Exploring the relationship between social-emotional risk and literacy outcomes: evidence from a collective impact, targeted mentoring initiative

Social-emotional learning (SEL) has become a cornerstone of educational strategies aimed at fostering well-rounded student development (CASEL, 2021). Defined as the process by which students acquire the skills necessary for emotional regulation, interpersonal effectiveness, and responsible decision-making, SEL has been shown to positively impact a range of academic and personal outcomes (CASEL, 2021). Programs focused on SEL aim to help students develop critical life skills, such as empathy, self-regulation, and resilience, which are foundational to both social interactions and academic achievement. Research in social-emotional behavior (SEB) suggests that students who struggle with self-regulation, social awareness, and emotional stability are at increased risk for a range of academic and behavioral issues, as these competencies play a direct role in a student’s ability to engage with learning materials and persevere through challenges (Durlak et al., 2011). As such, there is growing recognition of the need to integrate SEB-focused interventions within the school setting, as students who are socially and emotionally supported tend to experience better overall educational outcomes (Jones & Kahn, 2017).

In response to the growing recognition of social-emotional needs, schools have increasingly implemented programs and supports aimed at enhancing students’ SEB competencies. One approach focuses on strengthening protective factors, which are elements in students’ lives that promote resilience and positive outcomes despite adversities (Comeau & Boyle, 2018). Schools are actively working to cultivate these protective factors by creating inclusive environments, integrating SEL into the curriculum, and providing access to counselors or SEB-focused specialists (Jones & Kahn, 2017). Additionally, partnerships with community organizations allow schools to offer tailored support services that address specific SEB needs, such as conflict resolution programs, self-regulation skill-building, and peer support groups. These initiatives help equip students with the skills to manage emotions, build healthy relationships, and approach challenges with resilience, ultimately fostering a well-rounded foundation for academic and life success.

Notably, support from non-family adults, particularly through mentoring, has gained recognition for its significant impact on student development and well-being (Rhodes, 2020). Mentors provide guidance, encouragement, and role modeling, often filling gaps that family members may not be able to address. Youth mentoring—a unique, supportive dyadic relationship between non-parental adults and young people—has become a valuable addition to school-based SEB support, particularly for students from underserved backgrounds (DuBois et al., 2011). Research shows that approximately 50–80% of American children and adolescents report having a meaningful relationship with non-parental adults, offering stability, advice, and advocacy (e.g. Rhodes et al., 2002). However, a concerning one-third of students, particularly those in the lowest socioeconomic quartile, report never experiencing such a relationship (e.g. Raposa et al., 2018). Mentoring relationships have been associated with a wide range of positive outcomes in behavioral, socioemotional, academic, and vocational domains. Specifically, students who participate in mentoring programs demonstrate higher self-esteem, academic achievement, and peer relationships, along with reductions in substance misuse, aggression, depressive symptoms, and delinquent behaviors (e.g. Wilson et al., 2020). As schools strive to enhance SEB support, integrating mentoring programs serves as an effective strategy to reinforce protective factors that foster positive, long-term outcomes for students.

School-university partnerships serve as powerful vehicles for educational transformation, particularly in addressing SEL and SEB risk among students from under-resourced backgrounds (Christensen et al., 2020). These collaborations leverage the expertise, resources, and research capabilities of universities while grounding interventions in the realities of K-12 educational settings. Universities play a critical role in developing evidence-based SEL interventions, training educators in best practices, and conducting rigorous evaluations of program effectiveness (Greenberg, 2023). Meanwhile, schools provide direct access to students and ensure that interventions are responsive to the lived experiences of their communities. By fostering reciprocal relationships, school-university partnerships facilitate the integration of SEL frameworks into school-based initiatives, ensuring that social-emotional competencies are not only taught explicitly but also embedded within broader instructional and behavioral support systems (Bal et al., 2015a).

One of the key benefits of school-university partnerships in the realm of SEL and SEB interventions is their ability to sustain and scale mentoring programs that address the complex needs of at-risk students. Universities contribute to the training and professional development of mentors, equipping them with research-backed strategies for fostering self-regulation, emotional resilience, and academic persistence in students (Goldner & Ben-Eliyahu, 2021). Moreover, these partnerships create opportunities for graduate students and faculty researchers to engage directly with schools, applying theoretical frameworks to real-world educational challenges (Darling-Hammond, 2017). This bidirectional learning enhances the capacity of both schools and universities to implement interventions that are responsive to the evolving needs of students, particularly those facing social-emotional challenges. By embedding SEL and mentoring initiatives within a collective impact framework, school-university partnerships amplify the effectiveness of interventions, ensuring that students receive comprehensive, multi-tiered supports that promote both social-emotional and academic success (Bal et al., 2015).

In tandem with the rise of SEL, literacy remains a critical focus in educational systems across the United States, as reading proficiency is foundational to success in almost all subject areas. The ability to read fluently, comprehend complex texts, and think critically about what is read is essential for academic achievement and lifelong learning (Duke & Cartwright, 2021). However, literacy achievement in the U.S. has remained a significant concern, with recent data from the National Assessment of Educational Progress (NAEP, 2022) indicating that nearly two-thirds of fourth-grade students are not reading at a proficient level. These findings underscore the urgent need for effective literacy interventions, especially those that consider the diverse learning needs of students. Research on reading interventions has highlighted the importance of tailored approaches that account for students’ individual needs, but traditional literacy programs often overlook the role of social and emotional factors that can significantly impact a child’s ability to engage fully with reading instruction (Snow et al., 1998).

Several studies have examined the intersection of SEB and literacy, revealing a complex relationship in which social-emotional competencies can play a substantial role in reading achievement. For instance, Morris and Holloway (2019) found that students with high SEB skills demonstrated greater resilience and focus during literacy tasks, leading to improved reading comprehension. These findings align with broader research suggesting that students who can effectively regulate their emotions and maintain positive relationships are more likely to approach reading tasks with confidence and persistence, key factors in successful literacy development (McTigue et al., 2016). Additionally, targeted SEL interventions have shown promise in supporting students who are at risk for poor literacy outcomes by reducing disruptive behaviors and increasing time spent on reading tasks, ultimately leading to improved academic performance (Schonert-Reichl et al., 2015).

