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Academic Coaching and Its Relationship to Student Performance, Retention, and Credit Completion

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Title: Academic Coaching and Its Relationship to Student Performance, Retention, and Credit Completion
Language: English
Authors: Alzen, Jessica L. (ORCID 0000-0002-1706-2975), Burkhardt, Amy, Diaz-Bilello, Elena, Elder, Eryn, Sepulveda, Alicia, Blankenheim, Audrey, Board, Lily
Source: Innovative Higher Education. Oct 2021 46(5):539-563.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 25
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Coaching (Performance), Academic Support Services, Academic Achievement, Academic Persistence, School Holding Power, College Students, College Credits, Grade Point Average, Program Effectiveness, Correlation
DOI: 10.1007/s10755-021-09554-w
ISSN: 0742-5627
Abstract: Student retention is a key outcome measure for post-secondary education, but data show relatively stagnant retention rates over the past decade. Longstanding interventions such as counseling, academic advising, and mentoring provide crucial student services, but little change in retention rates suggest there is still need for additional student supports. Within the landscape of higher education, academic coaching is a relatively new, yet burgeoning intervention designed to increase student retention and success. Despite rapid growth of the intervention, little empirical work has been done to systematically describe and evaluate such programs. In this study, we provide a rich description of one academic coaching program and use a quasi-experimental design to evaluate the program's effects on student outcomes. We investigate two research questions: (1) how does academic coaching influence key student outcomes?; and (2) to what extent do these effects vary by amount of coaching received? On average, we found that students with prior semester grade point averages from 1.0-2.0 who participate in the academic coaching program earn grade point averages about 0.4 points higher during the coaching semester, are about 10% more likely to enroll in the semester following coaching, and earn about two more credits in the semester following coaching than students who choose not participate in the program. Outcomes varied minimally based on the number of coaching appointments students attended.
Abstractor: As Provided
Notes: https://github.com/REMCU/academic-coaching-manuscript
Entry Date: 2021
Accession Number: EJ1311084
Database: ERIC
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  Value: <anid>AN0152771176;ihe01oct.21;2021Oct05.06:16;v2.2.500</anid> <title id="AN0152771176-1">Academic Coaching and its Relationship to Student Performance, Retention, and Credit Completion </title> <p>Student retention is a key outcome measure for post-secondary education, but data show relatively stagnant retention rates over the past decade. Longstanding interventions such as counseling, academic advising, and mentoring provide crucial student services, but little change in retention rates suggest there is still need for additional student supports. Within the landscape of higher education, academic coaching is a relatively new, yet burgeoning intervention designed to increase student retention and success. Despite rapid growth of the intervention, little empirical work has been done to systematically describe and evaluate such programs. In this study, we provide a rich description of one academic coaching program and use a quasi-experimental design to evaluate the program's effects on student outcomes. We investigate two research questions: 1) how does academic coaching influence key student outcomes? and 2) to what extent do these effects vary by amount of coaching received? On average, we found that students with prior semester grade point averages from 1.0—2.0 who participate in the academic coaching program earn grade point averages about 0.4 points higher during the coaching semester, are about 10% more likely to enroll in the semester following coaching, and earn about two more credits in the semester following coaching than students who choose not participate in the program. Outcomes varied minimally based on the number of coaching appointments students attended.</p> <p>Keywords: Academic coaching; Student retention; Higher education; Coarsened exact matching</p> <hd id="AN0152771176-2">Introduction</hd> <p>Colleges across the country invest in and implement many support programs designed to increase student retention such as academic advising, mentoring, and counseling. While these services surely provide value to students, retention data over the past decade shows little change over time. Approximately 20% of students across all 4-year degree-granting institutions do not return for a second year (National Center for Education Statistics [NCES], [<reflink idref="bib16" id="ref1">16</reflink>]). While more selective institutions retain approximately 95% of first-time students, less selective institutions only retain about 65% of first-time students (NCES, [<reflink idref="bib16" id="ref2">16</reflink>]). Consequently, research about the efficacy of interventions aimed at increasing student retention is critical to helping administrators effectively direct resources to meet institutional retention goals.</p> <p>Academic coaching recently emerged in post-secondary education as an intervention designed to increase student retention and success. The intervention first appeared in higher education in 2000 and within the last ten years, more institutions have started programs that fall under the umbrella of academic coaching (Robinson, [<reflink idref="bib19" id="ref3">19</reflink>]). However, there is substantial variability in the types of programs labeled as academic coaching. For example, sometimes academic coaching is offered virtually to students through an outside agency contracted by the institution (Bettinger & Baker, [<reflink idref="bib3" id="ref4">3</reflink>]; Lehan et al., [<reflink idref="bib13" id="ref5">13</reflink>]). At other institutions, academic coaching is offered by on-campus staff to specific groups of students such as students with documented disabilities, athletes, or those who are academically at-risk (Bellman et al., [<reflink idref="bib2" id="ref6">2</reflink>]; Robinson, [<reflink idref="bib19" id="ref7">19</reflink>]). In this study, we take up the definition of academic coaching provided by Robinson ([<reflink idref="bib19" id="ref8">19</reflink>]):Academic coaching is an interactive process that focuses on the personal relationship created between the student and the coach. It is important for the coach to encourage the student to become more self-aware of their strengths, values, interests, purpose, and passion - and develop those attributes. (p. 126)</p> <p>Similarly, the Coaching in Higher Education Consortium (CHEC), a new professional community launched in 2020, defines coaching as "fostering an individualized relationship with students that promotes their agency, self-understanding, growth, effectiveness, and persistence within the realm of education and across their lifespan" (CHEC, [<reflink idref="bib7" id="ref9">7</reflink>], slide 22). A salient feature of these definitions is the interactive and personal relationships that occur between students and coaches over time as well as the dedicated focus on building student agency and self-awareness. These elements are what set academic coaching apart from other common interventions.</p> <p>Despite the more recent and rapid proliferation of academic coaching programs in higher education, these programs have not been the focus of much empirical research, particularly in traditional college settings. In this article, we first explain the clear distinctions between academic coaching and other common higher education interventions. We then describe the sparse research regarding academic coaching programs as we define them. Next, we provide a rich description of academic coaching at a large research institution in the Rocky Mountain region of the United States (referred to as Silverbill University) to ensure the context of this study is clear and the results are easily comparable with future research. Following, we present the results regarding the relationship between participating in academic coaching at Silverbill and several key student outcomes of interest. Specifically, we answer two research questions:</p> <p></p> <ulist> <item> What is the effect of academic coaching on grade point average (GPA), student retention, and credits earned?</item> <p></p> <item> To what extent do these effects vary by amount of coaching received?</item> </ulist> <hd id="AN0152771176-3">Background</hd> <p>As academic coaching is a burgeoning field, it is important to identify how it differs from other interventions in higher education such as academic advising and counseling. Surely there is some overlap in these different student services. However, those committed to developing academic coaching as a unique intervention emphasize some defining characteristics.</p> <hd id="AN0152771176-4">Academic Coaching as a Unique Intervention</hd> <p>One of the most common student support services in higher education is academic advising. Academic advisors can be described as student support staff who provide guidance regarding degree planning, academic goal setting, major(s) and course selection, navigation of academic policies, and registration, within a broader curricular and co-curricular perspective. Academic advisors often carry large student caseloads, typically meeting once or twice a semester or year (McClellan & Moser, [<reflink idref="bib14" id="ref10">14</reflink>]). Conversely, academic coaches focus on personal and academic growth related to the student's goals more broadly through both formal and informal support in a variety of settings (Brock, [<reflink idref="bib5" id="ref11">5</reflink>]). Additionally, academic coaches meet more frequently with fewer students over shorter periods of time.