Ashley R. Sanders, Ph.D.

Executive Summary

UCLA Digital Humanities Minor: Student Success, Equity, and Program Insights (2011–2024)

This report presents a comprehensive analysis of student-level data from the UCLA Digital Humanities (DH) minor, examining retention and completion outcomes across key demographic groups from 2011 to 2024. Using interactive Tableau dashboards, descriptive statistics, and logistic regression modeling, the study identifies patterns of student success and areas where equity-focused support can be enhanced.

Key Findings:

• The overall completion rate for the DH minor is 77%, exceeding national undergraduate benchmarks.
• First-generation students complete the minor at the highest rate (77%), suggesting the program effectively supports this group.
• Female students outperform male students (74.4% vs. 68.9%), and non-transfer students slightly outperform transfers.
• Completion rates vary by race/ethnicity: international (82%), white and Hispanic (~73%), and Black and Native American students (≤60%).
• A significant interaction between URM status and gender suggests that URM male students may benefit from unique supports or motivations, as they had higher-than-expected predicted completion rates.

Recommendations:

• Target additional outreach and support to URM female students, particularly those who are not first-gen.
• Investigate the success of URM male students for scalable best practices (e.g., peer mentorship, advising).
• Continue to monitor and support transfer and continuing-generation students, particularly those declaring the minor late.
• Institutionalize data-informed decision-making by collecting and analyzing outcomes data annually.

The DH program serves as a promising model for inclusive academic success in interdisciplinary, computational humanities education.

UCLA Digital Humanities Program Overview

Founded in 2011 and reporting to the Dean of Humanities, the Digital Humanities (DH) program at UCLA consists of a “free-standing” undergraduate minor and a graduate certificate. This program combines the traditional strengths of a liberal arts education with training in computational methods. By implementing lab- and studio-based classes structured around interdisciplinary analysis, collaborative research, and experimental thought and design, the DH program prepares students for a wide range of twenty-first-century creative and analytical jobs that now demand a melding of technology, data, and the humanities. DH makes a unique and timely contribution to research and pedagogy at UCLA by offering practical engagements with computational tools, data analysis, networked technology, and digital media in their crucial roles in contemporary culture and society. In so doing, it draws directly on the interpretative and critical methods of the humanities, both to analyze the workings of digital technologies and to develop new tools to document and analyze all aspects of human culture and society.

NOTE: All of the data analyzed in this report is for declared DH Minors; the data from the College of Engineering on the students who declared DH as their “Technical Breadth Area,” which is the equivalent of a minor for engineering students at UCLA, was unavailable for analysis.

Demographic Overview of Students in the DH Program

Faculty and leadership within the DH program have cultivated a safe environment that seeks to reinforce the idea that all students are welcome in the program, regardless of their major, race, ethnicity, gender, transfer or first-generation status, or other identities. Between 2011 and 2024, the percentages of students identifying as female, first-generation, transfer, or non-white have all risen. As shown in the interactive dashboard below, not only has the program grown dramatically since its inception, it has also become predominately female and non-white, and the proportion of Asian-Pacific Island students has steadily increased.

Overall, the program comprised 18% underrepresented minority (URM) students (URM here includes Black, Latinx, and Native American students), about 28% first-generation college students, and 25% transfer students (students who entered UCLA as transfers). International students comprised roughly 13% of the minors.

To provide a sense of how the program has evolved, we examine the 2022-2023 academic year – the most recent full academic year for which we have data. The DH minor student body included about 20% URM students, about 28% first-generation college students, 35% transfer students, and roughly 9% of minors were international student – the lowest proportion since its inception, likely due to carry-over effects of the Covid-19 pandemic, as we see a rebound to 12% in the fall of 2023.

Further statistical analysis uncovers several statistically significant and meaningful correlations, suggesting the importance of observing students’ intersectional identities. For instance, more Hispanic students in the program are also transfer students than for any other racial or ethnic identity.

