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Connecting the Dots: Enhancing Outcomes for Students with Emotional Disturbance through Integrated Student SupportMichel, Evan Burton January 2021 (has links)
Thesis advisor: Mary E. Walsh / Out-of-school factors including poverty, mobility, and violence contribute to student learning and development where need often influences negative outcome gaps over time (Coalition for Community Schools, 2018; Mattison & Aber, 2007; Moore, 2014; Moore & Emig, 2014). A subset of students face these and additional challenges with emotional disturbance (ED). The ED designation is a strong predictor of poorer outcomes even with special education practice in place (de Voursney & Huang, 2016; IDEA, 2004; Lewis et al., 2017; Moore et al., 2017; Olivier et al., 2018). These findings heighten calls to reform support systems around students, especially those students facing the most need. Integrated Student Supports (ISS) emerged as a systemic approach to comprehensively service in and out-of-school needs (Moore, 2014; Moore & Emig, 2014; Lee-St. John et al., 2018; Moore et al., 2017). However, limited research exists on the impact of tandem ISS services on special education accommodation for students with ED. This study focused on an approach to ISS, City Connects, on academic and behavior outcomes for students with ED impairment. City Connects offers tailored support for the whole child and implementation has resulted in positive outcomes (City Connects, 2010, 2012, 2016, 2018, 2020; Walsh et al., 2014). The study had two aims. First, to determine if students with ED designation (N=4,427) scored lower on academic and thriving outcomes than students never in special education (N=14,475). The second was to assess if ever participating in City Connects (N=5,067) moderated the relationship between ED impairment and outcomes. School-fixed effects regressions assessed these aims. Results revealed that students with ED scored significantly lower across all outcomes. Analyses for the second study aim were variable. Math scores were significantly higher for City Connects students than children without these supports. Writing and MCAS-ELA scores did not significantly differ between the two groups. Reading and behavior marks were significantly lower for City Connects students. The predicted moderation of City Connects only met significance for reading scores. Findings partially support hypotheses and promote greater attention to investigations of subsets of students and the mechanisms behind the response to City Connects and ISS more broadly. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.
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TheImpact of an Integrated Student Support Program on Non-Cognitive Outcomes for Students with Social-Emotional-Behavioral Needs: A Longitudinal AnalysisRene, Kirsten M. January 2020 (has links)
Thesis advisor: Mary E. Walsh / Poverty has many deleterious effects on child development, including negative impacts on social-emotional health, a developmental domain schools refer to as non-cognitive skills (Dearing, 2008). Unfortunately, children growing up poor often have underdeveloped non-cognitive skills, which significantly predict academic success and well-being (Farrington et al., 2012). Integrated Student Support (ISS) is one emerging approach that holistically supports cognitive and non-cognitive student development (Moore & Emig, 2014). While ISS has been found to improve academic outcomes, limited research examines its impact on social-emotional outcomes. This study focused on one ISS intervention, City Connects, which provides tailored student support plans to every child in a school via school and community-based services (Walsh et al., 2014). The study had three aims. The first was to examine the percentage of City Connects students with and without a social-emotional-behavioral (SEB) Need across levels of risk and service characteristics (i.e., domains, intensity levels, types) in second grade (N=896). The second was to examine improvement in three teacher-rated non-cognitive student outcomes (Prosocial Behavior, Self-Regulated Learning, Academic Effort) from second-fifth grade for City Connects students with and without a SEB Need (N=896). The third was to compare improvement in the same three non-cognitive student outcomes from second-fifth grade for students with a SEB Need in City Connects schools and comparable schools without the intervention (N=1,778). Multilevel modeling assessed aims 2 and 3. Significantly more students with a SEB Need were deemed higher risk and received more health, early intervention, and SEB/counseling services compared to students without a SEB Need in City Connects schools. Further, significant improvements from second-fifth grade were found in Self-Regulated Learning and Academic Effort for City Connects students with a SEB Need compared to those without a SEB Need. Significant improvements were also found over time in Academic Effort for students with a SEB Need in City Connects schools compared to those in comparison schools. Findings support that ISS improves non-cognitive functioning for students attending high-poverty schools. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.