When examining SEB and literacy in elementary students from under-resourced backgrounds, the need for integrated support becomes even more critical (McTigue et al., 2016). These students—particularly those from low-income families or communities with limited access to educational resources—face additional barriers that can hinder both SEL and academic achievement. For these students, external stressors related to socioeconomic challenges, such as food insecurity and unstable housing, can amplify SEB risk factors and, in turn, impact their ability to achieve literacy milestones (Jensen, 2009). Research by Comeau and Boyle (2018) highlights that students from under-resourced communities often enter school with lower levels of emotional regulation and literacy preparedness, placing them at a distinct disadvantage. In response, educational programs that combine SEL with targeted literacy instruction are increasingly recognized as a way to address both social-emotional and academic needs simultaneously. By fostering emotional stability and resilience, these interventions not only help students from under-resourced backgrounds engage more effectively with reading but also work to close the achievement gap in literacy, providing a foundation for lifelong learning and success.

School-university partnerships are instrumental in developing, implementing, and assessing SEL and literacy initiatives. By bridging research and practice, universities contribute to refining intervention models, mentor training, and program evaluation, ensuring that social-emotional and academic supports are both evidence-based and contextually relevant to the needs of students from under-resourced backgrounds. For instance, the U.S. Department of Education’s white paper on designing and implementing SEL programs emphasizes the importance of equity and inclusion in creating effective SEL interventions, highlighting the role of educational institutions in fostering these initiatives (e.g. Gagnier et al., 2022b).

Mentoring programs have been widely recognized as a key intervention strategy for addressing both academic and social-emotional barriers in at-risk youth (Rhodes, 2020). Research suggests that mentoring relationships, particularly those that are structured and evidence-based, can enhance self-regulation, resilience, and motivation, leading to improved student engagement and academic performance (Martins et al., 2024). By integrating structured mentoring into a collective impact model, this initiative seeks to provide marginalized students with consistent, individualized support that extends beyond traditional classroom interventions (Grossman & Rhodes, 2002).

Mentoring is grounded in several psychological and educational theories that explain its effectiveness in promoting both social-emotional and academic development among youth. One of the most widely recognized frameworks is Rhodes’ (2005) model of youth mentoring, which posits that mentoring relationships facilitate positive youth outcomes through three interconnected pathways: social-emotional development, cognitive development, and identity formation. This theory suggests that when youth engage in consistent, high-quality mentoring relationships, they experience increased feelings of belonging, emotional security, and self-regulation, all of which contribute to improved academic motivation and achievement (Rhodes, 2020).

Additionally, Vygotsky’s (1978) sociocultural theory provides a foundational perspective on mentoring, emphasizing that learning occurs through guided interactions with more experienced individuals. Mentors serve as “more knowledgeable others”, helping youth develop self-regulation, executive function skills, and resilience through scaffolded support. This aligns with research indicating that structured mentoring relationships provide essential emotional and cognitive supports that enable students to navigate academic challenges more effectively (Gordon et al., 2019a, b).

From a developmental perspective, mentoring also aligns with Erikson’s (1968) psychosocial theory, which highlights the critical role of supportive relationships in identity formation. Adolescents and young children who receive mentoring experience increased self-efficacy, improved decision-making skills, and greater emotional resilience, all of which are linked to long-term academic and personal success (Larose et al., 2019).

Meta-analytic studies further confirm the efficacy of mentoring as a tool for improving both social-emotional and academic outcomes. DuBois et al. (2011) found that structured, evidence-based mentoring programs led to significant gains in students’ self-regulation, executive function, and school engagement, particularly among at-risk youth. Similarly, mentoring programs that incorporate SEL-based strategies and cognitive-behavioral approaches have been shown to reduce disciplinary incidents, enhance school connectedness, and promote persistence in academic settings (Christensen et al., 2020).

By integrating structured mentoring within a collective impact model, this initiative ensures that students from under-resourced backgrounds receive consistent, individualized support that extends beyond traditional classroom interventions. The combination of theoretical frameworks and empirical evidence underscores the essential role of mentoring in fostering both academic achievement and social-emotional well-being, particularly for students facing adversity.

Targeting social-emotional behavior (SEB) support to improve literacy outcomes is an emerging focus in education, especially as research reveals the deep connection between emotional well-being, self-regulation, and academic performance (Morris & Holloway, 2019). In particular, SEB competencies like executive function (EF) and active self-regulation are increasingly recognized as crucial to reading success, allowing students to engage effectively with complex texts and literacy tasks (McTigue et al., 2016). Addressing SEB support in literacy interventions can empower students, not only in reading proficiency but in developing cognitive and social skills that enhance their lifelong learning and adaptability. To understand the foundational aspects of literacy learning and how SEB skills fit into this framework, it is essential to examine established and evolving models of reading, including the Simple View of Reading, the Active View of Reading, and the newly proposed Active View of Reading+.

The Simple View of Reading, a foundational framework established by Hoover and Gough (1990), proposes that reading comprehension is the result of two core components: decoding and linguistic comprehension. Decoding refers to the ability to translate text into spoken words, while linguistic comprehension encompasses understanding the meaning of language. This model has become a widely adopted foundation in literacy education, with states across the United States increasingly aligning their reading standards and teacher training around it (Schwartz, 2022). While the Simple View of Reading provides a useful framework, it has limitations in addressing the complex, dynamic processes that impact reading comprehension. Specifically, it does not account for the role of self-regulation and executive function, which are crucial in helping students maintain attention, organize information, and apply cognitive strategies during reading tasks.

Recognizing the need for a more comprehensive model, Duke and Cartwright (2021) introduced the Active View of Reading, which incorporates the concept of active self-regulation into the reading process. Active self-regulation involves both metacognitive and executive function processes that enable students to monitor and control their comprehension strategies. This includes planning, setting reading goals, adjusting strategies as needed, and reflecting on understanding—all of which contribute to improved comprehension and retention. The Active View of Reading emphasizes that skilled reading requires not only decoding and comprehension but also the ability to actively engage with and navigate text (Duke & Cartwright, 2021). By including executive functions like working memory and cognitive flexibility, this model highlights the importance of mental processes that are often overlooked in traditional literacy instruction.

