I am having a difficult time trying to build the Literature review. My writing is more of a summary rather than providing the argument of why the selected theory (Tinto's Integration Model) is justified for me to use it. I am using the Meal Plan method of paragraph structure and do not want to have any citations in the first or last sentences.

Review of the Literature Related to the Problem of Practice Understanding the intricate dynamics of student retention in community colleges, especially among first-year students, has been a focal point of scholarly inquiry. Grounded in Tinto's Student Integration Model, numerous studies delve into the multifaceted aspects influencing students' persistence in higher education. Research endeavors have investigated various interventions, including academic advising, peer mentoring programs, and extracurricular involvement, to decipher their impact on retention rates. While these studies often uncover positive correlations between engagement and persistence, they also unveil a nuanced landscape, with differential factors influencing retention among diverse student cohorts and institutional settings. Some emphasize the pivotal role of academic factors like early performance and faculty engagement. In contrast, others underscore the significance of social integration and a sense of belonging within the campus community. This literature review component aims to synthesize these diverse findings, clustered according to the key dimensions of student integration, to inform effective strategies tailored to address the unique needs of students and institutional contexts. Theme 1: Factors Influencing Student Retention The theme of factors influencing student retention focuses on identifying various elements that contribute to students' persistence in their studies, particularly in STEM and related fields. Identifying contributing elements is crucial for developing effective strategies to improve retention rates. Belser et al. (2018) conducted a study using a large dataset and rigorous statistical methods to identify key predictors of persistence among STEM majors, such as prior academic achievement, financial aid status, and engagement in extracurricular activities. The study's comprehensive approach and robust methodology are significant strengths. Still, it is limited by its focus on a single academic discipline and reliance on self-reported data, which may introduce bias. Various techniques are required to create effective prediction models due to the complexity of the factors impacting student retention. In another study, Sass et al. (2018) developed a structural model to predict student retention based on factors like positive and negative sacrifice, student engagement, and student satisfaction. They used a large sample to Increase the probability of including a more generalized population to enhance the query's generalizability. However, the model's complexity limits its practical application (Elder, 2021). Additionally, the study's cross-sectional design makes it difficult to establish causal relationships between the identified factors and retention outcomes. One of the most critical factors in community college student success is the impact of expectations on retention rates. James (2022) investigated the role of expectations in student retention, finding that both student and institutional expectations significantly impact persistence rates. However, the qualitative nature of this study limits its generalizability and ability to capture the full range of quantitative data relevant to the issue. Similarly, Lucey (2018) focused on the role of motivation in online learning environments, highlighting the importance of motivation-enhancing policies. While this study provides valuable insights into student motivation, it is limited in scope and needs to fully address the broader factors influencing student persistence across different learning modalities. Theme 2: Early Identification of At-Risk Learners and Student Retention The early identification of at-risk learners is a recurring theme within the literature on student retention in community colleges. A significant body of research underscores the potential of early identification systems to detect and address challenges that students may face before they escalate and lead to dropout. For instance, Ortiz-Lozano et al. (2023) highlighted the importance of analyzing various data points to identify vulnerable student populations and provide tailored support. Similarly, Villano et al. (2018) employed survival analysis techniques to assess the effectiveness of early-alert systems in predicting student outcomes, revealing a positive correlation between early intervention and increased retention rates. However, implementing and utilizing early identification systems effectively poses challenges for institutions. Ortiz-Lozano et al. (2023) noted the financial burden of implementing comprehensive systems and the potential difficulties in generalizing findings across different institutional contexts. Likewise, the statistical methods employed by Villano et al. (2018) may be complex for some practitioners to interpret and utilize, who need more statistical and analytical skills, limiting the practical applicability of their findings (Berens et al., 2018). Despite these challenges, the research consistently underscores the importance of timely intervention for students at risk of dropping out, emphasizing the need for institutions to invest in effective early identification and support systems. Theme 3: Role of Staff, Faculty, and Mentorship in Student Retention Staff and Faculty involvement and mentorship are not just roles but positions of significant influence and responsibility in student retention. Their engagement can significantly shape a student's academic journey and persistence. Tinto (2017) found that Staff-student interaction positively impacts student retention rates. This study demonstrated that students who have meaningful interactions with Staff and Faculty members are more likely to stay enrolled and complete their studies. The study's strength is its longitudinal approach, which provides a comprehensive view of the impact over time. However, its limitation lies in the potential variability in faculty engagement across different institutions. Similarly, Campbell