Examining Growth Among College Students with Intellectual and Developmental Disability: A Longitudinal Study

Abstract

Inclusive postsecondary education programs for adults with intellectual and developmental disabilities (IDD) are proliferating across the United States. Although college can be a formative time for any student, there has been limited research on the growth that college students with IDD may experience during their time on campus. We address this gap by using a longitudinal design to examine the adaptive behavior, self-determination, executive functioning, and social skills of college students with IDD across three points in time—upon initial entry into the program, at the end of their first year, and at the end of their second year. Analyses suggested significant improvements in adaptive behavior and self-determination across the first year of the program. We offer recommendations for research and practice aimed at documenting and promoting growth for students with IDD throughout their collegiate experience.

College can be a time of considerable growth and development for adults (Kuh, 1993; Mayhew et al., 2016). Although the undergraduate experience provides valuable preparation for a future career, it offers much more than a pathway to the world of work. Throughout their time on campus, college students forge new relationships that can strengthen their interpersonal skills and lead to new friendships. They deepen their capacity to direct their own lives and advocate for their needs. They increase their independence skills and learn to manage their lives more effectively. And they encounter new perspectives that can shape how they see themselves and the world. This formative time of learning and life can bend the trajectories of students in new and exciting directions (Oreopoulos & Petronijevic, 2013).

Federal legislation, such as the Higher Education Opportunity Act of 2008, has supported increased access to these influential and indelible experiences for adults with intellectual and developmental disabilities (IDD; Grigal et al., 2019). Nearly 300 higher education institutions across the United States now offer formal programs designed to support the involvement of students with IDD in important aspects of the undergraduate experience (see www.thinkcollege.net). Although these programs vary widely in their design and delivery, thousands of students with IDD are now accessing academic courses, work experiences, residential life, social opportunities, and many other aspects of campus life. Indeed, the college campus has become the next horizon for the growing inclusive education movement.

As with any college student, adults with IDD are also expected to be positively impacted by their time on campus. Much of the early advocacy for this movement appealed to the array of benefits students with IDD might accrue (e.g., Grigal & Hart, 2010; Uditsky & Hughson, 2008; Zafft et al., 2004). For example, inclusive higher education has been proposed as a pathway for promoting independent living skills, enhancing self-determination, teaching students to direct their behavior, and building social competence (Griffin et al., 2010; Hart et al., 2010; Grigal & Papay, 2018). Moreover, follow-up studies suggest participation in these programs may have a positive impact on the later employment, residential, and community outcomes of graduates (e.g., Butler et al., 2016; Moore & Schelling, 2015; Ross et al., 2013). For example, Sannicandro and colleagues (2018) found that students with IDD who had participated in inclusive college programs were more likely to be employed and have higher earnings than students without these experiences.

To date, the empirical literature has been surprisingly quiet when it comes to the growth students with IDD actually experience during their time in college. A small number of qualitative studies have asked program staff, parents, and students themselves about the benefits they perceive students accrue during their time in college (see reviews by Alqazlan et al., 2019; Whirley et al., 2020). For example, Miller et al. (2018) interviewed parents about how their daughters and sons with IDD were affected by the college experience. Parents described benefits in the areas of independent living (e.g., personal care, use of technology, handling of personal affairs) and self-perceptions (e.g., self-confidence, pride, self-advocacy). Likewise, Van Hees and colleagues (2015) interviewed college students with autism spectrum disorder who reported their college experience had improved aspects of their executive functioning and independent living skills. However, these studies rarely use formal, established measures to capture the ways in which students may be affected by their college experiences.

When prior studies of college students with IDD used standardized measures (e.g., curriculum-based measurement), they typically did so to measure responses to more targeted interventions implemented in a particular college settings or focused on a particular skill (Ford & Kaldenberg, 2019; Hosp et al., 2018). Only one study to date has examined more general growth (i.e., not in response to a specific intervention) among college students with IDD across multiple time points and using standardized measures. Prohn and colleagues (2018) asked parents to complete an adaptive behavior measure for six college students with IDD at the beginning and end of a single academic year. All six students showed small improvements over this time period. Additional longitudinal studies are needed to examine student development over the course of their participation in higher education programs in key areas of potential impact, such as adaptive behavior (Berg et al., 2017), self-determination (Ankeny & Lehmann, 2011; Shogren et al., 2018), executive functioning (Van Hees et al., 2015), and social skills (Miller et al., 2018).

It is also important to examine the profiles of students with IDD who are just starting their college programs. Entering students are likely to possess a combination of strengths and needs across different transition domains. For example, Shogren et al. (2018) examined self-determination scores for a convenience sample of 251 first-year college students with IDD. These students had self-determination scores that were consistent with other individuals with IDD, but self-determination also varied widely across students. The profiles of students in other areas (e.g., adaptive behavior, executive functioning, social skills) have yet to be explored. Such initial information could provide a baseline against which later improvements are compared.

The present study addressed these research gaps by using a longitudinal design and validated, well-established measures to examine the growth of students with IDD during their first two years of college. Specifically, we examined four areas of student growth: adaptive behaviors, self-determination, executive functioning, and social skills. Our research questions were:

• RQ1: What were the profiles of entering students with IDD in each of these areas?

