The share of first-generation students (those who are the first in their family to attend college) in Ivy League schools varied from school to school. For the Class of 2028 (students beginning university in the Fall of 2024), **** percent of Cornell University's freshman class were first-generation college students.

This statistic shows the percentage of students identifying as first-generation in the United States in 2016, by gender and ethnicity. As of 2016, about ** percent of the first-generation American students, broken down by gender, were female. Almost ** percent of the first-generation students identified themselves as Native Americans in the United States in 2016.

This qualitative longitudinal study aims to explore the well-being and academic readiness of first-generation undergraduates over time. The study involves two rounds of 1-hour Zoom interviews, conducted at two different time points (e.g., first and second semester of an academic year). The sampling procedure will ensure a diverse and representative sample of first-generation students while maintaining feasibility and ethical considerations.Target population is defined with an inclusion criteria: First-generation undergraduate students (defined as students whose parents/guardians did not complete a bachelor’s degree); and enrolled full-time in a degree program at the participating institution(s). Exclusion criteria includes students who do not identify as first-generation.Sampling strategy is by purposive sampling and snowball sampling. Purposive sampling ensures a diverse sample that reflects the heterogeneity of first-generation students in terms of, gender, ethnicity/race, socioeconomic background, academic major, year of study (e.g., freshmen, sophomores), and student status as local, mainland, or international. Snowball sampling invites participants to refer other first-generation students who meet the inclusion criteria. This will help expand the sample and include students who may not be easily reachable through institutional channels.Since the population of first-generation students is minor in a university, sample size was aimed at 30 participants for the interviews and fifty students were recruited to join round 1 interviews. This sample size is manageable for in-depth qualitative analysis and allows for attrition in the second round. Round 1 interviews were conducted at the beginning of the academic year (e.g., September–October). Round 2 interviews were conducted at the end of the academic year (e.g., April–May). A 10% attrition rate was anticipated between the two rounds due to scheduling conflicts or withdrawal from the study. Forty-five students from round 1 continued the interview in round 2. The data files are transcripts form both round 1 and round 2 interviews.

According to a survey conducted in 2024, ** percent of Generation Z students reported that they would be the first in their family to attend college in the United States.

In Harvard University's Class of 2025, **** percent of Hispanic or Latinx students were first-generation college students. A further **** percent of South Asian students at Harvard in the Class of 2025 were first-generation students.

Long-lived educational trajectories of people from lower classes are frequent objects of sociological reflection. Relevant research has allowed for knowledge advances concerning practices that favor the emergence of some of these unlikely cases. This article addresses this theme based on data obtained through in-depth interviews with 25 people presenting popular origins, who represent the first generation of their families to attend higher education. The aim is to problematize the image commonly attributed to these trajectories, which usually focuses on the suffering, the feeling of not belonging and the cultural rupture with the original working class environment. It is argued herein that the concept of such an image is influenced by the mobilization of the concept of class defector. Through an empirical analysis, this article criticizes the application of such a concept in favor of interpretations that consider not only the diverse cultural references to which these individuals are potentially subject to, but also those that consider them competent social actors for the reading of different social contexts.

In 2020, ** percent of students at Brown University in the United States were first-generation college students. This is an increase from the previous year, when ** percent of students at Brown were first-generation college students.

This repository contains data and replication code for the following article: "A Summer Bridge Program for First-Generation Low-Income Students Stretches Academic Ambitions with No Adverse Impacts on First-year GPA" The data is deidentified in a way that protects student privacy.

This reflective paper explores the intersectionality of social identity, trauma, and education through the lens of a first-generation college student (FGCS) who is a neurodivergent Army veteran. I share my personal journey and experiences, highlighting marginalized communities’ challenges in the education system. I delve into the impact of cultural invasion, the transmission of trauma across generations, and the importance of critical consciousness in addressing educational inequality. I also discuss the role of spatial thinking and language in shaping learning experiences. I emphasize the need for cultural awareness, inclusivity, and equity in educational spaces and highlight the transformative power of embracing one’s differences. Overall, I explain the complex dynamics of social identity, trauma, and education and call for a deeper understanding and critical examination of these issues.

Summary

dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.

Introduction

This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents. The dataset aims to provide valuable insights for educators, policymakers, and researchers to enhance strategies for fostering student retention and academic achievement.

