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Social-Emotional Learning Foundations Curriculum for K-1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles

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Title: Social-Emotional Learning Foundations Curriculum for K-1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles
Language: English
Authors: Michelle M. Cumming (ORCID 0000-0002-1803-1900), Daniel V. Poling (ORCID 0000-0002-4469-824X), Yuxi Qiu, Nastasia Schreiner, Brian Jones, Ann P. Daunic, Nancy Corbett, Stephen W. Smith
Source: Exceptional Children. 2026 92(3):264-285.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 22
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Early Childhood Education
Elementary Education
Kindergarten
Primary Education
Grade 1
Descriptors: Social Emotional Learning, Kindergarten, Grade 1, Executive Function, Intervention, At Risk Persons, Emotional Disturbances, Behavior Disorders, Student Behavior, Program Effectiveness, Student Characteristics, Classroom Environment
Assessment and Survey Identifiers: Systematic Screening for Behavior Disorders
DOI: 10.1177/00144029251381300
ISSN: 0014-4029
2163-5560
Abstract: Due to executive function's (EF) foundational role in students' school and lifelong success, school-based interventions that build EF (i.e., inhibitory control, working memory, cognitive flexibility) and related skills are critical, especially for students at risk for emotional and behavioral disorders (EBD). Social Emotional Learning Foundations (SELF), a Tier 2 literacy-integrated, social-emotional learning (SEL) intervention with a universal component, is one of the few SEL interventions focusing on K-1 students at risk for EBD. This intervention has shown promise in enhancing students' social-emotional competence, behavioral functioning, and EF. Yet, no prior studies have examined whether SELF's effectiveness varied based on student EF strengths and difficulties (i.e., EF risk profiles), which has implications for SELF implementation and adaptation. Thus, using data (n = 1,154) from a randomized control trial, we sought to determine whether SELF had differential effects based on student EF risk latent profiles. Secondarily, we wanted to explore if classroom quality (i.e., emotional support, classroom organization, and instructional support) moderated these effects. Results from multivariate regression analysis showed differential effects of SELF on students' social-emotional, behavioral, and EF outcomes. Latent transition analysis revealed that SELF was associated with changes in student EF risk profile membership, with gender and grade level differences. Exploratory results indicated that classroom quality may have served to moderate the relationships between SELF, EF risk profiles, and outcomes. We discuss implications for intervention efforts and future research.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1500098
Database: ERIC
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  Value: <anid>AN0192372881;exc01apr.26;2026Mar23.04:45;v2.2.500</anid> <title id="AN0192372881-1">Social-Emotional Learning Foundations Curriculum for K–1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles </title> <p>Due to executive function's (EF) foundational role in students' school and lifelong success, school-based interventions that build EF (i.e., inhibitory control, working memory, cognitive flexibility) and related skills are critical, especially for students at risk for emotional and behavioral disorders (EBD). Social Emotional Learning Foundations (SELF), a Tier 2 literacy-integrated, social-emotional learning (SEL) intervention with a universal component, is one of the few SEL interventions focusing on K–1 students at risk for EBD. This intervention has shown promise in enhancing students' social-emotional competence, behavioral functioning, and EF. Yet, no prior studies have examined whether SELF's effectiveness varied based on student EF strengths and difficulties (i.e., EF risk profiles), which has implications for SELF implementation and adaptation. Thus, using data (n = 1,154) from a randomized control trial, we sought to determine whether SELF had differential effects based on student EF risk latent profiles. Secondarily, we wanted to explore if classroom quality (i.e., emotional support, classroom organization, and instructional support) moderated these effects. Results from multivariate regression analysis showed differential effects of SELF on students' social-emotional, behavioral, and EF outcomes. Latent transition analysis revealed that SELF was associated with changes in student EF risk profile membership, with gender and grade level differences. Exploratory results indicated that classroom quality may have served to moderate the relationships between SELF, EF risk profiles, and outcomes. We discuss implications for intervention efforts and future research.</p> <p>Keywords: executive function; latent profile analysis; emotional and behavioral disorders; at risk for EBD; elementary school</p> <p>Early elementary school students' executive function (EF)—a set of neurocognitive processes that support regulation of thought, emotion, and action ([<reflink idref="bib36" id="ref1">36</reflink>]; [<reflink idref="bib62" id="ref2">62</reflink>])—lays the foundation for student learning and is highly predictive of their academic achievement ([<reflink idref="bib54" id="ref3">54</reflink>]), mental health ([<reflink idref="bib61" id="ref4">61</reflink>]), and earning potential ([<reflink idref="bib37" id="ref5">37</reflink>]). To enhance students' short-term (e.g., academic achievement, social-emotional skills) and long-term (e.g., employment) outcomes, scholars have increasingly advocated for schools to implement programming known to support student EF development in early elementary school ([<reflink idref="bib56" id="ref6">56</reflink>]). Specifically, social-emotional learning (SEL) programs, which focus on building students' social and emotional skills (e.g., self-regulation, emotion identification, problem-solving), have gained greater recognition and use. SEL programs are not only effective at improving students' social and emotional competencies, behavior, and academic achievement (see [<reflink idref="bib20" id="ref7">20</reflink>], for meta-analyses), but also show promise in enhancing students' EF skills (see [<reflink idref="bib11" id="ref8">11</reflink>]; [<reflink idref="bib56" id="ref9">56</reflink>], for meta-analysis). SEL programming may be especially important for students with or at risk for emotional and behavioral disorders (EBD) due to both their social, emotional, and EF needs (e.g., [<reflink idref="bib12" id="ref10">12</reflink>]) and their persistent negative outcomes (e.g., school dropout, incarceration; [<reflink idref="bib7" id="ref11">7</reflink>])—underscoring the need for targeted school-based prevention and intervention efforts. Yet, there are limited SEL programs for students at risk for EBD.</p> <p>To address the paucity of SEL programs for students at risk for EBD, and given that early elementary school is a critical prevention period, [<reflink idref="bib18" id="ref12">18</reflink>] developed Social Emotional Learning Foundations (SELF). Created as a Tier 2 (i.e., targeted) intervention with a universal (i.e., class-wide) component, SELF is a literacy-integrated, SEL intervention for K and 1st grade students, identified through a validated behavior screener, as at risk for EBD. Research supports SELF's effectiveness in enhancing students' SEL skills (e.g., social-awareness, self-management), contextualized EF (i.e., EF use in the classroom), behaviors (e.g., externalizing), and social-emotional vocabulary ([<reflink idref="bib18" id="ref13">18</reflink>]; [<reflink idref="bib17" id="ref14">17</reflink>]). Although these findings are promising, the effectiveness of interventions may vary based on students' unique characteristics ([<reflink idref="bib8" id="ref15">8</reflink>]), such as their EF strengths and needs. For instance, [<reflink idref="bib49" id="ref16">49</reflink>] found that the effects of the Head Start Research-Based, Developmentally Informed (REDI) intervention varied based on high, moderate, and low EF student developmental trajectories. More recently, [<reflink idref="bib15" id="ref17">15</reflink>] conducted a cross-sectional latent profile analysis with SELF data to determine if students at risk for EBD demonstrated unique patterns of EF skills based on (a) a teacher report of students' application of contextualized EF skills in school settings (Behavior Rating Inventory of Executive Function—Second Edition [BRIEF-2; [<reflink idref="bib25" id="ref18">25</reflink>]) and (b) a performance-based EF measure (Head-Toes-Knees-Shoulders [HTKS; [<reflink idref="bib46" id="ref19">46</reflink>]) at pre-test. [<reflink idref="bib27" id="ref20">27</reflink>] recommend the use of both types of EF measures to capture the breadth and nuance of EF characteristics. Results indicated a theoretically supported, three-profile solution of mild, moderate, and clinical EF risk profiles, with profile names based on BRIEF-2 manual guidelines. Students in the mild EF risk profile demonstrated occasional challenges with EF-related skills (e.g., attention, organization). Students in the moderate EF risk profile exhibited greater EF needs, such as working memory and emotional control. Students in the clinical EF risk profile exhibited significant EF challenges. Yet, there is a lack of insight into SELF's impact based on these EF profiles, which would have implications for its delivery and adaptation. Thus, the primary purpose of our study is to determine whether SELF varied in effectiveness based on student EF risk profiles.</p> <hd id="AN0192372881-2">Executive Function and Student Outcomes</hd> <p>There is general agreement that EF underlies goal-directed behaviors and is made up of inhibitory control, working memory, and cognitive flexibility ([<reflink idref="bib36" id="ref21">36</reflink>]; [<reflink idref="bib35" id="ref22">35</reflink>]), with active maturation throughout childhood and early adulthood ([<reflink idref="bib3" id="ref23">3</reflink>]). Inhibitory control is the conscious controlling of a habitual or prepotent response, such as when a student refrains from engaging in aggression during an altercation or abstains from calling out. Working memory involves keeping information in mind and then using it, such as when a student remembers and uses rules to work through a social disagreement or to complete a multi-step assignment. Cognitive flexibility consists of shifting thoughts and behaviors, such as when a student imagines a friend's perspective or thinks of multiple ways to solve a problem.</p> <p>Over 2 decades of research has underscored EF's foundational role in students' short- and long-term success. Students with EF competence demonstrate increased proficiency in reading, math, science, and social-emotional skills (e.g., [<reflink idref="bib12" id="ref24">12</reflink>]; [<reflink idref="bib39" id="ref25">39</reflink>]; [<reflink idref="bib54" id="ref26">54</reflink>]), and tend to retain information and learn more deeply ([<reflink idref="bib2" id="ref27">2</reflink>]). EF is also pivotal to health, financial stability, and prosocial behaviors in adulthood ([<reflink idref="bib37" id="ref28">37</reflink>]). Conversely, students with EF difficulties are at increased risk for academic (e.g., [<reflink idref="bib57" id="ref29">57</reflink>]), mental health, and behavioral challenges (e.g., [<reflink idref="bib61" id="ref30">61</reflink>]). Overall, EF supports individuals in effectively navigating the demands of school, work, and relationships.</p> <p>Although research highlights the salience of EF in learning (e.g., [<reflink idref="bib57" id="ref31">57</reflink>]), attention (i.e., attention-deficit/hyperactivity disorder), and mental health ([<reflink idref="bib61" id="ref32">61</reflink>]), there is a paucity of EF research with students with or at risk for EBD. Yet, evidence suggests that students with or at risk for EBD score lower on EF tasks (e.g., [<reflink idref="bib12" id="ref33">12</reflink>]). Also, students at risk for EBD with increasing levels of EF difficulties demonstrate heightened behavioral needs ([<reflink idref="bib15" id="ref34">15</reflink>]). Thus, due to early elementary being an active EF maturation period ([<reflink idref="bib3" id="ref35">3</reflink>]), considering ways to foster EF may be essential to enhancing the quality and effectiveness of early prevention and intervention for students at risk for EBD.