• RQ2: What changes took place across their freshman year in each of these areas?

• RQ3: What changes took place across their second year in each of these areas?

We hypothesized that college students with IDD would demonstrate improvements in each of these four areas during the two years of their program.

Method

Participants

This study included 30 college students enrolled in Next Steps at Vanderbilt, an inclusive postsecondary education (IPSE) program in a southeastern state. To participate in Next Steps at Vanderbilt, students had to (a) be 18–26 years old; (b) have an intellectual or developmental disability; (c) have completed high school and received a standard or alternate diploma (i.e., occupational or special education); (d) not meet eligibility requirements for admission into a standard college program; and (e) exhibit sufficient communicative and functional skills as determined by program staff through a day-long observation and by contacting references (e.g., communicate reliably with others, exhibit socially responsible behavior on campus).

Student demographics are presented in Table 1. At program entry, the mean age of students was 20.45 years (SD = 2.08) with a range from 18 to 25 years. Most were male (63.3%, n = 19) and more than one third were racial/ethnic minorities (36.7%, n = 11). The most commonly reported disabilities included intellectual disability, autism spectrum disorder, and unspecified developmental disability. Nearly all students lived at home with their parents as Next Steps at Vanderbilt did not offer on-campus residential opportunities.

Table 1.

Student and Parent Demographic Information

	Total sample
Variable	%	n
Student’s gender
Female	36.7	11
Male	63.3	19
Student race/ethnicity
Asian	3.3	1
Black or African-American	13.3	4
White	63.3	19
Hispanic	6.7	2
Other	13.3	4
Student disabilitya
Autism spectrum disorder (i.e., Asperger’s syndrome)	29.6	8
Intellectual disability	29.6	8
Learning disability	13.3	4
Attention Deficit Hyperactivity Disorder	11.1	3
Developmental delay	11.1	3
Cerebral palsy	7.4	2
Other (i.e., Epilepsy, Hearing impairment, Other syndrome)	16.7	5
Student’s residential arrangement
In parent’s home	92.9	28
Independent with some support	6.7	2
Parent educational levelb
High school or GED	3.3	1
Courses towards college	16.7	5
College degree	56.67	17
Graduate degree	23.33	7
Total household income level
$20,000 or less	6.7	2
$20,001 – $40,000	3.3	1
$40,001 – $60,000	6.7	2
$60,001 – $80,000	10.0	3
$80,001 – $100,000	3.3	1
More than $100,000	40.0	12
I prefer not to answer	26.7	8
Parent marital statusb
Married	80.0	24
Re-married	16.7	5
Widowed	3.3	1

^aMore than one disability could be reported

^bParent demographics refer to the responding parent

Some study assessments were also completed by the parents of these students. The mean age of parent respondents was 52.86 years (SD = 5.65), ranging from 36 to 63 years old. Almost three-fourths of respondents (73.3%, n = 22) were mothers, 20.0% (n = 6) were fathers, and the rest were others (6.6%, n = 2). Although family income ranged widely, the median fell in the range of more than $100,000. Nearly all parents (96.7%, n = 29) were married.

Recruitment

All entering first-year students were eligible to participate in the study. At the start of their first year in Next Steps at Vanderbilt, the program director sent all parents a recruitment email and flyer outlining the goals and time commitment of the study. The researchers also attended program orientation events to explain the study and answer parent and student questions. Students with IDD and parents who expressed interest were then consented by a study team member. This study was approved by University’s Institutional Review Board and all families provided written consent (parents) and assent or consent (students with IDD). Parents and students were assured throughout the study that participation was voluntary.

IPSE Program

Next Steps at Vanderbilt is situated within Vanderbilt University, a mid-sized, private university with nearly 7,000 undergraduate students. The research-intensive university offers nearly 70 majors in its four undergraduate colleges. Over the four years of the study, the percentage of minority undergraduates ranged from 31% to 39% and the percentage of international students ranged from 9% to 11%. Two-year retention rates were 97% across all years.

Since 2010, the university has offered Next Steps at Vanderbilt for students with IDD. At the time of this study, the program was non-residential and two years in duration, a length consistent with most programs nationally at the time. Next Steps at Vanderbilt support students to access all aspects of campus life, including academic coursework, career development, student organizations, service-learning experiences, and campus events. Students audit 1–2 typical university classes each semester based on their interests, take 3 specialized seminars with other students with IDD in their cohort each semester (e.g., Health and Wellness, Personal Finance, Interpersonal Skills, Living on Your Own, Sexual Awareness, Emotional Regulation, Food Preparation and Safety), access student organizations and extracurriculars, participate in on- and off-campus internships, and experience other campus activities based on their personal interests. In addition to the semester-long orientation program, students also participate in many other orientation events held during their freshman year. Students graduate with a certificate of completion.