</p> <p>Another support model that can be confounded with academic coaching is that of mental health counseling. Most characterizations of counseling emphasize social and emotional wellbeing above and beyond what would be expected of an academic coach (Kaplan et al., [<reflink idref="bib11" id="ref12">11</reflink>]). Counselors must also meet a stricter credentialing standard focusing on mental and emotional health than academic coaches (Prevatt, [<reflink idref="bib18" id="ref13">18</reflink>]). Blankenheim and Ehrnstrom ([<reflink idref="bib4" id="ref14">4</reflink>]) describe mental health counseling as an intervention that provides mental health assessment, diagnosis, and intervention, whereas academic coaching emphasizes academic skills and resilience. While academic coaching and counseling are distinct, Blankenheim and Ehrnstrom ([<reflink idref="bib4" id="ref15">4</reflink>]) describe overlap between the two services via evidence-based approaches including motivational interviewing and compassion training.</p> <p>In a study designed to understand the practices that make coaching a distinct service, Sepulveda ([<reflink idref="bib21" id="ref16">21</reflink>]) found six practices made up coaching: (<reflink idref="bib1" id="ref17">1</reflink>) coaching beliefs; (<reflink idref="bib2" id="ref18">2</reflink>) coaching skills; (<reflink idref="bib3" id="ref19">3</reflink>) coaching meetings using a conversational framework; (<reflink idref="bib4" id="ref20">4</reflink>) coaching over multiple meetings; (<reflink idref="bib5" id="ref21">5</reflink>) training, growth, and development as a coach; and (<reflink idref="bib6" id="ref22">6</reflink>) the coaching role in the context of higher education. Further, Sepulveda ([<reflink idref="bib21" id="ref23">21</reflink>]) found student support services may use some of these practices, but it is the consistent combination of the six together that make coaching distinct.</p> <p>Some key characteristics of the differences between academic advising, coaching, and counseling from the literature are illustrated in Fig. 1. Counseling focuses on social emotional wellbeing, may be administered by mental health professionals, and usually entails a series of meetings. Academic advising focuses on guiding students' degree planning process and typically entails periodic meetings throughout student enrollment. Coaching focuses on developing skills from a holistic perspective, including the co-construction of students' goals and steps to achieve those goals over a series of frequent meetings during a focused duration of time. The salient elements of academic coaching that differentiate it from other long-standing services may fill important gaps in student needs that have resulted in unmoving retention rates to date.</p> <p>Graph: Fig. 1 Overlap and Distinctions Among Common Student Support Services</p> <p>Despite the existence of literature that differentiates academic coaching from other interventions, programs that hold the title of academic coaching are not universal in design. This is largely due to the nascent nature of the field (Robinson, [<reflink idref="bib19" id="ref24">19</reflink>]). These programs are new and still developing on many campuses. Some academic coaching programs are housed in student success centers, serving under the umbrella of student success, while others offer stand-alone programs.</p> <p>Given the varied nature of academic coaching programs, designing and conducting studies to provide evidence of the effects of academic coaching is a challenge (Lehan et al., [<reflink idref="bib13" id="ref25">13</reflink>]). At the time of this writing, when using search terms such as "academic coaching" and "academic coaching post-secondary education," we found only five peer-reviewed articles or reports that attempted to provide such evidence for academic coaching programs designed for the general population of students in higher education. In holding to Robinson's ([<reflink idref="bib19" id="ref26">19</reflink>]) definition and the delineation of academic coaching from other interventions provided above, we purposefully limit our literature review to these studies.</p> <hd id="AN0152771176-5">Previous Coaching Efficacy Studies</hd> <p>The largest study on academic coaching focuses on a remote coaching model from a company called InsideTrack. This company works with institutions across the US to offer remote services with company coaches (Bettinger & Baker, [<reflink idref="bib3" id="ref27">3</reflink>]). Services include at least five connections between student and coach by phone, email, text messaging, and/or social networks for two consecutive semesters. Coaches assist students in overcoming academic and non-academic barriers by helping them identify resources and ways to advocate for themselves (Bettinger & Baker, [<reflink idref="bib3" id="ref28">3</reflink>]).</p> <p>Students in the study varied in multiple ways, including year in school, full- versus part-time, and athlete status. Additionally, students came from 2- and 4-year institutions, public and private not-for profit, and proprietary colleges. Within each university's sample, InsideTrack randomly divided students into coached and non-coached groups. The authors found that students who received coaching were ~ 5% more likely to remain enrolled six months from the coaching term and ~ 12% more likely to remain enrolled one year after the end of coaching.</p> <p>Although Bettinger & Baker provided some information regarding the relationship between coaching and persistence in college, this study has some limitations for generalizability. First, the sample included 51% male students with an average age of 31, which is not reflective of traditional college campuses (Bettinger & Baker, [<reflink idref="bib3" id="ref29">3</reflink>]). Second, students and coaches interacted remotely, and it is possible that remote coaching has differential effects than in-house coaching programs staffed with individuals familiar with specific campus resources, curricula, and initiatives.</p> <p>In another study of remote coaching, Lehan et al. ([<reflink idref="bib13" id="ref30">13</reflink>]) investigated academic coaching for graduate students in an online degree program. Students met with their academic coaches up to two times per week for a semester. Coaches helped students develop their competence in navigating the institution and supported broader skill development such as time management and how to be a self-directed learner (Lehan et al., [<reflink idref="bib13" id="ref31">13</reflink>]). Specifics about how coaches met these goals with students were not provided.</p> <p>The researchers selected a random sample of 160 students from those students who used academic coaching services for the treatment group and found a matched sample of students based on institutional demographic data (i.e., course enrollment, race/ethnicity, gender, age, department, grade point average, and time at the institution). Lehan et al. found that students who used coaching services at least once in their first year increased their odds of persistence by a factor of 2.66. Similar to Bettinger & Baker, Lehan et al. found positive relationships between academic coaching and persistence. A unique aspect of the Lehan et al. study was that the students were graduate students rather than undergraduates in the Bettinger & Baker study. Once again, the results from this study cannot generalize to traditional undergraduate institutions as the sample consists of students quite unlike traditional undergraduates.</p> <p>The last three publications available at the time of this writing focus on student populations similar to that of the current study. Sepulveda et al. ([<reflink idref="bib22" id="ref32">22</reflink>]) investigated the relationship between academic coaching and retention as well as cumulative GPA at the end of the first year at a public, mid-sized institution in the western United States. Using the results from an intent to return survey, the researchers randomly assigned students who indicated they were not likely to return to treatment (offered coaching services) and control groups (not offered coaching services). Coaching included building relationships with students to understand them as individuals and to inform students of campus resources. Additionally, coaches used subsequent meetings to make deeper connections with students and review steps taken between meetings. This also gave students and coaches the opportunities to prioritize needs and help students determine how to take action (Sepulveda et al., [<reflink idref="bib22" id="ref33">22</reflink>]). The researchers found no statistically significant difference in retention or GPA between coached and uncoached students, but there was low compliance with the intervention. Only half of the treatment group participated in one coaching meeting and only about one-quarter of the group attended three or more meetings (Sepulveda et al., [<reflink idref="bib22" id="ref34">22</reflink>]).</p> <p>In contrast to Sepulveda et al.'s ([<reflink idref="bib22" id="ref35">22</reflink>]) study, Robinson and Gahagan ([<reflink idref="bib20" id="ref36">20</reflink>]) studied a coaching program at a large, public 4-year university that requires first-year students with GPAs below 2.0 to meet with an academic coach in the spring semester. Coaching at this institution focused on student self-reflection, planning, goal setting, and individual support (Robinson & Gahagan, [<reflink idref="bib20" id="ref37">20</reflink>]). The study employed a single group pre-post design, so it did not include a control group. Ninety-two percent of students who participated in coaching improved their GPAs and "demonstrated academic achievement after one academic year" (Robinson & Gahagan, [<reflink idref="bib20" id="ref38">20</reflink>], p. 29). The details behind these assertions are not available. Thus, it is not possible to make thoughtful comparisons between results reported in their study and future empirical work.