I need help adding the approaches I will use for your project. I need help adding one sentence after each paragraph to denote my data collection, data analysis, and method selection, research design selection. These sentences will support your use of these approaches.

A structured framework tailored to the Applied Improvement Project Model (AIPM) enhances the depth and focus of investigations into student engagement and retention, building upon the valuable insights gained from earlier studies like Tight (2020). Frameworks such as Springer and Aragon's (2020) cyclical model or Springer's (2014) iterative action research process resonate with the AIPM's emphasis on continuous improvement. These frameworks provide a roadmap for ongoing evaluation and refinement, ensuring that interventions remain responsive to the evolving needs of students. In the initial stages of the AIPM, Spaulding and Falco's (2013) problem-focused framework can also be instrumental in diagnosing the root causes of low engagement or retention rates. By providing specific prompts and strategies for each phase of the investigation, these frameworks enable a comprehensive and targeted exploration of the multi-faceted factors influencing student success. Tinto's student integration model (1975) serves as the theoretical lens through which the intervention to improve staff engagement will be viewed, recognizing staff 'engagement's pivotal role in student success. Research has shown a gap between engaged staff members and higher job satisfaction, improving student outcomes as they are likelier to go above and beyond their roles (Eldridge et al., 2021). Tinto's model emphasized the significant influence of a student's academic and social integration within the institution on their retention (Tinto, 1975). While not explicitly addressing staff engagement, the model implies that a supportive and engaged staff is essential for creating an environment conducive to student success. In educational settings, engaged staff can foster positive interactions with students, provide effective academic support, and contribute to a vibrant campus community, thus aligning with Tinto's (1975) emphasis on academic and social integration. Research supports the positive impact of staff engagement on student success and retention. For instance, Kim and Sax (2017) found that engaged advisors are more likely to build rapport with students and provide them with the guidance and support they need to overcome academic challenges. The literature reveals a multi-faceted approach to enhancing student success and retention in community colleges, integrating academic support services and predictive analytics as a promising strategy. Research by Smith and Jones (2020) found that tutoring programs significantly increased student persistence and graduation rates, highlighting the importance of comprehensive academic support in empowering students and fostering their academic achievement. Similarly, Fong et al. (2017) demonstrated the positive impact of mentoring on student engagement and academic performance. The approach aligns with the growing body of research emphasizing the importance of data-driven decision-making and individualized support in optimizing student outcomes (Garcia et al., 2021). The research has further solidified the understanding that a comprehensive approach integrating academic support services and predictive analytics can create a more supportive and responsive learning environment for community college students, potentially leading to increased retention rates. Academic support services remain central to boosting student enrollment and retention. Research on this theme has investigated enablers such as tutoring, mentoring, and structured student study assistance (Slatyer et al., 2016). A study by Zientek et al. (2022) and Slatyer et al. (2016) focused on a peer tutoring system in a community college context, revealing that students who received tutoring had enhanced performance and higher retention rates than those who did not. Further research by Bowman et al. (2021) investigated the effectiveness of supplemental instruction sessions in high-risk courses, finding that students who frequently attended these sessions achieved better results than their counterparts who rarely or never participated. These findings underscore the importance of academic support services in improving student outcomes and retention. As a form of academic support, tutoring has been widely studied in literature. Research indicates that tutoring programs can significantly enhance student learning and academic achievement. Tutoring programs can be particularly effective in helping students who may be underprepared or facing challenges in specific subjects. The literature suggests that effective tutoring programs should be tailored to meet student's individual needs and integrated into the overall academic support system. Mentoring has also been identified as a crucial component of academic support services. Studies have shown that mentoring can provide students with valuable guidance, support, and encouragement, which can help them navigate academic and personal challenges (Crisp et al., 2017). The literature suggests that effective mentoring programs should pair students with mentors who can provide them with the necessary support and guidance and create a supportive and inclusive environment where mentors and mentees can build meaningful relationships. Supplemental instruction (SI) is another form of academic support that effectively improves student learning and retention. SI sessions typically involve peer-led study groups linked to specific courses and provide students with opportunities to review course material, clarify concepts, and develop study strategies. The SI can be particularly beneficial for students struggling in high-risk courses and may need additional support to succeed. Predictive analytics is crucial in identifying at-risk students and enabling timely interventions. Research by Romero and Ventura (2020) examined the application of early warning systems (EWS) in community colleges, finding that they facilitated early interventions that increased retention levels. A growing body of research, including studies by Latif et al. (2023) and Fahd et al. (2021), has explored the potential of machine learning algorithms in predicting enrollment retention. These studies successfully identified students at high risk of dropping out, leading to improved retention rates. The research significantly contributes to the burgeoning field of predictive analytics in education. While significant strides have been made in understanding and addressing student retention, the literature review reveals several areas requiring further exploration. Despite the promise of predictive analytics and academic support services, their implementation faces challenges like data accuracy, student privacy, and the need for continuous model updates (Fahd et al., 2021). As Zientek et al. (2022) point out, the success of early warning systems and predictive models hinges on the quality of data and analysis methodologies, which may vary across institutions and contexts. Additionally, ensuring consistent engagement and providing adequate training for tutors and mentors in academic support programs are critical. The effectiveness of these programs can be influenced by factors such as tutor-student matching, the availability of resources, and the institutional climate. To effectively tackle these challenges, robust data practices, continuous assessment, and a focus on ethical considerations are crucial. Further research is needed to explore the contextual factors that impact the success of retention interventions and to develop tailored strategies for diverse student populations. The literature review has deepened the understanding of the problem of practice by highlighting both the potential and the challenges associated with integrating academic support services and predictive analytics in community colleges. It has emphasized that while these approaches offer promising solutions, their effectiveness hinges on careful implementation, ongoing assessment, and student involvement. The enhanced understanding has shaped the approach to the problem by emphasizing the need for a comprehensive and nuanced strategy that considers the diverse needs of students and the ethical implications of data-driven approaches. The future direction involves leveraging the strengths of academic support services and predictive analytics while addressing the challenges through robust data practices, ongoing assessment, and student involvement to create a more supportive, responsive, and equitable learning environment.