Person-centered planning meetings provide the context for making decisions about all aspects of a student’s college experience—academic courses, work experiences, campus involvement, and individualized supports. Students also receive weekly advising to discuss their progress toward personal goals, coursework, career aspirations, independence, and other topics. Within courses, students could access accommodations provided by the Disability Services office and/or peer supports from within the course. Beyond the classroom, students are connected to a circle of 4–6 peer mentors who provide support in one or more areas of campus life: (a) academics; (b) daily planning, scheduling, or organizational skills; (c) eating meals together; (d) work or internships; (e) social activities; (f) exercise; and/or (g) campus activities. Finally, between 3–5 Next Steps at Vanderbilt program staff are available to support students and collaborating faculty, peers, employers, and other campus or community partners. A full description of Next Steps at Vanderbilt can be found in Bethune-Dix et al. (2020) and at https://peabody.vanderbilt.edu/departments/nextsteps/.

Data Collection

Data were collected from four cohorts of Next Steps at Vanderbilt students who entered the program from 2014 to 2017. Assessment measures were collected from parents and students with IDD at three time points: at the start of the program (generally within the first month) (Time 1); within the last month or the summer after students’ first year in the program (Time 2); and the last month or summer after students’ second year in the program (Time 3). At each time point, we conducted a structured interview with a parent to assess adaptive behaviors, asked parents to complete measures of executive functioning and social skills, and asked students to complete measures of self-determination and executive functioning. When needed, research staff provided students with IDD support to complete measures. This included providing questions both in oral and visual ways, restating the directions, expanding or explaining questions, and defining unfamiliar words. Students and parents generally completed the testing in 1-hour blocks, and breaks were given when requested or when research staff observed that they might be helpful to reduce test fatigue. With the exception of some additional demographic questions at Time 1, the interviews and questionnaires were identical at each wave of data collection. The same parent participated across all waves. All surveys and structured interviews were completed via hard copy forms. Responses were subsequently entered into an electronic database.

A total of 30 students were eligible for participation across four years and all (100%) agreed to participate. Five students entered in 2014, seven in 2015, nine in 2016, and nine in 2017. The larger numbers in the later cohorts reflect program growth due to receipt of federal Transition and Postsecondary Programs for Students with Intellectual Disabilities (TPSID) funding. Students from the 2017 cohort did not complete the Time 3 survey because research funding ended. Thus, they were not included in analyses for Research Question 3. Of the 21 students available to participate in Time 3, three were lost to attrition, resulting in a sample of 18 families for analyses examining Time 2 to Time 3 change. The sample characteristics of this subsample were similar to the full sample (analyses available by request).

Measures

All assessment measures below were collected at all three timepoints.

Adaptive behavior

The Vineland Adaptive Behavior Scales–Second Edition (VABS-II) is a structured interview that assesses students’ day-to-day adaptive behaviors (Sparrow et al., 2005). Trained research staff administered the interview to parents. This measure provides an overall adaptive behavior total comprised of three domains: Communication, Daily Living Skills, and Socialization. The Communication domain evaluates receptive, expressive, and written language skills. The Daily Living Skills domain assesses personal daily living skills, domestic daily living skills, and community skills. The Socialization domain assesses interpersonal relations, play and leisure, and coping skills. The VABS-II has good test–retest reliability with correlations ranging from .80 to .95, and good interrater reliability with correlation coefficients from .75 to .85 (Sparrow et al., 2005). Internal consistency of the domains at Time 1 in this sample ranged from .89 to .92. For the present study, we used standard scores (M = 100, SD = 15) for the adaptive behavior total and each of the three domains, with higher scores representing greater adaptive behavior skills. When statistically significant change was observed in a domain score, we examined sub-domain scores. For sub-domain scores, we used v-scale scores which describe an individual’s relative level of functioning on the sub-domains compared with others of the same age. The subdomain v-scale scores have a mean of 15 (SD = 3).

Self-determination

The Arc Self-Determination Scale (SDS; Wehmeyer & Kelchner, 1995) is a 72-item self-report questionnaire that addresses students’ overall self-determination. This scale assesses individual performance in four essential characteristics of self-determination: autonomy, self-regulation, psychological empowerment, and self-realization. The autonomy sub-domain consists of 32 items that address one’s independence as well as acting on the basis of personal values. Responses are on a 4-point, Likert-type scale ranging from (0) I do not even if I have the chance to (3) I do every time I have the chance. Scores range from 0 to 96. The self-regulation subdomain consists of 14 items that address problem-solving and goal setting skills. There are two subsections. The first asks students to identify what he/she considers the best solution to a problem based on stories. Depending on the effectiveness of the solution, responses are scored on a scale of (0) students either gave no answer or fail to achieve the indicated ending to the story to (2) acceptable, adequate way to achieve the indicated ending. In the second subsection, students are asked to identify a goal and the steps to achieve these goals. Responses were coded based on the presence of a goal and the number of steps identified to reach that goal ranged from (0) no plan or goal, (1) identify the goal, but no steps to reach that goal, (2) identify the goal, have one or two steps to reach that goal, to (3) identify the goal, have three or four steps to reach that goal. Scores on the self-regulation sub-domain range from 0 to 28. The psychological empowerment sub-domain addresses locus of control and self-efficacy. It includes 16 questions, each with two statements, and students are asked which statement best describes them (e.g., “I can make my own decisions” or “Other people make decisions for me.”). Responses are coded as 0 (does not reflect a psychologically empowered belief or attitude) or 1 (reflects a psychologically empowered belief or attitude); sub-domain scores range from 0 to 16. The self-realization sub-domain measures self-awareness and self-knowledge. This section has 15 items and the response options were either (0) disagree or (1) agree, with sub-domain scores ranging from 0 to 15. The total raw scores were converted into percentile scores for the sample description (Research Question 1). However, we used raw scores for analyses of change based on the SDS manual. Higher scores indicate higher levels of self-determination. In this study, Cronbach’s alpha for the SDS total score was .76.

