Risk Factors and Effectiveness of Implemented Academic Interventions on Student Retention at a Hispanic-Serving Institution

Abstract

We examined three large freshman cohorts from Texas State University, a Hispanic-Serving Institution (HSI), to identify risk factors that might affect academic retention. Risk factors supported by empirical studies such as first-generation, ethnicity, gender, financial aid need, high school performance, and living off campus were examined. We also determined the effectiveness of four academic interventions in achieving retention of students that were on academic probation for two consecutive semesters. Statistical analyses demonstrated that being a first-generation college student and receiving financial aid were significant risks for attrition in all cohorts. Living off-campus and being an URM student were not. Retention in two cohorts was dependent on the combinations of three peer mentoring, two academic coaching, and two to three academic advising sessions. A probation predictive model was developed. Finally, we discussed the importance of examining risk factors and interventions that are tailored to each academic institution.

Keywords

HSI attrition risk factors academic interventions

Risk factors associated with attrition of university students and academic interventions designed to improve success have been reported (Johnson et al., 2018; Lopez & Horn, 2020; Pascarella et al., 2004; Pratt et al., 2019; Reyes, 2011; Toven-Lindsey et al., 2015). Some higher education institutions analyze their own students’ risk factors, or refer to the literature to identify factors that might apply to their own students and develop success plans to improve student retention. As a possible solution for low student retention, several higher education academic institutions have implemented various interventions. Academic interventions are implemented using assumptions as to the reasons for low student retention without considering the institution’s own student population. Targeted interventions aimed at students’ needs might be more effective in retaining students rather than the use of a disparate set of interventions, which might not be effective at increasing retention (Mattern et al., 2015). Therefore, it is important to explore and determine the specific risk factors that affect students in an academic institution and implement strategies and programs tailored to the retention of such students.

Texas State University PACE Center

In the fall 2011 semester, Texas State University (TXST) began implementing its comprehensive success plan focused on centralizing services for freshman students. This plan included two interrelated goals: (1) help students clarify their career goals, and (2) assist students in developing and implementing an educational plan to meet their goals. A year later, TXST University opened the doors to the PACE (Personalized Academic and Career Exploration) Center that provided direct services such as total intake advising, career exploration, and mentoring for freshman. The PACE Center’s intentional and comprehensive approach as a one-stop shop with the goals of improving a student’s academic first-year experience and enhancing their engagement experiences, is a similar model adopted by other institutions (Dalton & Crosby, 2014). Complementing these programs within the PACE Center is a Freshman Seminar, a required one-credit course that introduces students to university education and includes a career exploration curriculum designed to achieve specific goals and objectives. A first-year seminar is one strategy that some universities are employing to engage freshman students and increase retention (Dalton & Crosby, 2014; Kimbark et al., 2016; Kuh et al., 2008; Miller & Lesik, 2015; Padgett & Keup, 2011; Vaughan et al., 2014; Young, 2020).

One of the main tasks of the PACE Center is to reduce the number of students that are not retained after two consecutive semesters on probation. The PACE Center identifies students that may become academically unsuccessful using a set of risk factors identified in the literature. Seven potential risk factors are collected from the University: gender, ethnicity, first-generation status, high school performance, financial aid eligibility, enrolling in less than 14 credit/hours, and living off campus.

Interventions at the TXST University

Four types of interventions are used to support student success. Three of which are classified as engagement interventions: Freshman Seminar, Peer Mentoring, Academic Coaching and Academic Advising.

Freshman Seminar is a designed to help first-year students learn strategies to better navigate their new academic environment. Courses are designed to build students’ practical skills and concepts that will promote their academic success and encourage them to explore connections between university study and long-term career paths. Courses include presenters to showcase university services available around student needs. For example, instructors invite Academic Coaches, Student Learning Assistance Center (SLAC) tutors, or financial aid staff who present to classes, provide support, and guide students.

