

Correction Standard

A four-point Likert scale was also used to obtain the study sample’s responses to the Self-Regulation Scale according to the following degrees of agreement: Always, often, sometimes, rarely. This scale was expressed quantitatively by giving each of the previous statements a score according to the following: Always = 4, often = 3, sometimes = 2, Rarely = 1. Based on this, the means obtained were categorized as follows, according to the formula:(Upper Value - Lower Value) ÷ Number of Levels = 1.00 (category length). The self-regulation scale used a four-point Likert scale to encourage participants to give decisive answers and reduce neutrality, as this dimension often requires clear indicators of behavior repetition.

Table 6. The Four-Way Likert Scale Measurement for the Self-Regulation Scale

Simple Mean Average (SMA)	Degree of Approval
1.00 - 2.00	Low
2.01 - 3.00	Medium
3.01 - 4.00	High

A five-point Likert scale was also used to obtain the study sample’s responses to the Social Interaction Scale according to the following degrees of agreement: Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree. Then, this scale was expressed quantitatively by giving each of the previous statements a score according to the following: Strongly Agree = 5, Agree = 4, Neutral = 3, Disagree = 2, Strongly Disagree 1. Based on this, the arithmetic means obtained were categorized as follows, according to the formula:(Upper Value - Lower Value) ÷ Number of Levels = 1.33 (category length). The Social Interaction Scale used a five-point Likert scale to capture a broader range of perceptions and allow for more precise responses regarding social behaviors. The use of two distinct scales was intentionally chosen to suit the nature of the dimension being measured and to achieve a balance between clarity and variety of responses.

Table 7. The Five-Way Likert Scale Measurement for Self-Regulation Scale

Simple Mean Average (SMA)	Degree of Approval
1.00 - 2.33	Low
2.34 - 3.66	Medium
3.67 - 5.00	High

This study examined the extent, frequency, and types of negative behaviors experienced by children with ASD, as well as the role of educational programs in reducing these behaviors from the perspective of families. Because the study focused on improving self-regulation and social interaction, we also looked at negative experiences that can interfere with these skills, such as bullying, social isolation, and other challenging behaviors. To account for individual differences, the analysis considered how often these behaviors occurred in relation to the child’s gender, age, and severity of the disorder. Families also reported on how effective they believed educational programs were in reducing negative behaviors and helping children adapt socially across different learning environments. Appropriate statistical methods were used to compare groups and test for differences, with the goal of understanding whether these variables influenced the children’s academic experiences and the families’ views of program effectiveness. The findings can help inform more specific recommendations that reflect the needs and characteristics of different groups of children with ASD.

Statistical Analysis

The study used descriptive statistics to calculate the means and standard deviations for the self-regulation and social interaction domains. Pearson's correlation coefficients were used to verify construct validity, and Cronbach's alpha coefficient was applied to assess internal consistency, with results indicating a high level of reliability for both scales. Prior to conducting the Analysis of Variance (ANOVA), assumptions of normality and homogeneity of variance were evaluated to confirm the appropriateness of the parametric analysis. The Kolmogorov-Smirnov test was used to assess the normality of score distributions, and Levene’s test was applied to evaluate the homogeneity of variances across groups.

Findings/Results

Results Related to Question One: What is the degree of impact of programs provided to children with ASD in improving their self-regulation behaviors from the perspective of their families?

To answer this question, the means and standard deviations of the responses of parents of children with ASD on the self-regulation scale were calculated, as shown in Table 8.

Table 8. Means and Standard Deviations of Parents' Responses on the Self-Regulation Scale

Rank	Item	Mean	Standard Deviation	Level
1	The behavior modification plan enhances my child's control over their reactions.	3.22	0.92	High
2	The educational plan provides effective strategies for time management.	3.16	0.76	High
3	The Individualized Education Plan (IEP) supports my child's growth and improves self-regulation.	3.05	0.82	High
4	Parental involvement positively influences the development of my child's self-regulation.	3.05	0.82	High
5	The plan includes clear goals that help my child control themselves.	3.00	0.68	Moderate
6	Various programs contribute to developing my child's life skills.	2.86	0.83	Moderate
7	The programs help organize my child’s thoughts and emotions.	2.82	0.72	Moderate
8	Using technology supports the development of my child’s self-regulation.	2.80	0.75	Moderate
9	Applied Behavior Analysis contributes to improving my child’s self-control.	2.78	0.78	Moderate
10	Multidisciplinary support enhances my child’s self-regulation ability.	2.78	1.14	Moderate
11	The curriculum promotes my child’s self-regulatory behavior.	2.75	0.72	Moderate
12	The offered programs increase my child's self-confidence.	2.73	0.74	Moderate
13	Selecting appropriate reinforcers helps my child improve self-control.	2.68	0.71	Moderate
14	The programs support my child’s ability to adapt to different environments.	2.68	0.82	Moderate
15	The programs help my child build self-worth when exhibiting positive behavior.	2.64	0.93	Moderate
16	Positive reinforcement enhances my child's ability to self-regulate.	2.59	0.73	Moderate
17	Strategies such as social stories and modeling are used to promote self-regulation.	2.52	0.77	Moderate
18	Programs rely on problem-solving to improve my child's self-regulation ability.	1.96	0.93	Low
19	Programs improved my child’s ability to assess consequences before taking action.	1.74	1.01	Low
Overall Scale Score	2.73	0.45	Moderate