Executive functioning

The Behavior Rating Inventory of Executive Function–Adult Version (BRIEF-A; Gioia et al. 2000) is a questionnaire with two versions (i.e., self-report and informant-report, both used in the present study), each consisting of 75 items assessing behavioral manifestations of executive problems in daily life (Roth et al., 2005). Items comprise nine non-overlapping clinical scales reflecting different aspects of executive function including inhibitory control, self-monitoring ability, planning and organizational skill, emotional control, and working memory. Participants were asked: “During the past month, how often has each of the following behaviors been a problem?” Potential responses were: (0) Never, (1) Sometimes, and (2) Often. We computed the total score (i.e., Global Executive Composite), as well as the two domain scores (i.e., index scores) that make up this composite: Behavioral Regulation Index and Metacognition Index. Behavioral Regulation represents the individual’s ability to maintain appropriate regulatory control of his or her behavior and emotional responses, and Metacognition represents the individual’s ability to systematically solve problems via planning and organization while sustaining these task-completion efforts in active working memory. We used t scores for the total score and domain scores. Higher scores indicate more executive functioning difficulties and T scores of 65 or higher are categorized as clinically significant. Previous research reported high internal consistency ranging from .80 to .98 (Roth et al., 2005). Reliability of both the BRIEF-A informant-report and self-report was strong for the current sample (αs = .93 and α = .86, respectively)

Social skills

The Social Skills Improvement System–Rating Scales (SSIS; Gresham & Elliott, 2008) is a parent-report measure that examines social skills in the following sub-domains: communication, cooperation, assertion, responsibility, empathy, engagement, and self-control. It is comprised of 46 items (e.g., “Expressed feelings when wronged”) rated on a 4-point, Likert-type scale from (0) never to (3) almost always. Based on the total sum of raw scores, standard scores were generated and used for the analyses. According to the SSIS manual, scores can be interpreted as “below average” if the standard score is below 85, “average” if the score is between 85 to 115, and “above average” if the score is higher than 115. The SSIS has good internal consistency reliability ranging from .74 to .96 (Gresham & Elliott, 2008). For the current study, reliability of the SSIS was also strong (α = .86). Higher scores indicate more social skills.

Data Analysis

Preliminary analyses and missing data

We first calculated descriptive statistics for outcome measures to examine variance, normality, outliers, and missing data. All data were normally distributed. There were some missing data for scales, such as when students did not complete the questionnaire or the parent did not complete the interview. Rates of missing data on Time 1 and Time 2 outcomes ranged from 0% to 10%. To avoid deletion of subjects, we employed multiple imputation for missing data using SPSS statistical software. Multiple imputation is a statistical approach for replacing missing values with predicted values based on known values and is robust even with a large amount of missing information (Little & Rubin, 2002). Schafer (1997) recommends imputing at least one data set per percentage of data missing. Thus, the results for Research Question 2 present the estimates pooled across ten imputed datasets. Given the smaller sample size, Research Question 3 (i.e., change from Time 2 to Time 3) is considered exploratory. Rates of missing data on outcomes at Time 3 for the 18 families included in this analysis ranged from 5.6% to 11%. Multiple imputation was again used, and the results for Research Question 3 present the estimates pooled across 11 imputed datasets (one for each percentage of data missing: Shafer, 1997).

Research question 1

We used descriptive statistics to summarize the profiles of students with IDD at Time 1 with respect to adaptive behaviors, self-determination, executive functioning, and social skills. For descriptive purposes, we condensed scores into categories (e.g., “low,” “moderately low”) based on guidelines defined by the assessment developers.

Research question 2

We used paired t-tests using pooled multiple imputed datasets to test for differences in student outcomes from Time 1 (i.e., entry into program) to Time 2 (i.e., end of Year 1). Adaptive behavior and executive functioning (self- and parent-report) total and domain scores were examined. When domain scores showed statistically significant growth, sub-domains were examined to determine which components seemed to be influencing the overall change. Similarly, when significant change was observed for total scores of self-determination and social skills, sub-domains were examined. We computed effect sizes (i.e., Cohen’s d) for each analysis by dividing the mean change scores from Time 1 to Time 2 by the pooled standard deviations to quantify the magnitude of change between Time 1 and Time 2. For Cohen’s d, small effect size = 0.2, medium = 0.5, and large = 0.8 (Cohen, 1988).