The Peer Mentoring Program is designed to help transition new students to their new life at the University, both socially and academically. Peer Mentors are assigned to three freshman seminar sections and serve a minimum of 69 students during their entire first year. Peer Mentors are undergraduate upperclassmen who are active in the campus community and have a minimum 3.0 GPA. Peer mentors are required to attend freshman seminar classes, hold office hours, and meet with each student on a group or individual basis twice a semester. During these meetings, Peer Mentors discuss academic issues, concerns, and questions with first-year students, and help map out academic goals. The Peer Mentoring program is certified by the College Reading and Learning Association’s (CRLA) International Peer Educator Training Program Certification (IPTPC) at the Mastery Level. The Peer Mentoring team meets weekly for an hour to discuss classroom progress, student concerns, and updates.

The Academic Coaching Program fosters a professional relationship between a coach and a student. Academic coaching is an action-oriented approach to enable students to become self-advocates for their education guided by a positive supporter. Academic Coaches participate in a minimum of 80 hours of annual professional development training. The training provides coaching strategies to help students gain the practical and professional skills needed to successfully complete college and secure gainful employment. Academic Coaches are graduate students who hold monthly meetings with undergraduate students that typically last 30 minutes to an hour. In their meetings, Academic Coaches provide guidance to students in their learning process enabling them to take a proactive approach in their education and overall well-being. The services provided by Academic Coaches can be divided into two types of interventions, academic and socio-emotional. Academic services include the development of goals and strategies for academic success, including academic skills such as note taking, study, problem solving, and test taking. Socio-emotional services include identifying personal development resources, building academic self-confidence and self-motivation, enhancing financial literacy, and teaching students how to achieve greater academic success.

Academic Advising helps students become familiar with navigating the education process at the University. Advisors focus on academic plan development by scheduling 30-minute meetings to explore majors, discussing course recommendations, assisting in navigating through academic endeavors, and teaching resources to help student success. Academic Advisors are trained in a two-week onboarding process structured to align with the National Academic Advising Association (NACADA) Core Competencies model (NACADA, 2020). This training balances conceptual, information, and relational competencies.

Purpose of the Present Study

In this study, we examined retention of second semester sophomores who began their university studies at TXST University, a large, residential HSI. Three cohorts (2016-2017, 2017-2018, and 2018-2019) consisting of 17,159 students were used in an empirical study to determine which risk factors presented in the literature were statistically significant predictors of attrition at the studied institution. In addition, four interventions were examined to determine the effectiveness in achieving retention after two semesters of academic probation. Finally, a binomial regression analysis was used to develop a probation model for students attending San José State University (SJSU) to determine if the studied risk factors at TXST University are the same at a different HSI.

Relevant Literature

Several predictive models have been proposed to examine student retention (Bean, 1985; Kerby, 2015; Murtaugh et al., 1999; Pascarella & Terenzini, 1977; Spady, 1970; Tinto, 1975). These predictive models include pre-college factors, early academic success indicators, and identification of evidenced based at-risk factors (Cardona et al., 2020). Kerby’s (2015) predictive model contains five components: external factors, pre-college factors, internal factors, adaptation, and outcomes and suggested that predictive models of retention should include pre-college factors (high school GPA, ACT and SAT scores).

Additional studies demonstrated the impact of students’ initial experiences on a college campus is linked to their likelihood of degree completion. These early academic outcomes, such as first semester GPA and number of earned hours, have a major impact on the student’s successful adjustment to college (Pascarella & Terenzini, 1991). Other risk factors include gender, ethnicity, first-generation, socioeconomic status, and living off campus among others (Bean, 1985; Glenn, 2003; Lopez & Horn, 2020; Mattern et al., 2015, Mbuva, 2011; Pascarella et al., 2004). Mattern et al. (2015) performed cluster analysis to examine retention after the first year of college using a national database that includes diverse students and academic institution profiles. Mattern et al. (2015) found that the cluster with the highest percentage of non-retained students is comprised of students with at least one non-college educated parent, is 30% Black or Latinx, and has the lowest high school GPA of the examined clusters.

Academic interventions that have been shown to increase student retention include: supplemental instruction and tutoring (Balzer Carr & London, 2019; Laskey & Hetzel, 2011), first-year seminar (Dalton & Crosby, 2014; Padgett & Keup, 2011), academic advising (Swecker et al., 2013; Zhang et al., 2017), student coaching (Capstick et al., 2019; Dalton & Crosby, 2014), and peer mentoring (Kiyama & Luca, 2014; Reyes, 2011). Glenn (2003) suggested that most effective interventions aimed at increasing the retention of Black students is to identify students with risk factors at enrollment and track their academic progress.