Table 8 shows that the overall mean score for the self-regulation scale was Moderate, with an arithmetic mean of 2.73. The item stating "The behavior modification plan enhances my child's control over their reactions" ranked first with an arithmetic mean of 3.22, rated high. Meanwhile, the item "Programs help my child make sound decisions" ranked last with an arithmetic mean of 1.74, rated low.

Results Related to Question Two: What is the degree of impact of programs provided to children with ASD in improving their social interaction behaviors from the perspective of their families?

To address this question, the means and standard deviations of the responses of parents of children with ASD on the social interaction scale were calculated, as shown in Table 9.

Table 9. Means and Standard Deviations of Parents' Responses on the Social Interaction Scale

Rank	Item	Mean	Standard Deviation	Level
1	Educational programs provide a supportive environment for my child to improve interaction skills with peers.	4.22	0.78	High
2	The programs offered to my child help them adapt to different environments.	4.08	0.97	High
3	Strategies such as social stories and modeling contribute to improving my child’s interaction.	4.03	0.92	High
4	The programs include positive reinforcement strategies to encourage social interaction.	3.99	0.92	High
5	Inclusion in the educational environment improves my child’s social skills.	3.92	0.96	High
6	The Individualized Education Plan focuses on developing my child's social communication skills.	3.89	1.10	High
7	The behavior modification plan helped integrate my child within peer groups.	3.81	1.01	High
8	Group activities help develop my child’s interaction skills with others.	3.81	1.01	High
9	Social modeling improves my child’s ability to initiate conversations and regulate responses.	3.78	0.93	High
10	The behavior modification plan contributes to improving my child’s social interaction.	3.77	1.04	High
11	The programs offered to my child increase their social interaction.	3.75	0.95	High
12	Group learning improves my child’s cooperative and interactive skills.	3.73	1.04	High
13	Family involvement in educational programs improved my child’s social skills.	3.69	1.07	High
14	Training in social communication skills enhanced my child’s interaction with others.	3.69	0.83	High
15	The Individualized Education Plan contributes to my child’s ability to form new friendships.	3.65	0.98	Moderate
16	The programs offered to my child boost their self-confidence when interacting with others.	3.53	1.06	Moderate
17	The use of technology enhances my child’s social communication.	3.41	1.19	Moderate
18	The programs help my child initiate conversation and communicate with others.	3.34	1.04	Moderate
19	The programs include problem-solving strategies to enhance my child’s social interaction.	2.06	0.96	Low
Overall Scale Score	3.69	0.43	High

Table 9. indicates that the overall mean score for the social interaction scale was high, with an arithmetic mean of (3.69). The item 'Educational programs provide a supportive environment for my child to improve interaction skills with peers’ ranked first with an arithmetic mean of (4.22), rated high. Meanwhile, the item " The programs include problem-solving strategies to enhance my child’s social interaction" ranked last with an arithmetic mean of 2.06, rated low.

Results Related to Question Three: Are there statistically significant differences at the level of (α=0.05) in the degree of improvement in self-regulation behaviors among children with ASD attributed to the child’s gender, age, or severity of the disorder from their families’ perspective?

To answer this question, the arithmetic means and standard deviations of parents' responses on the self-regulation scale were calculated according to the variables (child’s gender, age, severity of the disorder), as shown in Table 10.

Table 10. Arithmetic Means and Standard Deviations of Parents' Responses on the Self-Regulation Scale by Study Variables

Variable	Category	N	Arithmetic Mean	Standard Deviation
Gender	Male	120	2.72	0.43
	Female	30	2.75	0.54
Age	3-7 years	67	2.75	0.39
	8-14 years	45	2.71	0.45
	14-21 years	38	2.71	0.54
Severity	Mild	70	2.80	0.44
	Moderate	30	2.88	0.37
	Severe	50	2.54	0.45

The results in Table 10. indicate apparent differences in the arithmetic means of the study sample responses on the self-regulation scale according to the variables (child’s gender, age, severity of the disorder). To determine the significance of these differences, an ANOVA test was conducted, as shown in Table 11.