Research question 3

We conducted paired t-tests using pooled multiple imputed data to test for differences in Time 2 (i.e., end of Year 1) to Time 3 (i.e., end of Year 2) scores for the dependent variables. Similar to Research Question 2, we examined sub-domains scores only when differences in domain scores or total scores of adaptive behavior, self-determination, executive functioning (self and parent), and social skills were statistically significant.

Results

What are the Profiles of Students with IDD at the Start of College?

Descriptive statistics for total and domains scores of adaptive behaviors, self-determination, executive functioning, and social skills at program entry are reported in Table 2. Students’ mean score for overall adaptive behavior was low, below the 1^st percentile of the normed general population (i.e., mean standard score was lower than 70). Based on VABS-II guidelines (Sparrow et al., 2005), two thirds of students (n = 20) had overall adaptive behavior scores in the “low” range and one third (n = 10) had scores in the “moderately low” range. For the communication domain, 73.3% (n = 22) of students had scores in the “low” range and the rest had scores indicating “moderately low” communication. For the daily living skills domain, the majority of students had scores at the “low” (56.7%, n = 17) or “moderately low” (36.7%, n = 11) level. Students had higher scores in socialization compared to the other domains. Two thirds (n = 20) of students had socialization scores in the “moderately low” range and the rest (n = 10) were in the “low” range.

Table 2.

Student Outcome Measures at Time 1 and Time 2, using Multiple Imputation

	Time 1		Time 2		t	d
Measure	M	SE	M	SE
Adaptive behaviors total	67.20	1.41	70.95	1.49	2.75**	.47
Adaptive behaviors: Communication	63.81	2.30	70.04	2.09	2.96**	.49
Receptive communication	9.23	0.57	9.80	0.57	.94	.17
Expressive communication	9.84	0.37	11.56	0.51	3.54**	.56
Written communication	9.07	0.25	9.67	0.29	2.27*	.39
Adaptive behaviors: Daily living skills	70.04	1.72	70.88	1.55	.62	.12
Adaptive behaviors: Socialization	74.21	1.83	79.75	2.14	2.53**	.43
Interpersonal relationship	9.93	0.29	10.58	0.39	1.78	.32
Play and leisure time	10.40	0.50	11.08	0.53	1.31	.24
Coping skills	12.72	0.49	13.82	0.47	2.08*	.37
Self-determination total	92.21	2.47	97.06	2.54	2.70**	.45
Autonomy	58.45	1.67	59.68	1.99	.72	.14
Self-regulation	9.64	0.74	11.91	0.84	3.72**	.58
Psychological empowerment	13.54	0.32	13.69	0.27	.49	.09
Self-realization	10.91	0.29	11.52	0.25	2.18*	.38
Executive functioning-parent total	53.07	1.75	52.95	2.07	−.11	−.02
Executive functioning-parent: Behavioral regulation	48.21	1.77	49.64	2.03	1.15	.14
Executive functioning-parent: Metacognition	55.56	1.98	55.21	2.31	−.31	−.03
Executive functioning-self total	54.35	1.39	53.74	1.73	−.49	−.09
Executive functioning-self: Behavioral regulation	53.04	1.58	53.14	1.88	.07	.01
Executive functioning-self: Metacognition	55.61	1.49	53.68	1.80	−1.82	−.21
Social skills	96.79	3.11	96.92	3.45	.07	.01

Note.

^*p < .05,

^**p < .01

We converted mean self-determination scores (see Table 2) into percentiles in which the normed sample was students with disabilities. For overall students’ self-determination, the mean score of 92 was below the 35^th percentile, meaning only 35% of students of their ages with disabilities have the same scores or lower. When examining sub-domains of self-determination, students reported better self-regulation (i.e., 54^th percentile) than autonomy (i.e., 36^th percentile) or self-realization (i.e., 37^th percentile). Further, students’ mean score in psychological empowerment was at the 44^th percentile, meaning that scores were above 44% of other students with disabilities.

Likewise, we converted mean executive functioning scores (see Table 2) into percentiles; here, the normed sample was individuals in the general population. Mean scores for executive functioning problems were elevated for both parent and student report for the total score and both domains (metacognition and behavior regulation), with percentile scores ranging from the 62^nd to the 69^th percentile (an exception to this was parent report of behavioral regulation, which was at the 51^st percentile). Despite these elevated scores, fewer than 10% of participants (n = 3) had scores that indicated their overall executive functioning challenges were in the “clinical” range based on BRIEF-A guidelines (i.e., T scores at or greater than 65). This was true for both parent and student report, as well as overall executive functioning problems and both domains.

Social skills ratings were generally high. Based on SSIS guidelines (Gresham & Elliott, 2008), 73.3% of students (n = 22) reported average or above average social skills while only 26.7% of students (n = 8) reported social skills below average.

What Changes Took Place Across Their Freshman Year?