</p> <hd id="AN0192372881-3">Social Emotional Learning and EF Skill Development</hd> <p>Although EF maturation has a genetic component ([<reflink idref="bib35" id="ref36">35</reflink>]), it is also sensitive to environmental experiences (e.g., classroom quality; [<reflink idref="bib10" id="ref37">10</reflink>]) and is malleable to intervention ([<reflink idref="bib56" id="ref38">56</reflink>]); thus, targeted school-based programming may be critical for EF development. In particular, SEL focused programs are especially effective at improving students' EF skills, particularly amongst students with disabilities (see [<reflink idref="bib56" id="ref39">56</reflink>], for meta-analyses). These positive effects may be due to the theorized link between EF and self-regulation ([<reflink idref="bib4" id="ref40">4</reflink>]; [<reflink idref="bib40" id="ref41">40</reflink>]), which include both top-down processes—intentional, deliberate, and goal-directed—and bottom-up processes—automatic, reactive responses (e.g., stress, emotion). Yet, despite the effectiveness of SEL programs in fostering student EF skills, the majority have not been developed for students at risk for EBD.</p> <hd id="AN0192372881-4">The social-emotional learning foundations intervention</hd> <p>To address the lack of SEL research with students at risk for EBD, [<reflink idref="bib16" id="ref42">16</reflink>], [<reflink idref="bib18" id="ref43">18</reflink>]) developed SELF, which includes both a class-wide component and a small group intervention that integrates SEL with literacy instruction for K–1 students at risk for EBD. They used the five core SEL competencies (social awareness, self-awareness, self-management, decision-making, relationship skills; [<reflink idref="bib19" id="ref44">19</reflink>]) to develop 17 broad SEL topics that span 52 K and 54 first-grade 20-min lessons, to be delivered three times a week. Each topic relies upon developmentally appropriate, culturally diverse, and commercially available storybooks with embedded SEL-focused activities (see [<reflink idref="bib18" id="ref45">18</reflink>], for description).</p> <p>In SELF's initial pilot study, [<reflink idref="bib16" id="ref46">16</reflink>] found that students in the treatment group improved on school-related competence, internalizing behavior, and teacher-reported EF. In a pre-test–post-test cluster randomized efficacy trial, [<reflink idref="bib18" id="ref47">18</reflink>] reported treatment effects on contextualized EF, SEL-related competencies, behavior, and social-emotional vocabulary, with a mean intervention dosage of 88.4% (calculated as the percentage of total SELF lessons received, based on teacher-recorded attendance for each lesson). A secondary analysis testing mediation found that direct effects on competence and internalizing behavior were mediated by treatment effects on social-emotional learning competence, such that higher social-emotional scores led to increased competence and reduced internalizing behaviors ([<reflink idref="bib17" id="ref48">17</reflink>]). Although promising, no research has been conducted to determine whether SELF had differential effects based on students' EF strengths (e.g., demonstrates competence on EF tasks) and needs (e.g., has difficulty using EF in classroom settings).</p> <hd id="AN0192372881-5">Using Latent Profiles to Determine Differential Intervention Impacts</hd> <p>A person-centered approach may be an effective means by which to gain insight into students' EF strengths and needs and, in turn, the differential impact of school-based programming, such as SELF. A latent profile analysis (LPA) is a person-centered approach used to identify groups that share unique patterns of characteristics ([<reflink idref="bib22" id="ref49">22</reflink>]; [<reflink idref="bib21" id="ref50">21</reflink>])—such as EF—and has the potential to provide insight into whether some groups of students have different outcomes and responses to interventions than other groups, based on these characteristics. Traditionally, intervention effectiveness is based on variable-centered analysis, which assumes sample homogeneity and accounts for average responses ([<reflink idref="bib9" id="ref51">9</reflink>]; [<reflink idref="bib32" id="ref52">32</reflink>]). This approach may result in limitations, such as inability to detect (a) heterogeneity within and between individuals, (b) nonlinear outcomes, and (c) differential intervention effectiveness ([<reflink idref="bib8" id="ref53">8</reflink>]). For instance, a traditional variable-centered analytic approach may miss entire groups of students who may or may not benefit from an intervention. Thus, this approach has the risk of limiting insight into what works for whom and under what conditions.</p> <p>Rather than focusing solely on the average experiences of the overall intervention group via variable-centered analysis ([<reflink idref="bib32" id="ref54">32</reflink>]), LPA offers insight into differential intervention responses based on unique group characteristics (e.g., EF). For example, [<reflink idref="bib44" id="ref55">44</reflink>] used LPA to understand the impact of a conduct problems prevention program based on five profiles of child and parent risk characteristics, and found that children in the higher risk profiles demonstrated the most positive responses over 3 years. In one of the few studies to use LPA to examine the relationship between EF profile trajectories (low, moderate, high) and the Head Start REDI intervention, [<reflink idref="bib49" id="ref56">49</reflink>] found effects for students with low EF growth trajectories between PK and third grade. Moreover, LPA research using the pre-test scores from SELF ([<reflink idref="bib18" id="ref57">18</reflink>]) found (a) three distinct EF risk profiles (i.e., mildly, moderately, clinically) for students at risk for EBD and (b) each profile uniquely related to outcomes (e.g., behavior; [<reflink idref="bib15" id="ref58">15</reflink>]). Given SELF's promising impact and research on differential intervention effectiveness based on LPA profiles (e.g., [<reflink idref="bib49" id="ref59">49</reflink>]), additional insight is needed into whether SELF's effectiveness varied based on student EF risk profiles.</p> <hd id="AN0192372881-6">EF risk profile membership change: Considering SELF and student demographics</hd> <p>It is essential to also determine the role SELF and student demographics potentially played in possible EF risk profile membership change. Research indicates that EF develops rapidly during PK and matures with age ([<reflink idref="bib3" id="ref60">3</reflink>]); thus, grade-level differences in EF growth and related EF risk profile change may exist. Also, there is some evidence of gender differences in EF growth rates. For example, [<reflink idref="bib52" id="ref61">52</reflink>] found that PK girls had stronger lateral prefrontal brain activation than boys, allowing them to apply EF skills such as cognitive flexibility to disengage and switch to another task more easily than boys. Thus, in elementary settings, there may be possible gender differences in EF risk profile membership change. Further, there is some evidence of racial/ethnic differences in EF ([<reflink idref="bib47" id="ref62">47</reflink>]), although scholars caution about making strong conclusions due to within-group heterogeneity, differences in outcomes based on assessment type, and that EF development may be contextually contingent ([<reflink idref="bib34" id="ref63">34</reflink>]). Thus, there may be racial/ethnic differences in EF risk profile change, which is salient due to the disproportionate identification of students from specific underrepresented groups (i.e., overrepresentation of Black and Indigenous) with EBD ([<reflink idref="bib41" id="ref64">41</reflink>].</p> <hd id="AN0192372881-7">Considering classroom quality in intervention effectiveness</hd> <p>Classroom quality may play an important role not only in student EF and SEL development, but also in intervention effectiveness. Both EF and SEL competence development are sensitive to school-based environmental experiences. For instance, classrooms characterized by low or high levels of emotional support (most measured via the Classroom Assessment Scoring System; [<reflink idref="bib43" id="ref65">43</reflink>]) are uniquely associated with student EF ([<reflink idref="bib10" id="ref66">10</reflink>]). Classroom climate, based on the quality of teacher–student interactions, serves to either hinder or support students' social-emotional development ([<reflink idref="bib42" id="ref67">42</reflink>]). Classroom climate has also been found to be associated with the effectiveness of SEL related interventions ([<reflink idref="bib23" id="ref68">23</reflink>]) and programs aimed at reducing problem behavior ([<reflink idref="bib59" id="ref69">59</reflink>]). Thus, investigating whether SELF's impact was moderated by classroom quality and student EF profiles is warranted.</p> <hd id="AN0192372881-8">Purpose</hd> <p>Given EF's foundational role in students' school and lifelong success ([<reflink idref="bib5" id="ref70">5</reflink>]; [<reflink idref="bib54" id="ref71">54</reflink>]), school-based interventions that build EF and related skills are essential, particularly for students at risk for EBD. SELF, an SEL intervention for students at risk for EBD ([<reflink idref="bib18" id="ref72">18</reflink>]), has shown promise in enhancing student EF (teacher reported), social-emotional competence, and behaviors. Yet, no studies have explored whether SELF's effectiveness varies based on student EF risk profiles, and if classroom quality moderates this relationship. To address this gap in the literature, we posed the following research questions:</p> <p></p> <ulist> <item> 1. Does SELF have differential effects on student outcomes (i.e., behavior, social-emotional competence, EF) based on EF risk profile (pre-test) compared to business-as-usual (BAU) peers?</item> <p></p> <item> 2a. To what extent is SELF related to changes in student EF risk profiles from pre- to post-test?</item> <p></p> <item> 2b. Are there differences in EF risk profile change based on student gender, grade level, and racial/ethnic background?</item> <p></p> <item> We additionally posed the following exploratory question:</item> <p></p> <item> 3. Does classroom quality moderate SELF effectiveness for students in different EF risk profiles?</item> </ulist> <hd id="AN0192372881-9">Method</hd> <p></p> <hd id="AN0192372881-10">Participants and Setting</hd> <p>For the current study, we used data from a 3-year (2016–2019) randomized controlled trial of SELF, which was approved by the university IRB ([<reflink idref="bib18" id="ref73">18</reflink>]). Fifty-two, Title I schools within 11 school districts in one southeastern state with 163 K and 141 first-grade classrooms participated in the study. An average of 5.85 K–1 teachers per school (<emph>n</emph> = 304 total) allowed for typical rates of attrition and ensured an adequate sample size for analyses.</p> <p>After district approval, school principals and K–1 teachers were invited to participate, with an equal chance of assignment to either SELF or BAU. A total of 1,154 students (627 K, 527 first grade) identified as at risk for EBD participated in the study. In the Fall, teachers selected eligible students at risk for EBD via the Systematic Screening for Behavior Disorders, Second Edition (SSBD-2; [<reflink idref="bib58" id="ref74">58</reflink>]), which is a standardized assessment for identifying children with elevated behavioral or emotional challenges. Use of the SSBD was for the research study and not a regular school practice. In the first stage, teachers reviewed evidence-based descriptions of externalizing and internalizing behaviors. Using these descriptions as a guide, they identified the top three to four students in each category. In the second stage, teachers used the Combined Frequency Index and Critical Events Index to further evaluate students, ultimately nominating two or three individuals in each category for potential participation in the study. The SSBD-2 has demonstrated evidence of internal consistency (α =.84–.86), test-retest reliability (ρ =.74–.88), interrater reliability (ρ =.72–.94), and convergent validity ([<reflink idref="bib26" id="ref75">26</reflink>]; [<reflink idref="bib31" id="ref76">31</reflink>]).