The Active View of Reading + further expands on these ideas by incorporating three additional foundational elements essential for self-regulation in literacy: felt-safety, connection, and executive function. Developed by Wilson et al. (2024), this model asserts that students must feel safe and connected within their learning environment to fully engage in literacy tasks. Felt-safety, the sense of security and trust in the classroom, is essential for students, particularly those from challenging backgrounds, as it reduces cognitive load associated with survival responses, allowing focus to shift to learning. Connection refers to students’ sense of belonging and trust within the learning environment, which can enhance motivation and persistence in literacy tasks. Finally, executive function skills—like cognitive flexibility, working memory, and inhibitory control—are seen as central, enabling students to regulate their approach to reading comprehensively. This holistic model suggests that fostering self-regulation through felt-safety and connection is as critical as traditional decoding and comprehension skills in achieving proficient reading (Wilson et al., 2024).

In sum, these evolving frameworks illustrate a progressive shift from a strictly skill-based view of reading to one that acknowledges the integral role of SEB factors, particularly executive function and self-regulation. By integrating SEB support, educators can more effectively address literacy challenges, particularly among students facing socioemotional and environmental barriers to learning (Gagnier et al., 2022a). This integrated approach, as described by the Active View of Reading+, offers a pathway for addressing the literacy crisis by focusing on underlying SEB competencies that enable students to achieve reading proficiency in meaningful and lasting ways.

Bringing together social-emotional behavior (SEB), literacy, mentoring, and collective impact reveals a powerful strategy for addressing the complex challenges facing students today (Garringer & MacRae, 2019). SEB skills, including emotional regulation and executive function, are foundational not only to students’ social development but also to their academic success, especially in literacy (Durlak et al., 2011). Research consistently shows that students who can regulate their emotions, manage stress, and persist through challenges are more engaged and perform better academically (Kahn & Jameel, 2024). Mentoring plays a crucial role in fostering these SEB competencies by providing students with supportive relationships, guidance, and encouragement—often filling gaps left by other support systems (Christensen et al., 2020a). When integrated within a collective impact framework, these efforts become even more effective. Collective impact brings together schools, community organizations, families, and policymakers, aligning resources and strategies to achieve a common goal: improving student outcomes holistically (Garringer & MacRae, 2019). This approach enables the layering of SEB and literacy supports, ensuring that students have access to not only academic interventions but also the emotional and relational support necessary for sustained success. In this way, the integration of SEB, literacy, mentoring, and collective impact serves as a comprehensive approach that addresses students’ needs on multiple levels, creating an environment in which they can thrive academically, socially, and emotionally (Christensen et al., 2020a).

In addition to the work done within school-university partnership through collective impact, school-university partnerships are instrumental in developing, implementing, and assessing social-emotional learning (SEL) and literacy initiatives. By bridging research and practice, universities contribute to refining intervention models, mentor training, and program evaluation, ensuring that social-emotional and academic supports are both evidence-based and contextually relevant to the needs of marginalized students. For instance, a study by Flushman et al. (2021) highlights the importance of such collaborations in supporting new teachers’ SEL competencies, which in turn benefits student outcomes. Additionally, Nabors et al. (2022) discuss a university and community-based partnership that implemented after-school mentoring activities to support positive mental health for refugee children, demonstrating the role of universities in mentor training and program evaluation. These examples underscore the critical role of school-university partnerships in integrating SEL frameworks into school-based initiatives, ensuring that social-emotional competencies are explicitly taught and embedded within broader instructional and behavioral support systems.

Despite extensive research on the importance of social-emotional behavior (SEB) skills in education, there remains a critical gap in understanding how SEB specifically impacts literacy outcomes, particularly for students from under-resourced backgrounds in Title I elementary schools. While studies have shown the benefits of SEL interventions and mentoring for overall student well-being, few have examined the direct relationship between SEB risk and English Language Arts (ELA) outcomes within underserved urban districts. Additionally, the role of targeted mentoring initiatives in influencing literacy performance remains underexplored, leaving questions about the specific academic impacts of mentoring on students at elevated SEB risk. This study addresses these gaps through three research questions: (1) What is the impact of a targeted mentoring initiative on the outcomes of these students? (2)What is the relation between SEB and ELA outcomes in students from under-resourced backgrounds attending Title I elementary schools in an urban district? And finally, (3) does SEB risk at the beginning of the school year predict ELA performance on the benchmark assessment? By exploring these questions, this research aims to provide deeper insights into the ways SEB interventions and mentoring can influence literacy achievement for vulnerable populations, offering guidance for more effective, integrated educational strategies.

The study included 396 students in grades K-6 attending Title I schools in a large urban school district in the Midwest. The participating schools primarily served economically disadvantaged students, with an average of 92.3% qualifying for free or reduced-priced lunch. The sample represented a diverse demographic profile, with the majority of students identifying as members of under-resourced communities. All participants were enrolled in the Prime Fit Youth Foundation’s mentoring program, a structured, collective impact initiative aimed at supporting students through targeted social-emotional and academic interventions. Students in the program were identified as at-risk for social-emotional behavior (SEB) challenges by school personnel. Parental consent was obtained and then the student was referred to the mentoring program. Table 1 provides an overview of participants.

The selection of mentees for the program was conducted at the school level, with each school team responsible for identifying students who would benefit most from participation. The selection of mentees for the program was conducted at the school level, with each school team responsible for identifying students who would benefit most from participation. School teams, including educators, counselors, school psychologists, behavior specialists, and administrators, collaborated to ensure a data-driven selection process that aligned with the program’s focus on mitigating SEB risks while supporting academic growth.

The selection process for mentees involved a multi-step identification approach incorporating both quantitative and qualitative data sources. Quantitative data included screening tools, academic performance records, attendance reports, and behavioral incident reports, while qualitative input was gathered from school personnel with direct knowledge of students’ needs. Selection criteria were based on three primary areas: social-emotional and behavioral challenges, academic concerns, and family or environmental risk factors.

Social-emotional and behavioral challenges were identified through persistent difficulties in self-regulation, impulse control, or emotional regulation, as well as struggles with peer relationships and social interactions. Additionally, students who had documented incidents of aggressive behavior, withdrawal, or defiance, along with a history of suspensions, disciplinary referrals, or frequent office visits for behavioral concerns, were considered for the program.