Table 2 shows the results of paired t-tests and effect sizes from Time 1 to Time 2 for total and domain scale scores. On average, total adaptive behaviors improved significantly between Time 1 and Time 2 with a medium effect size (d = .47; p < .01), though standard scores associated with the means were in the “low” range at both time points. Among adaptive behavior domains, communication and socialization significantly improved over the first year of the program. When further probing aspects of communication, there was improvement in expressive and written communication, but not in receptive communication. Of note, the effect size was bigger for expressive communication (d = .56) than the other subcomponents. When using standardized data, the mean expressive communication score at Time 1 was in the “low” range but it moved into “moderately low” at Time 2. Changes in socialization were mainly driven by increasing coping skills, with means coping skills scores falling into the “moderately low” range at Time 1, and the “adequate” range at Time 2.

Overall self-determination also significantly increased over the freshman year, with a medium effect size (d = .45). Converting the mean score at each time point to percentiles suggested that self-determinations improved from the 35^th percentile (Time 1) to the 44^th percentile (Time 2). These changes could be attributed to improvements in self-regulation and self-realization, with no significant changes in autonomy or psychological empowerment. The largest effect size observed for self-determination components was self-regulation (d = .58). The mean self-regulation score at Time 1 was at the 54^th percentile, but at the 74^th percentile at Time 2, meaning that 74% of students their age with disabilities have the same scores or lower.

There were no statistically significant changes in total or domains of executive functioning (parent or self-report) nor in social skills, with effect sizes close to zero in both areas.

What Changes Took Place Across Their Second Year?

Table 3 shows the results of paired t-tests and effect sizes from Time 2 to Time 3 for overall and domain scores. Because of the smaller sample size, these findings are more exploratory. The daily living skills domain of adaptive behavior significant increased during the second year of the IPSE program (d = .51) (from the “low” range at Time 1 to the “moderately low” range at Time 2); this change was mainly accounted for by improvements in domestic living skills (e.g., household tasks).

Table 3.

Student Outcome Measures at Time 2 and Time 3, using Multiple Imputation

	Time 2		Time 3		t	d
Measure	M	SE	M	SE
Adaptive behaviors total	70.59	1.93	74.09	2.06	1.67	.35
Adaptive behaviors: Communication	73.13	2.26	70.53	4.58	−.68	−.18
Adaptive behaviors: Daily living skills	70.53	2.28	76.33	3.45	2.17*	.51
Personal daily living	10.27	.43	11.27	.75	1.22	.42
Domestic daily living	9.87	.70	11.60	.93	3.22**	.54
Community daily living	9.80	.50	10.40	.41	1.66	.34
Adaptive behaviors: Socialization	82.13	3.50	82.20	3.55	.02	.01
Self-determination total	97.86	3.30	107.79	3.51	3.79**	.65
Autonomy	59.54	2.58	69.20	2.79	4.41**	.70
Self-regulation	13.27	0.87	13.71	1.02	.45	.10
Psychological empowerment	13.80	0.30	14.06	0.46	.59	.13
Self-realization	11.42	0.30	11.07	0.44	−.75	−.16
Executive functioning-parent total	54.42	2.53	52.08	1.83	−1.59	−.33
Executive functioning-parent: Behavioral regulation	50.40	3.18	49.13	2.33	−.68	−.11
Executive functioning-parent: Metacognition	55.33	3.33	54.87	2.58	−.62	−.04
Executive functioning-self total	52.88	1.82	54.04	2.09	.65	.14
Executive functioning-self: Behavioral regulation	52.41	2.16	53.06	2.81	.27	.06
Executive functioning-self: Metacognition	53.18	2.11	54.59	2.58	.42	.14
Social skills	95.14	3.97	99.02	2.99	1.37	.29

Note.

^*p < .05,

^**p < .01

Self-determination also significantly increased in the second year of the program. When examining percentiles, the mean self-determination score was at the 44^th percentile at Time 1, and increased to the 67^th percentile at Time 2. Changes in self-determination were mainly driven by improvements in autonomy, with an effect size approaching a large effect (d = .70). No significant changes were found in the area of executive functioning, though there was some suggestion that parents might be rating executive functioning as decreasing (d = −.33, p = ns). On average, gains maintained in other areas (e.g., adaptive behaviors) were maintained from Time 2 to Time 3 (i.e., scores did not significantly worsen). Social skills ratings also increased modestly (d = .29), but those improvements were not statistically significant.

Discussion

As the inclusive higher education movement proliferates, it is important to understand the growth students with IDD experience throughout their programs. Although several studies have addressed the ways in which students might benefit from their college experience, longitudinal studies have been largely absent from the literature. To address this gap, we examined changes in adaptive behavior, self-determination, executive functioning, and social skills over the course of students’ two-year participation in Next Steps at Vanderbilt. Our findings add to the literature in several important ways.

First, we found that students with IDD who were entering Next Steps at Vanderbilt presented a profile comprised of both strengths and needs. Although our study focused on a particular program and cannot be generalized to the nearly 300 programs nationally (Grigal & Papay, 2018), these portraits at entry are interesting to consider. Many programs nationally focus on a subset of students with IDD who do not have significant cognitive impairments or pervasive support needs (Grigal et al., 2012a). For example, the admissions criteria for the IPSE program in this study required that students exhibit sufficient communicative and functional skills to successfully navigate the campus and its activities with limited support. Overall, the social skills of entering students were rated by parents as being at or near average and socialization ratings were higher than other areas of adaptive behavior. As the social dimensions of campus life—both within and beyond the classroom—can be substantial (Grigal et al., 2012a), it is not surprising that IPSE programs may draw—or prioritize—applicants who possess the social-related skills that could help them access these opportunities. However, it is important to note that this likely varies based on the entrance criteria and social expectations/opportunities of specific IPSE programs. Likewise, self-determination ratings tended to be somewhat comparable to those of other students with disabilities (cf., Shogren et al., 2018). Notably, self-regulation (i.e., goal setting, problem-solving strategies) was an area of relative strength for students, compared to autonomy or self-realization.