</p> <p>The majority of student participants were male (62%) and educated in Title 1 schools (81%). Overall, 64% of participants were White, 20% Black, 9% Hispanic/Latinx[<reflink idref="bib5" id="ref77">5</reflink>], and 7% Other. Also, 3% were English language learners and 20% received special education services. Chi-square tests to compare demographics between the BAU and SELF groups indicated no significant group differences (see [<reflink idref="bib18" id="ref78">18</reflink>] for more detailed information).</p> <hd id="AN0192372881-11">Measures</hd> <p>To address each research question, a variety of measures were used to assess contextualized EF, performance-based EF, social-emotional competencies, behaviors, and classroom quality<emph>.</emph> With the exception of the classroom quality observation, which was conducted only once in the Spring, the remaining measures were administered in the Fall (i.e., pre-test) and in the Spring (i.e., post-test). Subscale scores from respective measures were used for data analysis.</p> <hd id="AN0192372881-12">Behavior Rating Inventory of Executive Function—Second Edition</hd> <p>The 63-item Behavior Rating Inventory of Executive Function-2 (BRIEF-2; [<reflink idref="bib25" id="ref79">25</reflink>]) teacher form was used to assess students' contextualized EF in school settings. Teachers used a Likert-type scale to rate each item as <emph>never</emph>, <emph>sometimes</emph>, or <emph>often</emph>, with EF difficulties indicated by higher scores. The BRIEF-2 includes nine clinical scales (Inhibit, Shift, Emotional Control, Initiate, Working Memory, Plan/Organize, Organization of Materials, Task-Monitor, Self-Monitor) that form three indices of Cognitive Regulation, Behavior Regulation, and Emotion Regulation. Yet, in a recent psychometric evaluation of the BRIEF-2 with the current K–1 sample ([<reflink idref="bib15" id="ref80">15</reflink>]), results supported a 53-item modified model that consisted of nine scales and only two indices: Behavior-Emotion Regulation and Cognitive Regulation. We used this modified version in the current analysis, with strong internal consistency (omega coefficients range =.88–.99). Correlations between the pre-and post-test varied from.58 to.76, suggesting acceptable stability.</p> <hd id="AN0192372881-13">Head-Toes-Knees-Shoulders (HTKS)</hd> <p>The Head-Toes-Knees-Shoulders (HTKS; [<reflink idref="bib46" id="ref81">46</reflink>]) was used to measure students' overall performance-based EF skills. Specifically, the HTKS is a measure administered directly to students ages 4 to 6 under standardized conditions. Administered in a game-like manner, assessors first instructed students to either touch their head or toes for 10 trials. Over the next 10 trials, students were directed to do the opposite of the assessors' instructions (i.e., touch your toes when asked to touch your head). Assessors then asked students to touch their shoulders or knees, followed by the opposite. The assessment concluded by asking students to respond to the opposite of all four directions. Each attempt was scored as 0 (<emph>incorrect</emph>), 1 (<emph>self-correct</emph>), or 2 (<emph>correct</emph>), with a total score (0–52). Sample derived Cronbach's alpha was.95 and pre- and post-test correlation was.63.</p> <hd id="AN0192372881-14">Devereux Student Strengths Assessment</hd> <p>The Devereux Student Strengths Assessment (DESSA; [<reflink idref="bib33" id="ref82">33</reflink>]) gauged students' social-emotional competencies. Teachers completed the 72-item measure using a 5-point scale ranging from <emph>never</emph> to <emph>very frequently</emph> across eight scales: Goal-Directed Behavior (e.g., work[s] hard on projects), Optimistic Thinking (e.g., speak[s] about positive things), Personal Responsibility (e.g., follow[s] rules), Self-Awareness (e.g., give[s] an opinion when asked), Self-Management (e.g., pay[s] attention), Social-Awareness (e.g., share[s] with others), Decision Making (e.g., seek[s] advice), and Relationship Skills (e.g., offer[s] to help). In the current sample, Cronbach's alphas ranged from.88 to.94. Stability was assumed given the correlation between pre-test and post-test scores ranged between.57 and.70.</p> <hd id="AN0192372881-15">Clinical Assessment of Behavior Teacher Rating Form</hd> <p>The 70-item Clinical Assessment of Behavior (CAB; [<reflink idref="bib6" id="ref83">6</reflink>]) measured students' behavioral functioning and school adjustment. Teachers rated each item from 1 (<emph>always or very frequently</emph>) to 5 (<emph>never</emph>), with lower scores reflecting difficulties. We used data only from the Externalizing (e.g., insults others), Internalizing (e.g., cries easily), Social Skills (e.g., considerate of others), and Competence (e.g., learns new things easily) scales. Alphas ranged from.88 to.94 in the current sample. Correlations between pre- and post-test were.66 (Externalizing),.79 (Internalizing),.73 (Social Skills), and.74 (Competence).</p> <hd id="AN0192372881-16">Classroom Assessment Scoring System</hd> <p>The Classroom Assessment Scoring System (CLASS; [<reflink idref="bib43" id="ref84">43</reflink>]) was used to assess classroom quality. The CLASS for Grades K–1 addresses 10 dimensions organized into the domains of Emotional Support (Positive and Negative Climate, Teacher Sensitivity, and Regard for Student Perspectives), Classroom Organization (Behavior Management, Productivity, and Instructional Learning Formats), and Instructional Support (Concept Development, Quality of Feedback, and Language Modeling). Trained observers scored each dimension supporting the domain using a 1–7 scale (low = 1–2; mid = 3–5; high = 6–7) from at least four observations, which were averaged to create broad domain scores. Observations occurred after the start of the intervention, with no noted differences between the intervention and BAU condition on CLASS scores. To be certified, CLASS observers participated in 16 hours of training and scored 80% or higher across five separate observations coded by CLASS Master Coders. CLASS domains have demonstrated moderate to high factor loadings and appropriate internal consistency ([<reflink idref="bib43" id="ref85">43</reflink>]). For the current sample, aside from Negative Climate (factor loading =.67), all CLASS dimensions loaded above.80 onto respective domains. The three-domain model fit was as follows: <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>χ</mi><mn>2</mn></msup><mo stretchy="false">(</mo><mn>32</mn><mo stretchy="false">)</mo><mo>=</mo><mn>278.23</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> , Root Mean Square Error of Approximation <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">RMSEA</mi></mrow></mrow><mo stretchy="false">)</mo><mo>=</mo><mn>.17</mn></math> </ephtml> , Comparative Fit Index <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">CFI</mi></mrow></mrow><mo stretchy="false">)</mo><mo>=</mo><mn>.91</mn></math> </ephtml> , Tucker-Lewis Index <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">TLI</mi></mrow></mrow><mo stretchy="false">)</mo><mo>=</mo><mn>.87</mn></math> </ephtml> , Standardized Root Mean Square Residual <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">SRMR</mi></mrow></mrow><mo stretchy="false">)</mo><mo>=</mo><mn>.06</mn></math> </ephtml> ). We proceeded with the original CLASS domain structure due to the underlying theoretical structure.</p> <hd id="AN0192372881-17">Data Analysis</hd> <p>To answer each research question, we conducted multivariate regression analysis (research questions [RQs] 1 and 3) and latent transition analysis (LTA; RQ 2). We fitted all models using Mplus (Version 8.8; de-identified data and codes are available upon request). To account for students being nested within classrooms, we specified CLUSTER = TEACHERID in conjunction with TYPE = COMPLEX, which makes corrections on standard errors and <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>χ</mi><mn>2</mn></msup></math> </ephtml> test of model fit. Additionally, we used a robust maximum likelihood (MLR) estimator. See Figure S1 for a visualization of the connections between RQs, measures, and statistical methods described.</p> <hd id="AN0192372881-18">Multivariate regression</hd> <p>Multivariate regression is an extension of multiple regression in situations where more than one outcome variable shares the same set of predictors. In our study, this method was adequate to answer RQs 1 and 3 due to three sets of outcomes of interest: behavior (CAB-T; [<reflink idref="bib6" id="ref86">6</reflink>]), social-emotional competencies (DESSA; [<reflink idref="bib33" id="ref87">33</reflink>]), and EF (BRIEF-2; [<reflink idref="bib25" id="ref88">25</reflink>]; HTKS; [<reflink idref="bib45" id="ref89">45</reflink>]). Within each set, outcomes were interrelated (four from the CAB, eight from the DESSA, and 10 from the BRIEF and HTKS). For each outcome set, we included the following covariates in the model, research conditions (one dummy variable; BAU = 0, SELF = 1), EF risk profile (mild EF risk, moderate EF risk, and clinical EF risk dummy coded variables with mild EF risk profile as the reference group), classroom quality (three continuous variables including Emotional Support, Classroom Organization, Instructional Support), and interactions of these predictors (SELF × EF risk profile, SELF × classroom quality, EF risk profile × classroom quality, SELF × EF risk profile × classroom quality). EF risk profiles chosen for this analysis were based on LPA findings from [<reflink idref="bib15" id="ref90">15</reflink>] analysis of the current data set, which determined a theoretically and empirically supported 3-profile solution of mild, moderate, and clinical EF risk profiles. We reviewed bivariate correlations between predictor variables concerning multicollinearity and found none exhibited extremely high correlations. Continuous predictors were mean-centered. For CAB and DESSA outcomes, pre-test data were included as a covariate. For EF outcome analysis, we did not include pre-test EF data because EF risk profiles were based on pre-test. To assess model fit, we used [<reflink idref="bib30" id="ref91">30</reflink>] guidelines: statistic and corresponding <emph>p</emph> value (non-significant results suggest perfect fit), CFI ≥.95, TLI ≥.95, RMSEA ≤.08, and SRMR ≤ 0.08.</p> <hd id="AN0192372881-19">Latent Transition Analysis</hd> <p>For RQ 2, we conducted LTA to explore the extent to which students' EF risk profiles changed after participating in SELF. LTA is an extension of latent class/profile analysis to longitudinal data and affords examinations of how participants shift between latent classes/profiles across multiple time points. We followed [<reflink idref="bib63" id="ref92">63</reflink>]) suggested steps. In the first step, we estimated latent EF risk profiles based on BRIEF and HTKS scores from pre-test and post-test using LPA separately. We began with a one-profile model as the baseline, then gradually introduced one extra profile to the LPA. Next, we reviewed (a) the Lo-Mendell-Rubin likelihood ratio test (significant results suggest the model with one extra profile fits better); (b) relative fit indices (lower values suggest better fit), including the Akaike Information Criterion ([<reflink idref="bib1" id="ref93">1</reflink>]), Bayesian Information Criterion ([<reflink idref="bib50" id="ref94">50</reflink>]), and sample-size adjusted Bayesian Information Criterion ([<reflink idref="bib51" id="ref95">51</reflink>]); and (c) entropy (above.80 for acceptable classification quality) to determine the number of latent profiles to retain. We then reviewed the estimates of EF risk profile indicators, class prevalence, and the interpretability of potential latent profiles to select the final LPA model for pre- and post-test measures. In the second step, we fitted two unconditional LTA models: one with equality constraints on profile indicators across time (invariant LTA) and the other without (non-invariant LTA). We then compared both models based on the likelihood ratio test to examine if measurement invariance of EF risk profiles could be assumed. Results from this step informed which LTA model we used in subsequent analyses. In the third step, we estimated the prevalence of EF risk profiles and transition probabilities. Last, we added condition (BAU = 0, SELF = 1), grade (K = 0, 1 = 1), gender (male = 0, female = 1), and three dummy variables of race/ethnicity (White was the reference group) to the LTA model as covariates to examine their impact on transition probabilities. Variances of the LTA models were specified as equal across profiles.