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Evidence for “Tailoring” in the Matching of Integrated Services to Students’ Developmental Needs in City Connects Schools Using Pattern Analysis and Latent Class Analysis:Tran, Quang Dominic January 2023 (has links)
Thesis advisor: Mary E. Walsh / With an increase in emphasis on individual uniqueness and multi-contextual influences, developmental and intervention/prevention science along with similar fields of research (e.g., personalized medicine, personalized learning, health communication, business marketing) have promoted the design and implementation of interventions that would tailor responses and strategies to optimize targeted outcomes based on individual needs and variability (Joyner & Paneth, 2019; Kreuter et al., 1999; Vesanen, 2007). However, in spite of the effort and resources invested in personalization in the past decades, evidence for the realization and utility of tailored interventions have been more anecdotal than quantitatively empirical. The majority of person-centered studies have been qualitative (Lerner et al., 2019). While there is little agreement on what “tailoring” means across the different fields of study, there is a consensus that the term “tailoring” and tailoring-related terms (e.g., personalization, individualization, differentiation, and customization) lack a common and feasible theoretical foundation. Consequently, this semantic crisis has made the construct increasingly difficult to conceptualize and operationalize (e.g. Economist Group, 2021; Shemshack & Spector, 2020). Drawing on insights from the Specificity Principle, Orthogenetic Principle, and Developmental Contextualism in developmental science, this dissertation proposed a provisional definition of “tailoring”: the process of matching unique patterns of services based on each student’s cumulative strengths and needs and the availability of services (e.g., Bornstein, 2015; Lerner et al., 1998; Walsh et al., 2002; Werner & Kaplan, 1956). Guided by this definition, this dissertation sought to find evidence of “tailoring” in one “whole-child,” school-based/evidence-based Integrated Student Support (ISS): City Connects. City Connects partners with school personnel and multiple community agencies to systematically and cost-effectively allocate services/resources to students and their families from low-income communities in order to promote strengths, address needs, and mitigate risks (Moore & Emig, 2014; Dearing et al. 2016; Walsh & Theodorakakis, 2017). After establishing a theoretically-informed basis for “tailoring” as an operationalizable construct, this dissertation employed a comprehensive, three-dimensional approach to data analysis: nomothetic (for finding general/ “universal” trends), differential (for finding differences between groups), and idiographic (for finding differences between individuals) (e.g., Lerner et al., 2019; Overton, 2015; Salvatore & Valsiner, 2010). This was to magnify the descriptive power of the data and findings. In order to accomplish this, the two exploratory substudies in this dissertation employed 1) descriptive analysis, 2) a novel approach for comparing the service patterns matched to each student’s unique sets of strengths and needs, and 3) Latent Class Analysis (LCA). The major findings suggest that “tailoring” in City Connects schools is occurring in five ways: 1) students with higher needs receive more support than students with fewer needs; 2) City Connects is adaptive in responding to the emerging needs of individuals as circumstances change in the course of time; 3) there are unique patterns of services that are either shared (two more students have the same combination of services/types of services) or unshared (only one student has a particular service pattern); 4) service patterns are related to students’ developmental needs (i.e., higher risk level->higher percentages of individualized service patterns); and 5) service pattern matching is purposeful and does not occur randomly. The implications that these findings have on theory, research, and practice are discussed. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.
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Strengthening Causal Inferences: Examining Instrument-Free Approaches to Addressing Endogeneity Bias in the Evaluation of an Integrated Student Support ProgramLawson, Jordan L. January 2019 (has links)
Thesis advisor: Laura M. O'Dwyer / Education researchers are frequently interested in examining the causal impact of academic services and interventions; however, it is often not feasible to randomly assign study elements to treatment conditions in the field of education (Adelson, 2013). When assignment to treatment conditions is non-random, the omission of any variables relevant to treatment selection creates a correlation between the treatment variable and the error in regression models. This is termed endogeneity (Ebbes, 2004). In the presence of endogeneity, treatment effect estimates from traditionally used regression approaches may be biased. The purpose of this study was to investigate the causal impact of an integrated student support model, namely City Connects, on student academic achievement. Given that students are not randomly assigned to the City Connects intervention, endogeneity bias may be present. To address this issue, two novel and underused statistical approaches were used with school admissions lottery data, namely Gaussian copula regression developed by Park and Gupta (2012), and Latent Instrumental Variable (LIV) regression developed by Peter Ebbes (2004). The use of real-world school admissions lottery data allowed the first-ever comparison of the two proposed methods with Instrumental Variable (IV) regression under a large-scale randomized control (RCT) trial. Additionally, the researcher used simulation data to investigate both the performance and boundaries of the two proposed methods compared with that of OLS and IV regression. Simulation study findings suggest that both Gaussian copula and LIV regression are useful approaches for addressing endogeneity bias across a range of research conditions. Furthermore, simulation findings suggest that the two proposed methods have important differences in their set of identifying assumptions, and that some assumptions are more crucial than others. Results from the application of the Gaussian copula and LIV regression in the City Connects school lottery admissions study demonstrated that receiving the City Connects model of integrated student support during elementary school has a positive impact on mathematics achievement. Such findings underscore the importance of addressing out-of-school barriers to learning. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.
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