Academic concerns included below-grade-level performance on English Language Arts (ELA) benchmark assessments, declining grades in core subject areas, and documented low engagement in classroom activities. Students with a history of low motivation, difficulty completing assignments, or those in need of additional academic support were also prioritized.

Family and environmental risk factors further influenced the selection process. Students who had experienced adverse childhood experiences (ACEs), including parental incarceration, placement in foster care, or homelessness, were given consideration. Unstable housing or economic insecurity were also factors, as well as limited access to familial academic support, such as when primary caregivers were unable to assist with schoolwork due to work schedules, language barriers, or educational background. Additionally, referrals from child welfare agencies, community organizations, or social workers helped identify students in need of targeted mentoring and social-emotional support.

To identify and refer students for participation, each school implemented a tiered selection approach designed to prioritize students with the greatest needs while maintaining a balanced mentor-to-mentee ratio. This process began with a preliminary screening, in which schools reviewed Social, Academic, and Emotional Behavior Risk Screener (SAEBRS) scores to identify students demonstrating risk factors. Additionally, attendance and disciplinary records were analyzed to detect patterns of chronic absenteeism, disengagement, or behavioral concerns that could indicate a need for additional support.

Following the initial screening, a team-based review and referral process took place, in which school teams convened to discuss students flagged during screening. These teams, composed of educators, counselors, and administrators, prioritized students who exhibited a lack of strong protective factors and who would benefit most from a mentoring relationship. Teachers and school personnel provided valuable contextual insights into students’ social-emotional and academic challenges, ensuring that decisions were informed by both quantitative data and professional judgment.

Once students were identified for participation, the parent and guardian notification and consent process was initiated. Families of selected students received written consent forms outlining the program’s objectives, expected benefits, and participation requirements. In cases where families expressed hesitation, school staff facilitated informational sessions to address concerns and provide further clarification about the program. To ensure ethical and voluntary participation, students were only enrolled upon receipt of explicit parental consent.

By utilizing this multi-step, data-informed selection framework, the program ensured that students with the highest levels of need were accurately identified and adequately supported. Moreover, this structured process promoted family engagement while enhancing the program’s ability to deliver targeted, high-impact mentoring interventions tailored to each student’s unique social-emotional and academic needs.

The mentoring program was implemented as a collaborative initiative between a university, a school district, and a nonprofit, aligning with best practices in school-university partnerships. The university’s role was central in mentor training, ensuring that mentors were equipped with research-backed strategies for fostering self-regulation, emotional resilience, and academic persistence among students.

The mentoring program was structured as a collaborative partnership between a nonprofit organization providing targeted mentoring services, a university, and a large urban school district. This collaboration exemplified collective impact principles, uniting three entities with a common agenda to improve student outcomes by enhancing social-emotional and academic supports within the school setting. Through a shared vision, continuous communication, and alignment of resources, the nonprofit and the district jointly addressed multifaceted student needs. Together, they implemented a model that provided tiered, evidence-based interventions rooted in relationship-building, with service intensity tailored to each mentee’s and family’s needs.

Mentors were chosen through a rigorous multi-step selection process designed to ensure that they possessed the necessary qualifications, skills, and commitment to support students effectively. The selection process involved an application, screening, and training phase, emphasizing alignment with the program’s social-emotional learning (SEL) and academic intervention goals.

The recruitment process was a joint effort between the university, the nonprofit organization, and the school district, with each entity playing a critical role in identifying, vetting, and preparing mentors. The university provided a pipeline of undergraduate and graduate students, many of whom were studying education, psychology, social work, or related fields, ensuring that mentors had foundational knowledge in youth development and evidence-based interventions. The nonprofit recruited mentors from the broader community, prioritizing individuals with prior experience in mentoring, youth advocacy, or social services, while the school district recommended staff and community partners who had established relationships with youth from under-resourced backgrounds.

Once candidates were identified, a comprehensive screening process was conducted to assess their suitability for mentoring roles. This included background checks, reference verifications, and structured interviews to evaluate candidates’ experience working with children and adolescents, their understanding of trauma-informed care, and their ability to build strong, trust-based relationships. Applicants were also assessed on their commitment to the program’s core principles of consistency, cultural responsiveness, and asset-based mentoring.

Following selection, mentors participated in an intensive pre-service training led by the university in collaboration with school district professionals and nonprofit staff. This training covered essential topics such as trauma-informed mentoring, trust-based relational intervention (TBRI), self-regulation strategies, and culturally responsive practices. Mentors were also introduced to SAEBRS data interpretation, goal-setting strategies, and progress monitoring, ensuring that they were equipped to integrate both social-emotional and academic support into their interactions with mentees.

Mentors were then matched with students based on compatibility in interests, personality, and needs to foster meaningful connections and long-term engagement. Throughout the program, mentors received ongoing professional development, supervision, and support through university-facilitated reflection sessions and coaching from nonprofit and district personnel. This structured mentor selection and preparation process ensured that mentors were well-equipped to serve as stable, supportive figures capable of fostering self-regulation, emotional resilience, and academic growth among their mentees.

At the core of the program was a tiered prevention approach, reflecting best practices in public service fields, including education, health, youth services, and crime prevention. By offering varying levels of intervention intensity based on individual circumstances, the program responded to mentees’ social-emotional behavior (SEB) risk factors, academic needs, and familial challenges. Through daily interactions and a formal, empirically supported structure, the program aimed to create positive, sustained changes in student behavior, attendance, academic achievement, and self-regulation skills.

Research showed that positive student outcomes often stemmed from nurturing relationships that fostered a sense of connection and trust (Sethi & Scales, 2020). Grounded in these findings, the mentoring program was built upon daily, intentional relationship-building activities designed to enhance mentees’ self-efficacy, school connectedness, and emotional regulation. Through daily check-ins and weekly group sessions, mentors worked closely with mentees to promote prosocial behaviors, address conflicts, and support both immediate and long-term personal development. This structured model ensured consistency and predictability, reinforcing trust and stability for mentees over the entire school year.

