Literature Review Synthesized from Ernest Kwakye’s Annotated Bibliography by Alison Winiarski
Student success in university settings can be conceptualized through student performance, such as a student’s GPA, and retention. The following details variables that have been found to impact both constructs in hopes to understand how data can be used to improve student success.
Student Performance and Retention
From a search of the current literature on student success, there were similar findings of constructs that influence both student performance and retention. For instance, students with a high school GPA of 2.5 or higher and who have access to academic support programs are likely to perform better and finish their degrees (Garton et al., 2000; Grace-Odeleve, 2020). Similarly, better performance and retention rates were characteristic of students in their first year who repeatedly use the university’s academic resources such as the library (Kot, 2014; Tewell, 2015). When considering students from underrepresented populations, those that have higher confidence in their academic abilities are likely to have higher GPAs and continue in college, however, SAT scores are not a good predictor of student success from this population (Creighton, 2007). With these findings to begin this discussion, the next sections go more in-depth about findings for student performance and retention independently.
When evaluating predictors of student performance, four categories emerged from the literature: pre-entry factors, variables characteristic of first-year students, use of university resources, and personal or social influences.
Pre-entry factors, or existing student variables before entering college, have been found to influence student performance. These factors include math and science test scores along with ACT scores, while the best predictor of academic success in the first year first semester of college is high school GPA (Beck & Davidson, 2001; Garton et al., 2000; Gersten, 2018; Veenstra, 2009). Outside of academic outcomes, college performance is also affected by how prepared they feel for college. For instance, first-generation students who lack confidence in their preparedness for college were found cause students to prefer less rigorous coursework as well as lower interest in STEM programs (Pratt et al., 2019). It was noted that math skills can indicate student readiness, however, a more thorough look at existing literature is needed to understand other influences (Veenstra, 2009). From these insights, these pre-entry factors can be used to identify students who are most likely to succeed in the first semester of their first year as well as how feelings of college readiness can inform students’ decisions regarding their academic trajectory.
First Year Student Variables
Once students enter college, several variables were noted to affect academic performance. Similar to first-semester college students, first-year students’ first-semester GPA in college was the best predictor of academic success in their second semester (Gersten, 2018). Not only does students’ first semester GPA predict their subsequent GPA, but students that do not achieve their target GPA, or the GPA they aspired to have at the beginning of the semester, caused students to lower their target GPA for their second semester (Thibodeaux et al., 2017). However, students who exceeded their first-semester target GPA planned to socialize more in the subsequent semester (Thibodeaux et al., 2017).
Looking beyond GPA, other factors specifically impacting student performance in their first year are students’ class behaviors and student-faculty interactions. Regarding the former, students that have poor study habits, poor class attendance, and late submission of assignments poorly predict first-time students’ first-semester GPA (van der Meer et al., 2018). Additionally, student-faculty interactions have been found to contribute to the success of college students along with academic advising support (Turner & Thompson, 2014). Specifically, those who interact with faculty either in or out of class regarding ideas or career aspirations had a higher overall GPA (Webber et al., 2013). Overall, first semester GPA, class behaviors, and student-faculty interactions for first-year students impacted student success in terms of academic performance.
At most universities, resources are available for all students. However, intervention programs such as Freshman Learning Communities (FLC) target those first-year students who are performing lower than average (Hotchkiss et al., 2007; Sweat, 2016). These programs usually last for about half of the first year of college for freshmen, however, the effectiveness of these programs is mixed (Hotchkiss et al., 2007; Sweat, 2016). FLCs have been found to increase GPAs from about three quarters to a full letter grade with no effect for white females, however, an additional and more recent study has found no relationship between FLC programs and GPA or retention rates (Hotchkiss et al., 2007; Sweat, 2016). This suggests that FLC programs have varied outcomes on student performance.
Other systems have been created to detect and predict students’ performance in class and flag students that have a lower trajectory, such as Purdue’s Course Signals. This system has been useful in giving students feedback and signaling the need for potential interventions or additional support to low-performing students (Arnold & Pistilli, 2012). Although the effectiveness of this system is not known, the variables informing this system include student grades in individual courses, demographic characteristics, past academic history, and student engagement through the university’s Learning Management System (Arnold & Pistilli, 2012).
Looking beyond interventions tailored to first-year students, other available university resources include library and counseling services. Regarding library services, book loans, database logins, electronic journal logins, and library workstation logins were positively associated with students’ GPAs (Tewell, 2015). Thus, students who utilize library resources typically have higher GPAs. Additionally, students who encounter academic difficulties sometimes decide to utilize the university’s counseling services. Students with these difficulties that consider counseling services within their first year are likely to perform better than those that do not (Veenstra, 2009). Knowing this, it could be advantageous to create interventions that are tailored for low-performing students who are not using library or counseling services to engage students and ensure they are being supported in the ways they need.