Method

Study Sites and Data Collection

Data from two HSIs with similar student population sizes were examined. TXST University (38,808 students) is located in the South-Central region of the U.S. SJSU (32,773 students) is located in the Pacific region of the U.S. Historical data from three student cohorts (17,159 students) of TXST University from the 2016-2017, 2017-2018, and 2018-2019 academic years were used in this study. Comparison data from 739 students from the Department of Biological Sciences at SJSU were used. Data from both institutions did not contain identifiable personal information (such as name, home address, or student ID). Demographic characteristics such as gender, ethnicity, probation status, retention status, first-generation status, financial aid eligibility, living on or off campus, high school performance, and number of enrolled credit/hours were used in the University analysis. Intervention characteristics at TXST University such as: grades in the freshman seminar, number of peer mentoring, academic coaching, and academic advising sessions attended were also used in the analysis.

For comparison purposes, data for incoming freshman students in all Biological Sciences programs at SJSU for academic years 2014-2015, 2015-2016, and 2016-2017 were obtained from the SJSU Office of Institutional Effectiveness and Analytics. The variables in the database for each student were ethnicity, gender, first generation college student, whether the student is eligible for financial aid, part-time or full-time status, age group, native or transfer student, community college attended, SAT or ACT score, and probation status. Grades in freshman Biology (Biology 30/1B and Biology 31/1A) and Chemistry (Chemistry 1A-1B) core courses and upper division Genetics (Biology 115) at SJSU were included in the comparison group. Course grades are categorized into pass (for a grade of C and above), and fail for lower grades.

For the TXST University and SJSU analysis, ethnicity was categorized into underrepresented minority (URM) for students whose reported ethnicity is Latinx or Black; not URM for students whose reported race/ethnicity is Asian or Caucasian; and other for students whose reported ethnicity is not Asian, Black, Caucasian, or Latinx.

Statistical Analyzes

From the TXST University, the numbers of students who were retained, not retained after two consecutive semesters on probation, and unknown status were calculated from academic years 2016-2017, 2017-2018, and 2018-2019 (Table 1). The students that were categorized as unknown status were those in good academic standing, not on probation, but were not retained after a year of attending TXST University. Either these students transferred to other academic institution or took a gap break from enrollment, possibly due to financial reasons. TXST University does not track these students.

Table 1.

Retained, Not Retained, and Unknown Status Students at TXST University.

Academic year^a	Registered full time freshmen	Retained	Not retained	Unknown status^b
2016–2017	5,573	4,332 (78%)	590 (10%)	652 (12%)
2017–2018	5,682	4,432 (78%)	627 (11%)	625 (11%)
2018–2019	5,904	4,521 (77%)	672 (11%)	713 (12%)

^aTwo years were used.

^bStudents in good standing who did not return to the institution.

For this study, comparisons were made for students on probation for two consecutive academic semesters in each cohort: Group 1 (not retained) and Group 2 (retained). Two sample T-tests were used to compare passing (C or better) and failing grades (DFUW) for the Freshman Seminar, and the number of interventions between Groups 1 and 2. Chi square tests of independence were used to determine which risk factors were related to attrition.

Binomial logistic regression was used to determine whether academic probation at SJSU (the binary outcome variable) could be predicted based on the following predictors: URM status, gender, first-generation status, financial aid eligibility, part-time or full-time status, age group, native or transfer student, ACT or SAT score, and grade for each course that the student had already completed.

Results

Student Demographics

Table 2 shows the gender and ethnicity distribution of the three TXST University cohorts. The percentage of enrolled female freshman for all three cohorts was 62%, 64%, and 63% in 2016-2017, 2017-2018, and 2018-2019, respectively. Latinx students were 39% in the 2016-2017 cohort, 40% in the 2017-2018, and 41% in the 2018-2019. The other ethnic group with high percentages was the Caucasian group; 43% in the 2016-2017 and the 2017-2018 cohorts, and 40% in 2018-2019 cohort. The remaining ethnic groups with low enrollment percentages included: Black, Asian, multiracial, American Indian/Native Alaskan, Hawaiian/Pacific islanders, and few students who did not report their ethnicity.