The dashboard below is interactive so the data can be sliced in many different ways and viewed for different periods of time using the slider in the lower right corner.

Academic Majors and Departments of DH Minor Students

Declared DH Minors come primarily from the Social Sciences. Economics/Business Economics, Sociology, and Communication are the top majors from this division. Many DH Minors are also pursuing majors in the Life Sciences, primarily Cognitive Science and Psychology. Examining the two most recent years reveals the growing trend of students coming from the Physical Sciences, mostly from Statistics & Data Science, as well as Data Theory.

Student Completion Rates

The completion rate for the Digital Humanities minor at UCLA stands at 77%, which is 13 percentage points higher than the national average for four-year degree completion. This minor is rigorous and research-focused, attracting highly committed students. However, some students do not finish the program, often because they apply too late in their undergraduate careers, leaving insufficient time to complete both the coursework and the capstone project. To address this issue, increasing awareness of the minor earlier in students’ academic journeys could significantly help more students successfully complete the program.

First-Generation Students: As we examine the outcomes for students within each demographic category, we notice that first-generation college students had the highest completion rate (77%), outperforming their continuing-generation peers (71%). This suggests that the DH minor may offer strong support or alignment with the goals of first-gen students, making it a particularly inclusive program in that regard.

Gender: Completion rates favored female students (74.4%) over male students (68.9%), aligning with national trends. (NCES Report)

Transfer Students: Among students who entered UCLA as freshmen (non-transfers), the completion rate for the minor was only slightly higher (73.3%) compared to transfer students (72.1%). This small gap reflects positively on the program’s accessibility for students from different pathways. Notably, this outcome highlights the program’s efforts to support transfer students by reserving seats in DH courses, allowing them to enroll in these impacted classes immediately upon entering UCLA. By facilitating early enrollment for transfer students, the program has effectively eliminated the completion gap between those entering as freshmen and those entering as transfers.

International students had the highest completion rate (82.2%) out of all demographic categories.

Looking at race and ethnicity, White non-Hispanic and Hispanic students completed the minor at equivalent rates ((73%) and Asian/Pacific Islander students (71.9%) closely followed. In contrast, Black students (60%), students of unknown/other race (57.7%), and American Indian/Alaskan Native students (50%, n=2) had noticeably lower completion rates. These gaps indicate a need for additional outreach, support, or structural review to ensure that traditionally underrepresented students have equitable access to success in the minor.

While these descriptive statistics offer valuable insight into broad trends in completion rates, they do not account for the ways in which multiple demographic characteristics may intersect to influence student outcomes. To better understand these relationships—and to identify which combinations of factors significantly predict minor completion—I conducted a logistic regression analysis. This approach estimates the likelihood of completion while controlling for overlapping variables such as race/ethnicity, gender, first-generation status, and transfer status, and to explore potential interaction effects between them.

Logistic Regression Analysis: Predicting DH Minor Completion

Method

To better understand which student demographics are associated with successful completion of the DH minor, I developed a series of logistic regression models using student-level data from 2011–2024. The outcome variable was whether a student completed the minor (1) or did not complete (0).

I evaluated both main effect models and models including two-way interaction terms to account for how overlapping identities (e.g., being URM and male) influence outcomes. Our models included the following student characteristics:

• Race/Ethnicity
• Gender
• First-Generation College Status
• Transfer Status

Findings

In the initial model using only main effects, no individual demographic factor reached statistical significance at p < 0.05, although one variable approached marginal significance:

• Being not first-generation was associated with lower odds of completion, with an odds ratio (OR) of 0.68 and a marginal p-value of 0.0546.
• Being male, URM, or a transfer student was also associated with lower odds of completion, but none of these reached statistical significance in the main effects model.
• Some race/ethnic categories (e.g., Asian, Hispanic, International, White) had higher odds of completion relative to the reference group (likely URM or “Unknown”), but these effects were not statistically significant either.