Personal and Social Influences
Although many campus-wide factors influence student performance, other variables outside of the university setting also have an impact. For instance, students who live on campus are positively and strongly related to cumulative GPA (Webber et al., 2013). Thus, those who live on campus tend to have higher cumulative GPAs compared to those that do not live on campus. Not only does the student’s living situation affect GPA, but peer groups can also have an influence. Students who have varied peer groups were found to have negative relationships with cumulative GPA (Webber et al., 2013). Therefore, the more varied the student’s peer group, the lower their cumulative GPA will be. Both the living situation and peer groups are difficult to track from an institutional level, however, these could be key explanatory variables that affect student performance.
Apart from factors affecting student performance, literature has also found distinctive findings on student retention. Generally, ACT and SAT scores have been found to positively predict the likelihood of students persisting through college, meaning students with high test scores tend to continue in their studies (Barbera et al., 2020). Additionally, financial status is the most pressing challenge to college retention regardless of the student’s race, whereas students with financial aid are more likely to persist through college (Barbera et al., 2020; Stewart et al., 2015; Xu & Webber, 2018). When considering this, it should be noted that retention rates do not vary by gender (Stewart et al., 2015). Although retention has been explored generally in all students, there are also factors found to specifically influence first-year students and first-generation students.
First Year Students
First-year students’ retention rates are influenced by a myriad of variables. These include peer tutoring, collaborations among students, peer mentoring, peer study groups, new student orientations, academic convocations, campus activity engagement (i.e., social gatherings, community engagement projects), and early warning or alert systems (Grace-Odeleve, 2020; Turner & Thompson, 2014). Although these influences were mentioned, nuanced relationships between each and retention were not evaluated. In addition to these social and behavioral factors, quantitative metrics also influence retention.
Quantitative measures, such as GPA and test scores, before and during college influence retention. Similar to student performance, high school GPA and the SAT/ACT composite scores have been found to indicate who is likely to persist through college, although high school GPA was a better indicator (Barbera et al., 2020; Garton et al., 2000; Sweat, 2016; Veenstra, 2009). Along with quantitative measures, students who were prepared academically for college are more likely to reenroll (Stewart et al., 2015; Turner & Thompson, 2014). Thus, students’ performance and college readiness are important factors for retention. Once students are enrolled in college, GPA remains an important predictor for retention. For instance, it has been found that having a GPA of at least 2.5 to 3.0 and above during their first year is likely to continue in their academic journey (Davis, 2010; Garton et al., 2000; Sweat, 2016). In addition to GPA, the academic intensity of the student’s high school curriculum also predicts retention (Barbera et al., 2020).
While GPA before and during college was found to be a highly important indicator of college retention rates, several factors were found to not help. For instance, students who did not have access to or were made aware of advising information and curriculum planning were unlikely to reenroll after their first year of college (Davig & Spain, 2003). Program information that was unable to be communicated effectively to first-year students only harmed students’ likelihood of returning the following year. Additionally, students who were not exposed to social group activities during their first year were also unlikely to continue in their studies (Davig & Spain, 2003). Finally, those students who were unprepared and had ineffective study skills had high withdrawal rates within their first year (Davig & Spain, 2003; Turner & Thompson, 2014). Having students be informed of program-related information, ways they can get connected with the university community, and effective ways of studying are all important facets to focus on to increase retention.
When evaluating retention rates of first-time students, a few findings were specifically relevant to underrepresented populations. In the first year, students from these populations have higher rates of continuing college after their first year if they are culturally and socially connected to the college (Creighton, 2007). Beyond this, housing problems, financial aid, and scholarship support also affect retention (Creighton, 2007). These insights should help gain an understanding of areas that affect underrepresented populations and how they can differ from other first-year students. This could be relevant when researching these populations.
First Generation Students
Apart from first-year students, first-generation students also have notable variables that influence the likelihood of continuing to enroll in school. Similar to underrepresented populations, it is important for retention that first-generation students feel connected to their college’s social structure. If they are not able to find other college students to relate to, these students are more likely to drop out (Pratt et al., 2019). Additionally, financial stability is a major cause of first-generation students dropping out (Pratt et al., 2019). This is similar to the overall finding for all college students; however, it is important to know this is also true and an important factor for this population.
Throughout this extensive search regarding factors that affect student success, several overarching findings have been shown. First, student success is often conceptualized as being student performance and retention rates. Second, student performance and retention are both influenced by GPA and the frequency a student uses university resources. Third, student performance in college is affected by variables from before college such as GPA in high school as well as actions that take place during college. GPA was the most important predictor of student performance, but student behavior, student-faculty interaction, use of university resources, and personal and social influences also impact it. Fourth, retention is influenced by variables including GPA, financial status, and having support socially and academically. Together, these relationships have been found to impact student success.
Throughout this literature search, some operationalized terms have emerged that can help with future research. For instance, Tewell (2015) conceptualized academic success by looking at both students’ cumulative GPA and fall-to-fall retention. Additionally, Tewell also focused on how much students use university resources. Therefore, student library use was measured through student logins to library databases and websites, the use of electronic books and journals, chat reference questions, and workshop signups. Finally, Beck and Davidson (2001) used various constructs to identify students who were a risk for poor grades. This was done through the results of their Survey of Academic Orientation, high school GPA, and other unspecified academic variables. These are a few variables that have been previously used that could be helpful in the future.