Table 2.

Demographic Characteristics of the 2016-2019 TXST University Freshmen Cohorts.

Cohort	Gender	Latinx	Caucasian	Black	Asian	Multiracial	American Indian/Native Alaskan, Hawaiian/Pacific Islander	International	DNR
2016–2017	3,474 (F) 2,099 (M)	2,177	2,407	733	113	98	24	15	6
2017–2018	3,650 (F) 2,032 (M)	2,283	2,435	708	114	100	24	12	6
2018–2019	3,737 (F) 2,167 (M)	2,434	2,348	835	146	100	21	14	6

Note. DNR = did not report.

Attrition Risk Factors

Two significant attrition risk factors were found in all cohorts: financial aid and first-generation status (Tables 3 and 4). However, each cohort had additional attrition risk factors that were not shared with each other. In the 2016 cohort, three attrition risk factors (financial aid, first- generation status, and enrolling in 13 units or less) were found (Table 3). These three risk factors and attrition were not related in the 2016 cohort (p=0.127; Table 4). The 2017 cohort contained four attrition risk factors: financial aid, first generation status, high school performance, and registered in 13 units or less (Table 3). There was no relationship between these four risk factors and attrition in the 2017 cohort (p=0.958; Table 4). In the 2018 cohort, there were two risk attrition factors: financial aid and first-generation status (Table 3). There was a relationship between these two factors and attrition in the 2018 cohort (p=0.041; Table 4).

Table 3.

Risk Factors That Result in Attrition at TXST University.

Cohort	Tested risk factor	Chi-square test of independence	Attrition risk factor
2016–2017	EFC<$2,000	X²(1)=8.233, p=0.004	Yes
	First-generation status	X²(1)=4.651, p=0.031	Yes
	Females	X²(1)=1.409, p=0.235	No
	Males	X²(1)= 0.314, p=0.578	No
	URM	X²(1)= 0.182, p=0.786	No
	High School Performance	X²(1)=0.0301, p=0.8622	No
	Registered in 13 units or less	X²(1)=33.667, p<0.00001	Yes
	Living off campus	X²(1)=3.230, p=0.0722	No
2017–2018	EFC<$2,000	X²(1)=9.318, p=0.002	Yes
	First-generation status	X²(1)=61.162, p<0.00001	Yes
	Females	X²(1)=2.834, p=0.092	No
	Males	X²(1)=3.079, p=0.079	No
	URM	X²(1)=0.0782, p=0.923	No
	High school performance	X²(1)=36.420, p<0.00001	Yes
	Registered in 13 units or less	X²(1)=5.04, p=0.024	Yes
	Living off campus	X²(1)=1.338, p=0.247	No
2018–2019	EFC<$2,000	X²(1)=9.384, p=0.002	Yes
	First-generation status	X²(1)=9.301, p=0.002	Yes
	Females	X²(1)=0.676, p=0.410	No
	Males	X²(1)=0.640, p=0.423	No
	URM	X²(1)=0.235, p=0.678	No
	High school performance	X²(1)=0.006, p=0.935	No
	Registered in 13 units or less	X²(1)=1.508, p=0.298	No
	Living off campus	X²(1)=0.190, p=0.662	No

Note. EFC<$2,000: family can only contribute $2,000 or less for their daughter/son university education. FG: first-generation. HS Performance: bottom 25% of their high school graduated seniors. Bold face indicates significant results.

Table 4.

Relationship Between Risk Factors and Attrition.

Cohort	Risk factors	X² test of independence	Relationship
2016–2017	EFC<$2,000, FG, Registered in 13 units or less	X²(2)=4.124, p=0.127	Independent
2017–2018	FG, EFC<$2,000, Registered in 13 units or less, and HS performance	X²(3)=6.899, p=0.958	Independent
2018–2019	FG, and EFC<$2,000	X²(2)= 6.366, p=0.041	Dependent

Gender (female or male), living off campus, and being an URM were not attrition risk factors for the cohorts examined (p>0.05; Table 3).