</p> <p>Finally, Capstick et al. ([<reflink idref="bib6" id="ref39">6</reflink>]) conducted a study most similar to the current work. Students with under 60 completed credits and GPAs below 2.0 received referrals to academic coaching at a mid-sized, urban research university in the southeastern United States. At this institution, the coaches focus on a collaborative relationship to help students focus on personal and professional goals. Coaches help students develop self-awareness, identify strengths, and determine academic plans, all while incorporating students' interests and values. They also help students to identify internal and external barriers to their goals and identify strategies for overcoming those barriers such as study skills, test-taking strategies, and time management (Capstick et al., [<reflink idref="bib6" id="ref40">6</reflink>]).</p> <p>The researchers compared eligible students who did and did not participate in academic coaching. Their methods included repeated measures ANOVA and regression analyses. The researchers found that coached students earned GPAs about 0.5 points higher than their non-coached counterparts. Additionally, coached students were about 20% more likely to return in the semester after coaching than those who did not receive coaching (Capstick et al., [<reflink idref="bib6" id="ref41">6</reflink>]). This study used a sample and methods most similar to the current study and provides a baseline for comparing results. However, Capstick et al.'s study used a non-equivalent groups design rather than a quasi-experimental design. Although their study provides some insight into the effectiveness of academic coaching, and they controlled for the available administrative variables, it is often the case that non-equivalent groups are different on variables related to both treatment and the outcome of interest. Research designs that are able to account for at least some of these differences, as is done in the current study, have a stronger warrant to make causal claims about the effectiveness of academic coaching.</p> <p>The emergent field of academic coaching claims to meet important needs for students and to provide crucial services to support student retention, particularly for students with low GPAs and most at-risk of leaving an institution. As academic coaching gains momentum across the country, and administrators make decisions regarding how to spend limited campus resources, research that investigates the effectiveness of such programs is necessary. Due to the limitations of the previous studies, we anticipate that findings from this study can provide important baseline information for future studies examining the effects of coaching. The key contributions of our study are to add to the sparse research base and to inform higher education about the potential impacts of academic coaching using common institutional secondary data sources. We next turn to a description of the academic coaching program under review to set the context for the current study.</p> <hd id="AN0152771176-6">Academic Coaching Program (ACP) at Silverbill University</hd> <p>Silverbill began its academic coaching program (ACP) within the academic advising unit in the College of Arts and Sciences in spring 2016. The office targets first-year and continuing students with cumulative GPAs below 2.0 as an intervention for students at-risk of leaving the institution. At the time of the study, the ACP staff included three full-time academic coaches and one assistant director. All academic coaches hold, at minimum, a bachelor's degree, and more often, master's degrees in fields such as education, social work, and psychology. Coaches receive initial training and on-campus professional development in areas such as best practices in the field of coaching, student development and learning theory, campus policies and procedures, and academic skill-building tools and strategies. Since the program serves students across the College of Arts and Sciences, academic coaches serve specific disciplinary areas. For instance, one academic coach serves students in the physical sciences while another serves students in the social sciences. This specific assignment practice helps coaches further specialize in learning strategies, consistently partner with departmental faculty and staff, streamline referrals from (and to) disciplinary academic advisors, and individualize the support provided to students.</p> <p>Silverbill's ACP emphasizes co-construction of individualized plans so that each student works in partnership with their coach to define goals and objectives based on unique needs. Goals and objectives may include connections and campus resources, metacognition, motivation, reading and note-taking, test preparation and test-taking, time management, mindset, and wellness. The purposeful and student-driven approach allows coaches to foster trusting relationships that help students feel a sense of belonging in the university community. This is a key element of the academic coaching relationship, given that research indicates that students who lack a sense of belonging are more likely to leave an institution (Drake, [<reflink idref="bib8" id="ref42">8</reflink>]; Kuh et al., [<reflink idref="bib12" id="ref43">12</reflink>]).</p> <p>At the start of each fall semester, the ACP sends an invitation to every student in the College of Arts and Sciences who earned a cumulative GPA below 2.0. During spring semesters, the invitation is sent to only first-year and first-year transfer students with GPAs below 2.0. These limitations exist due to limited staffing resources available in the ACP. There are other coaching resources beyond individual appointments available to students with higher GPAs (e.g., drop-in appointments), but those are outside of the scope of the current study.</p> <p>Students who wish to receive services sign up for a coaching appointment. During the first meeting, coaches spend time building relationships with students and clarifying expectations and roles. Coaches also begin the process of purposefully seeking to understand the student by asking questions about their current life and school experiences. Other objectives during the first meeting include gaining an understanding of the student's main goals in the coaching process and explaining a coaching agreement. Agreements are individualized based on student goals, but consistent factors are that students agree to attend at least four coaching appointments (previously three), utilize a campus resource, meet with their academic advisor, implement strategies and reflect on them between appointments, and complete a closeout reflection meeting during the semester.</p> <p>Each subsequent coaching meeting follows a meeting "flow" that consists of six phases developed by ACP staff: relationship building and making connections to previous appointments, purposefully exploring to understand the student, prioritizing what is most important to the student, co-creating solutions to problems or ideas to grow, determining action and next steps, and summarizing and wrapping up the appointment. Meetings begin with relationship building where the coach makes connections with the student and to the previous meeting by following up on personal notes and goals. Next, the coach explores what is currently going on in the student's life and helps to identify any challenges before transitioning to helping the student prioritize what is most important to focus on during the meeting. Once the coach and student identify a focal topic, they work together to co-create solutions to the student's current challenges or areas of growth. After a variety of ideas have been voiced by both the coach and student, the coach helps the student determine the best next steps and goal setting for the following meeting. Finally, the coach closes by summarizing the meeting and scheduling the next. This process is repeated at each meeting for the duration of the semester. Students are encouraged to complete at least 4 meetings that follow this flow (meeting approximately once every two to three weeks), but they are welcome to utilize more than 4 meetings in a semester if it meets their individualized needs.</p> <p>We next describe the data and methods used to examine the potential relationship between the ACP at Silverbill and a variety of student outcomes. We look at these relationships for all students who participate in the ACP for any number of appointments, students who complete at least three appointments, and students with lower and higher prior semester GPAs. These analyses will help us to better understand potentially differential effects for various student groups.</p> <hd id="AN0152771176-7">Data</hd> <p>In this study, we focus on students enrolled in the College of Arts and Sciences who received an invitation to the ACP from fall 2017 to spring 2019 (36,105 total students). Although the ACP began serving students in spring 2016, we limit the sample due to data quality issues. We began with a sample of 1,857 students (~ 5% of total students) who were invited to coaching. Of those students, we exclude 179 because of missing GPA data due to course withdrawals or incompletes. Our final sample includes 1,678 students. We present descriptive demographic information for this full group of students split into four different groups (See Fig. 2): students who were invited to coaching but never participated (i.e., Non-participants) and students who were invited to coaching and participated, regardless of how much they participated (i.e., Participants). We further divide Participants into two additional sub-samples: students who participated and completed fewer than three coaching appointments (i.e., Non-completers) and those who participated in at least three coaching appointments (i.e., Completers).