The program was implemented five days a week throughout the school year and followed a daily schedule of mentor-mentee check-ins to provide a consistent touchpoint for relationship-building and support. Services were delivered on site at schools during school hours. These daily check-ins, guided by Trust-Based Relational Intervention techniques, focused on developing mentees’ executive function, self-regulation, and self-efficacy skills. Each check-in also included goal setting, problem-solving, and advocacy, allowing mentors to address mentees’ immediate challenges and promote self-directed growth. The daily check-ins were conducted either in the classroom, or in a hallway or other space provided by the school.

Additional layers of support were systematically integrated to align with each student’s unique needs which reflects best practice (Garringer & MacRae, 2019). Tier 1 support included daily mentor-mentee interactions and weekly group sessions designed to strengthen social-emotional competencies and establish positive peer associations. These group sessions, held within the school setting (typically in a classroom or library), introduced mentees to concepts of self-regulation, conflict resolution, and self-efficacy. Typical group size was eight mentees per mentor. Weekly group goals were collaboratively set, with mentors guiding mentees in identifying challenges and strategizing effective responses. Tier 1 also included family engagement opportunities and community asset referrals as needed, strengthening the mentees’ support networks both inside and outside of school. Family Engagement and Training Night provided a structured opportunity to reinforce positive school-family connections. These events were designed not only to provide educational support but also to cultivate positive family interactions that enhanced students’ learning experiences and overall development.

Tier 2 support offered more intensive, one-on-one academic interventions, along with on-call de-escalation services to prevent disciplinary exclusion. By providing in-class academic assistance and small-group instruction twice a week, academic mentors helped mentees develop critical literacy and learning skills, addressing the link between academic performance and SEB risk. For mentees in crisis or at risk of suspension, on-call de-escalation was provided to help students remain in school, reduce behavioral incidents, and create a more supportive learning environment.

Outcomes for this collective impact initiative were multidimensional, encompassing both social-emotional and academic growth. The program’s overarching goals included improved academic achievement, attendance, behavior, sense of school connectedness, self-efficacy, and self-regulation. By integrating evidence-based practices within a cohesive, relationship-centered framework, this model aimed to foster long-term positive outcomes for students facing socioemotional and academic challenges. Through its tiered approach and alignment with collective impact principles, the mentoring program offered a comprehensive, replicable model for enhancing student success and resilience in under-resourced urban school districts.

Data for this study were collected through district-provided student records and mentee self-reported measures of school connectedness, self-efficacy, and self-regulation strategies. The following sections describe each type of measure in detail.

For each student, the district provided the following data:

Attendance data: This included records of student attendance for the academic year, allowing for analysis of changes in school attendance patterns as a potential indicator of program impact.

Disciplinary data: This included information on any out-of-school suspensions and expulsions each student received during the academic year. Disciplinary data were analyzed as a measure of behavioral improvement over time.

Academic data: Academic performance was evaluated through English Language Arts (ELA) benchmark progress monitoring data. The benchmark data provided insight into students’ progress toward grade-level literacy expectations throughout the year. The district utilizes the FastBridge English Language Arts (ELA) benchmark assessment to monitor and evaluate students’ reading proficiency and progress throughout the academic year. FastBridge offers a suite of research-based assessments designed to provide educators with actionable data on students’ literacy skills. The specific version used for the participants was aReading (Adaptive Reading). Designed for students in grades 2 through 8, aReading is a computer-administered, adaptive assessment that evaluates broad reading abilities, including comprehension, vocabulary, and decoding skills. The adaptive nature of the test adjusts the difficulty of questions based on student responses, providing a personalized assessment experience. The assessment can be administered in a group setting and typically takes about 15–30 minutes to complete.

FastBridge assessments are administered multiple times throughout the school year—commonly in the fall, winter, and spring—to establish benchmarks and monitor student progress. These benchmarks help educators identify students who are on track, as well as those who may require additional support to meet end-of-year reading goals. FastBridge assessments are grounded in extensive research and have demonstrated strong psychometric properties, including high reliability and validity. The assessments are designed to align with national norms and benchmarks, providing educators with a reliable tool for measuring student reading achievement and growth over time.

Social-emotional risk data: To measure social-emotional risk, the district administers the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS). The SAEBRS is a brief, standardized screening tool designed to identify students at risk for social, academic, and emotional behavior difficulties that may impact their overall school success. This tool consists of 19 items and is completed by teachers, who assess each student’s behaviors across three distinct subscales: Social Behavior, Academic Behavior, and Emotional Behavior. (1) Social Behavior Subscale: This subscale measures students’ ability to interact positively with peers and teachers, assessing aspects of social competence and peer relationships. (2) Academic Behavior Subscale: This subscale evaluates students’ engagement with academic tasks, including attention, persistence, and organizational skills critical to academic success. (3) Emotional Behavior Subscale: This subscale focuses on students’ emotional regulation and mental health, assessing behaviors that reflect mood stability and emotional well-being.

Each subscale score falls within one of three risk categories—low risk, moderate risk, or high risk—indicating the level of intervention a student may require. Low-risk scores suggest typical development, while moderate- and high-risk scores signal the need for targeted or intensive interventions, respectively. The district uses the categories of College Prepared, Low Risk, Some Risk, and High Risk.

Psychometric properties of the SAEBRS indicate strong reliability and validity for use in school settings. Internal consistency for the overall scale is high, with a Cronbach’s alpha of 0.93, and the subscales also demonstrate good reliability, with alphas ranging from 0.85 to 0.92. The SAEBRS has also shown robust construct validity, correlating well with other established behavioral and academic measures, making it a reliable tool for identifying SEB risks that may impact academic and social outcomes.

In addition to district-provided data, mentees completed three self-reported measures pre- and post-intervention, which assessed their sense of school connectedness, self-efficacy, and use of self-regulation strategies. Each of these measures was adapted from established scales, with strong psychometric properties ensuring reliability and validity for use in the school setting.

Student Sense of Connectedness Scale: Adapted from Brew et al. (2004), the Student Sense of Connectedness Scale assessed students’ feelings of belonging and connection within the school environment. This scale consisted of 33 items rated on a 4-point Likert scale (1 = Strongly Disagree to 4 = Strongly Agree). Items covered aspects of relational support from peers and teachers, as well as students’ perceptions of being valued and accepted at school. Previous studies have demonstrated the scale’s high internal consistency, with a Cronbach’s alpha of 0.87, indicating strong reliability in measuring school connectedness.