Academic Interventions

Four academic interventions were examined in this study: Freshman Seminar, peer mentoring, academic coaching, and academic advising. Receiving a failing grade in the Freshman Seminar resulted in attrition for all three cohorts (Figure 1, p≤0.032).

Figure 1.

Failing the Freshman Seminar Was Related to Attrition. * p≤0.032.

Furthermore, no participation in peer mentoring, academic coaching, and academic advising sessions resulted in attrition in all three cohorts (p≤0.015). A significant difference was found in the number of interventions between the students that were retained, and attended these three interventions several times (Figure 2). For the 2016-2017 and the 2017-2018 cohorts, a minimum of three peer mentoring, two academic coaching, and three advising sessions resulted in retention (p≤0.03). For the 2018-2019 cohort, three peer mentoring, two academic coaching, and two academic advising sessions resulted in retention (p≤0.001). For the 2016-2017 cohort, there was no relationship between the number of interventions identified (Table 5, p=0.9095). However, for the other two cohorts, there were a relationship between retention and the three identified interventions (Table 5, p≤0.0005).

Figure. 2.

Minimum Number of Interventions that Resulted in Retention. *p≤0.03. **p≤0.001.

Table 5.

Relationship Between the Combination of Peer Mentoring, Academic Coaching and Academic Advising Sessions and Retention.

Cohort	Interventions	X² Test of Independence	Relationship
2016–2017	3PM, 2AC, 3AA	X²[2]= 0.1896, p=0.9095	Independent
2017–2018	3PM, 2AC, 3AA	X²[2]= 21.93, p=0.000017	Dependent
2018–2019	3PM, 2AC, 2 AA	X²[2]= 14.96, p=0.000564	Dependent

Note. PM: peer mentoring. AC: academic coaching. AA: academic advising.

Probation Model at SJSU

For each analysis, the outcome variable is whether or not the student entered academic probation. *Multiple indicates that because the variable had multiple groups, the dummy coding resulted in multiple coefficients and odds ratios. The p-value provided is for the variable overall.

To examine if the risk factors identified in the large cohorts from TXST University were the same in a different academic setting, we developed a predictive model for probation for the Biological Sciences Department at SJSU (Table 6). Using a binomial regression analysis, significant predictors of academic probation status were gender, URM status, SAT score, and grade for each course taken. Females were less likely to be on academic probation than males. URM students were more likely to be on academic probation than other ethnicities. Students with higher SAT scores were less likely to be on academic probation, and academic probation was associated with lower grades in the courses examined.

Table 6.

Results of Binomial Logistic Regressions on Each Predictor Variable.

Predictor	B (log-odds ratio)	Odds ratio	p-value
ACT score (n=193)	−0.069	0.933	0.107
SAT score (n=540)	−0.002	0.998	0.014*
First-generation college student (n=739)	−0.011	0.989	0.952
Financial aid eligibility (n=739)	−0.110	0.896	0.644
Full-time vs. part-time student (n=739)	0.335	1.399	0.207
Gender (n=739)	−0.341	0.711	0.05*
Age group (n=739)	Multiple*	Multiple*	>0.05
Under-represented minority (n=739)	Multiple*	Multiple*	≤0.05*
Native vs. transfer student (n=739)	0.096	1.101	0.633
Chemistry 1A pass or fail (n=433)	3.005	20.186	≤0.05*
Chemistry 1B pass or fail (n=269)	2.458	11.687	≤0.05*
Biology 30/1B pass or fail (n=385)	2.895	18.083	≤0.05*
Biology 31/1A pass or fail (n=333)	2.638	13.982	≤0.05*
Biology 115 pass or fail (n=292)	2.643	14.059	≤0.05*

Bold face and italics represent significant statistical results.

Additional analyses determined the proportion of students on probation for the various combinations of ethnicity and gender (Figure 3). Male URM students were most likely to be on academic probation and female students who are not URM were least likely be on academic probation.

Figure 3.

Proportion of Students on Probation by Gender and Ethnicity From the Biological Sciences Department at the State University.