[<reflink idref="bib1" id="ref44">1</reflink>]</p> <p>Graph: Fig. 2 Student Groups of Interest</p> <p>Table 1 provides the raw data for the outcomes of interest for these students. We consider three outcomes for each sample: coaching semester GPA (a measure of student performance during the coaching term), percentage enrolled at Silverbill in the semester following the invitation to coaching (a measure of student retention), and number of credits earned in the semester following the coaching invitation. We use credits earned in the next semester as one way to understand the longer-term influence of the coaching program.</p> <p>Table 1 Descriptive statistics: Outcomes of interest</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="2"><p>Invited</p></th><th align="left" colspan="2"><p>Invited and Participated</p></th></tr><tr><th align="left"><p>All Students</p></th><th align="left"><p>Non-participants</p><p>n = (1152)</p></th><th align="left"><p>Participants</p><p>n = (526)</p></th><th align="left"><p>Non-completers</p><p>n = (251)</p></th><th align="left"><p>Completers</p><p>n = (275)</p></th></tr></thead><tbody><tr><td align="left"><p>Coaching Semester GPA Mean (SD)</p></td><td align="left"><p>1.96 (1.02)</p></td><td align="left"><p>2.23 (0.87) ***</p></td><td align="left"><p>2.04 (0.94)</p></td><td align="left"><p>2.41 (0.77) ***</p></td></tr><tr><td align="left"><p>% Enrolled Next Semester</p></td><td align="left"><p>76</p></td><td align="left"><p>85 ***</p></td><td align="left"><p>77</p></td><td align="left"><p>92 ***</p></td></tr><tr><td align="left"><p>Next Semester Credits Mean (SD)</p></td><td align="left"><p>7.77 (6.21)</p></td><td align="left"><p>9.19 (5.99) ***</p></td><td align="left"><p>8.12 (6.38)</p></td><td align="left"><p>10.17 (5.44) ***</p></td></tr></tbody></table> </ephtml> </p> <p>* <emph>p</emph> < 0.05, ** <emph>p</emph> < 0.01, *** <emph>p</emph> < 0.0001</p> <p>The first two columns under the "Invited" header of Table 1 compare results for Non-participants and Participants invited to coaching. The last two columns under "Invited and Participated" provide the same information regarding differences between Non-completers and Completers who accepted the coaching invitation. For each set of comparisons, we flag statistically significant differences between groups. In general, Participants tend to have more favorable outcomes than Non-participants, and Completers have more favorable outcomes than Participants. It also appears that Completers drive the observed differences between Participants and Non-participants as the Non-participants and Non-completers columns are similar to one another.</p> <p>The information in Table 1 includes only raw data. Participating in and completing the ACP at Silverbill is completely voluntary, and the opportunity is only open to a select group of students. We know that we cannot treat the two groups as random and cannot take the differences in student outcomes shown in Table 1 as causally related to receiving academic coaching. In other words, there are differences between the students who do and do not participate in coaching that may be attributed to other factors outside of coaching.</p> <p>Since students are not randomly assigned to receive coaching services, we use three different matching techniques to strengthen claims about the effectiveness of the ACP. Although these techniques are imperfect since we are not able to account for all possible variables that may influence differences between groups (e.g. student motivation and feelings of self-efficacy), these approaches provide viable strategies for examining differences in the absence of random assignment. Within each of these approaches, we use administrative data to match students and limit the sample to students who share more similar characteristics to one another than in the full sample. By creating groups consisting of individuals who share similar characteristics on these variables, we create samples that more closely resemble randomly assigned groups.</p> <p>To illustrate issues with simply comparing results using unmatched data, we present data in Table 2 on a few key variables between the full sample of Participants and Non-participants who received a coaching invitation. The top panel of Table 2 includes variables the research literature suggests are linked to students' likelihood of persisting in college (McFarland et al., [<reflink idref="bib15" id="ref45">15</reflink>]). The variables in the bottom panel are related to prior achievement. Some studies (Bettinger & Baker, [<reflink idref="bib3" id="ref46">3</reflink>]; Capstick et al., [<reflink idref="bib6" id="ref47">6</reflink>]) use prior achievement as predictors of future academic success. We use credits at entry as a measure of motivation and first and prior semester GPA as measures of prior achievement. We use GPA at Silverbill for prior achievement rather than admissions test scores or high school GPA as this is a more consistent indicator. Although we expect some variability in GPAs from within the institution, this is likely less variable than the policies and decisions governing high school GPAs that vary by teacher, school, and district context, and the admissions test scores that vary by version and type of admissions test.</p> <p>Table 2 Demographic Descriptive Statistics—All students</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Non-participants</p><p>n = (1152)</p></th><th align="left"><p>Participants</p><p>n = (526)</p></th></tr></thead><tbody><tr><td align="left" /><td align="left" colspan="2"><p>Proportion</p></td></tr><tr><td align="left"><p>Student of Color</p></td><td align="left"><p>0.42</p></td><td align="left"><p>0.42</p></td></tr><tr><td align="left"><p>Female</p></td><td align="left"><p>0.29</p></td><td align="left"><p>0.41 ***</p></td></tr><tr><td align="left"><p>First Generation</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.23</p></td></tr><tr><td align="left"><p>Transfer Student</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.25</p></td></tr><tr><td align="left"><p>Pell Grant Recipient</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.24</p></td></tr><tr><td align="left"><p>Received Financial Aid</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.20</p></td></tr><tr><td align="left" /><td align="left" colspan="2"><p>Mean (SD)</p></td></tr><tr><td align="left"><p>Credits at Entry</p></td><td align="left"><p>11.1 (17.86)</p></td><td align="left"><p>13.38 (21.23) *</p></td></tr><tr><td align="left"><p>First Semester GPA</p></td><td align="left"><p>1.53 (0.72)</p></td><td align="left"><p>1.57 (0.68)</p></td></tr><tr><td align="left"><p>Prior Semester GPA</p></td><td align="left"><p>1.51 (0.52)</p></td><td align="left"><p>1.53 (0.47)</p></td></tr></tbody></table> </ephtml> </p> <p>* <emph>p</emph> < 0.05, ** <emph>p</emph> < 0.01, *** <emph>p</emph> < 0.0001</p> <p>The data in Table 2 suggest that these groups share similar characteristics. However, statistically significant differences exist between the groups in the percentage of female students and credits at entry. A higher proportion of participants are female and participants have more credits at entry. The retention literature indicates that female students tend to persist to college graduation more than males (McFarland et al., [<reflink idref="bib15" id="ref48">15</reflink>]), and we might expect students who entered with more credits to be more likely to persist in college. Thus, it is reasonable to expect that these differences in our student groups may bias the results from the raw data in favor of the ACP. We next describe the matching approaches taken to create comparable groups for our analysis.</p> <hd id="AN0152771176-8">Methods</hd> <p>Evaluating any program's effectiveness after implementation limits the researcher to using historical administrative data for analysis. This is the case in the current study as well as two of the studies included in the literature review for this paper (c.f. Capstick et al., [<reflink idref="bib6" id="ref49">6</reflink>]; Robinson & Gahagan, [<reflink idref="bib20" id="ref50">20</reflink>]). We know from the method of assignment to treatment status as well as the descriptive statistics in Tables 1 and 2 that there are differences between groups, so we created more comparable groups using three different matching approaches to the data: nearest neighbor and optimal methods of propensity score matching (PSM) as well as Coarsened Exact Matching (CEM). We used matching as a method for quasi-experimental design for several reasons. First, since we used historical administrative data, we were unable to use random assignment. Second, matching was a method used in one of the prior studies cited here (c.f. Lehen et al., 2018). Finally, matching is a commonly accepted approach for studies that use observational data (Iacus et al., [<reflink idref="bib10" id="ref51">10</reflink>]).</p> <p>We consider three different approaches to matching to investigate if the results are sensitive to method. Nearest neighbor matching occurs for one treatment (Participant) case at a time. For each student in the sample, we estimate the likelihood of taking up academic coaching given our data (i.e., a propensity score). The algorithm then finds the control (Non-participant) case with the nearest propensity score to create pairs of students for the matched sample (Ho et al., [<reflink idref="bib9" id="ref52">9</reflink>]). Nearest Neighbor matching generates matches one case at a time, and there is no consideration for the total distance between propensity scores among pairs of students. Thus, we also use an Optimal matching approach in which the matched sample occurs such that there is minimal average absolute distance between propensity scores across the matched pairs (Ho et al., [<reflink idref="bib9" id="ref53">9</reflink>]). In other words, Nearest Neighbor prioritizes minimum distance between individual pairs, and Optimal prioritizes minimum total distance across all pairs.