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Dataset

The dataset includes information known at the time of student enrollment – academic path, demographics, and social-economic factors.

- Marital status: Categorical variable indicating the marital status of the individual. (1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated)

- Application mode: Categorical variable indicating the mode of application. (1 - 1st phase - general contingent 2 - Ordinance No. 612/93 5 - 1st phase - special contingent (Azores Island) 7 - Holders of other higher courses 10 - Ordinance No. 854-B/99 15 - International student (bachelor) 16 - 1st phase - special contingent (Madeira Island) 17 - 2nd phase - general contingent 18 - 3rd phase - general contingent 26 - Ordinance No. 533-A/99, item b2) (Different Plan) 27 - Ordinance No. 533-A/99, item b3 (Other Institution) 39 - Over 23 years old 42 - Transfer 43 - Change of course 44 - Technological specialization diploma holders 51 - Change of institution/course 53 - Short cycle diploma holders 57 - Change of institution/course (International)).

- Application order: Numeric variable indicating the order of application. (between 0 - first choice; and 9 last choice).

- Course: Categorical variable indicating the chosen course. (33 - Biofuel Production Technologies 171 - Animation and Multimedia Design 8014 - Social Service (evening attendance) 9003 - Agronomy 9070 - Communication Design 9085 - Veterinary Nursing 9119 - Informatics Engineering 9130 - Equinculture 9147 - Management 9238 - Social Service 9254 - Tourism 9500 - Nursing 9556 - Oral Hygiene 9670 - Advertising and Marketing Management 9773 - Journalism and Communication 9853 - Basic Education 9991 - Management (evening attendance)).

- evening attendance: Binary variable indicating whether the individual attends classes during the daytime or evening. (1 for daytime, 0 for evening).

- Previous qualification: Numeric variable indicating the level of the previous qualification. (1 - Secondary education 2 - Higher education - bachelor's degree 3 - Higher education - degree 4 - Higher education - master's 5 - Higher education - doctorate 6 - Frequency of higher education 9 - 12th year of schooling - not completed 10 - 11th year of schooling - not completed 12 - Other - 11th year of schooling 14 - 10th year of schooling 15 - 10th year of schooling - not completed 19 - Basic education 3rd cycle (9th/10th/11th year) or equiv. 38 - Basic education 2nd cycle (6th/7th/8th year) or equiv. 39 - Technological specialization course 40 - Higher education - degree (1st cycle) 42 - Professional higher technical course 43 - Higher education - master (2nd cycle)).

- Nationality: Categorical variable indicating the nationality of the individual. (1 - Portuguese; 2 - German; 6 - Spanish; 11 - Italian; 13 - Dutch; 14 - English; 17 - Lithuanian; 21 - Angolan; 22 - Cape Verdean; 24 - Guinean; 25 - Mozambican; 26 - Santomean; 32 - Turkish; 41 - Brazilian; 62 - Romanian; 100 - Moldova (Republic of); 101 - Mexican; 103 - Ukrainian; 105 - Russian; 108 - Cuban; 109 - Colombian).

- Mother's qualification: Numeric variable indicating the level of the mother's qualification.
(1 - Secondary Education - 12th Year of Schooling or Eq. 2 - Higher Education - Bachelor's Degree 3 - Higher Education - Degree 4 - Higher Education - Master's 5 - Higher Education - Doctorate 6 - Frequency of Higher Education 9 - 12th Year of Schooling - Not Completed 10 - 11th Year of Schooling - Not Completed 11 - 7th Year (...