Limitations and Future Directions
When evaluating early indicators of “D” or “F” grades or withdraws (DFWs) in college students, there are very few to no resources available. Instead, most articles are focused on DFWs as variables in a model to predict the retention of college students. Future research could focus on continuing this search or doing an internal analysis to find early indicators of DFW. This could benefit both faculty and the university to identify students who display early indicators of DFW grades and intervene in hopes to prevent this outcome and mitigate DFW rates.
Future directions could also include continuing to look at how previous studies conceptualize student success, retention, and graduation. Additionally, focusing on what is more commonly published and what is unique could be helpful in future research.
Arnold, K. E. & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. ACM Internal Conference Proceeding Series, 267-270. http://doi.org/10.1145/2330601.2330666
Barbera, S. A., Berkshire, S. D., Boronat, C. B., & Kennedy, M. H. (2020). Review of Undergraduate Student Retention and Graduation Since 2010: Patterns, Predictions, and Recommendations for 2020. Journal of College Student Retention: Research, Theory & Practice, 22(2), 227–250. https://doi.org/10.1177/1521025117738233
Beck, H. P. & Davidson, W. D. (2001). Establishing an early warning system: Predicting low grades in college students from survey of academic orientations scores. Research in Higher Education, 42(6), 709-723. https://doi.org/10.1023/A:1012253527960
Creighton, L. M. (2007). Factors affecting the graduation rates of university students from underrepresented populations. International Electronic Journal for Leadership in Learning, 11(7).
Davig, W. & Spain, J. W. (2003). Impact on freshmen retention of orientation course content: Proposed persistence model. Journal of College Retention, 5, 305-323.
Davis, D. (2010). College success for all students: An investigation of early warning indicators of college readiness. https://digital.library.unt.edu/ark:/67531/metadc33141/
Garton, B.L., Dyer, J.E. & King, B. O. (2000). The use of learning styles and admission criteria in predicting academic performance and retention of college freshmen. Journal of Agricultural Education, 41(2), 46-23. https://doi.org/10.5032/jae.2000.02046
Gersten, B. M. (2018). Learning communities and early student success. UNLV Theses, Dissertations, Professional Papers, and Capstones. 3360. http://dx.doi.org/10.34917/14139875
Grace-Odeleye, B. (2020). Integrated support strategies for promoting of students’ retention and achievement during first year of college. International Journal of Contemporary Education, 3(1). https://doi.org/10.11114/ijce.v3i1.4725
Hotchkiss, J. L., Moore, R. E. & Pitts, M.M. (2007). Freshmen learning communities, college performance and retention. Educational Economics, 14(2), 197-210. https://doi.org/10.1080/09645290600622947
Kot, F. C (2014). The impact of centralized advising on first-year academic performance and second-year enrollment behavior. Research in Higher Education, 55(6), 527-563. http://dx.doi.org/10.1007/s11162-013-9325-4
Pratt, I. S., Harwood, H. B., Cavazos, J. T., & Ditzfeld, C. P. (2019). Should I stay or should I go? Retention in first-generation college students. Journal of College Student Retention: Research, Theory & Practice, 21(1), 105–118. https://doi.org/10.1177/1521025117690868
Stewart, S., Lim, D. H. & Kim, J. (2015). Factors influencing College Persistence for first-time students. Journal of Development Education, 38(3), 12-16, 18-20).
Sweat, A. M. (2016). The effect of learning communities on freshman student retention rates and GPA at a Public 4-Year institution of higher education. Online Theses and Dissertations, 434. https://encompass.eku.edu/etd/434
Tewell, E. C. (2015). Use of Library services can be associated with a positive effect on first-year students GPA and Retention. Evidence Based Library and Informational Practice, 10(1), 79-81.
Thibodeaux, J., Deutsch, A., Kitsantas, A. & Winsler, A. (2017). First-year college students’ time use. Journal of Advanced Academics, 28(1), 5-27. https://dor.org/10.1177/1932202X16676860
Turner, P. & Thompson, E. (2014). College Retention Initiatives Meeting the Needs of Millennial Freshman Students. College Student Journal, 48(1), 94-104.
van der Meer, J., Scott, S., & Pratt, K. (2018). First semester academic performance: The importance of early indicators of non-engagement. Student Success, 9(4), 1-12. https://doi.org/10.5204/ssj.v9i4.652
Veenstra, C.P. (2009). A strategy for improving freshman college retention. The Journal for Quality and Participation, 31(4)19-23
Webber, K.L., Krylow, R.B., & Zhang, Q. (2013). Does involvement really matter? Indicators of college student success and satisfaction. Journal of College Student Development 54(6), 591-611. https://doi:10.1353/csd.2013.0090
Xu, Y. J.& Webber, K. L. (2018). College student retention on a racially diverse campus: A theoretically guided reality check. Journal of College Retention: Research, Theory & Practice, 20(1), 2-28. https://doi/10.1177/1521025116643325