</p> <p>Finally, with the CEM matching approach, variables are temporarily "coarsened." For example, GPA might be coarsened into two categories: less than 1.0 and greater than or equal to 1.0. We use the default approach in the CEM package in R, which coarsens all variables into 12 strata. Once variables are coarsened, cases are sorted into strata and any cases without at least one matching pair in the respective stratum are removed from the dataset. This approach is similar to Optimal matching in that the goal is to minimize the total difference between groups on matching variables. CEM is different from the other two approaches in that it does not use a propensity score to match cases but rather generates a match in each of these strata for each of the matching variables.</p> <p>In order to understand the relative balance achieved between groups, we report the <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>L</mi><mn>1</mn></msub></math> </ephtml> value for each of these matching approaches in Table 3 along with sample sizes for each approach. <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>L</mi><mn>1</mn></msub></math> </ephtml> provides an indication of the distance between multivariate histograms. If <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>L</mi><mn>1</mn></msub><mo>=</mo><mn>1</mn></mrow></math> </ephtml> , the two histograms are completely separate and there is no balance between the two groups. If <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>L</mi><mn>1</mn></msub><mo>=</mo><mn>0</mn></mrow></math> </ephtml> , the two histograms exactly coincide and there is complete balance (Iacus et al., [<reflink idref="bib10" id="ref54">10</reflink>]).</p> <p>Table 3 Balance among Samples</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Group Construction</p></th><th align="left"><p># Non-participants</p></th><th align="left"><p># Participants</p></th><th align="left"><p><math xmlns="http://www.w3.org/1998/Math/MathML"><msub xmlns=""><mi>L</mi><mn>1</mn></msub></math><inline-graphic href="10755_2021_9554_Article_IEq5.gif" /></p></th></tr></thead><tbody><tr><td align="left"><p>Raw Data</p></td><td align="left"><p>1152</p></td><td align="left"><p>526</p></td><td char="." align="char"><p>0.76</p></td></tr><tr><td align="left"><p>Nearest Neighbor</p></td><td align="left"><p>526</p></td><td align="left"><p>526</p></td><td char="." align="char"><p>0.73</p></td></tr><tr><td align="left"><p>Optimal</p></td><td align="left"><p>526</p></td><td align="left"><p>526</p></td><td char="." align="char"><p>0.77</p></td></tr><tr><td align="left"><p>CEM</p></td><td align="left"><p>476</p></td><td align="left"><p>274</p></td><td char="." align="char"><p>0.52</p></td></tr></tbody></table> </ephtml> </p> <p>The information in Table 3 suggests that the amount of balance achieved with each PSM approach does not differ substantially from that in the raw data. We can achieve better balance using a CEM approach, but at the cost of losing a notable proportion of our sample. Table 4 shows that the students dropped from the CEM analysis are significantly different from the matched students on all variables. Students in the dropped sample are more likely to be students of color, female, first-generation students, transfer students, and financial aid recipients. They also entered Silverbill with more credits at entry and had lower first-semester and prior semester GPAs. The choice regarding which analytic approach to take involves considering some tradeoffs. Choosing one of the PSM approaches affords better generalizability of the results of the study, but also suffers from the same limitations of controlling for differences between student groups in previous research (c.f., Capstick et al., [<reflink idref="bib6" id="ref55">6</reflink>]). Conversely, choosing the CEM approach limits the generalizability of the results to a specific subpopulation at Silverbill, albeit with a stronger warrant for causal claims regarding the efficacy of the ACP. We proceed with reporting results from CEM to contribute to the research regarding effectiveness of academic coaching differently from prior research but acknowledge the limited generalizability of the results. We provide results from all three approaches in the Appendix to allow for independent comparison across methods.</p> <p>Table 4 CEM Matched Students and Dropped Students Descriptive Statistics</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p>CEM Matched (n = 750)</p></th><th align="left"><p>CEM Dropped (n = 928)</p></th></tr></thead><tbody><tr><td align="left" colspan="3"><p>Proportion</p></td></tr><tr><td align="left"><p> Student of Color</p></td><td align="left"><p>0.39</p></td><td align="left"><p>0.45 *</p></td></tr><tr><td align="left"><p> Female</p></td><td align="left"><p>0.29</p></td><td align="left"><p>0.35 *</p></td></tr><tr><td align="left"><p> First Generation</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.30 ***</p></td></tr><tr><td align="left"><p> Transfer Student</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.36 ***</p></td></tr><tr><td align="left"><p> Pell Grant Recipient</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.33 ***</p></td></tr><tr><td align="left"><p> Received Financial Aid</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.27 ***</p></td></tr><tr><td align="left" colspan="3"><p>Mean (SD)</p></td></tr><tr><td align="left"><p> Credits at Entry</p></td><td align="left"><p>4.41 (10.4)</p></td><td align="left"><p>17.80 (22.03) ***</p></td></tr><tr><td align="left"><p> First Semester GPA</p></td><td align="left"><p>1.62 (0.59)</p></td><td align="left"><p>1.47 (0.78) ***</p></td></tr><tr><td align="left"><p> Prior Semester GPA</p></td><td align="left"><p>1.57 (0.47)</p></td><td align="left"><p>1.48 (0.53) **</p></td></tr></tbody></table> </ephtml> </p> <p>* <emph>p</emph> < 0.05, ** <emph>p</emph> < 0.01, *** <emph>p</emph> < 0.0001</p> <p>Next, we present the results from the dataset constructed under CEM. In order to be convinced that the difference from the matched samples reflects an unbiased estimate of the average treatment effect, we must be convinced that assignment to treatment and control groups are as good as random after matching on available variables (Austin, [<reflink idref="bib1" id="ref56">1</reflink>]). As noted in the Data section, this assumption may not hold true since the groups may still be unbalanced on other variables such as student motivation and feelings of self-efficacy. Based on the potential violation of this assumption, what we present next are results that lie somewhere between unbiased causal effects and known biased differences in raw data. In other words, although we are sure these results are better than reporting on the raw differences, we are not convinced they can be classified as completely unbiased estimates of the causal effect of the ACP on student outcomes.</p> <hd id="AN0152771176-9">Results</hd> <p>We present results for Participants and Completers as compared to Non-participants. Recall that Participants are those students who attend one or two coaching appointments. Completers are those who attend three or more appointments. We also compare results across all three groups who received an invitation to coaching (Non-participants, Participants and Completers) based on GPA classification.</p> <p>The mean GPA for students who received an invitation to coaching is 1.52, and the standard deviation is 0.5. We characterize "higher" GPAs as > = 1.0 and "lower" GPAs as < 1.0 as a 1.0 cutoff represents about one standard deviation below average. We perform this analysis in order to better understand if we can observe any differential outcomes for students with varying prior semester GPAs.[<reflink idref="bib2" id="ref57">2</reflink>]</p> <hd id="AN0152771176-10">General Effects on Student Outcomes</hd> <p>Our first research question is: What is the effect of academic coaching on GPA, student retention, and credits earned? Results related to this question are important not only for building the research literature regarding the effectiveness of academic coaching programs overall but also for understanding the extent to which academic coaching can influence these outcomes for a variety of student groups. As is common with intervention programs, resources are often scarce, and empirical research that supports how to best direct resources is useful for program design and development.</p> <hd id="AN0152771176-11">Coaching Semester GPA</hd> <p>We begin by sharing results related to coaching semester GPA. Figure 3 presents the difference in GPAs during the semester in which students had the opportunity to participate in the ACP. The vertical y-axis illustrates the change in GPA for the indicated group compared to Non-participants, and the horizontal x-axis shows the different groups of interest. The GPA for Non-participants is not illustrated directly, but instead Non-participants serve as a reference group. Bars extending above 0 indicate that Participants or Completers had higher GPAs than Non-participants, and bars extending below 0 indicate that Participants or Completers had lower GPAs than Non-participants. The error bars illustrate ± two standard errors around each estimated difference between the group of interest (i.e. Participants or Completers) and Non-participants. If the bars cross over zero, then the difference is not statistically significant. That is, we do not have sufficient evidence to convince ourselves there is a non-zero difference found in the coaching semester GPA between the relevant group and Non-participants.</p> <p>Graph: Fig. 3 Effect of Academic Coaching on GPA</p> <p>The first set of bars on the left illustrates the outcomes for Participants (white) and Completers (gray). Participants earned GPAs ~ 0.3 points higher than Non-participants and Completers earn GPAs ~ 0.5 points higher (Exact values in Appendix Tables 5 and 6). These results are similar to those found by Capstick et al. ([<reflink idref="bib6" id="ref58">6</reflink>]) and provide more evidence to the claims regarding the effectiveness of academic coaching in helping students raise their GPAs. When we look at these differences for students with higher prior GPAs, the differences look similar. Finally, although we show results for students with lower prior semester GPAs, we are cautious about claims made from these data as our sample sizes are very small, and there is a lot of variability in the results.