Student Self-Efficacy Scale: Adapted from Fertman and Primack (2009), the Student Self-Efficacy Scale measured students’ beliefs in their ability to succeed in school-related tasks and to influence outcomes in their own learning. This scale included 15 items, also rated on a 4-point Likert scale (1 = Strongly Disagree to 4 = Strongly Agree), covering domains such as academic confidence, problem-solving abilities, and perseverance. The scale demonstrated strong psychometric properties, with a Cronbach’s alpha of 0.89, supporting its reliability in assessing self-efficacy within an educational setting.

Self-Regulation Strategies Questionnaire: Adapted from Plasch (2011), the Self-Regulation Strategies Questionnaire measured students’ use of cognitive and behavioral strategies to manage emotions, stay focused, and persist through academic tasks. This 6-item scale was scored on a 4-point Likert scale (1 = Rarely to 4 = Always) and included items related to goal setting, emotional regulation, and attention control. The scale demonstrated strong internal consistency with a Cronbach’s alpha of 0.85, making it a reliable tool for evaluating self-regulation among school-age students.

By using these district-provided data and validated self-reported measures, the study captured a comprehensive view of student outcomes across multiple domains, from objective academic and behavioral indicators to subjective perceptions of connectedness, self-efficacy, and self-regulation. This multi-dimensional approach allowed for an in-depth analysis of the mentoring program’s impact on mentees’ social-emotional and academic development.

To address the first research question (i.e. What is the impact of a targeted mentoring initiative on the outcomes of these students?), descriptive statistics and paired samples t tests were computed on variables measuring social-emotional behavior (SEB) risk, as measured by the SAEBRS, and literacy outcomes, assessed through FastBridge ELA benchmark data. The descriptive statistics provide an overview of key variables, including attendance, disciplinary actions, and measures of self-efficacy, school connectedness, and self-regulation strategies. Additionally, frequency distributions of SEB risk and ELA risk were examined. These descriptive statistics highlight changes in key outcomes from pre-intervention (fall) to post-intervention (spring), providing insight into the mentoring program’s impact on both behavioral and academic domains.

Table 2 provides descriptive statistics for measures of attendance, discipline, as well as sense of school connection, self-efficacy, and use of self-regulation strategies at pre (fall) and post intervention (spring).

The descriptive statistics revealed notable changes across several key measures from fall to spring. Attendance data showed an increase in average days present, reflecting improved engagement with the school environment. Disciplinary data indicated a reduction in out-of-school suspensions and expulsions, suggesting positive behavioral shifts among mentees. For social-emotional outcomes, the Student Sense of Connection Survey demonstrated a statistically significant improvement, with mean scores rising from 74.7 (SD = 10.4) in the fall to 84.1 (SD = 10.7) in the spring, t(395) = 63.9, p < 0.001. Similarly, the Student Self-Efficacy Scale showed a significant increase, with scores improving from a fall mean of 39.7 (SD = 5.5) to a spring mean of 44.5 (SD = 5.8), t(395) = 52.9, p < 0.001. Lastly, the Self-Regulation Strategies Survey scores significantly increased from a fall mean of 14.9 (SD = 2.9) to 17.8 (SD = 3.2) in the spring, t(395) = 55.83, p < 0.001. These findings indicate statistically significant growth in social-emotional competencies and behavior among students over the course of the program, underscoring the effectiveness of the intervention.

The next phase of the analysis focused on the frequency distribution of risk levels as measured by the SAEBRS and the FastBridge ELA benchmark assessments. This analysis aimed to identify patterns in SEB and academic performance among mentees, offering insight into the prevalence of risk factors and their potential impact on literacy outcomes. SAEBRS scores were particularly significant, as they were used to help identify mentees for the program and serve as a proxy for executive function, highlighting areas where additional support was most needed. Table 3 presents the results of the frequency analysis of ELA risk categories, while Table 4 provides the frequency distribution of SEB risk categories.

The frequency distribution of English Language Arts (ELA) risk levels revealed notable shifts from fall to spring across the four risk categories. The percentage of students in the College Prepared category increased from 11.9% in the fall (N = 47) to 14.9% in the spring (n = 56), indicating positive academic progress for some mentees. Similarly, the Low Risk category saw a modest increase, rising from 23.0% (N = 91) to 26.0% (N = 98). Conversely, the Some Risk category remained relatively stable, with a slight decrease from 30.9% (N = 122) in the fall to 30.2% (N = 114) in the spring. Importantly, the percentage of students in the High Risk category decreased from 34.2% (N = 135) in the fall to 28.9% (N = 109) in the spring, reflecting a reduction in the number of students at the greatest academic risk. These shifts suggest that the mentoring intervention may have contributed to mitigating high levels of academic risk and promoting progress toward greater ELA readiness.

The next analysis focused on the distribution of fall SAEBRS data, which provided a snapshot of mentees’ SEB risk levels at the beginning of the academic year. Unlike the ELA risk data, this analysis does not examine growth or changes over time but instead highlights the baseline SEB risk levels used by schools to identify students for the mentoring program. Understanding these initial risk distributions helps to contextualize the needs of the mentees and illustrates how the program targets support to those facing the greatest social-emotional and behavioral challenges. The following section presents the frequency distribution of SEB risk categories for mentees at the start of the year.

The distribution of fall SEB risk categories as measured by the SAEBRS revealed notable patterns across the Social, Academic, and Emotional subscales, highlighting areas where mentees required varying levels of support. For the Social subscale, the mentees were relatively evenly distributed, with 34.7% classified as Low Risk, 33.2% as Some Risk, and 32.0% as High Risk, suggesting that approximately one-third of the mentees faced significant social challenges. The Academic subscale showed a different pattern, with the majority of mentees (50.3%) falling into the Low Risk category, 34.4% classified as Some Risk, and only 15.3% identified as High Risk, indicating that academic challenges were less pervasive than social or emotional concerns. In contrast, the Emotional subscale displayed a higher concentration of mentees in the Low Risk category (46.0%) and Some Risk category (38.4%), while 15.6% were classified as High Risk. These findings suggest that while a significant portion of mentees demonstrated strengths in academic and emotional areas, a notable subset faced substantial challenges, particularly in social functioning, underscoring the need for targeted, individualized mentoring interventions.