</p> <hd id="AN0192372881-20">Missing Data</hd> <p>Reviewing summary statistics of the demographic and outcome variables (see Table S1 in the online supplementary material) indicated missingness spanned from.09% to 6.76% for pre- and.43% to 12.31% for post-test. We examined the missing mechanism using [<reflink idref="bib28" id="ref96">28</reflink>]) steps in the MissMech package ([<reflink idref="bib29" id="ref97">29</reflink>]) in R. Nonparametric test results were nonsignificant (<emph>p</emph> =.29 for pre-test and <emph>p</emph> =.24 for post-test), suggesting data were missing at random.</p> <hd id="AN0192372881-21">Results</hd> <p>Because RQs 1 and 3 are answered by the same model, which is focused on different aspects of the results (i.e., two-way interaction effect of <emph>SELF × EF risk profiles</emph> for RQ 1, and three-way interaction effect of <emph>SELF × EF risk profiles × CLASS</emph> for RQ 3), we first report RQ 1 and exploratory RQ 3 results and then RQ 2 LTA results. Multivariate regression results are based on standardized path coefficients. Tables 1–4 highlight significant findings, with complete results in supplementary materials.</p> <p>Table 1. Selected Estimates of Regression Coefficients and <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>R</mi><mn>2</mn></msup></math> </ephtml> From Multivariate Regression Analysis of Behavioral Outcomes.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" /><th align="left" colspan="2">Internalizing</th><th align="left" /><th align="left" colspan="2">Externalizing</th><th align="left" /><th align="left" colspan="2">Social Skills</th><th align="left" /><th align="left" colspan="2">Competence</th></tr><tr><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th></tr></thead><tbody><tr><td>SELF</td><td>0.12**</td><td>0.04</td><td /><td>0.05</td><td>0.03</td><td /><td>0.09*</td><td>0.04</td><td /><td>0.06</td><td>0.03</td></tr><tr><td>Moderate</td><td>−0.12**</td><td>0.04</td><td /><td>−0.08*</td><td>0.04</td><td /><td>−0.09*</td><td>0.04</td><td /><td>−0.14***</td><td>0.04</td></tr><tr><td>Clinical</td><td>−0.22***</td><td>0.04</td><td /><td>−0.17***</td><td>0.04</td><td /><td>−0.17***</td><td>0.04</td><td /><td>−0.30***</td><td>0.04</td></tr><tr><td>SELF × Moderate</td><td>0.07</td><td>0.04</td><td /><td>0.07*</td><td>0.04</td><td /><td>0.10*</td><td>0.04</td><td /><td>0.12**</td><td>0.04</td></tr><tr><td>SELF × Clinical</td><td>0.09*</td><td>0.05</td><td /><td>0.12**</td><td>0.04</td><td /><td>0.14**</td><td>0.04</td><td /><td>0.12**</td><td>0.04</td></tr><tr><td>SELF × Moderate × IS</td><td>0.09</td><td>0.05</td><td /><td>0.06</td><td>0.04</td><td /><td>0.08</td><td>0.05</td><td /><td>0.13**</td><td>0.05</td></tr><tr><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup xmlns=""><mi>R</mi><mn>2</mn></msup></math></p></td><td>0.498</td><td /><td /><td>0.605</td><td /><td /><td>0.548</td><td /><td /><td>0.604</td><td /></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> Estimates of the intact list of predictors are reported in Table S2 in the online supplementary material. SELF = Social Emotional Learning Foundations; Moderate = moderate executive function risk profile; Clinical = clinical executive function risk profile; IS = Instructional Support.</p> <p>2 *** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.001</mn></math> </ephtml> , ** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.01</mn></math> </ephtml> , * <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.05</mn></math> </ephtml> .</p> <p>Table 2. Selected Estimates of Regression Coefficients and <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>R</mi><mn>2</mn></msup></math> </ephtml> From Multivariate Regression Analysis of Social Emotional Outcomes.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="2">Self- Awareness</th><th align="left" colspan="2">Self- Management</th><th align="left" colspan="2">Social- Awareness</th><th align="left" colspan="2">Relationship Skills</th></tr><tr><th /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th></tr></thead><tbody><tr><td>SELF</td><td>0.16**</td><td>0.05</td><td>0.12*</td><td>0.05</td><td>0.13*</td><td>0.05</td><td>0.13*</td><td>0.05</td></tr><tr><td>Moderate</td><td>−0.16***</td><td>0.04</td><td>−0.17***</td><td>0.04</td><td>−0.14***</td><td>0.04</td><td>−0.12**</td><td>0.04</td></tr><tr><td>Clinical</td><td>−0.29***</td><td>0.05</td><td>−0.37***</td><td>0.05</td><td>−0.32***</td><td>0.05</td><td>−0.24***</td><td>0.05</td></tr><tr><td>SELF × Moderate</td><td>0.12*</td><td>0.05</td><td>0.09</td><td>0.05</td><td>0.07</td><td>0.05</td><td>0.10*</td><td>0.05</td></tr><tr><td>SELF × Clinical</td><td>0.13*</td><td>0.05</td><td>0.12*</td><td>0.05</td><td>0.13*</td><td>0.06</td><td>0.12*</td><td>0.06</td></tr><tr><td>SELF × Moderate × IS</td><td>0.09</td><td>0.06</td><td>0.14*</td><td>0.07</td><td>0.14*</td><td>0.06</td><td>0.14*</td><td>0.07</td></tr><tr><td>SELF × Clinical × ES</td><td>−0.14</td><td>0.10</td><td>−0.22*</td><td>0.11</td><td>−0.23*</td><td>0.11</td><td>−0.08</td><td>0.11</td></tr><tr><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup xmlns=""><mi>R</mi><mn>2</mn></msup></math></p></td><td>0.38</td><td /><td>0.42</td><td /><td>0.37</td><td /><td>0.33</td><td /></tr></tbody></table> </ephtml> </p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" /><th align="left" colspan="2">Goal-Directed Behavior</th><th align="left" /><th align="left" colspan="2">Personal Responsibility</th><th align="left" /><th align="left" colspan="2">Decision Making</th><th align="left" /><th align="left" colspan="2">Optimistic Thinking</th></tr><tr><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th></tr></thead><tbody><tr><td>SELF</td><td>0.11*</td><td>0.05</td><td /><td>0.14**</td><td>0.05</td><td /><td>0.12*</td><td>0.05</td><td /><td>0.16**</td><td>0.05</td></tr><tr><td>Moderate</td><td>−0.15***</td><td>0.04</td><td /><td>−0.12**</td><td>0.04</td><td /><td>−0.15***</td><td>0.04</td><td /><td>−0.12**</td><td>0.05</td></tr><tr><td>Clinical</td><td>−0.33***</td><td>0.04</td><td /><td>−0.36***</td><td>0.04</td><td /><td>−0.32***</td><td>0.05</td><td /><td>−0.28***</td><td>0.05</td></tr><tr><td>SELF × Clinical</td><td>0.08</td><td>0.05</td><td /><td>0.09</td><td>0.05</td><td /><td>0.11*</td><td>0.05</td><td /><td>0.13*</td><td>0.06</td></tr><tr><td>SELF × Moderate × IS</td><td>0.20**</td><td>0.06</td><td /><td>0.18**</td><td>0.07</td><td /><td>0.13</td><td>0.07</td><td /><td>0.16*</td><td>0.07</td></tr><tr><td>SELF × Clinical × ES</td><td>−0.16</td><td>0.09</td><td /><td>−0.19*</td><td>0.09</td><td /><td>−0.16</td><td>0.10</td><td /><td>−0.15</td><td>0.10</td></tr><tr><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup xmlns=""><mi>R</mi><mn>2</mn></msup></math></p></td><td>0.44</td><td /><td /><td>0.48</td><td /><td /><td>0.39</td><td /><td /><td>0.37</td><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note.</emph> Estimates of the intact list of predictors are reported in Table S3 in the online supplementary material. SELF = Social Emotional Learning Foundations; Moderate = moderate executive function risk profile; Clinical = clinical executive function risk profile; IS = Instructional Support; ES = Emotional Support.</item> <item>4 *** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.001</mn></math> </ephtml> , ** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.01</mn></math> </ephtml> , * <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.05</mn></math> </ephtml> .</item> </ulist> <p>Table 3. Selected Estimates of Regression Coefficients and <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>R</mi><mn>2</mn></msup></math> </ephtml> From Multivariate Regression Analysis of EF Outcomes.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" /><th align="left" colspan="2">Inhibit</th><th align="left" /><th align="left" colspan="2">Self-Monitor</th><th align="left" /><th align="left" colspan="2">Shift</th><th align="left" /><th align="left" colspan="2">Emotional Control</th><th align="left" /><th align="left" colspan="2">Initiate</th></tr><tr><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th></tr></thead><tbody><tr><td>SELF</td><td>−0.04</td><td>0.04</td><td /><td>−0.01</td><td>0.04</td><td /><td>−0.01</td><td>0.05</td><td /><td>−0.02</td><td>0.05</td><td /><td>−0.03</td><td>0.04</td></tr><tr><td>Moderate</td><td>0.32***</td><td>0.05</td><td /><td>0.38***</td><td>0.05</td><td /><td>0.25***</td><td>0.05</td><td /><td>0.22***</td><td>0.05</td><td /><td>0.38***</td><td>0.04</td></tr><tr><td>Clinical</td><td>0.61***</td><td>0.04</td><td /><td>0.67***</td><td>0.04</td><td /><td>0.51***</td><td>0.05</td><td /><td>0.42***</td><td>0.05</td><td /><td>0.79***</td><td>0.04</td></tr><tr><td>SELF × Moderate</td><td>−0.07</td><td>0.05</td><td /><td>−0.10</td><td>0.05</td><td /><td>−0.09</td><td>0.05</td><td /><td>−0.05</td><td>0.05</td><td /><td>−0.10*</td><td>0.04</td></tr><tr><td>SELF × Clinical</td><td>−0.09</td><td>0.05</td><td /><td>−0.12*</td><td>0.05</td><td /><td>−0.12</td><td>0.06</td><td /><td>−0.07</td><td>0.06</td><td /><td>−0.07</td><td>0.05</td></tr><tr><td>SELF × Clinical × ES</td><td>0.13</td><td>0.11</td><td /><td>0.05</td><td>0.10</td><td /><td>0.23*</td><td>0.10</td><td /><td>0.29*</td><td>0.12</td><td /><td>0.19*</td><td>0.09</td></tr><tr><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup xmlns=""><mi>R</mi><mn>2</mn></msup></math></p></td><td>0.24</td><td /><td /><td>0.27</td><td /><td /><td>0.18</td><td /><td /><td>0.13</td><td /><td /><td>0.43</td><td /></tr></tbody></table> </ephtml> </p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" /><th align="left" colspan="2">Working Memory</th><th align="left" /><th align="left" colspan="2">Plan/Organize</th><th align="left" /><th align="left" colspan="2">Task-Monitor</th><th align="left" /><th align="left" colspan="2">Organization of Materials</th><th align="left" /><th align="left" colspan="2">HTKS</th></tr><tr><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th><th align="left" /><th align="left"><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi xmlns="">β</mi></math></p></th><th align="left"><italic>SE</italic></th></tr></thead><tbody><tr><td>SELF</td><td>−0.02</td><td>0.04</td><td /><td>−0.09*</td><td>0.04</td><td /><td>−0.03</td><td>0.04</td><td /><td>−0.11**</td><td>0.04</td><td /><td>0.03</td><td>0.04</td></tr><tr><td>Moderate</td><td>0.42***</td><td>0.04</td><td /><td>0.38***</td><td>0.04</td><td /><td>0.45***</td><td>0.04</td><td /><td>0.19***</td><td>0.04</td><td /><td>−0.11*</td><td>0.04</td></tr><tr><td>Clinical</td><td>0.86***</td><td>0.04</td><td /><td>0.81***</td><td>0.04</td><td /><td>0.84***</td><td>0.04</td><td /><td>0.73***</td><td>0.04</td><td /><td>−0.30***</td><td>0.07</td></tr><tr><td>SELF<p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo xmlns="">×</mo></math></p>Moderate</td><td>−0.12**</td><td>0.04</td><td /><td>−0.05</td><td>0.04</td><td /><td>−0.11*</td><td>0.04</td><td /><td>0.02</td><td>0.04</td><td /><td>0.08</td><td>0.04</td></tr><tr><td>SELF<p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo xmlns="">×</mo></math></p>Clinical</td><td>−0.11*</td><td>0.05</td><td /><td>−0.04</td><td>0.05</td><td /><td>−0.10</td><td>0.05</td><td /><td>−0.06</td><td>0.06</td><td /><td>0.02</td><td>0.08</td></tr><tr><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup xmlns=""><mi>R</mi><mn>2</mn></msup></math></p></td><td>0.48</td><td /><td /><td>0.45</td><td /><td /><td>0.45</td><td /><td /><td>0.39</td><td /><td /><td>0.08</td><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>5 <emph>Note.