These findings suggest that no single demographic characteristic alone was a strong predictor of minor completion. However, the presence of marginal effects and theory-driven concerns warranted deeper analysis.

then tested a logistic regression model including all pairwise (2-way) interactions between URM status, gender, first-gen status, and transfer status. This revealed a statistically significant interaction:

• URM × Male: This interaction term was highly significant (p = 0.0015). The odds ratio for the interaction was 6.10, suggesting that being both URM and male results in a different (and significantly more positive) effect on completion than either variable alone would predict.
  • URM students alone had lower predicted odds of completion (OR = 0.63).
  • Male students alone had lower odds (OR = 0.55).
  • But URM male students had higher predicted odds, suggesting the negative effects of each identity might not compound — or that this subgroup benefits from unseen supports or unique motivations.

No other interaction terms were statistically significant.

The strong significance of the URM × Male interaction suggests that intersectional identities play a meaningful role in student success, and that not all students experience risk equally.

Some subgroups — such as URM, non-first-gen women, and non-URM, first-gen male transfer students — show notably lower predicted completion probabilities (around 50–60%), compared to the cohort average (77%). In contrast, URM males (especially first-gen) showed high predicted probabilities of completion, likely reflecting the interaction effect and perhaps strong formal and informal support structures.

It is also worth noting that the model predicts that URM students who are female, not first-generation, and entered UCLA as freshmen have only a 50.8% probability of completing the DH minor. This is significantly lower than the program average (77%) and suggests that this subgroup may face unique challenges. In contrast, first-generation URM students show higher predicted probabilities, suggesting that first-gen-specific programming may be providing meaningful support that could be extended to a broader set of URM students.

To test for more nuanced effects, I explored models including 3-way interactions among URM status, gender, first-generation college status, and transfer status. However, none of these models significantly improved fit compared to the 2-way interaction model, as determined by likelihood ratio tests (p > 0.05). This suggests that the primary variation in completion outcomes is explained by 2-way interactions.

Recommendations

• Pay attention to intersectional risks: Certain subgroups (e.g., URM + female + not first-gen) appear especially vulnerable to non-completion. Consider providing additional support and/or mentoring to groups at high risk of dropping the minor.
• Investigate high-performing URM male students: Their unexpectedly high predicted success may reflect positive supports (peer networks, faculty mentoring, etc.) that could be replicated across groups.
• Monitor non-first-gen outcomes: Overturning assumptions that non-first-gen students are less at risk, this analysis suggests first-gen students may be thriving, while their continuing-gen peers could use more support.

Conclusion

The UCLA Digital Humanities minor has emerged as a dynamic, inclusive, and academically rigorous program that serves a diverse cross-section of the undergraduate population. With an overall completion rate of 77%—well above national averages for undergraduate program completion—the minor is clearly providing value to students across majors and demographic backgrounds. The program’s growth, especially in attracting first-generation, female, and non-white students, reflects successful outreach and relevance in a rapidly evolving academic and professional landscape.

However, disaggregated analyses reveal meaningful variation in outcomes that warrant further attention. While first-generation students and international students are thriving in the program, certain subgroups—particularly URM students who are not first-gen and female—show lower completion probabilities. These findings underscore the importance of looking beyond single demographic characteristics and toward the compounded effects of identity, time to degree, and access to support. A significant interaction between URM status and gender, for example, highlights that male URM students may experience a unique set of conditions—possibly protective—that merit further exploration and potentially replication.

To build on these findings, the DH program should consider implementing targeted advising and early outreach for students, particularly URM students who are not first-generation. Additionally, qualitative investigation into the supports and experiences of successful URM male students may reveal scalable strategies for broader application. Finally, the program would benefit from developing a routine data-monitoring framework to assess equity and retention across future cohorts, ensuring that interventions are timely, evidence-based, and responsive to the evolving student body. Through these steps, the DH minor can continue to model inclusive excellence in a technology-enhanced humanities curriculum.

Code Repository