Discussion

Institutional Setting

Two significant attrition risk factors were found in the cohorts examined. These were: first-generation status and financial need. First-generation status is associated with low retention (Antonelli et al., 2020; Atherton, 2014; Byrd & MacDonald, 2005; Bui, 2002; Cho et al., 2008; Davis, 2010; Dennis et al., 2005; Engle & Tinto, 2008; Folger et al., 2004; Forbus et al., 2011; Garriott et al., 2015; Harvey & Heinz Housel, 2011; Ishitani, 2006; Lohfink & Paulsen, 2005; Nunez et al., 1998). This study’s results supported Johnson et al. (2018) first model indicating that first-generation status is a risk factor of retention. Schelbe et al. (2019) qualitative study of first-generation students (n=25) identified five services that helped retention of those students. These included: student’s support, clear expectations, role models, preparation before they began college, and resources. The PACE Center’s interventions support the findings of the study by Schelbe et al. (2019), as these services are provided among the interventions of TXST University PACE Center. Ishitani (2003) found that students with parents without a college degree have a 71% higher attrition rate than students with both parents having college degrees. The literature on first generation and retention describes the lack of understanding of university culture and financial aid need as two of the reasons that prevent many first-generation college students to be retained (Pascarella et al., 2004). However, having at least one parent with a college degree may provide clarity of understanding of university culture as this significantly correlates with persistence and retention (Wells, 2008). Pascarella et al. (2004) longitudinal study found that first-generation students have significant academic differences in their experiences (for example number of credits obtained, GPA) when this group is compared with students whose both parents earned bachelor’s degrees. These differences seem to explain low GPA obtained during college, and the length it takes for first-generation students to complete their bachelor’s degrees (Pascarella et al., 2004). However, parental involvement may have a positive impact. McCulloh (2020) qualitative study of 12 first generation students suggested that parental involvement was important for the retention of those students.

Financial difficulty of first-generation college students is an interconnected attrition risk factor. Xu and Webber (2018) used regression analysis and determined that financial need significantly influenced attrition regardless of ethnicity (p<0.05). These findings are supported by Pratt et al. (2019), Haverila et al. (2020), and Park et al. (2020) studies. Pratt et al. (2019) study showed that financial insecurity was the highest predictor of attrition for first generation students. They also demonstrated that students who worked while attending the first year of college had a significant lower retention rate (p<0.001). Haverila et al. (2020) compared retention rate intention differences between domestic and international students using 15 variables. Financial reasons were a significant attrition intention factor of domestic students. Park et al. (2020) found that low-income students were the lowest retained as compared to students with middle or high income.

In this study, a low high school GPA was a significant attrition risk factor for the students of the 2017-2018 cohort (p<0.00001). For the 2016-2017 and 2018-2019 cohorts, high school performance was not (p>0.05). These results reflect the literature on the effect of high school GPA and retention. Several studies showed high school GPA to be a strong predictor of college students’ retention (Johnson et al., 2018; Lopez & Horn 2020; McCabe et al., 2020; Murtaugh et al., 1999), while others did not (Laskey & Hetzel, 2011; Saunders-Scott et al., 2018). Johnson et al. (2018) study concluded that a low high school GPA is the strongest predictor for no-retention. Lopez and Horn (2020) found high school GPA to be a strong predictor of retention in a southwest, midsized, comprehensive, 4-year HSI; while McCabe et al. (2020) found that first semester college GPA is not a predictive factor for retention but that high school GPA is. Two studies that did not find high school GPA to be a risk factor for retention were the studies by Laskey and Hetzel (2011) and Saunders-Scott et al. (2018). Both of these studies used midwestern universities. In the study conducted by Laskey and Hetzel (2011), high school GPA was not a contributing factor of retention in a private university. Similar results were shown in the study by Saunders-Scott et al. (2018) as high school was a predictor for college GPA, but not retention.