</emph> Estimates of the intact list of predictors are reported in Table S4 in the online supplementary material. SELF = Social Emotional Learning Foundations; Moderate = moderate executive function risk profile; Clinical = clinical executive function risk profile; ES = Emotional Support; HTKS = Head-Toes-Knees-Shoulders.</item> <item>6 *** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.001</mn></math> </ephtml> , ** <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.01</mn></math> </ephtml> , * <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo><mn>.05</mn></math> </ephtml> .</item> </ulist> <p>Table 4. Multinomial Logistic Estimates of the Effect of SELF, Grade, Gender, Race/Ethnicity on Transition of EF Risk Profiles from Pre- to Post-Test.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" /><th align="left" colspan="14">Post-Test EF Risk Profiles Conditioned on</th></tr><tr><th align="left" /><th align="left" colspan="4">Mild EF Risk (Pre-Test)</th><th align="left" /><th align="left" colspan="4">Moderate EF Risk (Pre-Test)</th><th align="left" /><th align="left" colspan="4">Clinical EF Risk (Pre-Test)</th></tr><tr><th align="left" /><th align="left" rowspan="2">Estimate</th><th align="left" rowspan="2">SE</th><th align="left" colspan="2">95% CI</th><th align="left" /><th align="left" rowspan="2">Estimate</th><th align="left" rowspan="2">SE</th><th align="left" colspan="2">95% CI</th><th align="left" /><th align="left" rowspan="2">Estimate</th><th align="left" rowspan="2">SE</th><th align="left" colspan="2">95% CI</th></tr><tr><th align="left" /><th align="left">LL</th><th align="left">UL</th><th align="left" /><th align="left">LL</th><th align="left">UL</th><th align="left" /><th align="left">LL</th><th align="left">UL</th></tr></thead><tbody><tr><td>Mild EF vs. Clinical EF (Post-Test)</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> SELF</td><td>0.88</td><td>1.07</td><td>0.08</td><td>9.59</td><td /><td><bold>6</bold><bold>.</bold><bold>44</bold></td><td><bold>3</bold>.<bold>39</bold></td><td><bold>2</bold>.<bold>29</bold></td><td><bold>18</bold>.<bold>10</bold></td><td /><td>2.40</td><td>1.22</td><td>0.88</td><td>6.50</td></tr><tr><td> Grade</td><td>2.24</td><td>2.91</td><td>0.18</td><td>28.49</td><td /><td>1.02</td><td>0.50</td><td>0.39</td><td>2.66</td><td /><td>1.00</td><td>0.48</td><td>0.39</td><td>2.55</td></tr><tr><td> Gender</td><td>4.03</td><td>5.18</td><td>0.33</td><td>49.94</td><td /><td><bold>3</bold>.<bold>56</bold></td><td><bold>1</bold>.<bold>98</bold></td><td><bold>1</bold>.<bold>20</bold></td><td><bold>10</bold>.<bold>59</bold></td><td /><td>1.39</td><td>0.62</td><td>0.58</td><td>3.32</td></tr><tr><td> Black</td><td>0.61</td><td>0.77</td><td>0.05</td><td>7.21</td><td /><td>1.12</td><td>0.69</td><td>0.34</td><td>3.76</td><td /><td>0.88</td><td>0.49</td><td>0.30</td><td>2.60</td></tr><tr><td> Hispanic/Latinx</td><td>0.22</td><td>0.23</td><td>0.03</td><td>1.78</td><td /><td>1.48</td><td>1.06</td><td>0.37</td><td>5.99</td><td /><td>1.11</td><td>0.81</td><td>0.27</td><td>4.62</td></tr><tr><td> Other</td><td>-</td><td>-</td><td>-</td><td>-</td><td /><td>3.10</td><td>3.84</td><td>0.27</td><td>35.19</td><td /><td>0.87</td><td>0.91</td><td>0.11</td><td>6.76</td></tr><tr><td>Moderate EF vs. Clinical EF (Post-Test)</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> SELF</td><td>0.40</td><td>0.53</td><td>0.03</td><td>5.47</td><td /><td>2.24</td><td>1.14</td><td>0.83</td><td>6.05</td><td /><td><bold>2</bold>.<bold>68</bold></td><td><bold>0</bold>.<bold>77</bold></td><td><bold>1</bold>.<bold>53</bold></td><td><bold>4</bold>.<bold>72</bold></td></tr><tr><td> Grade</td><td>3.50</td><td>4.74</td><td>0.25</td><td>49.56</td><td /><td>0.95</td><td>0.44</td><td>0.38</td><td>2.37</td><td /><td><bold>1</bold>.<bold>89</bold></td><td><bold>0</bold>.<bold>49</bold></td><td><bold>1</bold>.<bold>14</bold></td><td><bold>3</bold>.<bold>13</bold></td></tr><tr><td> Gender</td><td>3.40</td><td>5.07</td><td>0.18</td><td>63.34</td><td /><td>2.36</td><td>1.37</td><td>0.76</td><td>7.34</td><td /><td>1.32</td><td>0.37</td><td>0.76</td><td>2.30</td></tr><tr><td> Black</td><td>2.06</td><td>2.80</td><td>0.14</td><td>29.61</td><td /><td>0.79</td><td>0.47</td><td>0.25</td><td>2.52</td><td /><td>1.13</td><td>0.38</td><td>0.59</td><td>2.17</td></tr><tr><td> Hispanic/Latinx</td><td>0.47</td><td>0.65</td><td>0.03</td><td>7.11</td><td /><td>1.13</td><td>0.78</td><td>0.29</td><td>4.37</td><td /><td>0.99</td><td>0.49</td><td>0.38</td><td>2.59</td></tr><tr><td> Other</td><td>-</td><td>-</td><td>-</td><td>-</td><td /><td>1.69</td><td>2.25</td><td>0.12</td><td>23.02</td><td /><td>1.20</td><td>0.69</td><td>0.39</td><td>3.70</td></tr></tbody></table> </ephtml> </p> <p>7 <emph>Note.</emph> Significant estimates are highlighted in bold. The clinical EF risk profile is the reference group. For race/ethnicity, White is the reference group. Estimates for Other were not estimated due to no observations in the joint distribution of pre- and post-test EF risk profiles. EF = executive function; SELF = Social Emotional Learning Foundations.</p> <hd id="AN0192372881-22">SELF: Differential Effects Based on At-Risk EF Profiles: RQ 1</hd> <p></p> <hd id="AN0192372881-23">Behavioral Outcomes</hd> <p>As shown in Table 1, we found several main effects of SELF and EF risk profiles on CAB ([<reflink idref="bib6" id="ref98">6</reflink>]) scales. When compared to BAU, SELF had an impact on Internalizing ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ) and Social Skills ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.09</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ) but not on Externalizing and Competence. SELF students scored.12 and.09 units higher on Internalizing behaviors and Social Skills than BAU students (higher scores indicate more competence). Also, there were differences on all CAB scales for moderate versus mild EF risk profiles ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn><mspace width="0.25em" /></math> </ephtml> for Internalizing; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.08</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn><mspace width="0.25em" /></math> </ephtml> for Externalizing; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.09</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> for Social Skills; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.14</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn><mspace width="0.25em" /></math> </ephtml> for Competence), and for clinical versus mild EF risk profiles ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.22</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn><mspace width="0.25em" /></math> </ephtml> for Internalizing; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.17</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> for Externalizing; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.17</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn><mspace width="0.25em" /></math> </ephtml> for Social Skills; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.30</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn><mspace width="0.25em" /></math> </ephtml> for Competence). Those students in the moderate and clinical EF risk profiles scored lower on behavioral outcomes compared to peers in the mild EF risk profile. Last, we found a <emph>SELF</emph><ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo>×</mo></math> </ephtml><emph>moderate EF risk profile</emph> effect on Externalizing ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.07</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.05</mn></math> </ephtml> ), Social Skills ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.10</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> ), and Competence ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ) and a <emph>SELF</emph><ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo>×</mo></math> </ephtml><emph>clinical EF risk profile</emph> effect on all CAB scales ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.09</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.04</mn></math> </ephtml> for Internalizing <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mo>;</mo><mspace width="0.25em" /><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> for Externalizing, <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.14</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> for Social Skills, and <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> for Competence). When compared to the BAU, students who received SELF and were in the moderate and clinical EF risk profiles scored higher than peers in the mild EF risk profile on several behavior-related outcomes, with stronger effects noted for the clinical EF risk profile.</p> <hd id="AN0192372881-24">Social-Emotional Outcomes</hd> <p>We found main effects of SELF on all scales of the DESSA ([<reflink idref="bib33" id="ref99">33</reflink>]; see Table 2). Compared to BAU students, SELF students scored higher on Self-Awareness, Self-management, Social-Awareness, Relationship Skills, Goal-Directed Behavior, Personal Responsibility, Decision-making, and Optimistic Thinking ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> varied from.11 to.16). We also found differences on all DESSA scales for moderate versus mild EF risk profiles ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> = −.17 to −.12) and clinical versus mild EF risk profiles ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> = −.37 to −.24), indicating students in the moderate and clinical EF risk profiles scored lower on social emotional competence compared to peers in the mild EF risk profile. Findings indicated a <emph>SELF × moderate EF risk profile</emph> effect on Self-Awareness ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> ) and Relationship Skills ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.10</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.04</mn></math> </ephtml> ), and a <emph>SELF × clinical EF risk profile</emph> on Self-Awareness ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.13</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ), Self-Management ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), Social-Awareness ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.13</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ), Relationship Skills ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ), Decision Making ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.11</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), and Optimistic Thinking ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.13</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ). Compared to BAU, students who participated in SELF and were in the moderate and the clinical EF risk profiles scored higher than peers in the mild EF risk profile on multiple DESSA skills.</p> <hd id="AN0192372881-25">Executive Function Outcomes</hd> <p>We found main effects of SELF and student EF risk profiles on specific student EF outcomes as assessed via teacher report (BRIEF-2; [<reflink idref="bib25" id="ref100">25</reflink>]) and performance-based (HTKS; [<reflink idref="bib46" id="ref101">46</reflink>]) measures. As reported in Table 3, there was a main effect of SELF on the Plan/Organize ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.09</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ) and Organization of Materials ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.11</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ) BRIEF-2 scales. When compared to BAU, on average, SELF students scored lower on specific aspects of teacher-reported EF (a lower score indicates stronger EF). Yet, there was no main effect of SELF on the HTKS. Also, we noted significance on all BRIEF-2 subscales and the HTKS when comparing students in the moderate and clinical EF risk profiles to students in the mild EF risk profile. The average difference between moderate versus mild EF risk profiles ranged from.19 (Organization of Materials) to.45 (Task-Monitor), and the difference between clinical versus mild EF risk profiles varied from.42 (Emotional Control) to.86 (Working Memory) on BRIEF-2 scales. Similar trends were noted for the HTKS for students in the moderate ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> = −.11) and clinical ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> = -30) EF risk profiles (lower scores indicate less EF proficiency), such that students in the moderate and clinical EF profiles demonstrated less EF proficiency than peers in the mild EF risk profile. Finally, we found an effect of <emph>SELF × moderate EF risk profile</emph> on the BRIEF-2 scales of Initiate ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.10</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), Working Memory ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ), and Task-Monitor ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.11</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> ). We also found an effect of <emph>SELF × clinical EF risk profile</emph> on Self-Monitor ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.12</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ) and Working Memory ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.11</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ). When compared to BAU, those students who received SELF and were in the moderate and the clinical EF risk profiles scored better than peers in the mild EF risk profile on some teacher-reported EF skills. Yet, we found no significant interaction effects on other BRIEF-2 scales and the HTKS.