Having established the distribution of SEB risk levels and ELA performance among mentees and investigating the impact of the mentoring program on student outcomes (research question 1), the next phase of the analysis examined the relationship between these two key variables. Understanding how SEB risk factors correlated with ELA outcomes was critical for identifying how social-emotional and behavioral challenges impacted literacy development in students from under-resourced backgrounds. By analyzing this relationship, the study aimed to uncover patterns that could inform targeted interventions and provide deeper insight into the unique needs of students attending Title I elementary schools in an urban district.

To further investigate the relationship between social-emotional behavior (SEB) risk and literacy outcomes, the analysis explored the creation of a composite variable for SEB risk by combining the three subscales of the SAEBRS: Social Behavior, Academic Behavior, and Emotional Behavior. Bivariate correlations among the three SAEBRS subscales (Social Behavior, Academic Behavior, and Emotional Behavior) indicated that the subscales were highly correlated with one another, with correlation coefficients ranging from r = 0.54 to r = 0.62, p < 0.001. Given the strong correlations among the SAEBRS subscales, a composite SEB variable was created to provide a more comprehensive measure of overall social-emotional behavior risk. This composite variable aimed to provide a holistic measure of SEB risk, reflecting the interconnected nature of these domains and their cumulative impact on student outcomes.

With the composite SEB variable established to provide a holistic measure of social-emotional behavior risk, the next phase of the analysis focused on its relationship with ELA outcomes. By examining the correlations between SEB risk and ELA performance, we aimed to uncover patterns that illuminate how social-emotional and behavioral challenges impact literacy achievement. Table 5 showcases the results of these correlation analyses.

The correlation analysis revealed small but statistically significant relationships between SEB risk and reading performance. SEB risk was positively correlated with fall reading scores (r = 0.11, p < 0.05) and spring reading scores (r = 0.12, p < 0.05), suggesting that lower SEB risk (i.e. higher scores means less risk) was modestly associated with better reading outcomes. Additionally, fall reading scores and spring reading scores were strongly and significantly correlated (r = 0.97, p < 0.001), reflecting a high level of consistency in students’ reading performance over time. These findings indicate that SEB risk had a small but positive relationship with literacy outcomes.

Finally having established the correlations between SEB risk and ELA outcomes, the next phase of the analysis addresses the third research question: Does SEB risk at the beginning of the school year predict ELA performance on the benchmark assessment? This analysis aims to determine whether initial SEB risk levels served as a significant predictor of literacy achievement, providing insight into the extent to which social-emotional and behavioral factors influence academic performance. The results of regression analysis conducted to examine this predictive relationship was statistically significant, F(1, 391) = 5.04, p < 0.03, with fall SEB risk (t = 2.3, p = 0.03) as a significant predictor of spring ELA scores. These findings suggest that social-emotional and behavioral factors at the start of the year play a meaningful role in shaping students’ academic achievement, underscoring the importance of addressing SEB risk to improve literacy performance.

This study investigated the relationship between social-emotional behavior (SEB) risk and literacy outcomes among students from under-resourced backgrounds attending Title I elementary schools in an urban district. Using data from the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS) and FastBridge ELA benchmark assessments, we explored three research questions: (1) What is the impact of a targeted mentoring initiative on the outcomes of these students? (2) What is the relationship between SEB and ELA outcomes? and (3) Does SEB risk at the beginning of the school year predict ELA performance on the benchmark assessment? Findings from this study provide critical insights into the interplay between SEB and academic achievement, underscoring the potential of targeted mentoring interventions to improve both social-emotional and academic outcomes for at-risk students.

These outcomes were achieved through a collective impact framework that brought together the collaborative efforts of a large urban school district, a nonprofit organization, and a university. This partnership-based approach highlights the potential for university faculty, educators, and community organizations to work together in designing and implementing effective, evidence-based interventions that address both behavioral and academic risk factors. The district played a key role in identifying mentees and providing academic and behavioral data, while the nonprofit delivered the mentoring program with a tiered approach tailored to individual student needs. The university contributed research and evaluation expertise, ensuring data-informed decision-making and program refinement over time.

Through this collaborative, school-university-community partnership, the mentoring program was able to integrate evidence-based practices that addressed the multifaceted needs of students from under-resourced backgrounds. The results emphasize the importance of leveraging collective impact models to create sustained, systemic change in education. This approach ensured that interventions were not only comprehensive but also scalable and replicable, providing a framework for other communities seeking to enhance social-emotional and academic outcomes for at-risk populations (Teitel, 2003).

Findings from this study provide compelling evidence that targeted mentoring interventions yield significant benefits for students’ social-emotional and academic growth. From fall to spring, mentees exhibited notable increases in school connectedness, self-efficacy, and self-regulation strategies, with large effect sizes observed in each area. These results align with existing literature emphasizing the value of relationship-building and targeted supports in fostering positive student development (Rhodes, 2020; Sethi & Scales, 2020). Additionally, the program contributed to a reduction in disciplinary incidents and absenteeism, further supporting its effectiveness in addressing behavioral and engagement challenges. These findings are consistent with the work of Durlak et al. (2011), who highlighted the importance of integrating social-emotional learning (SEL) frameworks into interventions to meet the complex needs of at-risk students. By enhancing SEL competencies, the mentoring program created an environment conducive to long-term academic and personal success.

The relationship between SEB risk and literacy outcomes revealed statistically significant positive correlations. Lower SEB risk (higher scores) was modestly associated with better fall and spring ELA scores, suggesting that social-emotional factors, such as self-regulation and emotional stability, play a role in academic success. These findings align with research by McTigue et al. (2016), who noted that SEB competencies are crucial for sustaining focus and resilience during literacy tasks. The modest nature of the correlations underscores the interconnectedness of social-emotional and academic domains, particularly for students who often face compounded risk factors. This relationship highlights the potential for targeted SEL interventions to indirectly support academic achievement by bolstering foundational social-emotional skills (Schonert-Reichl et al., 2015).