Data for this study were collected at the University of California – Irvine (UCI) as part of the UCI-MUST (Measuring Undergraduate Success Trajectories) Project, a larger longitudinal measurement project aimed at improving understanding of undergraduate experience, trajectories and outcomes, while supporting campus efforts to improve institutional performance and enhance educational equity (Arum et. al. 2021). The project is focused on student educational experience at a selective large, research-oriented public university on the quarter system with half of its students first-generation and 85 percent Hispanic, Asian, African-American, Pacific Islander or Native American. Since Fall 2019, the project has tracked annually new cohorts of freshmen and juniors with longitudinal surveys administered at the end of every academic quarter. Data from the Winter 2023 end of term assessment, administered in the first week of April, was pooled for four longitudinal study cohorts for this study (i.e., Fall 2019-2022 cohorts). There was an overall response rate of 42.5 percent for the Winter 2023 end of term assessment. This allowed us to consider student responses from freshmen through senior years enrolled in courses throughout the university. Students completed questionnaire items about their knowledge and use of ChatGPT in and out of the classroom during the winter 2023 academic term. In total 1,129 students completed the questionnaire, which asked questions about: knowledge of ChatGPT (“Do you know what ChatGPT is?”); general use (“Have you used ChatGPT before?”); and instructor attitude (“What was the attitude of the instructor for [a specific course students enrolled in] regarding the use of ChatGPT?”). Of those 1,129 students, 191 had missing data for at least one variable of interest and were subsequently dropped from analysis, resulting in a final sample of 938 students. In addition, for this study we merged our survey data with administrative data from campus that encompasses details on student background, including gender, race, first-generation college-going, and international student status. Campus administrative data also provides course-level characteristics, including whether a particular class is a lower- or upper-division course as well as the academic unit on campus offering the course. In addition, we used administrative data on all students enrolled at the university to generate classroom composition measures for every individual course taken by students in our sample – specifically the proportion of underrepresented minority students in the class, the proportion of international students in the class and the proportion of female students in the class. For our student-level analysis [R1], we used binary logistic regressions to examine the association between individual characteristics and (1) individual awareness and (2) individual academic use of ChatGPT utilizing the student-level data of 938 students. Individual characteristics include gender, underrepresented minority student status, international student status, first generation college-going student status, student standing (i.e. lower or upper classmen), cumulative grade point average and field of study. Field of study was based on student major assigned to the broad categories of physical sciences (i.e. physical sciences, engineering, and information and computer science), health sciences (i.e. pharmacy, biological sciences, public health, and nursing), humanities, social sciences (i.e. business, education, and social sciences), the arts, or undeclared. We defined awareness of ChatGPT as an affirmative response to the question “Do you know what ChatGPT is?” Regarding ChatGPT use, we focused on academic use which was defined as an affirmative response of either “Yes, for academic use” or “Yes, for academic and personal use” to the question “Have you used ChatGPT before?” For our course-level analysis [R2], we constructed a measure – course-level instructor encouragement for ChatGPT use – based on student responses to the end of the term survey conducted at the completion of the Winter 2023 term. In the survey, students were asked to indicate the extent to which their instructors encouraged them to use ChatGPT in each of their enrolled courses. The response

The FITW program is designed to support the development, replication, and dissemination of innovative solutions and evidence for what works in addressing persistent and widespread challenges in postsecondary education for students who are at risk for not persisting in and completing postsecondary programs, including, but not limited to, adult learners, working students, part-time students, students from low-income backgrounds, students of color, students with disabilities, and first-generation students.

Data on student success and retention-in-major (DVs) for a sample of science and mathematics majors before and after a required peer-cooperative learning program was implemented. IVs include student major, course taken, SAT scores, and demographic data including gender, low-income student status, student-of-color status, and first-generation college student status.

This dataset consists of a selection of variables extracted from the U.S. Department of Education's College Scorecard 2015/2016. For the original, raw data visit the College Scorecard webpage. This dataset includes variables about institution types, proportion of degree types awarded, student enrollments and demographics, and a number of price and revenue variables. For 2005-2006 data, see here.Note: Data is not uniformly available for all schools on all variables. Variables for which there is no data (NULL), or where data is suppressed for reasons of privacy, are indicated by 999999999.

ATTRIBUTE DESCRIPTION EXAMPLE

ID2 1

UNITIDUnit ID for institution 100654

OPEID 8-digit OPE ID for institution 100200

OPEID6 6-digit OPE ID for institution 1002

State FIPS

1

State

AL

Zip

35762

City

Normal

Institution Name

Alabama A & M University

Institution Type 1 Public 2 Private nonprofit 3 Private for-profit 1

Institution Level 1 4-year 2 2-year 3 Less-than-2-year 1

In Operation 1 true 0 false 1

Main Campus 1 true 0 false 1

Branches Count of the number of branches 1

Popular Degree 1 Predominantly certificate-degree granting 2 Predominantly associate's-degree granting 3 Predominantly bachelor's-degree granting 4 Entirely graduate-degree granting 3