</p> <hd id="AN0192372881-26">Moderation Effect of Classroom Quality: RQ3</hd> <p>Specific aspects of classroom quality moderated SELF's effectiveness on student behavioral, social-emotional, and EF outcomes based on student EF risk profiles. See Tables 1–3 for significant results and Figures S2-S5 for visual representations. For CLASS ([<reflink idref="bib43" id="ref102">43</reflink>]) Instructional Support, we found a three-way interaction of <emph>SELF × moderate EF risk profile × Instructional Support</emph> ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.13</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ) on the CAB Competence scale only. As Instructional Support increased by one unit, students in the moderate EF risk profile increased.13 units more in Competence in the difference between SELF and BAU groups than those in the mild EF risk profile. We also found an effect of <emph>SELF × moderate EF risk profile × Instructional Support</emph> on the Self-Management ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.14</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), Social-Awareness ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.14</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ), Relationship Skills ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.14</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.04</mn></math> </ephtml> ), Goal-Directed Behavior ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.20</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo><</mo><mn>.05</mn></math> </ephtml> ), Personal Responsibility ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.18</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> ), and Optimistic Thinking ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.16</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.02</mn></math> </ephtml> ) DESSA scales. Overall, SELF's positive impact on competence and several social-emotional skills for students in the moderate EF risk profile depended on classroom instructional support quality.</p> <p>For CLASS Emotional Support, interaction effects were significant for aspects of social-emotional competence and EF outcomes for students in the clinical EF risk profile only. We found an effect of <emph>SELF × clinical EF risk profile × Emotional Support</emph> on the DESSA scales of Self-Management ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.22</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.04</mn></math> </ephtml> ), Social-Awareness ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.23</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), and Personal Responsibility ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mo>−</mo><mn>.19</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.04</mn></math> </ephtml> ). As Emotional Support increased by one unit, SELF students in the clinical EF risk profile decreased by.22,.23, and.19 units on Self-Management, Social-Awareness, and Personal Responsibility, respectively, in difference scores between SELF and BAU groups than those from the mild EF risk profile. We found an interaction effect of <emph>SELF × clinical EF risk profile × Emotional Support</emph> on the BRIEF-2 scales of Shift ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.23</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ), Emotional Control ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.29</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.01</mn></math> </ephtml> ), and Initiate ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi><mo>=</mo><mn>.19</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>.03</mn></math> </ephtml> ). SELF's effect on several social-emotional and contextualized EF skills for students in the clinical EF risk profile (compared to mild EF risk profile) depended on classroom emotional support, although findings were not in the expected direction. No Organizational Support interaction effects were found.</p> <hd id="AN0192372881-27">Transition of EF Risk Profile Membership: RQ2</hd> <p>We followed an iterative process to determine EF risk profile membership transition. First, we fitted separate LPAs to both pre- and post-test EF indicators (BRIEF-2 scales and HTKS) to determine the number of profiles for subsequent LTA. According to the model fit statistics of LPAs (Table S5), increasing the number latent profiles from baseline to two led to the greatest decrease in relative fit statistics (i.e., Akaike Information Criterion, Bayesian Information Criterion, sample-size adjusted Bayesian Information Criterion). Increasing the number of profiles to three still resulted in a considerable decrease in relative fit. However, adding four or more latent profiles did not lead to salient improvement in model fit. The Lo-Mendell-Rubin likelihood ratio test showed statistical significance in the comparison between 2-profile and baseline as well as 3-profile and 2-profile LPA models. Collectively, these findings supported the 2-profile and 3-profile solutions. After reviewing estimates of EF indicators (Table S6), we proceeded with the 3-profile solution—mild, moderate, and clinical EF risk profiles—for both measurement occasions. This is consistent with [<reflink idref="bib15" id="ref103">15</reflink>] previous LPA analysis with the current data and the resulting EF risk profiles used in RQ1 and RQ3 analysis.</p> <hd id="AN0192372881-28">Invariance of EF profiles (Step 2)</hd> <p>In the second step, we fitted two unconditional LTAs, one had EF indicator means freely estimated for pre- and post-test (i.e., non-invariant model), and the other introduced equality constraints on EF indicators so that, for each profile, the estimates on means were equal between pre- and post-test (i.e., invariant model). Results of likelihood ratio test (Table S7) indicated no significant difference between models: <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>χ</mi><mn>2</mn></msup><mo stretchy="false">(</mo><mn>30</mn><mo stretchy="false">)</mo><mo>=</mo><mo>−</mo><mn>115.81</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>p</mi><mo>=</mo><mn>1.00</mn></math> </ephtml> ), suggesting the invariant LTA model fits well. Thus, measurement invariance of the 3-profile solution can be assumed.</p> <hd id="AN0192372881-29">Unconditional LTA (Step 3)</hd> <p>As shown in Table S8, estimates on means of EF indicators for the unconditional invariant LTA were similar to their respective estimates from the LPAs (Step 1). Profile prevalence was 24% on mild, 39% on moderate, and 37% on clinical EF risk profiles at pre-test, and 42% on mild, 34% on moderate, and 24% on clinical EF risk profiles at post-test. From the transition probability matrix (see Table S8 and Figure S6 in the online supplemental material), there was a high probability (.91) of students in the mild EF risk profile retaining the same EF risk profile from pre- to post-test. For those in the moderate EF risk profile, there was a.40 probability of transitioning to the mild EF risk profile, a.52 probability of staying in the same profile, and only a.08 probability of moving to the clinical EF risk profile at post-test. For the clinical EF risk profile at pre-test, there was a.56 probability of students staying in the same profile,.35 probability of transitioning to the moderate EF risk profile, and a.09 probability of moving to the mild EF risk profile at post-test.</p> <hd id="AN0192372881-30">Conditional LTA with research condition, grade, and gender (Step 4)</hd> <p>Based on the unconditional invariant LTA, we added research conditions (i.e., BAU, SELF), grade, gender, and race/ethnicity to the model (see S9 for results). First, there was no effect of condition, grade, or race/ethnicity on the odds of being in the moderate or the mild EF risk profiles relative to the odds of being in the clinical EF risk profile at pre-test. Gender had an effect on membership assignment between the mild and clinical EF risk profiles ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>O</mi><mi>R</mi><mo>=</mo><mn>2.69</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mn>95</mn><mtext>%</mtext><mi>C</mi><mi>I</mi><mo>=</mo><mo stretchy="false">[</mo><mrow><mn>1.92</mn><mo>,</mo><mspace width="0.25em" /><mn>3.77</mn></mrow><mo stretchy="false">]</mo></math> </ephtml> ) only, indicating that the odds of being in the mild EF risk profile relative to the clinical EF risk profile was 2.69 units higher for girls compared to boys.</p> <p>For the effects of SELF, grade, gender, and race/ethnicity on the transition of EF risk profiles from pre- to post-test (Table 4), none of the covariates presented significance on the post-EF risk profile membership among the students in the mild EF risk profile at pre-test. For those that were in the moderate EF risk profile at pre-test, we found effects of SELF ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>O</mi><mi>R</mi><mo>=</mo><mn>6.44</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mn>95</mn><mtext>%</mtext><mi>C</mi><mi>I</mi><mo>=</mo><mo stretchy="false">[</mo><mrow><mn>2.29</mn><mo>,</mo><mspace width="0.25em" /><mn>18.10</mn></mrow><mo stretchy="false">]</mo></math> </ephtml> ) and gender ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>O</mi><mi>R</mi><mo>=</mo><mn>3.56</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mn>95</mn><mtext>%</mtext><mi>C</mi><mi>I</mi><mo>=</mo><mo stretchy="false">[</mo><mrow><mn>1.20</mn><mo>,</mo><mspace width="0.25em" /><mn>10.59</mn></mrow><mo stretchy="false">]</mo></math> </ephtml> ) on the comparison between mild and clinical EF risk profiles at post-test. For students in the moderate EF risk profile at pre-test, SELF students had a higher chance of moving into the mild EF risk profile relative to those in the BAU group at post-test. Also, girls were more likely to transition to the mild EF risk profile compared to boys at post-test. Last, for students in the clinical EF risk profile at pre-test, SELF ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>O</mi><mi>R</mi><mo>=</mo><mn>2.68</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mn>95</mn><mtext>%</mtext><mi>C</mi><mi>I</mi><mo>=</mo><mo stretchy="false">[</mo><mrow><mn>1.53</mn><mo>,</mo><mspace width="0.25em" /><mn>4.72</mn></mrow><mo stretchy="false">]</mo></math> </ephtml> ) and grade ( <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>O</mi><mi>R</mi><mo>=</mo><mn>1.89</mn></math> </ephtml> , <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mn>95</mn><mtext>%</mtext><mi>C</mi><mi>I</mi><mo>=</mo><mo stretchy="false">[</mo><mrow><mn>1.14</mn><mo>,</mo><mspace width="0.25em" /><mn>3.13</mn></mrow><mo stretchy="false">]</mo></math> </ephtml> ) were significant for the comparison in the transition probability between moderate and clinical EF risk profiles at post-test, such that SELF students were more likely to move to the moderate EF risk profile at post-test. Similarly, compared to K at post-test, first graders had a higher chance of transitioning to the moderate EF risk profile.