Table 11. ANOVA Test Results to Determine the Significance of Differences in Arithmetic Means of Study Sample Responses on the Self-Regulation Scale by Study Variables

Source	Sum of Squares	Df	Mean Square	F	Sig.
Gender	0.153	1	0.153	0.821	.367
Age	0.417	2	0.208	1.117	.330
Severity	3.228	2	1.614	8.652	.000*
Error	26.861	144	0.187
Total	30.172	149

Significance level (α=.05)

Table 11 shows no statistically significant differences in the arithmetic means of the study sample responses on the self-regulation scale according to the child's gender (F = 0.821, Sig. = 0.367) or age (F = 1.117, Sig. = 0.330). However, there were statistically significant differences according to the severity of the disorder (F = 8.652, Sig. = 0.000). In addition to statistical significance, the effect size was calculated using eta squared (η²), which indicated a moderate effect of severity of the disorder on self-regulation behaviors (η² = 0.126), suggesting that approximately 12.6% of the variance in self-regulation scores can be attributed to differences in severity (Cohen, 1988).

To determine the source of these differences, a Scheffe's test was conducted, as shown in Table 12.

Table 12. Scheffe's Test for Post Hoc Comparisons to Determine the Source of Differences in Self-Regulation Behaviors by Severity of Disorder

Severity	Mean Difference (I-J)	Sig.
Severe – Mild	-0.2576*	.007
Severe – Moderate	-0.3411*	.004
Moderate – Mild	0.0835	.676

The results indicate significant differences in self-regulation behaviors according to severity, where children with severe ASD showed lower self-regulation behaviors compared to those with mild and moderate ASD. However, no significant differences were found between the mild and moderate groups.

Results Related to Question Four: Are there statistically significant differences at the level of (α=.05) in the degree of improvement in social interaction among children with ASD attributed to the child’s gender, age, or severity of the disorder from their families’ perspective?

To address this question, the arithmetic means and standard deviations of parents' responses on the social interaction scale were calculated according to the variables (child’s gender, age, severity of the disorder), as shown in Table 13.

Table 13. Arithmetic Means and Standard Deviations of Parents' Responses on the Social Interaction Scale by Study Variables

Variable	Category	N	Arithmetic Mean	Standard Deviation
Gender	Male	120	3.72	0.42
	Female	30	3.58	0.47
Age	3-7 years	67	3.65	0.47
	8-14 years	45	3.68	0.46
	14-21 years	38	3.79	0.28
Severity	Mild	70	3.81	0.42
	Moderate	30	3.74	0.24
	Severe	50	3.50	0.46

The results in Table 13. indicate apparent differences in the arithmetic means of the study sample responses on the social interaction scale according to the variables (child’s gender, age, severity of the disorder). To determine the significance of these differences, an ANOVA test was conducted, as shown in Table 14.

Table 14. ANOVA Test Results to Determine the Significance of Differences in Arithmetic Means of Study Sample Responses on the Social Interaction Scale by Study Variables

Source	Sum of Squares	Df	Mean Square	F	Sig.
Gender	0.333	1	0.333	1.994	.160
Age	0.417	2	0.074	0.442	.644
Severity	2.479	2	1.239	7.428	.001*
Error	24.028	144	0.167
Total	27.520	149

Significance level (α=.05)

Table 14 shows no statistically significant differences in the arithmetic means of the study sample responses on the social interaction scale according to the child's gender (F = 1.994, Sig. = .160) or age (F = 0.442, Sig. = .644). However, there were statistically significant differences according to the severity of the disorder (F = 7.428, Sig. = .001*). In addition to statistical significance, the effect size was calculated using eta squared (η²), which indicated a moderate effect of severity of the disorder on social interaction behaviors (η² = 0.117), suggesting that approximately 11.7% of the variance in social interaction scores can be attributed to differences in severity (Cohen, 1988).

To determine the source of these differences, a Scheffe's test was conducted, as shown in Table 15.

Table 15. Scheffe's Test for Post Hoc Comparisons to Determine the Source of Differences in Social Interaction Behaviors by Severity of Disorder.