There were no statistically significant gender differences in groups of students that were on probation (p<0.05). Our study also did not find a significant association between living off campus and attrition, as most students in the three cohorts that were not retained resided on-campus (p>0.05). The literature of the effect of residential housing on campus compared with living off campus on student success (persistence and retention) is mixed. For instance, meta-analysis from studies published between 1966 to 1987 indicated that living on campus did not influence academic performance (Blimling, 1989). However, Schudde (2011) found that living on campus increases retention by 3.3 percentage points. It is important to note that Schudde (2011) study excluded campus-specific differences that may account to increased or no effect on retention.

Most of the ethnicity literature on retention is focused on Black and Latinx males. The qualitative study by Hood (1992) reported that Black males do not leave the university by their own choice. Three factors were attributed to high attrition of Black males: lack of role models at the institution, lack of counseling and guidance, and not taking their courses seriously. Park et al. (2020) examined retention rates of college students enrolled in STEM disciplines. They found that Black students had the lowest retention rates of the different ethnic groups in their sample. Our analyses demonstrated that being an URM student was not an attrition risk factor, since these groups have similar retention rates than non URM students (p>0.05). The predictive multi-variable retention model by Murtaugh et al. (1999) indicated that Black students are as likely as Caucasian students to be retained if both ethnic groups are equally prepared. Wells (2008) found that Latinx and Black students have less parental education than students from other race/ethnicities.

In this study, students that did not meet with peer mentors were not retained. Kiyama and Luca (2014) qualitative study analyzed their contributions of peer mentors on the retention of students they served. Their study showed three overlapping themes of peer mentoring. These were: structure of opportunity, community-building, and developing networks. These themes are also present in the Peer Mentoring Program. The Peer Mentors help students if: they are having trouble finding resources on campus, need someone to talk to about academics, having trouble adjusting to college life, and assistance in finding student organizations to join.

Our study showed that attending two sessions of academic coaching was one of the interventions students that were on probation for two consecutive semesters and were retained used. Furthermore, students that did not meet their academic coach were not retained. Researchers have shown that student coaching was associated with persistence and retention (Dalton & Crosby, 2014). Furthermore, the logistic regression analysis in the study by Capstick et al. (2019) demonstrated that academic coaching is a statistically significant predictor of retention. Students who participated in academic coaching and were on academic probation, were retained at higher percentages than students who did not participate (Capstick et al., 2019).

Academic advising has been shown to correlate with increased retention (Swecker et al., 2013; Zhang et al., 2017). Haverila et al. (2020) study found that student advising was found to be a contributing factor of retention. Our study showed that students that were retained beyond their two consecutive semesters of probation, met with their academic advisor between two to three times per semester. Students that failed to meet their academic advisor were not retained. Academic advising provides self-perceived gains (Mu & Fosnacht, 2019). Mu and Fosnacht (2019) found that an increased frequency of advising sessions resulted in students’ self-reported gains. Our study supports Swecker et al. (2013) finding that the number of academic advising sessions increases retention.

Recommendations for HSIs

Our study’s findings suggest that it is important to move away from generalizations and focus on the institutional identities and student population needs; since what may be important in some institutions may not be critical in others. Using empirical findings from studies based on aggregated data from many institutions is a strategy that is used to develop academic policy and practices at many colleges and universities. However, designing policy and programs based on generalizations, in many cases obscure the inherent differences of diverse academic institutions (Cardona et al., 2020). Due to sample selection, findings of aggregated institutional data are not universal and should not be applied to all institutions (Pascarella et al., 2004). Instead, a customized predictive model should be developed tailored to institutional identity and student profiles.

The issue of students on probation that are not retained would require HSIs to empirically analyze persistence and retention trends from their own institutions using multiple variables. HSIs can then examine data from different cohorts to objectively determine which possible attrition factors are statistically significant in their own institutions. This would require that the HSIs don’t assume that all of the potential attrition factors found in the literature apply to their students. This initial analysis may not require a large investment of funds. However, testing and implementing interventions may require external funding. TXST University in this study obtained funds to develop the PACE Center and implement interventions. Our study suggests that only one strategy is not sufficient to improve retention rates at an HSI. Instead, our study demonstrated that for two TXST University Freshman cohorts (2017-2018 and 2018-2019), retention was dependent on having a combination of three peer mentoring, two academic coaching, and two to three academic advising sessions (p≤0.0005). Finally, the last recommendation is to obtain institutional commitment by either providing funds or by facilitating the support of faculty and staff that are willing to improve retention at their respective HSI.