In other areas, however, more substantial needs were evident. For example, most students received low ratings in two areas of adaptive behavior: communication and daily living skills. Improving capacities in the areas of personal, domestic, and community living skills is one of the primary foci of many IPSE programs (for a review, see Whirley et al., 2020). Likewise, students entering the program tended to have more executive functioning challenges compared to peers without disabilities. However, these challenges rarely rose to the level of clinical problems, likely reflecting the requisite executive functioning skills needed for students to be successful in the program. Again, this may vary by IPSE program.

Second, we found that adaptive behavior ratings increased for students enrolled in the IPSE program. During the first year of college, increases were observed in the areas of communication and socialization. Although we did not see additional significant improvements in the second year in these domains, the skills students had developed were maintained. In contrast, improvements in the domain of daily living skills were not observed until the second year of the program. Our findings regarding increases in adaptive behavior are important, as these improvements may contribute to improvements in employment and independent living outcomes after graduation (Taylor & Mailick, 2014; Taylor & Seltzer, 2011). Pinpointing the specific source of those improvements in the present study is not possible. Students participated in a wide range of formal and informal learning experiences, both within and beyond the college classroom. For example, they accessed extracurricular activities, service projects, recreational endeavors, internships, and unstructured activities—all of which provided numerous opportunities to interact with peers, co-workers, and others.

We were somewhat surprised at the lack of changes in daily living skills during the first year of the program. Because Next Steps at Vanderbilt did not have residential offerings, it may be that opportunities to address skills within this domain were more limited—particularly during the first year of the program (when other skills might have been more the focus). Given the importance of adaptive behavior for future employment and independence (Taylor & Mailick, 2014; Taylor & Seltzer, 2011), studies should continue to examine growth in this construct across IPSE programs, including those that offer on-campus residential experiences, with an eye toward determining the specific components of programs that seem to be most influential in the development of adaptive behavior. Researchers may also want to analyze IPSE program curriculums to determine how and to what extent they foster daily living skills.

Third, we documented student growth in several areas of self-determination (i.e., autonomy, self-regulation, self-realization) across the freshman year. Moreover, self-determination was the only measure we examined that continued to improve across the second year of the program. Efforts to promote self-determination should permeate inclusive higher education programs (Getzel & Thomas, 2008; Grigal et al., 2012b). For example, students enrolled in Next Steps at Vanderbilt participated in person-centered planning sessions, set personal goals for both college and career, selected their own courses, worked with peer mentors in the area of daily planning, and took seminars that explicitly address self-determination skills. Although pinpointing the source of this change is not possible within the present study, the combination of instruction and practice opportunities may be a contributing factor. As with adaptive behavior, self-determination is a predictor of improved employment outcomes, independent living, and quality of life (Shogren et al., 2015; Wehmeyer & Palmer, 2003).

Fourth, we did not document significant changes in overall executive functioning or social skills among participating students. The executive functioning measure addresses constructs such as working memory and concentration. These aspects of cognition may not be addressed explicitly within the typical university curriculum or in the specific IPSE program’s seminars. The lack of change in social skills may be explained by the relative strengths that students with IDD exhibited in social skills upon entry into the IPSE program. However, we did observe improvements in the socialization domain of adaptive behavior. One potential reason for differences between these similar constructs (social skills vs. socialization) is that the measures may be capturing different aspects of social functioning—particularly because many of the SSIS items are more relevant to the classroom setting (e.g., “follows your directions,” “controls temper in conflict situations with peers”) whereas the Vineland items are more general (encompassing interpersonal relationships but also coping skills and how well the individual participates in leisure activities with others). Regardless, more targeted interventions may be needed to impact executive functioning and social skills. Interventions such as Unstuck and On Target (UOT; Kenworthy et al., 2014), and Social Competence Intervention (Stichter et al., 2010) have shown some promise and could be adapted to address the needs of college students with IDD.

Our challenges detecting growth in adaptive behavior, executive functioning, or social skills might also reflect limitations of the validated measures in IPSE program settings. As the use of validated measures in research on IPSE programs grows, it will be important to examine whether measures used elsewhere are still psychometrically sound within postsecondary settings for this group of students. Further, these measures may not be capturing the constructs of most importance to student growth in IPSE programs, or they may be capturing constructs in ways that are less applicable to IPSE programs. Newly developed measures specific to the IPSE context could provide an important complement to validated measures in understanding growth among college students with IDD. Researchers may consider developing and validating measures of constructs that have been shown to be important in qualitative studies of student growth in IPSE programs, such as employment skills and outcomes (Butler et al., 2016; Moore & Schelling, 2015; Ross et al., 2013; Sannicandro et al., 2018), independent living skills (Miller et al., 2018; Van Hees et al., 2015), academic achievement (Plotner & May, 2019), peer mentoring experiences (Athamanah et al., 2020), psycho-social adjustment, and quality of college life (Plotner & May, 2019). In addition, it may be helpful to systematically review growth measures developed for college students with disabilities (see a review by Feldman & Newcomb, 2019) and modify items and/or re-standardize these measures to be normed for college students with IDD.