Severity	Mean Difference (I-J)	Sig.
Severe – Mild	-.3159-*	.000
Severe – Moderate	-.2435-*	.039
Moderate – Mild	-0.0724	.719

The results indicate significant differences in social interaction according to severity, where children with severe ASD showed lower social interaction compared to those with mild and moderate ASD. However, no significant differences were found between the mild and moderate groups.

Discussion

This study evaluated the perceived impact of educational and training programs for children with ASD on their self-regulation and social interaction skills, from the perspective of their families. Findings showed that these programs had a "moderate" effect in improving self-regulation and a "high" effect in enhancing social interaction. Statistically significant differences were noted with regard to the severity of the disorder, while no differences were observed with respect to gender or age.

The overall self-regulation score suggests that the programs provided made a moderate contribution to the development of children's self-regulation skills. This finding is consistent with previous research (Sáez-Delgado et al., 2022; Wang et al., 2023). Considering principles of self-regulation, components such as goal setting, reinforcement systems, and behavior modification plans were reported to play a meaningful role in supporting children's progress. The item “The behavior modification plan enhances my child’s ability to control his or her responses” received the highest mean score, highlighting the parents’ perception of the effectiveness of strategies consistent with Applied Behavior Analysis (ABA). In contrast, items related to decision-making and problem-solving received the lowest ratings, reflecting a gap in programs targeting higher executive functions and suggesting that many interventions may focus on managing immediate behaviors without promoting long-term cognitive strategies. This is consistent with the findings of previous studies (Tanksale et al., 2020) that emphasized the importance of integrating mindfulness, cognitive behavioral therapy, and visual support to improve self-regulation. Furthermore, family systems theory supports the idea that child development is influenced by family context and family dynamics. The finding that parental involvement positively impacts self-regulation confirms this theory, enhancing the chances of sustained improvement when the family is actively involved in the intervention process.

Study results showed that programs for children with ASD have a significant impact on improving social interaction skills. This aligns with developmental theories that emphasize the critical role of peer interaction, inclusive environments, and communication-oriented instruction in promoting social and emotional development (Kumar & Jaiswal, 2025; Williams et al., 2022).

Group activities, inclusion strategies, and Individualized Education Plans (IEPs) not only support children's communication and relationship-building skills but also enhance strong collaboration between teachers and families. This consolidative approach aligns with family systems theory, which emphasizes the relationship between child development and the dynamics within the family unit. The use of strategies such as social stories, modeling, and reinforcement is consistent with evidence-based practices that target social exchange and practical communication skills.

It is worth noting that cultural values in Jordanian society, such as family bonding and social cohesion, may contribute to explaining the high levels of family participation in intervention programs. Arab culture values collective relationships, which may enhance the effectiveness of programs that directly engage families. It also highlights the importance of designing culturally sensitive interventions that respond to the children's needs and make families an integral part of the educational process.

The study revealed statistically significant differences in both self-regulation and social interaction related to the severity of the disorder. Children with severe conditions made less progress compared to their peers with mild or moderate severity, an expected but crucial finding.

These findings suggest that one approach does not fit all, and that children with more complex needs will require intensive and individualized support, possibly including assistive technologies such as robotics or digital applications (Rychalski et al., 2024).

No statistically significant differences were found across gender or age, suggesting that well-designed programs may be equally effective across these groups in the Jordanian context. This finding adds a new dimension to the global debate and offers insights from a Middle Eastern perspective, where cultural expectations may differ between males and females.

In the Jordanian context, where family relationships and parental responsibility are central, the family perspective provides a deep cultural understanding of the effectiveness of educational programs. The positive perceptions expressed by the parents involved in this study may reflect not only their views of their children’s progress but also their be an indicator of their emotional investment and expectations for program effectiveness. These views may be impacted by cultural norms that emphasize the family’s role in supporting children with special needs

By incorporating family systems theory, this study emphasizes the relationship between children’s behavior and family well-being. Previous research (Mutschler Collins et al., 2025; Wong et al., 2025) has shown that systematic caregiver involvement is essential for ensuring sustainable outcomes. Therefore, interventions in Jordan and similar contexts should continue to focus on parental education, psychosocial support, and program planning with family participation, recognizing the role of the family as a key factor in supporting the child’s development, rather than simply being a recipient of services.

Conclusion

This study explores the impact of programs offered in special education centers in supporting children with ASD, with a focus on developing self-regulation and social interaction skills. These key skills directly relate to a child's quality of life, independence, and social participation.

The results indicated that, from the perspective of the children's families, these programs clearly contribute to a significant improvement in social interaction skills and a moderate enhancement of self-regulation skills. It is should be noted that the severity of the disorder was the most influential variable in the effectiveness of the programs, indicating the importance of adapting program content and tools to the needs of each student.