Highest Degree 0 Non-degree-granting 1 Certificate degree 2 Associate degree 3 Bachelor's degree 4 Graduate degree 4

PCIP01 Percentage of degrees awarded in Agriculture, Agriculture Operations, And Related Sciences. 0.0446

PCIP03 Percentage of degrees awarded in Natural Resources And Conservation. 0.0023

PCIP04 Percentage of degrees awarded in Architecture And Related Services. 0.0094

PCIP05 Percentage of degrees awarded in Area, Ethnic, Cultural, Gender, And Group Studies. 0

PCIP09 Percentage of degrees awarded in Communication, Journalism, And Related Programs. 0

PCIP10 Percentage of degrees awarded in Communications Technologies/Technicians And Support Services. 0.0164

PCIP11 Percentage of degrees awarded in Computer And Information Sciences And Support Services. 0.0634

PCIP12 Percentage of degrees awarded in Personal And Culinary Services. 0

PCIP13 Percentage of degrees awarded in Education. 0.1268

PCIP14 Percentage of degrees awarded in Engineering. 0.1432

PCIP15 Percentage of degrees awarded in Engineering Technologies And Engineering-Related Fields. 0.0587

PCIP16 Percentage of degrees awarded in Foreign Languages, Literatures, And Linguistics. 0

PCIP19 Percentage of degrees awarded in Family And Consumer Sciences/Human Sciences. 0.0188

PCIP22 Percentage of degrees awarded in Legal Professions And Studies. 0

PCIP23 Percentage of degrees awarded in English Language And Literature/Letters. 0.0235

PCIP24 Percentage of degrees awarded in Liberal Arts And Sciences, General Studies And Humanities. 0.0423

PCIP25 Percentage of degrees awarded in Library Science. 0

PCIP26 Percentage of degrees awarded in Biological And Biomedical Sciences. 0.1009

PCIP27 Percentage of degrees awarded in Mathematics And Statistics. 0.0094

PCIP29 Percentage of degrees awarded in Military Technologies And Applied Sciences. 0

PCIP30 Percentage of degrees awarded in Multi/Interdisciplinary Studies. 0

PCIP31 Percentage of degrees awarded in Parks, Recreation, Leisure, And Fitness Studies. 0

PCIP38 Percentage of degrees awarded in Philosophy And Religious Studies. 0

PCIP39 Percentage of degrees awarded in Theology And Religious Vocations. 0

PCIP40 Percentage of degrees awarded in Physical Sciences. 0.0188

PCIP41 Percentage of degrees awarded in Science Technologies/Technicians. 0

PCIP42 Percentage of degrees awarded in Psychology. 0.0282

PCIP43 Percentage of degrees awarded in Homeland Security, Law Enforcement, Firefighting And Related Protective Services. 0.0282

PCIP44 Percentage of degrees awarded in Public Administration And Social Service Professions. 0.0516

PCIP45 Percentage of degrees awarded in Social Sciences. 0.0399

PCIP46 Percentage of degrees awarded in Construction Trades. 0

PCIP47 Percentage of degrees awarded in Mechanic And Repair Technologies/Technicians. 0

PCIP48 Percentage of degrees awarded in Precision Production. 0

PCIP49 Percentage of degrees awarded in Transportation And Materials Moving. 0

PCIP50 Percentage of degrees awarded in Visual And Performing Arts. 0.0258

PCIP51 Percentage of degrees awarded in Health Professions And Related Programs. 0

PCIP52 Percentage of degrees awarded in Business, Management, Marketing, And Related Support Services. 0.1479

PCIP54 Percentage of degrees awarded in History. 0

Admission Rate

0.6538

Average RetentionRate of retention averaged between full-time and part-time students. 0.4428

Retention, Full-Time Students

0.5779

Retention, Part-Time Students

0.3077

Completion Rate

0.1104

Enrollment Number of enrolled students 4505

Male Students Percentage of the student body that is male. 0.4617

Female Students Percentage of the student body that is female. 0.5383

White Percentage of the student body that identifies as white. 0.034

Black Percentage of the student body that identifies as African American. 0.9216

Hispanic Percentage of the student body that identifies as Hispanic or Latino. 0.0058