</p> <hd id="AN0192372881-31">Discussion</hd> <p>Due to the importance of EF in student success, particularly among students with or at risk for EBD (e.g., [<reflink idref="bib12" id="ref104">12</reflink>]), and the promise of SELF ([<reflink idref="bib18" id="ref105">18</reflink>]) for improving contextualized EF and related skills (e.g., social-emotional competencies, behavior; [<reflink idref="bib16" id="ref106">16</reflink>]; [<reflink idref="bib18" id="ref107">18</reflink>]), we sought to extend these findings by using a person-centered approach to determine what works for whom and under what conditions<emph>.</emph> First, we wanted to determine whether SELF had differential effects based on student EF risk latent profiles ([<reflink idref="bib15" id="ref108">15</reflink>]) and whether SELF improved EF risk profile membership. Second, as an exploratory question, we wanted to determine whether classroom quality moderated these effects. Results showed (a) differential effects of SELF on outcomes (e.g., behavior) based on student EF risk profiles; (b) SELF was associated with changes in student EF risk profile membership, with gender and grade level differences; and (c) classroom quality served as a moderator in the relationship between SELF, EF risk profiles, and outcomes. This study not only extends a small but growing EF research base with students at risk for EBD (e.g., [<reflink idref="bib15" id="ref109">15</reflink>]), but it is also the first, to our knowledge, to examine the differential effects of an SEL intervention based on student EF risk profiles with students at risk for EBD.</p> <hd id="AN0192372881-32">Differential Effects Based on EF Profiles</hd> <p>Compared to the BAU group, SELF had differential effects on social-emotional competence, behavior, and contextualized EF based on student EF risk profiles. Given that (a) EF is malleable and highly sensitive to experience ([<reflink idref="bib10" id="ref110">10</reflink>]; [<reflink idref="bib62" id="ref111">62</reflink>]), and (b) [<reflink idref="bib49" id="ref112">49</reflink>] found that students with a low EF profile trajectory benefited the most from the Head Start REDI intervention in terms of EF growth between PK and third grade, we anticipated that students with higher levels of EF risk profiles would increasingly benefit from SELF. In alignment with this expectation, students in the mild, moderate, and clinical EF risk profiles increasingly benefited from SELF, as evidenced by enhanced contextualized EF skills, social-emotional competence, and behaviors. Thus, EF risk profiles matter in terms of SELF's effectiveness for students at risk for EBD, especially for those within the clinical EF risk profile.</p> <p>Although all students across EF risk profiles showed improvement in contextualized EF after participating in SELF, unexpectedly, there was variability in effectiveness on specific teacher-reported EF subscales (BRIEF-2; [<reflink idref="bib25" id="ref113">25</reflink>]), and there was no impact on performance-based EF (HTKS: [<reflink idref="bib46" id="ref114">46</reflink>]). For instance, we found no differential treatment effects on the BRIEF-2 subscales of Plan/Organize, Organization of Materials, or Inhibit. Because Plan/Organize and Organization of Materials scales are intended to capture students' competency in managing current and future-related tasks in the areas of homework, writing, and tests, it may be that teachers did not have the opportunity to observe distinct EF-related improvements for these subscales due to the academic demands (e.g., fewer tests, homework) and structure in K–1 classrooms; yet, additional research is warranted.</p> <p>The lack of effects on performance-based EF may be due to multiple reasons. First, the HTKS ([<reflink idref="bib46" id="ref115">46</reflink>]) is often used as a global measure of EF. Given that there are different developmental maturation rates for each EF process (e.g., working memory; [<reflink idref="bib3" id="ref116">3</reflink>]), it may be that treatment effects were undetected due to the HTKS only providing an overall score. Second, although performance-based measures are considered the gold standard, their ecological validity has been questioned due to the process of measuring EF under standardized conditions over a short time that does not necessarily capture the multi-dimensional processes needed during real-world situations (Toplak et al., 2013). Thus, the BRIEF-2 ([<reflink idref="bib25" id="ref117">25</reflink>]) may have been more sensitive to detecting affective manifestations of EF in the classroom after receiving SELF.</p> <hd id="AN0192372881-33">SELF and Student Demographics in EF Risk Profile Membership Change</hd> <p>Compared to peers who did not receive SELF, students who participated in SELF tended to show EF risk profile improvement. We found that students who were in the clinical or moderate EF risk profile at pre-test, and participated in SELF, were more likely to move into the moderate EF risk and mild EF risk profile, respectively. Thus, SELF was effective in reducing student EF risk. Also, we noted differences in EF risk profile change based on gender and grade, such that girls in the moderate EF risk profile were more likely to move to the mild EF risk profile, and first graders in the clinical EF risk profile were more likely to transition to the moderate EF risk profile at post-test, controlling for the effects of SELF. Grade level and gender findings may be due to unique active EF development, such that older students and girls demonstrate better EF in early elementary ([<reflink idref="bib3" id="ref118">3</reflink>]; [<reflink idref="bib52" id="ref119">52</reflink>]). Overall, receiving SELF had a considerable effect on student EF risk profile change, even when controlling for gender and grade-level effects. Thus, both boys and girls and K and first graders benefited from SELF.</p> <p>Contrary to previous research ([<reflink idref="bib47" id="ref120">47</reflink>]), we did not find differences in EF risk profile change based on student race/ethnicity, which may be due to our use of both performance-based and rating scale measures of EF. Rea-Sandin et al. found racial/ethnic differences varied based on performance-based task type, suggesting that differences were due to the task properties versus EF ability, which are often normed with White monolingual participants. Performance-based measures also do not use ecologically valid tasks ([<reflink idref="bib34" id="ref121">34</reflink>]), allowing for the assessment of EF skills in a standardized manner but possibly limiting detection of EF use in school settings. Thus, the use of both types of EF measures may have mitigated potential racial/ethnic measurement bias and captured the breadth of participants' EF skills.</p> <hd id="AN0192372881-34">Classroom Quality as a Moderator</hd> <p>We found classroom quality moderated SELF's effectiveness for students in different EF risk profiles, yet there was variability based on classroom quality domain, level of quality, and EF risk profile. First, students who received SELF, and were educated in classrooms with high emotional support and instructional support, tended to show greater social emotional skills and behavioral competence compared to BAU peers, even if they were also educated in similar quality classrooms. These findings align with previous research that emotionally supportive classrooms are important for students' EF development ([<reflink idref="bib10" id="ref122">10</reflink>]). Thus, certain components of classroom quality may provide an added benefit to enhancing SELF's effectiveness. Yet, classroom organization did not serve as a moderator and there were multiple unexpected findings related to the moderating effects of emotionally and instructionally supportive classrooms. Given that this was an exploratory analysis, we caution readers from making strong conclusions.</p> <p>Contrary to our expectations, when classroom emotional support was below average compared to students in clinical EF risk profiles, students in the mild EF risk profile who received SELF scored worse on specific social-emotional competencies (i.e., self-management, social awareness, and personal responsibility) and contextualized EF skills (i.e., shift, emotional control, initiate) than BAU peers. Also, when compared to clinical EF risk profiles, students in the mild EF risk profile who received SELF and were educated in above average instructionally supportive classrooms performed worse on goal-directed behavior—a social emotional competence skill—than BAU mild EF risk profile students who were in above average instructionally supportive classrooms. Although unexpected, it may be that some teachers who implemented SELF perceived it as an additional demand that had an adverse effect on both their workload manageability and ability to create a quality classroom ([<reflink idref="bib13" id="ref123">13</reflink>]).</p> <p>Another consideration about unexpected findings may be related to the CLASS ([<reflink idref="bib43" id="ref124">43</reflink>]) measure. Given that reliability/validity were mediocre but within the acceptable range for our sample, and multiple findings were in the expected direction for other EF risk profiles (e.g., critical risk profile), we encourage researchers to further investigate the psychometric properties of the CLASS. Our sample had a majority of Title 1 schools and only K–1 classrooms, which contrasts with validation studies that spanned Grades 1–5, included a more racially/ethnically diverse sample, and fewer Title-1 schools (e.g., [<reflink idref="bib48" id="ref125">48</reflink>]).</p> <hd id="AN0192372881-35">Limitations and Future Research Directions</hd> <p>Our study included a large sample, utilized a variety of psychometrically tested observational and teacher report measures, and used a randomized-control design that allowed for causal conclusions; yet, there are limitations and future research to consider. First, our sample was majority male and White, educated in predominantly Title 1 schools, and from one southeastern state. Results may not be generalizable to other samples (e.g., underrepresented groups) and settings. Thus, although we found no effects in EF risk profile membership change based on race/ethnicity, we encourage researchers to examine the impact of SELF with samples that include underrepresented groups, from multiple socioeconomic backgrounds, and in different states. This is especially salient, given the disproportionate identification of students from underrepresented groups with EBD (e.g., OSEP, 2022). Also, to determine added effects, we encourage studies that embed culturally responsive practices ([<reflink idref="bib24" id="ref126">24</reflink>]) within SELF, such as by reflecting participants' cultural identities and incorporating family and community perspectives.</p> <p>There are possible limitations related to our analysis. Although there is value in a person-centered approach ([<reflink idref="bib8" id="ref127">8</reflink>]; [<reflink idref="bib32" id="ref128">32</reflink>]), LPA is an analytic approach that tends to be exploratory ([<reflink idref="bib55" id="ref129">55</reflink>]). Thus, we encourage readers to consider the exploratory nature of this analysis when interpreting findings and implications. Another limitation is the use of saved latent profile membership in multivariate regression, which does not take the probabilistic uncertainty of classification into account. Hence, we encourage future studies to incorporate the classification uncertainty in their analysis. Also, our pre-post design allowed for data collection at only two time points, limiting ability to determine the trajectory of profile membership over time, and maintenance effects. Therefore, we encourage researchers to conduct longitudinal analyses of SELF. Our exploratory analysis of classroom quality moderating effects may have been underpowered, limiting power to detect effects in the expected directions. Thus, we recommend future studies of classroom quality moderation with a robust sample size. Further, we did not include the role of students' socioeconomic status in our analysis due to limited sample variability (i.e., 81% low SES), an area of future research.