The regression analysis revealed that fall SEB risk significantly predicted spring ELA scores. This finding underscores the predictive value of SEB risk measures in identifying students who may require targeted academic and social-emotional interventions. The results are consistent with Schonert-Reichl et al. (2015), who demonstrated that SEL-focused interventions positively impact academic performance by addressing underlying social-emotional challenges. These findings further reinforce the importance of adopting holistic approaches that address both social-emotional and academic needs, particularly for students in under-resourced communities. By recognizing SEB risk as an early indicator of academic outcomes, schools can proactively implement interventions to support literacy achievement and overall student well-being (Jones & Kahn, 2017).

This study has important implications for university faculty who engage in teacher preparation, mentoring programs, and school-university partnerships. By integrating mentoring programs into teacher education coursework, faculty can equip pre-service teachers with strategies to support students’ social-emotional development in classroom settings (Greenberg et al., 2003). Moreover, university-based research centers can serve as hubs for evaluating mentoring programs, ensuring that schools implement evidence-based mentoring frameworks tailored to student needs (Zeichner, 2010).

For teachers and school personnel, this study highlights the critical role of mentoring in reinforcing SEL practices within school settings. Educators can use SEB risk data as an early warning system to identify students who would benefit from mentoring interventions. Additionally, by collaborating with trained mentors, teachers can provide targeted SEL support that enhances student engagement, emotional regulation, and academic persistence (Darling-Hammond, 2017).

For students, the findings reinforce the value of positive mentoring relationships in fostering self-confidence, academic motivation, and behavioral self-regulation. Schools should consider scaling mentoring initiatives to ensure that more students—particularly those at risk for academic underperformance or social-emotional difficulties—receive consistent, developmentally appropriate support (Grossman & Rhodes, 2002).

This study underscores the power of collective impact frameworks in addressing complex educational challenges. By aligning the efforts of schools, nonprofit organizations, and community partners, the mentoring program exemplified the principles of shared vision, coordinated action, and data-driven decision-making. The tiered prevention model, which tailored interventions to individual mentee needs, reflects best practices in public service fields and highlights the importance of collaboration for sustained impact (Garringer & MacRae, 2019).

These findings suggest that similar collective impact initiatives could be replicated in other educational contexts, including rural, suburban, and alternative school settings. Expanding mentoring efforts beyond elementary school and into middle and high school settings could ensure continuity of support as students transition between grade levels. Additionally, collective impact initiatives should explore how mentoring can be embedded within family engagement programs, ensuring that students receive reinforced SEL support at home and in school (Bal et al., 2015).

While this study provides valuable insights, several limitations should be noted. First, the sample was limited to mentees in a specific urban district, which may limit the generalizability of findings to other contexts. Second, the reliance on teacher-reported SAEBRS data may introduce bias, as teachers’ perceptions could influence the assessment of SEB risk. Third, while the study highlights associations and predictive relationships, it does not establish causation. Future research could incorporate experimental or longitudinal designs to further explore the causal impact of SEB risk on literacy outcomes. Additionally, examining the role of mentoring dosage and fidelity could provide deeper insights into which program components drive the observed outcomes.

Future research should explore the long-term impact of mentoring programs on both SEB and academic outcomes, including tracking mentees into secondary education. Investigating how the integration of SEB and academic supports can be scaled across different educational settings is also critical. Additionally, exploring the intersection of SEB risk and other domains, such as mental health and family engagement, could provide a more comprehensive understanding of factors influencing literacy development.

This study underscores the power of collective impact frameworks in addressing complex educational challenges. By aligning the efforts of schools, nonprofit organizations, and community partners, the mentoring program exemplified the principles of shared vision, coordinated action, and data-driven decision-making. The tiered prevention model, which tailored interventions to individual mentee needs, reflects best practices in public service fields and highlights the importance of collaboration for sustained impact. These findings suggest that similar collective impact initiatives could be replicated in other contexts to enhance social-emotional and academic outcomes for students from under-resourced backgrounds.

This study contributes to the growing body of evidence supporting the integration of SEL into academic interventions, particularly for students facing systemic barriers to educational success. By addressing SEB risk factors, mentoring programs can serve as a critical support system, fostering self-regulation, executive function, and school engagement, all of which are essential for academic achievement and personal development. Findings from this study emphasize that students’ social-emotional competencies are deeply interconnected with their ability to succeed in literacy and other core academic domains, highlighting the need for interventions that simultaneously support emotional well-being and academic persistence.

Beyond the immediate impact on student outcomes, this research underscores the value of relationship-centered mentoring models as a means of building protective factors, reducing academic disparities, and enhancing student resilience. The success of this initiative within a collective impact framework demonstrates that multi-sector partnerships—involving schools, universities, and community-based organizations—are essential for designing sustainable and scalable interventions. University faculty and researchers play a crucial role in this ecosystem by contributing evidence-based training, program evaluation, and mentorship support, while educators and school administrators provide critical on-the-ground insights that ensure interventions remain responsive to student needs.

Moving forward, educators, university faculty, policymakers, and community leaders must work together to expand and refine mentoring programs that target social-emotional and academic development simultaneously. Future initiatives should explore how to integrate mentoring into broader school-based SEL curricula, ensuring that students receive consistent, multi-tiered support beyond one-on-one interventions. Additionally, examining long-term outcomes—including high school completion, postsecondary success, and career readiness—will be essential in understanding the lasting effects of mentoring on student trajectories.

To achieve long-term, systemic educational change, mentoring interventions must be replicable, adaptable, and sustainable across diverse educational settings. This requires ongoing investment in professional development for mentors and educators, as well as policy-level support to ensure mentoring is recognized as a viable and evidence-based strategy for addressing opportunity gaps. Furthermore, leveraging data-driven decision-making and continuous improvement models will allow school-university-community partnerships to refine their approaches and scale interventions in ways that maximize student success.

By integrating SEL into literacy-focused mentoring programs, this study provides a model for addressing the academic and social-emotional challenges faced by underserved students. These findings reinforce the importance of holistic, relationship-centered approaches in creating environments where students feel supported, develop resilience, and gain the skills necessary for lifelong learning and achievement. Through continued collaboration, innovation, and strategic investment, the potential exists to redefine how educational systems support student well-being and academic achievement, ensuring that all learners—regardless of background—have the opportunity to thrive.

This study was reviewed and approved by the Wichita State University’s Institutional Review Board (IRB) (Approval Number: [5501]). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee. Informed consent was obtained from all individual participants included in the study (via signed parent permission slips and verbal assent by each child).