Conclusion

The literature cited in this study showed institutional differences in terms of risk factors. In this study, students’ data of risk factors supported by the literature were collected, but empirical analysis demonstrated that besides being a first-generation college student and financial aid need, the type of attrition risk factors varied widely in the cohorts examined. Furthermore, using similar risk factors from the University, our SJSU predictive model for probation indicated that URM students have the highest probability of being on probation, and that financial aid need or being a first-generation student were not predictive factors for probation. In each case, the empirical analysis and the predictive model are unique to each institution and indicated differences in the number and type of attrition risk factors. These unique sets of findings provide two examples of data analyses that can be used for the development of institutional policies and support programs targeted to each institution’s own students.

Limitations of Our Study

Our analyses were limited to the use of large sets of historic data points that are quantitative in nature. The historical data used in this study did not include notes from meetings with academic advisors, academic coaches, and peer mentors. In addition, exit interviews from students that were not retained were not conducted by the PACE Center. Surveys of socio-emotional characteristics of students that were on probation such as: engagement, sense of belonging, low-self efficacy, family issues, personality, and low grit (Laskey & Hetzel, 2011; Lopez & Horn, 2020; Pascarella et al., 2004; Saunders-Scott et al., 2018; Tinto, 1975) were not performed since the intention of the PACE Center was to provide interventions to help students on probation and not to formalize a qualitative study. Without qualitative data we were not able to infer if some or if any socio-emotional factors influenced retention in the cohorts examined. Qualitative information of these cohorts would be impossible to obtain since most of the students in these cohorts have either graduated or left the university.

TXST University is an HSI, but that doesn’t mean that all of their students are Latinx. The data set of students (three cohorts) on probation had a low number of American Indian (n=5), Asian (n=21), Multiracial (n=33), Native Alaskan (n=1), and Pacific Islander (n=1). Only the Black (n=322), Caucasian (n=700), and Latinx (n=803) groups were represented in large numbers. Nevertheless, ethnicity data were desegregated. ANOVA and the Tukey test were performed in order to determine if belonging to different ethnicities contributed to attrition at the University. However, since mean differences were too large among these ethnic groups, the ANOVA and Tukey results yielded misleading results. Therefore, only URM students were used in the chi-square ethnicity analysis and are shown (Table 3).

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The PACE Center was supported by the United States Department of Education Title V grant: P031S120.

ORCID iD

Julio G. Soto

Author Biographies

Daniel A. Brown established the framework for Personalized Academic & Career Exploration (PACE) as TXST: A Focus on Freshmen, Texas State's Quality Enhancement Plan (QEP). He coordinated programs enhancing the success of new students, directed the university's QEP, and obtained external funding in support of initiatives serving Hispanic and low-income students. Dr. Brown is currently the associate provost at Lamar University, Beaumont, TX.

Julio G. Soto, PhD, is a full professor at San José State University, Department of Biological Sciences. His publications include peer-reviewed articles in developmental biology, cancer biology, curriculum development, predictors of success at a freshman cell biology course, and undergraduate research. He received the 2013 Presidential Award for Excellence in Sciences, Mathematics, and Engineering Mentoring (PAESMEM). He is the corresponding author on this article.

Sulekha Anand, PhD, areas of expertise include experimental design, statistical and biostatistical analysis, neuroscience, scientific writing, and academic assessment. She teaches both undergraduate and graduate courses at SJSU and serves as a research and statistical consultant for faculty and their graduate students.

Nicholas Weimer serves as the director for Strategic Initiatives in University College at Texas State University. He conducts research and evaluation of student success initiatives in higher education with a focus on serving underrepresented undergraduate students. He has extensive experience directing federal, state, and local grants and leading student success efforts in higher education. Nicholas earned his MBA at The University of Texas Rio Grande Valley and his MPA at The University of Texas-Pan American.

Victoria A. Black, PhD, is the director of TXST PACE Mentoring Center. She advocates for first generation college students and explores the concept of mentor ability.

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