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Future Ready: Creating a Coherent Career Advising Strategy at The Denver Scholarship Foundation
No full textHigher education in the United States is at a crossroads. College enrollment rates have declined for the past 15 years, and many question whether a four-year degree is worth the time, money, and sacrifice it takes to persist to graduation. Most students cite securing a good job as their primary reason for pursuing higher education, illustrating an evolving education landscape where career conversations and programming must be increasingly prevalent to meet individual student and broader economic needs.
Colorado has a unique education paradox, as it leads the nation in post-secondary education attainment. However, Colorado’s high school graduation and college matriculation rates are below national averages, as a significant portion of its highly educated professional workforce is imported from other states. Post-secondary credentials are required to earn a living wage in Denver, making it crucial to strengthen the state’s education-to-career pipeline to fill available jobs. The context above informed my residency at The Denver Scholarship Foundation (DSF), which was created in 2006 to help make college possible for Denver Public School (DPS) students.
My capstone illustrates my effort to develop a coherent career advising strategy that prepares high school and college students for professional success. By creating psychological safety, focusing on organizational learning, and employing a facilitative leadership approach, I strived to create the conditions for future career advising success at DSF.
During my residency, I analyzed the nonprofit career advising landscape. I also conducted background research on theories and best practices for career advising, including career theory, mentorship and social capital formation, career exposure and experiences for high school students, the role of college internships in helping students avoid underemployment, and career metrics. In addition, I evaluated DSF’s existing career programming and reported on stakeholder insights to inform future program design.
I conclude with recommendations for DSF to clearly define career advising, build staff capacity, create more work-based learning and mentorship opportunities, and implement career metrics. I also share relevant sector-wide implications, such as embracing holistic advising and college student success coaching, strategically collecting and using student career data, facilitating transformational partnerships, and providing affordable pathways through postsecondary education.Educatio
Engineering an Open-source Motion Capture Platform to Characterize Abnormal Gaits for Resource-limited Environments
No full textGait abnormalities, a leading cause of death for older adults, are on the rise with the increasing
global median age. While existing technologies can detect and quantify these abnormalities,
their limited use in low-resource settings due to high costs and limited interoperability
poses a severe challenge. This thesis overcame that barrier by introducing a novel approach:
an open-source motion capture tool. The paper details creating a color-based marker detection
system that utilizes a Kalman filter for tracking and the methods for achieving 3D
marker coordination using a stereovision setup. The paper determined the necessary rotation
matrices to obtain physiologically relevant angles utilizing a series of markers placed at
anatomically relevant reference points. The motion capture system was validated by characterizing
and classifying four gaits at three different speeds across two participants. The paper
employed the motion capture system to assess the key features of normal, crouch, spastic,
and steppage gaits in both the time and frequency domains. The paper performed spline
interpolation, principal component analysis (PCA), linear discriminant analysis (LDA), logistic
regression, support vector machine (SVM), and k-nearest neighbor (KNN). The paper
found that nonlinear statistical methods slightly outperformed linear ones when classifying
gait. The paper showed that using the frequency domain can mitigate the impact of temporal
noise during windowing and provide a more effective approach to classifying speed.
The paper demonstrated that individually trained models have little generalizability, but
increasing the population over which model training occurs can mitigate this concern.Engineering Sciences A
Question-Driven Reasoning in AI-Assisted Decision-Making: A Content-Based Approach
No full textWe introduce the Question-Driven Theory of AI-Assisted Decision-Making, a content-based model of reasoning to explain human-algorithm interaction in high-stakes risk assessment contexts. According to the content-based theory of reasoning proposed by Koralus et al., human reason is driven by the goal of reducing the complexity of our questions as directly as possible, as opposed to alternative reasoning models that focus on maximizing expected utility by considering all possible alternatives. By emphasizing the role of the question-answering mechanism in human reasoning, this theory allows us to bring a new perspective to theoretically model and thereby understand the dynamics of human-algorithm interaction. According to our theory, AI-assisted decision-making can be understood in two phrases, the Question-Raising phrase and the Question-Answering phase. First, the algorithm prediction guides the decision-maker to raise an actionable question, while adding dependencies of concepts into their mental model representation. Then, the decision-maker reaches a decision by settling the question with their mental model representation. We propose the design of and conduct a behavioral experiment to test the applicability of a content-based theory on AI-assisted decision-making. We show that the presentation of an algorithm’s risk assessment predictions, such as logically equivalent information with either positive or negative probabilities, can influence the type of questions decision-makers pose and, subsequently, the decisions they make. We propose a framework to computationally model this procedure, as well as normative directions for designing AI assistance that prompt the right kind questions to ensure rational decision-making procedure and fair results.Computer Scienc
Large Language Models and How We Train Them
No full textLarge language models (LLMs) are the technological revolution of the decade, yet the mechanisms behind how they were developed or how they work fundamentally are often misunderstood by the public—and even debated among AI researchers and industry leaders—despite their popularity. As a result, for many individuals, these models have become unexplainable black boxes, which can lead to misuse or over-reliance on LLMs as people fail to grasp how they work or what their current limitations are. Due to a dramatic increase in AI discussion over the last five years, there is also the problem of information overload. There are many resources on AI from academic papers, blogs, news articles, and more that cover many different topics in a vast literature, but few unify these ideas into a coherent framework that explains how modern LLMs are built and trained. Many resources are also inaccessible and assume deep background knowledge, or focus on narrow implementation-level and mathematical details. This thesis is a structured, theoretical introduction to large language models that aims to combine theory with intuitive explanations and bridge the gap between understanding AI and its usage.Computer Scienc
Job Loss and Work Among Older Workers
No full textIn recent decades the older worker population has grown significantly, and its sociodemographic composition has become more diverse. Yet it is not clear what the implications of these demographic changes are for work in older age. Prior research on aging and work has predominately studied more advantaged workers. This dissertation improves our understanding of the labor market experiences of marginalized workers in older age.