</p> <hd id="AN0152771176-12">Next Semester Enrollment</hd> <p>We next share results for subsequent semester enrollment, which serves as an indicator of student retention. Figure 4 follows the same organization as Fig. 3 and illustrates the difference in enrollment rates for the semester after coaching was offered. Participants enroll in the following semester ~ 10% more than Non-participants, but Completers enroll ~ 15% more, and the differences between groups are both statistically significantly different from zero (Exact values in Tables 5 and 6). These results are similar to those found by Bettinger and Baker ([<reflink idref="bib3" id="ref59">3</reflink>]; i.e. 12% higher retention) and slightly more modest than those found by Capstick et al. ([<reflink idref="bib6" id="ref60">6</reflink>]; i.e. 20% higher retention). Similar to coaching term GPA, both participating in and completing the ACP has a statistically significant and positive effect on next semester enrollment as compared to not participating in the program for all students, on average.</p> <p>Graph: Fig. 4 Effect of Academic Coaching on Next Semester Enrollment</p> <p>When we separate students into high and low prior GPA, we see that both Participants and Completers with prior GPAs > = 1.0 enroll a little more than 10% more than Non-participants, and the differences are still statistically significant. Finally, as before, we do not have enough students in each low prior GPA group to make strong claims about the effect of the ACP on next semester enrollment, but we see descriptively that Completers with lower prior semester GPAs do appear to have higher enrollment rates than Participants.</p> <hd id="AN0152771176-13">Next Semester Credits</hd> <p>Finally, we present the results regarding credits earned in the semester following coaching in Fig. 5. This measure gives some information regarding potential long-term effects of the ACP. Similar to the results shown in Figs. 3 and 4, the results for all students and students with higher prior GPAs are similar. The findings in the first two comparisons reveal that Participants, on average, earn about 2 more credits in the following semester and Completers earn, on average, about 3 more credits relative to Non-participants. These differences are statistically significant for all groups. Although we cannot make conclusions about the effects for students with lower prior semester GPAs, we are encouraged by the positive patterns evident for ACP Completers and Participants. Prior studies regarding the effectiveness of academic coaching programs did not include next semester credits as an outcome of interest, but the effect on next semester credits appears to be consistent with our results regarding GPA and retention and in previous studies.</p> <p>Graph: Fig. 5 Effect of Academic Coaching on Next Semester Credits</p> <p>In summary, we found that students with prior semester GPAs > = 1.0 who participate in the ACP earn GPAs about 0.4 points higher during the coaching semester, are about 10% more likely to enroll in the semester following coaching, and earn about two more credits in the semester following coaching than students who do not participate in the ACP. The differences between participating in (attending one or two coaching appointments) and completing (attending three or more appointments) the ACP were modest. Although we are not able to make such claims about students with lower prior GPAs, we are encouraged by the patterns in the data.</p> <hd id="AN0152771176-14">Dosage Analysis</hd> <p>We next turn to a dosage analysis to answer our second research question: To what extent do the effects of the ACP vary by amount of coaching received? Rather than presenting differences in outcomes found in the matched sample as we did above, here we provide descriptive information (i.e., correlations and plots) regarding the patterns between student outcomes and number of coaching sessions.</p> <hd id="AN0152771176-15">Coaching Term GPA</hd> <p>Among the three outcome variables, coaching term GPA has the strongest correlation with the number of coaching appointments, though this correlation is still relatively weak at 0.21. Figure 6 illustrates two curves: a linear relationship (solid), and a locally weighted scatter-plot smoother, or a Loess curve (dashed). The linear relationship fits a line that assumes a consistent relationship between number of appointments and GPA. The Loess curve fits a smooth line between the two variables and tracks the relationship between the average coaching term GPA and each incremental number of coaching appointments. We see the relationship between number of coaching appointments and GPA shows a relatively consistent yet modest increase in GPA until six appointments (i.e. the solid and dashed lines follow similarly increasing patterns until about six appointments), but the change in magnitude from five to six appointments is minimal (~ 0.1), as is the change from four to five appointments (~ 0.2).</p> <p>Graph: Fig. 6 Number of Coaching Appointments and Coaching Semester GPA</p> <hd id="AN0152771176-16">Next Semester Enrollment</hd> <p>The correlation between number of coaching appointments and enrollment in the next semester is weak (0.15). As we move from one to four appointments in Fig. 7, we see a relatively consistent increase in the likelihood of a student returning in the following semester. After four appointments, there is no noticeable increase in next semester enrollment as indicated by the Loess line in the graph. Additionally, the magnitude of difference in enrollment between three and four appointments is marginal (< 5%), suggesting that either three or four appointments are most closely associated with the maximum increase in next semester enrollment.</p> <p>Graph: Fig. 7 Number of Coaching Appointments and Next Semester Enrollment</p> <hd id="AN0152771176-17">Next Semester Credits</hd> <p>Finally, the correlation between next semester credits and number of coaching appointments is the weakest of the three outcomes (0.13), which makes sense as it is the most removed from the ACP intervention. The Loess curve hits a maximum point around three or four appointments. Students who attended three or four coaching appointments completed, on average, about 10 credits in the semester following participation in the ACP (Fig. 8).</p> <p>Graph: Fig. 8 Number of Coaching Appointments and Next Semester Credits</p> <p>The results from the dosage analysis suggest that three to five appointments are associated with increased outcomes for students. However, the practical magnitude in those differences are small. These results, along with the results indicating small differences in outcomes for Participants as compared to Completers support the idea that there are marginal increases in outcomes for students up to around four or five academic coaching appointments.</p> <hd id="AN0152771176-18">Discussion</hd> <p>While longstanding interventions such as academic advising and counseling provide valuable services to students, the fact that these interventions, on average, have not resulted in a noticeable average increase in retention rates suggest additional supports are still needed (NCES, [<reflink idref="bib16" id="ref61">16</reflink>]). Student support staff responded to this need by expanding and innovating services to include academic coaching. In addition to implementing campus programs, academic coaches across the country formed the Coaching in Higher Education Consortium in 2020 as a professional community for developing and sharing resources and evidence-based practices (CHEC, [<reflink idref="bib7" id="ref62">7</reflink>]). In order for the consortium and larger field to expand their understanding about optimal coaching approaches and strategies, and for coaching to gain traction as a necessary support service on campuses, more research evidence is needed to understand to what extent—and for which groups—coaching provides the most value.</p> <p>In this study, we provide a clear explanation of how academic coaching overlaps with some existing student support services but also articulate how coaching uniquely expands those services. Additionally, we offer a detailed description of Silverbill's ACP, outline how it is situated within the institutional context, and identify the distinct way academic coaches support student retention and achievement. These details contribute to larger discussions about what sets coaching apart from other support services, provide useful information for emergent programs just beginning to design their approaches to coaching, and offer ideas about the gaps institutions need to fill in order to increase retention rates.</p> <p>Our results show that Silverbill's coaching intervention had positive impacts on GPA, credits earned, and retention. This is significant because Silverbill's ACP approach is consistent with the definitions of coaching provided by professionals and prior studies (c.f. CHEC, [<reflink idref="bib7" id="ref63">7</reflink>]; Robinson, [<reflink idref="bib19" id="ref64">19</reflink>]), and are similar to those in the sparse existing literature (c.f. Bettinger & Baker, [<reflink idref="bib3" id="ref65">3</reflink>]; Capstick et al., [<reflink idref="bib6" id="ref66">6</reflink>]). These results are also important as they suggest that academic coaching can move the needle on retention rates, particularly for students who need the most academic support. This work also provides a helpful comparison case for future research about academic coaching programs given the nature and detailed description of Silverbill's program.