</p> <p>Although we view the inclusion of both the teacher-report BRIEF-2 ([<reflink idref="bib25" id="ref130">25</reflink>]) and performance-based HTKS ([<reflink idref="bib46" id="ref131">46</reflink>]) to measure EF, and used a classroom observation measure to gauge classroom quality (CLASS; [<reflink idref="bib43" id="ref132">43</reflink>]) as strengths, there are possible limitations related to included measures. First, we relied on several teacher-report measures, which may be susceptible to bias. For instance, teachers completed the SSBD-2 ([<reflink idref="bib58" id="ref133">58</reflink>]), a measure of their perceptions of students' elevated behaviors to identify students at risk for EBD, which may have impacted eligibility. Thus, we encourage future research to include measures using multiple informants (e.g., self, caregivers). Also, given findings related to the HTKS, we suggest the use of measures, such as those in the NIH Toolbox ([<reflink idref="bib53" id="ref134">53</reflink>]), which assesses specific EF processes. We also underscore the need to consider factors when conducting and interpreting EF assessment results, such as acculturation, racism, within-group heterogeneity, and real-world context (e.g., [<reflink idref="bib47" id="ref135">47</reflink>]).</p> <hd id="AN0192372881-36">Implications for Practice and Conclusion</hd> <p>There are several implications for practice. First, although there is a wealth of school-based SEL programs ([<reflink idref="bib20" id="ref136">20</reflink>]), SELF is the only intervention, to our knowledge, that (a) simultaneously focuses on building students' SEL and literacy skills, (b) is designed for students at risk for EBD, and (c) is effective at building students' social-emotional functioning, EF, and behavioral competence. Thus, we encourage schools to use SELF. As we found differential effects of SELF ([<reflink idref="bib16" id="ref137">16</reflink>]) based on student EF risk profiles, we situate our recommendations for delivering SELF within a Tiered System of Adaptive Supports (TSAS; [<reflink idref="bib22" id="ref138">22</reflink>]). A core tenet of TSAS is the importance of adapting and tailoring tiered interventions based on students' needs and strengths. First, to determine students' needs and strengths, in addition to social-emotional competence and behavioral measures, we recommend schools administer initial screening and continued progress monitoring of student EF via performance-based measures (e.g., NIH Toolbox; [<reflink idref="bib53" id="ref139">53</reflink>]) <emph>and</emph> rating scales (e.g., BRIEF-2; [<reflink idref="bib25" id="ref140">25</reflink>]). For students in the mild EF risk profile, we encourage school professionals to implement SELF as designed, while also including Tier 1 supports to promote EF skill development, which require students to use and practice EF skills, such as through movement-based activities (e.g., aerobic exercises), circle games (e.g., Simon Says), mindfulness, and targeted EF training (see [<reflink idref="bib11" id="ref141">11</reflink>]; [<reflink idref="bib56" id="ref142">56</reflink>]). Given the increased risk for behavioral difficulties associated with heightened EF risk profiles ([<reflink idref="bib15" id="ref143">15</reflink>]), for students in the moderate EF risk profile, we recommend additional Tier 2 (small group) supports to simultaneously target student behavioral needs, such through Check-In Check-Out ([<reflink idref="bib60" id="ref144">60</reflink>]). Due to results suggesting SELF was particularly effective for students in the clinical EF risk profile, we also recommend Tier 3 (individualized) supports, such as implementing individualized plans that target behavioral needs, as well as expanding or re-teaching SELF lessons based on progress monitoring data. To support skill generalization and maintenance, we encourage school professionals to coordinate with caregivers and other community professionals to facilitate and reinforce practice opportunities.</p> <p>Due to our exploratory findings that aspects of classroom quality moderate SELF's effectiveness based on student EF risk profiles, we encourage school professionals to actively build classrooms and spaces that provide emotional and instructional support. These spaces are characterized by teacher sensitivity, positive climate, student perspective-taking, feedback, and effective learning formats ([<reflink idref="bib43" id="ref145">43</reflink>]). We also encourage pre- and in-service training that not only focuses on SEL and EF, but also on practices that foster supportive spaces. Last, we suggest school professionals consider students' backgrounds when implementing SELF by using culturally responsive practices ([<reflink idref="bib24" id="ref146">24</reflink>]), which affirm students' backgrounds and lived experiences. Although more research is warranted, evidence suggests SEL programs that include culturally responsive practices are effective ([<reflink idref="bib38" id="ref147">38</reflink>]).</p> <p>Given EF's role in students' school and life-long success ([<reflink idref="bib37" id="ref148">37</reflink>]; [<reflink idref="bib54" id="ref149">54</reflink>]) and the persistent EF and social-emotional needs of students with EBD, SELF ([<reflink idref="bib18" id="ref150">18</reflink>]) offers an effective means by which schools can enhance the EF and related skills of K and first graders at risk for EBD. Based on findings, we encourage school professionals and researchers to consider EF risk profiles to adapt SEL interventions, such as SELF, and provide multi-faceted supports based on student needs and strengths to maximize effectiveness.</p> <hd id="AN0192372881-37">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-ecx-10.1177_00144029251381300 for Social-Emotional Learning Foundations Curriculum for K–1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles by Michelle M. Cumming, Daniel V. Poling, Yuxi Qiu, Nastasia Schreiner, Brian Jones, Ann P. Daunic, Nancy Corbett and Stephen W. Smith in Exceptional Children</p> <ref id="AN0192372881-38"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref93" type="bt">1</bibl> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref27" type="bt">2</bibl> <bibtext> The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R324A160136 to the University of Florida. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Educatoin.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref23" type="bt">3</bibl> <bibtext> Michelle M. Cumming https://orcid.org/0000-0002-1803-1900 Daniel V. 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  Data: Social-Emotional Learning Foundations Curriculum for K-1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Michelle+M%2E+Cumming%22">Michelle M. Cumming</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1803-1900">0000-0002-1803-1900</externalLink>)<br /><searchLink fieldCode="AR" term="%22Daniel+V%2E+Poling%22">Daniel V. Poling</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4469-824X">0000-0002-4469-824X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yuxi+Qiu%22">Yuxi Qiu</searchLink><br /><searchLink fieldCode="AR" term="%22Nastasia+Schreiner%22">Nastasia Schreiner</searchLink><br /><searchLink fieldCode="AR" term="%22Brian+Jones%22">Brian Jones</searchLink><br /><searchLink fieldCode="AR" term="%22Ann+P%2E+Daunic%22">Ann P. Daunic</searchLink><br /><searchLink fieldCode="AR" term="%22Nancy+Corbett%22">Nancy Corbett</searchLink><br /><searchLink fieldCode="AR" term="%22Stephen+W%2E+Smith%22">Stephen W. Smith</searchLink>
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  Label: Source
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  Data: <searchLink fieldCode="SO" term="%22Exceptional+Children%22"><i>Exceptional Children</i></searchLink>. 2026 92(3):264-285.
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  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 22
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Kindergarten%22">Kindergarten</searchLink><br /><searchLink fieldCode="EL" term="%22Primary+Education%22">Primary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+1%22">Grade 1</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Social+Emotional+Learning%22">Social Emotional Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Kindergarten%22">Kindergarten</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+1%22">Grade 1</searchLink><br /><searchLink fieldCode="DE" term="%22Executive+Function%22">Executive Function</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Persons%22">At Risk Persons</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+Disturbances%22">Emotional Disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Disorders%22">Behavior Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Classroom+Environment%22">Classroom Environment</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Systematic+Screening+for+Behavior+Disorders%22">Systematic Screening for Behavior Disorders</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/00144029251381300
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0014-4029<br />2163-5560
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Due to executive function's (EF) foundational role in students' school and lifelong success, school-based interventions that build EF (i.e., inhibitory control, working memory, cognitive flexibility) and related skills are critical, especially for students at risk for emotional and behavioral disorders (EBD). Social Emotional Learning Foundations (SELF), a Tier 2 literacy-integrated, social-emotional learning (SEL) intervention with a universal component, is one of the few SEL interventions focusing on K-1 students at risk for EBD. This intervention has shown promise in enhancing students' social-emotional competence, behavioral functioning, and EF. Yet, no prior studies have examined whether SELF's effectiveness varied based on student EF strengths and difficulties (i.e., EF risk profiles), which has implications for SELF implementation and adaptation. Thus, using data (n = 1,154) from a randomized control trial, we sought to determine whether SELF had differential effects based on student EF risk latent profiles. Secondarily, we wanted to explore if classroom quality (i.e., emotional support, classroom organization, and instructional support) moderated these effects. Results from multivariate regression analysis showed differential effects of SELF on students' social-emotional, behavioral, and EF outcomes. Latent transition analysis revealed that SELF was associated with changes in student EF risk profile membership, with gender and grade level differences. Exploratory results indicated that classroom quality may have served to moderate the relationships between SELF, EF risk profiles, and outcomes. We discuss implications for intervention efforts and future research.
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  Data: As Provided
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  Label: Entry Date
  Group: Date
  Data: 2026
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  Data: EJ1500098
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        Value: 10.1177/00144029251381300
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      – Text: English
    PhysicalDescription:
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        PageCount: 22
        StartPage: 264
    Subjects:
      – SubjectFull: Social Emotional Learning
        Type: general
      – SubjectFull: Kindergarten
        Type: general
      – SubjectFull: Grade 1
        Type: general
      – SubjectFull: Executive Function
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      – SubjectFull: At Risk Persons
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      – SubjectFull: Student Characteristics
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      – SubjectFull: Systematic Screening for Behavior Disorders
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      – TitleFull: Social-Emotional Learning Foundations Curriculum for K-1 Students at Risk for EBD: Differential Effects Based on Student EF Risk Profiles
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