and Campbell (2017) emphasized the role of mentorship in retaining students, especially those from underrepresented groups. Their research indicated that mentorship programs help students navigate academic and personal challenges, leading to higher retention rates. While the study highlights the importance of structured mentorship programs, it is limited by its reliance on self-reported data and the variability in mentorship quality. These findings underscore the need for institutions to invest in faculty engagement and structured mentorship programs to enhance student retention. Staff and Faculty and mentorship are essential across various studies, indicating that personal support and guidance are crucial for student success. In conclusion, the literature review highlights various factors influencing student retention, including academic achievement, financial aid, engagement, expectations, early identification of at-risk learners, and the role of Faculty and mentorship. Quantitative studies provide insights into predictors and structural models, while qualitative research emphasizes the importance of expectations and motivation. Early identification and timely interventions are consistently emphasized despite practical challenges. Faculty involvement and mentorship are crucial for student success, indicating the need for institutions to invest in these areas. Critique and Synthesis The synthesis of findings from research on the early identification of at-risk students provides promising insights into retention improvement tactics while highlighting areas that require further exploration and refining. Ortiz-Lozano et al. (2023) emphasize the importance of varied data sources in identifying students needing tailored help. In contrast, Villano et al. (2018) highlight the effectiveness of statistical models in predicting student outcomes and guiding interventions. However, a thorough review of the research finds significant variance in the scheduling and implementation of early intervention programs across institutions, driven by resource availability, organizational structures, and student population demands. The literature on early identification of at-risk students in community colleges presents a compelling case for proactive intervention strategies to improve retention. Studies such as Ortiz-Lozano et al. (2023) and Villano et al. (2018) highlight the potential of data-driven approaches to identify students struggling academically or socially. By analyzing diverse data points, including academic performance, attendance, and demographic information, institutions can tailor support services to meet the unique needs of individual students. Predictive modeling, as demonstrated by Villano et al. (2018), can further enhance the accuracy and timeliness of these interventions. However, a critical literature synthesis reveals several key challenges and areas for improvement. The timing and implementation of early interventions vary widely across institutions, often hindered by resource constraints and organizational barriers. As Ortiz-Lozano et al. (2023) note, the financial burden of implementing comprehensive early warning systems can be a significant obstacle for many community colleges. Moreover, Villano et al. (2018)'s reliance on complex statistical models may be inaccessible to some practitioners who need more training and expertise. A more nuanced and comprehensive approach is needed to maximize the effectiveness of early identification efforts. The analysis includes sophisticated data analysis tools and a deep understanding of the diverse needs and challenges faced by students from different backgrounds. Tinto (1975) highlights the importance of faculty-student interaction, suggesting that a supportive and inclusive campus environment can significantly impact student persistence. Similarly, Campbell and Campbell (2017) emphasize the role of mentorship in fostering a sense of belonging and connection, particularly for underrepresented groups. Future research should focus on developing more comprehensive and flexible intervention models that integrate academic support with social and emotional support services. As James (2022) points out, student expectations play a crucial role in persistence, and interventions that address academic and non-academic factors are likely more effective. Additionally, institutions should invest in training and resources to ensure that Faculty and staff are equipped to identify and support students at risk of dropping out. By combining data-driven insights with a holistic understanding of student needs, community colleges can create a more supportive and responsive learning environment that promotes student persistence and success. This multifaceted approach holds the promise of not only improving retention rates but also fostering a more equitable and inclusive higher education system. For instance, Ortiz-Lozano et al. (2023) demonstrated the value of utilizing diverse data sources to pinpoint students needing targeted support. Villano et al. (2018) showed the effectiveness of statistical models in predicting student outcomes and guiding interventions. However, the research also reveals that the timing and implementation of early intervention strategies can vary significantly across institutions, depending on available resources, organizational structures, and the specific needs of the student population. A critical examination of the literature suggests that successful early identification initiatives require a multifaceted approach. This includes using robust data analysis and predictive modeling and a deep understanding of the unique challenges faced by diverse student subgroups. Furthermore, the effective implementation of early warning systems hinges on adequate resources, including trained personnel, appropriate intervention programs, and a supportive institutional culture. To optimize the impact of early identification on student retention, future research should focus on developing more comprehensive and flexible intervention models tailored to the specific needs of different student populations and institutional contexts. Additionally, further investigation is needed to identify the most effective timing and delivery methods for early interventions, ensuring that students receive the right support at the right time.