This study also highlights the importance of adopting a participatory model in intervention program design. Programs should include ongoing interaction between educators, specialists, and the children's families and should be based on well-thought-out strategies grounded in scientific evidence and practical applications. By focusing on families' perspectives, this study provides valuable insights that contribute to enriching the scientific literature on ASD interventions in Arabic-speaking settings. It emphasizes the importance of designing evidence-based programs that take participants' individual differences into account, and encourages further research that seeks family feedback to develop more effective educational and rehabilitative programming. Because it relies on a quantitative approach, this study provides data-driven findings that contribute to improving the quality and impact of special education interventions provided to children with ASD.

Recommendations

Based on the study's findings and discussion, the study presents a set of recommendations targeted at enhancing the effectiveness of educational and rehabilitation programs provided to children with ASD. One area for further exploration is the development of specialized programs tailored to the severity of the disorder, particularly for children with severe Autism, which could include intensive individualized interventions, where feasible, and the integration of assistive devices such as robotics and interactive technology. It is important to recognize that access to such technology may be limited by orgnaizational and financial factors, and its applicability may vary across settings. In addition, promoting greater family involvement in the design and implementation of educational plans may improve outcomes. Providing training workshops could equip caregivers to play a more active role in supporting their children within the home environment. The design of this program should be flexible and culturally responsive. Incorporating evidence-based strategies, such as social stories, modeling, and interactive art activities, may also be beneficial, given their proven effectiveness in improving social interaction. The study further suggests the potential value of reinforcing skills related to independence and self-planning. Programs might consider including activities that contribute to developing time management skills, decision-making, and goal-setting, using tools such as visual schedules and guided self-learning. To ensure the effectiveness of programs, the implementation of periodic program evaluations, particularly those that include feedback from families, could be a valuable strategy for assessing and refining program effectiveness. Finally, additional comparative research is recommended to examine the relative impact of different intervention models, such as Floortime and Son-Rise, to determine which are most effective in promoting self-regulation and social interaction, and to judge future program development

Limitations

The results of this study contribute to a deeper understanding of the impact of educational programs provided to children with ASD on developing self-regulation and social interaction skills, from the perspective of their families. Several limitations should be considered when reviewing the results of this study.

First, the current study relied on a targeted sample of 150 families of children enrolled in special education centers in Amman, Jordan, which, while appropriate for the study’s objectives, is a rather narrow sample. This sample limits the possibility of generalizing the results to other segments of society, particularly in rural environments or public schools, or in different geographical and cultural contexts.Second, the study relied solely on questionnaires expressing parents' perceptions. Although families offer a critical and unique perspective on their children’s development, self-reported data may be subject to bias, including social desirability, selective recall, or cultural norms that influence how disability is perceived and discussed. Additionally, the lack of other supporting sources, including teacher observations, direct behavioral assessment tools, or educational records, may affect the comprehensiveness and objectivity of the results.Third, the research design relied on a descriptive analytical approach within a cross-sectional framework, relying on data collected at a single point in time. This data collection method limits the ability to track changes or determine causal relationships between the variables researched. Moreover, while robotics technologies offer promising applications in supporting social and educational development, their effectiveness may vary depending on factors such as resource availability, user readiness, and cultural acceptance

Future studies could benefit from adopting longitudinal methodologies that track developmental trajectories and program outcomes over extended periods of time.Finally, the findings were obtained from a specific cultural and institutional context in Jordan.Cultural beliefs, stigma associated with ASD, family support patterns, and the structure of available services are likely to influence how families perceive their children's abilities and the effectiveness of interventions provided. These deeply rooted cultural factors must be taken into account when interpreting and applying findings to other contexts. This limitation calls for caution when generalizing the findings across diverse cultural settings and supports the case for future researchers to conduct comparative studies across different cultures to support a more comprehensive understanding of the effectiveness of ASD programs in a wide variety of cultural contexts.

Ethics Statements

This study involving human participants was reviewed and approved by the Ministry of Education in Jordan under approval number [3/10/20779] and by the Ministry of Social Development under approval number [3/6171]. The participants provided their written informed consent to participate in this study.

Conflict of Interest

The authors declare that they have no conflict of interest related to this study.

Generative AI Statement

No generative AI or AI-supported technologies were used in this study.

Authorship Contribution Statement

Ibrahim: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing-original draft, supervision, project administration. Shane: Investigation, resources, writing-review & editing. Karayazi-Ozsayin: Investigation, resources, writing—original draft. Hassanain: Conceptualization, methodology, resources, data curation, writing—original draft.

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