</p> <p>Although the current study provides evidence of academic coaching in improving student outcomes within one program at a university, the generalizability of results is limited. Future research is needed about similar programs at traditional undergraduate-serving institutions with more diverse student enrollment to verify whether these results apply to other sites and to better understand impacts for specific groups of students. For example, if academic coaching is particularly effective for increasing retention with academically struggling students who are disproportionately represented by historically marginalized groups, the intervention could serve as an important initiative to support equity and diversity goals in higher education. This is particularly important as retention rates tend to be lower on average for students of color than they are for white students (NSCRC, [<reflink idref="bib17" id="ref67">17</reflink>]). Further, it is necessary to study the effects of academic coaching at institutions with historically lower retention rates. Silverbill's retention rates have been over 80% for the better part of two decades, but the greatest need for implementing strategies to increase retention rates are in those institutions with lower retention rates (NCES, [<reflink idref="bib16" id="ref68">16</reflink>]). Other directions for future research in this emerging and evolving profession might also focus on tracking the long-term effects of these programs. While we did not have sufficient longitudinal data to support causal claims between academic coaching and persistence to graduation, the results here are promising for studying this relationship. Another area open for future research is to pinpoint the optimal dosage for coaching. Although we provide some information regarding dosage, there is no published research base with which to compare these results. Finally, coaching's consideration of student success encompasses students' social and emotional health and well-being. This dedicated focus implies that academic coaching potentially impacts students beyond academics. Understanding the potential relationship between coaching and students' social and emotional health and well-being would broaden the set of indicators used to evaluate the efficacy of these programs and may also benefit the field in terms of helping to refine coaching frameworks used to attend to students' well-being.</p> <hd id="AN0152771176-19">Conclusion</hd> <p>The results from this study suggest that the design of the academic coaching program implemented at Silverbill holds promise for improving key academic indicators, such as GPA and retention rates, that are closely monitored by all higher education institutions. As administrators prioritize limited resource allocations to support services offered in higher education, coaching interventions similar to that of Silverbill should be considered among those that can deliver a viable support service for academically struggling students and subsequently help retain these students. This work calls on the research field to continue building the evidence-base for the effectiveness of innovative student services programs such as academic coaching in order to improve retention rates at higher education institutions nationally.</p> <hd id="AN0152771176-20">Data Availability</hd> <p>Data transparency and custom code: https://github.com/REMCU/academic-coaching-manuscript</p> <hd id="AN0152771176-21">Code Availability</hd> <p>Data transparency and custom code: https://github.com/REMCU/academic-coaching-manuscript</p> <hd id="AN0152771176-22">Declarations</hd> <p></p> <hd id="AN0152771176-23">Conflicts of interest/Competing interests</hd> <p>Not Applicable.</p> <hd id="AN0152771176-24">Appendix</hd> <p>Table 5 Mean Difference (SE) Invited Vs. Participants</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Outcome Variable</p></th><th align="left" colspan="2"><p>Raw Difference (no matching)</p></th><th align="left"><p>Nearest Neighbor Matching</p></th><th align="left"><p>Optimal Matching</p></th><th align="left"><p>Coarsened Exact Matching</p></th></tr></thead><tbody><tr><td align="left" colspan="6"><p>All Students</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.27 (0.05) ***</p></td><td align="left" colspan="2"><p>0.26 (0.06) ***</p></td><td align="left"><p>0.2 (0.06) **</p></td><td align="left"><p>0.37 (0.07) ***</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.09 (0.02) ***</p></td><td align="left" colspan="2"><p>0.07 (0.02) *</p></td><td align="left"><p>0.06 (0.02) *</p></td><td align="left"><p>0.08 (0.03) *</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>1.42 (0.32) ***</p></td><td align="left" colspan="2"><p>1.45 (0.38) **</p></td><td align="left"><p>1.36 (0.37) **</p></td><td align="left"><p>1.9 (0.46) ***</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>1678</p></td><td align="left" colspan="2"><p>1052</p></td><td align="left"><p>1052</p></td><td align="left"><p>750</p></td></tr><tr><td align="left" colspan="6"><p>High Prior GPA</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.29 (0.05) ***</p></td><td align="left" colspan="2"><p>0.29 (0.06) ***</p></td><td align="left"><p>0.26 (0.06) ***</p></td><td align="left"><p>0.35 (0.07) ***</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.09 (0.02) ***</p></td><td align="left" colspan="2"><p>0.08 (0.02) *</p></td><td align="left"><p>0.08 (0.02) *</p></td><td align="left"><p>0.11 (0.03) *</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>1.55 (0.34) ***</p></td><td align="left" colspan="2"><p>1.43 (0.39) **</p></td><td align="left"><p>1.46 (0.4) **</p></td><td align="left"><p>2.09 (0.52) ***</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>1444</p></td><td align="left" colspan="2"><p>912</p></td><td align="left"><p>912</p></td><td align="left"><p>587</p></td></tr><tr><td align="left" colspan="6"><p>Low Prior GPA</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.13 (0.15)</p></td><td align="left" colspan="2"><p>-0.05 (0.18)</p></td><td align="left"><p>0.14 (0.17)</p></td><td align="left"><p>-0.19 (0.25)</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.02 (0.07)</p></td><td align="left" colspan="2"><p>0.06 (0.08)</p></td><td align="left"><p>0.04 (0.08)</p></td><td align="left"><p>-0.07 (0.13)</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>0.37 (0.85)</p></td><td align="left" colspan="2"><p>0.57 (1)</p></td><td align="left"><p>0.87 (0.99)</p></td><td align="left"><p>0.47 (1.6)</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>234</p></td><td align="left" colspan="2"><p>140</p></td><td align="left"><p>140</p></td><td align="left"><p>55</p></td></tr></tbody></table> </ephtml> </p> <p>* <emph>p</emph> < 0.05, ** <emph>p</emph> < 0.01, *** <emph>p</emph> < 0.0001</p> <p>Table 6 Mean Differences (SE) Invited Vs. Completers</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Outcome Variable</p></th><th align="left"><p>Raw Difference (no matching)</p></th><th align="left"><p>Nearest Neighbor Matching</p></th><th align="left"><p>Optimal Matching</p></th><th align="left"><p>Coarsened Exact Matching</p></th></tr></thead><tbody><tr><td align="left" colspan="5"><p>All Students</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.44 (0.06) ***</p></td><td align="left"><p>0.36 (0.07) ***</p></td><td align="left"><p>0.34 (0.08) ***</p></td><td align="left"><p>0.47 (0.09) ***</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.15 (0.03) ***</p></td><td align="left"><p>0.13 (0.03) ***</p></td><td align="left"><p>0.12 (0.03) ***</p></td><td align="left"><p>0.15 (0.04) ***</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>2.34 (0.4) ***</p></td><td align="left"><p>2.13 (0.5) ***</p></td><td align="left"><p>1.99 (0.5) ***</p></td><td align="left"><p>2.6 (0.58) ***</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>1678</p></td><td align="left"><p>550</p></td><td align="left"><p>550</p></td><td align="left"><p>566</p></td></tr><tr><td align="left" colspan="5"><p>High Prior GPA</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.39 (0.06) ***</p></td><td align="left"><p>0.36 (0.08) ***</p></td><td align="left"><p>0.35 (0.08) ***</p></td><td align="left"><p>0.43 (0.09) ***</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.14 (0.03) ***</p></td><td align="left"><p>0.09 (0.03) *</p></td><td align="left"><p>0.12 (0.03) **</p></td><td align="left"><p>0.13 (0.04) *</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>2.21 (0.42) ***</p></td><td align="left"><p>2.25 (0.52) ***</p></td><td align="left"><p>2.33 (0.53) ***</p></td><td align="left"><p>2.66 (0.66) ***</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>1444</p></td><td align="left"><p>490</p></td><td align="left"><p>490</p></td><td align="left"><p>417</p></td></tr><tr><td align="left" colspan="5"><p>Low Prior GPA</p></td></tr><tr><td align="left"><p>  Coaching Sem. GPA</p></td><td align="left"><p>0.57 (0.21) *</p></td><td align="left"><p>0.38 (0.26)</p></td><td align="left"><p>0.19 (0.26)</p></td><td align="left"><p>0.65 (0.31) *</p></td></tr><tr><td align="left"><p>  Enrolled Next Sem</p></td><td align="left"><p>0.22 (0.09) *</p></td><td align="left"><p>0.33 (0.12) *</p></td><td align="left"><p>0.3 (0.12) *</p></td><td align="left"><p>0.27 (0.17)</p></td></tr><tr><td align="left"><p>  Next Sem. Credits</p></td><td align="left"><p>2.34 (1.15) *</p></td><td align="left"><p>4.17 (1.42) *</p></td><td align="left"><p>3.77 (1.44) *</p></td><td align="left"><p>3.03 (2.21)</p></td></tr><tr><td align="left"><p>  N</p></td><td align="left"><p>234</p></td><td align="left"><p>60</p></td><td align="left"><p>60</p></td><td align="left"><p>37</p></td></tr></tbody></table> </ephtml> </p> <p>* <emph>p</emph> < 0.05, ** <emph>p</emph> < 0.01, *** <emph>p</emph> < 0.0001</p> <hd id="AN0152771176-25">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0152771176-26"> <title> References </title> <blist> <bibl id="bib1" idref="ref17" type="bt">1</bibl> <bibtext> Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. 