Asian Percentage of the student body that identifies as Asian. 0.0018

American Indian and Alaskan Native Percentage of the student body that identifies as American Indian or Alaskan Native. 0.0022

Native Hawaiian and Pacific Islander Percentage of the student body that identifies as Native Hawaiian or Pacific islander. 0.0018

Two or More Races Percentage of the student body that identifies as two or more races. 0

Non-Resident Aliens Percentage of the student body that are non-resident aliens. 0.0062

Race Unknown Percentage of the student body for whom racial identity is unknown. 0.0266

Percent Parents no HS Diploma Percentage of parents of students whose highest level of education is less than high school. 0.019298937

Percent Parents HS Diploma Percentage of parents of students whose highest level of education is high school 0.369436786

Percent Parents Post-Secondary Ed. Percentage of parents of students whose highest level of education is college or above. 0.611264277

Title IV Students Percentage of student body identified as Title IV 743

HCM2 Cash Monitoring Schools identified by the Department of Ed for Higher Cash Monitoring Level 2 0

Net Price

13435

Cost of Attendance

20809

In-State Tuition and Fees

9366

Out-of-State Tuition and Fees

17136

Tuition and Fees (Program) Tuition and fees for program-year schools NULL

Tution Revenue per Full-Time Student

9657

Expenditures per Full-Time Student

7941

Average Faculty Salary

7017

Percent of Students with Federal Loan

0.8159

Share of Students with Federal Loan

0.896382157

Share of Students with Pell Grant

0.860906217

Median Loan Principal Amount upon Entering Repayment

14600

Median Debt for Completed Students Median debt for student who completed a course of study 35000

Median Debt for Incompleted Students Median debt for student who did not complete a course of study 9500

Median Debt for Family Income $0K-$30K Median debt for students of families with less thank $30,000 income 14457

Median Debt for Family Income $30K-$75K Median debt for students of families with $30,000-$75,000 income 15000

Median Debt for Family Income over $75K Median debt for students of families with over $75,000 income 14250

Median Debt Female Students

16000

Median Debt Male Students

13750

Median Debt 1st Gen. Students Median debt for first generation college student 14307.5

Median Debt Not 1st Gen. Students Median debt for not first generation college students 14953

Cumulative Loan Debt Greater than 90% of Students (90th Percentile)

48750

Cumulative Loan Debt Greater than 75% of Students (75th Percentile)

32704

Cumulative Loan Debt Greater than 25% of Students (25th Percentile)

5500

Cumulative Loan Debt Greater than 10% of Students (10th Percentile)

3935.5

Accrediting Agency

Southern Association of Colleges and Schools Commission on Colleges

Website

www.aamu.edu/

Price Calculator

www2.aamu.edu/scripts/netpricecalc/npcalc.htm

Latitude

34.783368

Longitude

-86.568502

This statistic shows the acceptance and attendance rates of freshmen at first-choice colleges in the United States in 2016, by student generation. In 2016, about ** percent of the first-generation students were accepted to their first-choice college; however, the attendance rate of first-choice college by first-generation students was **** percent in the United States.

In the context of continued equity gaps in student success within and beyond STEM, this paper explored the extent to which the representation of underrepresented racial minority (URM) and first-generation college students predict grades in postsecondary STEM courses. The analyses examined 87,027 grades received by 11,868 STEM-interested students within 8,468 STEM courses at 20 institutions. Cross-classified multilevel models and student fixed effect analyses of these data both support the same conclusion: the proportion of URM and first-generation students within a class is positively associated with STEM grades among all students, and these relationships are stronger among students who are members of the minoritized group. Thus, promoting the representation of students with minoritized identities in STEM courses may lead to greater equity in college outcomes.

Disparities remain in the representation of marginalized students in STEM. Classroom-based experiential learning opportunities can increase student confidence and academic success; however, the effectiveness of extending learning to outdoor settings is unknown. Our objectives were to examine 1) demographic gaps in ecology and evolutionary biology (EEB) major completion, college graduation, and GPAs for students who did and did not enroll in field courses, 2) whether under-represented demographic groups were less likely to enroll in field courses, and 3) whether under-represented demographic groups were more likely to feel increased competency in science-related tasks (hereafter, self-efficacy) after participating in field courses. We compared the relationships among academic success measures and demographic data (race/ethnicity, socioeconomic status, first-generation, and gender) for UC Santa Cruz undergraduate students admitted between 2008 and 2019 who participated in field courses (N...