Implications for Research and Practice

In future studies, researchers should consider selecting outcome measures aligned to specific aspects of the IPSE programs. For example, the SSIS measure (i.e., communication, cooperation, assertion, responsibility) may not capture all the social aspects that are impacted by the college experience. Indeed, changes in social skills are different from changes in social interactions or social relationships. Improving the social connections of students can be valuable even in the absence of strengthening one’s social skills. Social participation is one of the key domains of quality of life for individuals with IDD (DaWalt et al., 2019). Therefore, future research may consider examining changes in social opportunities and relationships in addition to social skills.

In addition, future research should include multiple informants to understand students’ growth across multiple contexts. The current study collected data from students with IDD and their parents, as most students with IDD lived with their parents and these parents could observe students’ behaviors on a daily basis. Collecting data from other informants (such as peer mentors, program staff, or faculty instructors) would provide a richer context to understand skills and behaviors in the classroom and during social activities (Athamanah et al., 2020). This may be particularly important for students with IDD enrolled in a residential IPSE program, as other informants may have more daily interactions with students than parents.

The current study also provides implications for future practice. To date, most research examining the impact of IPSE programs focus on employment or independent living status after graduation. However, there are other promising benefits of college programs including development of new skills, changes in personal perspectives, improvements in behavior, and improvements in social networks. To better understand the impacts of IPSE programs, practitioners should identify individualized goals for students as they participate in the program and utilize assessments that can help with tracking progress toward those goals.

In addition, there should be embedded explicit instruction and practice opportunities to ensure students are likely to make progress in those identified areas (i.e., adaptive behaviors, self-determination, executive functioning, social skills). For example, peer mentoring has been identified as an effective practice to develop the social skills of college students with IDD (Farley et al., 2014). By incorporating peer mentoring systems and explicit instruction, students will have more opportunities to practice targeted social skills. Aligned with embedded explicit instruction, there is also a need to develop embedded data collection systems within IPSE programs to identify areas of need and indicators of progress. More rigorous data collection on the skills and behavioral development of students as they participate in IPSE programs will provide a better understanding of the characteristics of students who enter these programs as well as gains made over time. Note that given the wide heterogeneity in characteristics of post-secondary programs for students with IDD (e.g., college setting versus non-college setting, variability in level of inclusion), different measures and data collection systems will likely be needed based on setting and curriculum.

Limitations and Future Research

Several limitations highlight additional directions for future research. First, this study was conducted with a small sample from a single IPSE program. This is especially true in our analyses of data during Year 2. It is unclear to what extent findings from this small, socioeconomically advantaged sample might generalize to students with IDD more broadly. Furthermore, IPSE programs can vary widely in their focus and the students they serve (Grigal et al., 2012b; Grigal & Papay, 2018). Our study captures change over time within a particular IPSE program; findings from Next Steps at Vanderbilt may or may not generalize to other IPSE programs. This study should be replicated across other campuses to determine whether similar profiles and patterns are found. An even more compelling next step would be to combine samples of students with IDD across multiple IPSE programs. This would allow researchers to look at how variations in admission criteria, curriculum, and program experiences might be linked to different areas of student growth.

Second, we were not able to follow students after their graduation from the program. As such, it is unclear whether gains in skills were maintained and how these skills might contribute to post-graduation experiences. Researchers should extend longitudinal studies of students in IPSE programs to also follow former students after completion of the program. Third, we did not include a comparison group of similar-age adults with IDD who did not attend a college program. As a result, we cannot causally attribute any improvements to the program experiences. Though previous research suggests that young adulthood may be a time when improvements in skills and behaviors plateau (Smith et al., 2014), we are unable to speak directly to whether growth in the areas of adaptive behavior and self-determination observed in this study are typical of what might be seen among youth with IDD more generally during this time period. Future research should incorporate relevant comparison groups within their studies. Fourth, we did not include any direct measures of students’ growth. Though our self- and informant-report measures are well-validated, conclusions from this study would be strengthened by direct observational measures of student outcomes. Lastly, we could not use the most up-to-date Vineland measure as the 3^rd edition was not available when we began the study and consistent measurement across waves was critical.

Conclusion

Understanding the experiences of students with IDD within inclusive higher education is an important endeavor. As more individuals with disabilities and their families consider college pathways, it is important to address the specific ways in which students might benefit from their involvement each year. Likewise, program leadership should consider carefully the ways in which the experiences and supports they provide are leading to meaningful improvements in the skills and outcomes of students. We hope our findings will encourage other researchers to invest in longitudinal investigations of the impact of college on participating students with IDD.