In Chapter 2, I evaluate whether Black-White disparities in reemployment likelihood increase, decrease, or remain constant among older workers relative to younger workers and what factors explain these differences. I draw on scholarship on racial disparities in health and evaluate three hypotheses: cumulative dis/advantage (inequality increases over time), aging-as- leveler (inequality decreases over time), and persistent inequality (inequality does not change over time). I test these hypotheses using hazard models and data from the Survey of Income and Program Participation. I find that Black-White inequalities in reemployment narrow by about 70 percent in older age, supporting the aging-as-leveler perspective. Relative to their younger counterparts, White older workers experience a greater decline in reemployment likelihood than Black older workers. Though Black older workers face the lowest chance of reemployment. Prior experience, education, and skills do not explain Black-White differences in reemployment across age, suggesting that hiring discrimination and other post-job-loss factors likely underlie these differences.
In Chapter 3, I generate more comprehensive estimates of Black-White differences in the costs of job loss across age and test which theories account for these differences. Scholarship on job loss typically examines changes in earnings only for workers who reemploy and likely underestimates the financial implications of late-career job loss, particularly for minority older workers. In my sample I include individuals who do not reemploy to account for Black and older workers’ lower likelihood of reemployment. Using fixed effects models and data from the Survey of Income and Program Participation, I find that workers experience a large decline in earnings following job loss, ranging from 65 to 81 percent 1-3 years after job loss. Older and Black workers experience greater declines in earnings than their White and younger counterparts, but I do not find evidence of Black-White disparities changing across age. I find that job tenure partially accounts for age differences, pointing to human capital, job matching, and deferred compensation theories. However, age and race differences in earnings losses remain either partially or totally unexplained, suggesting that differences stem from unobserved factors that likely emerge after job loss, such as job search characteristics and age and racial discrimination.
In Chapter 4, I examine why less-educated men are more likely to exit the labor force than their more-educated counterparts prior to retirement age. In recent decades there has been an increase in early retirement (labor force exit by around age 60) among men, particularly among those without a college degree. This gap in labor force attachment by educational attainment (what I refer to as the education gap) is of concern because working into older age is critical for creating a sufficient nest egg for retirement. Is the education gap in labor force participation due to choice or constraint? I use the Health and Retirement Study to estimate how much of the education gap is a function of less-educated men disproportionately being unable to work (health status) and disproportionately lacking access to higher-quality jobs with employee retirement benefits that encourage working longer (pensions). I find that differences in health status and in pension coverage account for 67 percent and 22 percent of the gap, respectively. Together these findings suggest that less-educated men likely exit the labor force due to constraint, prior to saving enough for retirement. The education gap in labor force participation could therefore be a precursor to retirement security inequalities in older age.Social Polic
Reclaiming Alexandria: A Case for Historic Futures
No full textOver the past centuries, scenarios of menace resulting in unchecked urban pressures have degraded the historic core of Alexandria into vertical slums and abandoned warehouses, compromising the integrity of the original Alexandrian masterplan. The complexity of this deterioration involves numerous interconnected factors, including inadequate housing, environmental degradation, mobility challenges, resource scarcity, and the lack of policy frameworks governing the dynamics among various stakeholders. Consequently, Alexandria’s historic core remains in a state of decline, awaiting a transformative vision capable of revitalizing its latent potential. This thesis, thus, proposes a reinterpretation of Alexandria’s original masterplan as a theoretical, site-specific framework translated into a tangible urban design intervention. It explores how the conservation of Alexandria’s archeological urbanism can serve as a catalyst for urban renewal and provide an example of a future-oriented pathway deeply anchored in the city’s historic identity.Department of Urban Planning and Desig