IntroductionWe investigated differences in domain-general expectancy, value, and engagement in school by generation status and how the relationship among these constructs and academic performance differ by generation status.MethodsA total of 573 college students enrolled in introductory psychology courses participated in the study. We collected data on generation status, expectancy-value beliefs, school engagement, and official GPA data from participants, tested measurement invariance of expectancy-value beliefs and engagement between first-generation college students (FGCS) and continuing generation college students (CGCS), and conducted multigroup modeling to understand the differential relations of expectancy-value, engagement, and GPA between the two groups.ResultsWe discovered that the latent mean of expectancy beliefs differed significantly by generation status, with FGCS reporting higher expectancy than CGCS. There were no differences in the latent mean of task value. Multigroup structural equation modeling revealed that the effect of expectancy-value motivation on behavioral engagement was similar across groups, but its effect on cognitive engagement was greater for the FGCS than for the CGCS. For both groups, expectancy impacted academic performance via behavioral engagement. Finally, neither expectancy-value motivation nor cognitive engagement directly predicted academic performance for either group.DiscussionThe findings have important theoretical implications for understanding motivation and achievement of FGCS and CGCS and critical practical implications regarding undergraduate education.

IntroductionAlthough university education in Germany is without tuition fees, the parental educational background influences the decision to study as well as the choice of subjects, and is linked to challenges that students face during the course of study. However, little is known about these aspects in veterinary medicine. In this study, the differences between the first-generation students (FGS) and continuing-generation students (CGS) in veterinary medicine, as well as the challenges (study entrance, financial situation, concerns about the future), are examined in order to identify any need for supportive action.MethodsThe results of this study are based on a survey that was open to all German veterinary students in Spring 2023.ResultsResponses from 1,525 students were analyzed (response rate 24%). A considerable proportion of veterinary students (40%) in Germany are FGS. They are more likely to rate their financial situation as poor (20%) or very poor (7%) than CGS (poor: 10%, very poor: 3%). Even though FGS and CGS work for income alongside their studies in similar proportions (FGS: 71%; CGS: 67%), the motivation was different: CGS are more likely to work in order to earn extra money (CGS: 68%; FGS: 49%) and FGS because other sources of income are insufficient (FGS: 34%; CGS: 12%). A similar proportion of FGS and CGS frequently or consistently considers dropping out of university (FGS: 10%; CGS: 11%). However, in FSG this is more often due to financial reasons (FGS: 28%, CGS: 16%). Almost all students have a high school-based university entrance qualification (97%). Final school grades of CGS were better on average than those of FGS. More FGS than CGS had already completed vocational training prior to their studies (FGS: 47%, CGS: 30%).DiscussionIn conclusion, FGS and CGS differ in their own educational background and the financial challenges in veterinary education. It is therefore important to create an awareness of challenges related to the social background of veterinary students. Moreover, support services to counteract challenges of FGS (and CGS) are needed. There is a need for further research concerning the associations between parental educational background and academic and professional success in veterinary medicine.

The transition to higher education is a challenging period for many students and requires support. Because students’ backgrounds, such as being a first-generation in higher education student, shape experiences in higher education, it is important to consider these factors when organizing support. Using a quasi-experimental pre-test – post-test design, the current study examined the effects of an online pre-academic programme (PAP) specifically aimed to address background-related challenges, on early academic achievement, sense of belonging, academic self-efficacy, and mobilization of on-campus social capital. Multilevel regression analyses of achievement data (NPAP = 463; NControl = 948) and psychosocial data (NPAP = 115; NControl = 544) indicated a positive effect of PAP on achievement and sense of belonging, but not on self-efficacy. Mediation analyses showed that effects of PAP did not vary according to background factors. Path analysis further showed a positive association of PAP participation and mobilization of peer social capital, which partly mediated the effect on sense of belonging. No associations were found with mobilization of faculty social capital. The results suggest that PAP participation positively affects students’ transition to HE, in terms of early achievement, sense of belonging in HE, and mobilization of peer capital.