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Record W7065875371

Examining Transportation Roadblocks to Community Colleges: Pathways to Educational and Economic Opportunity

2025· other· en· W7065875371 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2025
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationSocioeconomic statusWork (physics)Access to Higher EducationPublic policyCommunity engagement
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines how transportation barriers impact community college (CC) students' ability to achieve their academic objectives. Although CCs serve as critical pathways to educational and economic opportunities for marginalized populations, transportation accessibility remains an overlooked variable in student success. The transition to a CC often signifies a meaningful achievement for a broad segment of the population. For some, securing adequate funding and initiating formal application procedures can seem like insurmountable hurdles. CCs, also known as two-year colleges or junior colleges, are a critical component of the U.S. higher education system. CCs offer an extensive range of courses that lead to associate degrees, certificates, or self-paced learning options, often allowing students to earn credits transferable to four-year institutions for bachelor's or advanced degrees (Kerr & Wood, 2022). CCs are critical to enhancing equitable access to higher education for first-generation college students, individuals from groups historically excluded from higher education, older learners, and workers seeking to enhance their skills (Community College Research Center [CCRC], 2021). An overarching goal of my dissertation is to bring awareness to the transportation challenges faced by CC students when accessing campus and to highlight the need for additional work in this area. There is some research that looks at how certain data points such as socioeconomic status, race, and access to a car are linked to CC access, but there is a lack of research on how access and associated barriers to it affects student completion, including reliable and affordable automobility, public transit, and other modes.This abstract describes the research objectives and overarching methodology and highlights key results from these contributions and concludes with a summary.These ongoing barriers remain prevalent among students in rural, suburban, and economically disadvantaged areas and can become the most significant determining factor in their academic outcomes. This research employs three complementary approaches: (1) a comprehensive literature review of transportation accessibility in higher education contexts, (2) spatial analysis comparing CC accessibility in Texas and California, and (3) case study analysis of hypothetical student experiences at Austin Community College.Chapter 1 - Obstacles and Opportunities for Improving Transportation Access to Community College EducationThe goal of Chapter 1 is to better comprehend the transportation access barriers faced by CCs and develop effective strategies to address them. I conducted an in-depth analysis of 88 studies examining transportation-related issues within higher education institutions. My findings revealed that while previous research has delved into the transportation challenges encountered by communities at universities, it has largely neglected the specific transportation issues faced by CC students. While some research exists in the context of CCs, there is a significant gap in empirical analyses that explore the modal preferences of CC students, examine the correlation between transportation barriers and the academic success of CC students, and evaluate the effectiveness of strategies aimed at improving access to CC campuses.To investigate past work relevant to college access, I performed a systematic literature review on transportation to higher education campuses. Since the existing body of work focusing on transportation access to colleges is limited, a broader search scope was employed to analyze enough studies. From these studies, relevant insights about college access were extracted. Key articles were identified using a hub-and-spoke search process. The analysis section aims to achieve three main objectives: 1) provide summaries of the 88 studies included in the literature review, 2) identify significant themes in the past work on transportation to higher education, and 3) explain why these themes are crucial for studying transportation access to colleges.Shaheen et al. (2017) introduced a framework called the STEPS to Transportation Equity Framework, which serves as a tool for analyzing the various transportation barriers that individuals may encounter during their travels. The STEPS framework revealed that long commute distances, conflicting transit and course schedules, and housing unaffordability can hinder transportation access to CCs. The literature underscores the use of public transit pass programs to assist CC students with transportation, but there's an opportunity to explore innovative approaches to meet the growing access requirements of CC students.Building on these insights from the literature review, Chapter 2 moves from theoretical understanding to geographic analysis by examining two major community college systems.Chapter 2 - A Tale of Two States: Exploring Transportation Accessibility to Community College Education in California and TexasI delve into the extent to which students must travel to reach their nearest CC campus, and the variety of degree and educational programs offered at each CC campus. To achieve this, I selected CCs in Texas and California as case studies to explore how access to education is influenced by the distinct policy models prevalent in these two states.This investigation is driven by a desire to comprehend how transportation access to the two largest CC networks in the U.S., Texas and California, which collectively encompass the two largest states in the country, impacts pathways to employment and transfer routes to universities. These two states play a significant role in shaping the economic, political, and social landscape of the United States.To explore accessibility to CCs, I selected a method of measuring access to CC campuses. Since education is a pathway to employment opportunities and other benefits, the variety of degree and certificate offerings provided by a CC is a measure of the opportunities available. To measure the number of offerings available at CCs, I utilize data maintained by the Integrated Postsecondary Education Data System (IPEDS). I also examine the number of opportunities available at CC campuses that are accessible to individuals in their vicinity.To gain insights into the characteristics of individuals with better access to CCs and those with limited access, I measure access proxy variables alongside three socioeconomic variables collected from the 2021 five-year American Community Survey (ACS) census tract estimates.To investigate our research objectives, I selected statistical tools that enable us to draw inferences from the data used to measure transportation access to pathways to employment and education through CCs. The method I utilized to assess sociodemographic affluence influences access to CCs and their educational offerings is crosstab analysis. Crosstab analysis involves organizing all data points measured into bins, or crosstabs, based on the magnitude of the variable used to define the crosstabs.When it comes to access to CC campuses and education, economic affluence plays a more significant role in California, while educational affluence holds a more prominent position in Texas. In contrast, mobility affluence has a minimal impact on access in both states. On average, educational offerings at the nearest CC campus tend to be higher in Texas than in California. This disparity can be attributed to the differences between California's centralized CC policy model and Texas' decentralized CC policy model. While one state may not clearly offer better CC campus access compared to the other, a comprehensive analysis of access provides a broad understanding of CC access in the two states. However, further exploration should be conducted through more detailed access case studies that are situated in both states.While Chapter 2 provides a macro-level analysis of accessibility patterns across two states, Chapter 3 zooms in to examine the lived experiences of individual students navigating these transportation systems.Chapter 3 - A Case Study Analysis of Transportation Accessibility to Community College on Public TransitThis study delves into the accessibility of public transit to CCs through a comprehensive case study analysis. The primary objectives are to:1) Understand the impact of transportation accessibility challenges on the lives of CC students.2) Show that transportation accessibility should be recognized as a significant barrier hindering CC students' ability to achieve their academic goals.3) Develop a framework to comprehend how equity factors influence student access to CCs via public transit.To address the real-world transportation accessibility challenges faced by CC students, I developed a qualitative research methodology based on hypothetical student profiles. This approach allowed us to gain a deeper understanding of their transportation burdens by presenting realistic barriers and challenges in a definable and descriptive manner. In the fall of 2019, StudentMoveTO, a Toronto survey, conducted the largest survey ever conducted to better understand student travel patterns, experiences, and preferences. The StudentMoveTO survey served as a proxy and framework for my approach to this methodology. Based on these assumptions, I analyzed the data and demographic characteristics of 200 students from the Toronto survey to create 10 hypothetical student profiles that served as a proxy for conducting our research with Austin Community College (ACC) students. For example, my analysis and coding of the student demographic characteristics in the sample revealed approximately 14 significant and repetitive elements (race, age, gender, income, enrollment status, employment, major parental status, transit time, frequency of travel, multiple campus transit, etc.) that correlated with transportation accessibility. Using this framework, I obtained publicly accessible data fro

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0140.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.244
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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