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Coaching: An International Journal of Theory, Research and Practice, 1–8. https://doi.org/10.1080/17521882.2019.1574847.</bibtext> </blist> </ref> <ref id="AN0152771176-27"> <title> Footnotes </title> <blist> <bibtext> We conducted analysis for first-year students separately as well and found the results to be comparable to the overall sample.</bibtext> </blist> <blist> <bibtext> Our sample includes only 26 Participants and 10 Completers with prior semester GPAs <1.0, so our ability to make inferences about the causal impact of the ACP for these groups is severely limited.</bibtext> </blist> </ref> <aug> <p>By Jessica L. Alzen; Amy Burkhardt; Elena Diaz-Bilello; Eryn Elder; Alicia Sepulveda; Audrey Blankenheim and Lily Board</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> <p></p> <p>Jessica L. Alzen is a Research Associate at the Center for Assessment, Design, Research and Evaluation. Her research focuses primarily on education evaluation in a variety of contexts including secondary and post-secondary STEM disciplines, higher education intervention programs, and teacher professional development. She focuses on using sound methodology conveyed in clear ways to help partners make the best decisions for their programs.</p> <p>Amy Burkhardt is a graduate from the University of Colorado Boulder School of Education Research & Evaluation Methodology program. She has a B.A. in Psychology, and her background consists of experience in survey development, data analysis, IQ test administration, and automated scoring for both formative and summative assessments. She is interested in measuring learning and problem solving skills.</p> <p>Elena Diaz-Bilello is the Associate Director at the Center for Assessment, Design, Research and Evaluation and collaborates with state agencies, school districts, and educational organizations to develop practical and sound approaches for addressing assessment and educational policy challenges. She draws on her background in measurement, evaluation and policy to help design, implement and evaluate educational reforms and initiatives, and to develop comprehensive assessment strategies to foster deeper learning for all students.</p> <p>Eryn Elder is the Assistant Director of Academic Coaching at University of Colorado Boulder. She is an experienced educator and student success professional who has developed frameworks and programs for student success initiatives. Eryn is passionate about collaborating with students to help them achieve their goals and holds a strong belief that each student should have the opportunity to recognize and develop their strengths.</p> <p>Alicia Sepulveda is an Academic Coach in the Academic Advising Center at University of Colorado Boulder. She is passionate about coaching college students, helping students overcome the many challenges they experience in college, and helping students design the life they want. Alicia has coached hundreds of college students across the country and believes coaching is an incredible tool that can unlock the potential of humans.</p> <p>Audrey Blankenheim is an Academic Coach in the Academic Advising Center at University of Colorado Boulder. She is passionate about student mental health and its influence on student academic success, and is a Licensed Professional Counselor in the state of Colorado. She has also worked on a number of instructional design projects, using her knowledge of psychology to improve the facilitation of online training programs.</p> <p>Lily Board is the Assistant Dean for Academic Advising and Student Success in the College of Arts and Sciences at University of Colorado Boulder. She oversees the College of Arts and Sciences' Academic Advising Center, which includes the college's Academic Coaching Program.</p> </aug> <nolink nlid="nl1" bibid="bib16" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib19" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib14" firstref="ref10"></nolink> <nolink nlid="nl5" bibid="bib11" firstref="ref12"></nolink> <nolink nlid="nl6" bibid="bib18" firstref="ref13"></nolink> <nolink nlid="nl7" bibid="bib21" firstref="ref16"></nolink> <nolink nlid="nl8" bibid="bib22" firstref="ref32"></nolink> <nolink nlid="nl9" bibid="bib20" firstref="ref36"></nolink> <nolink nlid="nl10" bibid="bib12" firstref="ref43"></nolink> <nolink nlid="nl11" bibid="bib15" firstref="ref45"></nolink> <nolink nlid="nl12" bibid="bib10" firstref="ref51"></nolink> <nolink nlid="nl13" bibid="bib17" firstref="ref67"></nolink>
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  Data: Academic Coaching and Its Relationship to Student Performance, Retention, and Credit Completion
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  Data: <searchLink fieldCode="AR" term="%22Alzen%2C+Jessica+L%2E%22">Alzen, Jessica L.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-1706-2975">0000-0002-1706-2975</externalLink>)<br /><searchLink fieldCode="AR" term="%22Burkhardt%2C+Amy%22">Burkhardt, Amy</searchLink><br /><searchLink fieldCode="AR" term="%22Diaz-Bilello%2C+Elena%22">Diaz-Bilello, Elena</searchLink><br /><searchLink fieldCode="AR" term="%22Elder%2C+Eryn%22">Elder, Eryn</searchLink><br /><searchLink fieldCode="AR" term="%22Sepulveda%2C+Alicia%22">Sepulveda, Alicia</searchLink><br /><searchLink fieldCode="AR" term="%22Blankenheim%2C+Audrey%22">Blankenheim, Audrey</searchLink><br /><searchLink fieldCode="AR" term="%22Board%2C+Lily%22">Board, Lily</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Innovative+Higher+Education%22"><i>Innovative Higher Education</i></searchLink>. Oct 2021 46(5):539-563.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 25
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  Data: 2021
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Coaching+%28Performance%29%22">Coaching (Performance)</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Support+Services%22">Academic Support Services</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Persistence%22">Academic Persistence</searchLink><br /><searchLink fieldCode="DE" term="%22School+Holding+Power%22">School Holding Power</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22College+Credits%22">College Credits</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Point+Average%22">Grade Point Average</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
– Name: DOI
  Label: DOI
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  Data: 10.1007/s10755-021-09554-w
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  Data: 0742-5627
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  Label: Abstract
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  Data: Student retention is a key outcome measure for post-secondary education, but data show relatively stagnant retention rates over the past decade. Longstanding interventions such as counseling, academic advising, and mentoring provide crucial student services, but little change in retention rates suggest there is still need for additional student supports. Within the landscape of higher education, academic coaching is a relatively new, yet burgeoning intervention designed to increase student retention and success. Despite rapid growth of the intervention, little empirical work has been done to systematically describe and evaluate such programs. In this study, we provide a rich description of one academic coaching program and use a quasi-experimental design to evaluate the program's effects on student outcomes. We investigate two research questions: (1) how does academic coaching influence key student outcomes?; and (2) to what extent do these effects vary by amount of coaching received? On average, we found that students with prior semester grade point averages from 1.0-2.0 who participate in the academic coaching program earn grade point averages about 0.4 points higher during the coaching semester, are about 10% more likely to enroll in the semester following coaching, and earn about two more credits in the semester following coaching than students who choose not participate in the program. Outcomes varied minimally based on the number of coaching appointments students attended.
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  Data: https://github.com/REMCU/academic-coaching-manuscript
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  Data: 2021
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    Identifiers:
      – Type: doi
        Value: 10.1007/s10755-021-09554-w
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 539
    Subjects:
      – SubjectFull: Coaching (Performance)
        Type: general
      – SubjectFull: Academic Support Services
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Academic Persistence
        Type: general
      – SubjectFull: School Holding Power
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: College Credits
        Type: general
      – SubjectFull: Grade Point Average
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
      – SubjectFull: Correlation
        Type: general
    Titles:
      – TitleFull: Academic Coaching and Its Relationship to Student Performance, Retention, and Credit Completion
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Alzen, Jessica L.
      – PersonEntity:
          Name:
            NameFull: Burkhardt, Amy
      – PersonEntity:
          Name:
            NameFull: Diaz-Bilello, Elena
      – PersonEntity:
          Name:
            NameFull: Elder, Eryn
      – PersonEntity:
          Name:
            NameFull: Sepulveda, Alicia
      – PersonEntity:
          Name:
            NameFull: Blankenheim, Audrey
      – PersonEntity:
          Name:
            NameFull: Board, Lily
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 0742-5627
          Numbering:
            – Type: volume
              Value: 46
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Innovative Higher Education
              Type: main
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