American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students’ final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a frequentist approach is used. For the standard six-year graduation rate method, the frequentist approach corresponds to counting the number of students who finished their program within six years and dividing by the number of students who entered that year. In the case of absorbing Markov chains, the frequentist approach is used to compute the underlying transition matrix, which is then used to estimate the graduation rate. In this paper, we apply a sensitivity analysis to compare the performance of the standard six-year graduation rate method with that of absorbing Markov chains. Through the analysis, we highlight significant limitations with regards to the estimation accuracy of both approaches when applied to small sample sizes or cohorts at a university. Additionally, we note that the Absorbing Markov chain method introduces a significant bias, which leads to an underestimation of the true graduation rate. To overcome both these challenges, we propose and evaluate the use of a regularly updating multi-level absorbing Markov chain (RUML-AMC) in which the transition matrix is updated year to year. We empirically demonstrate that the proposed RUML-AMC approach nearly eliminates estimation bias while reducing the estimation variation by more than 40%, especially for populations with small sample sizes.

The introduction and ongoing integration of unmanned aerial vehicles (UAV) into the National Airspace System requires appropriate management and regulation. To ensure safe access for all stakeholders the Federal Aviation Administration has established regulations for hobbyist and commercial operators of UAVs, partially supplemented with concurrent guidelines for hobbyist from the Academy of Model Aeronautics. Initially, many of the regulations regarding UAVs sought to maintain current safety standards by ensuring air traffic segregation between manned and unmanned aircraft. That said, much of the ongoing regulation development has occurred without data and evidence-driven practices; regulators have based decisions without accurate knowledge of the real-world usage of UAVs. In an attempt to support ongoing regulation and policy development this initial study reports statistics regarding pilot observance of UAV regulations and guidelines when performing mapping operations. Over 168,000 UAV trajectories, representing real-world UAV mapping operations, are analyzed to determine if pilots likely maintained visualline-of-sight, and followed 400 ft altitude guidelines and airspace restrictions. While global variations are noted, UAV pilots performing mapping operations within the United States are observed to widely comply with regulations. Troubling however, the analysis indicates that the small group of UAV pilots exceeding altitude regulations are much more likely to break other regulations as well, thereby increasing the risk of interaction with manned aircraft.

Simplified classifications have often led to college students being labeled as full-time or part-time students. However, student enrollment patterns can be much more complicated at many universities, as it is common for students to switch between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While previous studies have identified part-time enrollment as a risk factor to students’ academic success, limited research has examined the impact of enrollment patterns or strategies on academic performance. Unlike traditional methods that use a single-period model to classify students into full-time and part-time categories, in this study, we apply an advanced multi-period dynamic approach using a Hidden Markov Model to distinguish and cluster students’ enrollment strategies into three categories: full-time, part-time, and mixed. We then investigate and compare the academic performance outcomes of each group based on their enrollment strategies while taking into account student type (i.e., first-time-in-college students and transfer students). Analysis of undergraduate student records data collected at the University of Central Florida from 2008 to 2017 shows that the academic performance of first-time-in-college students who apply a mixed enrollment strategy is closer to that of full-time students, as compared to part-time students. Moreover, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Similarly, analysis of transfer students shows that a mixed-enrollment strategy is correlated with similar graduation rates as the full-time enrollment strategy and more than double the graduation rate associated with part-time enrollment. This finding suggests that part-time students can achieve better overall outcomes by increased engagement through occasional full-time enrollments.

Our study aims to collect data to understand ideological and extreme bias in text articles shared across various online communities, particularly focusing on the language used in subreddits associated with extremism and targeted violence. Initially, we gathered data from related online communities, specifically the r/Liberal and r/Conservative communities on Reddit, utilizing the Reddit Pushshift API to collect URLs shared within these subreddits. Our aim was to gather news, opinion, and feature articles, resulting in a corpus of 226,010 articles. We also curated a balanced subset of 45,108 articles and annotated 4000 articles to validate their relevance, facilitating understanding of language usage within ideological Reddit communities and insights into ideological bias in media content. Expanding beyond binary ideologies, we introduced a new category termed "Restricted" to encompass articles shared in private or banned subreddits. This third category encompasses articles shared in restricted, privatized, quarantined, or banned subreddits characterized by radicalized and extremist ideologies. This expansion yielded a large dataset of 377,144 articles. Additionally, we included articles from subreddits with unspecified ideologies, creating a holdout set of 922,522 articles. In total, our combined dataset of 1.3 million articles collected from 55 different subreddits will assist in examining radicalized communities and providing discourse analysis in associated subreddits, enhancing understanding of the language used in articles shared within radicalized Reddit communities and offering insights into extreme bias in media content. In summary, we collected 1.52 million articles to understand ideological and extreme bias, providing a comprehensive dataset that aids in understanding language usage within text articles posted in ideological and extreme Reddit communities.

With the long-term goal of supporting the training of air traffic controllers and ensuring safety through real-time monitoring of operations, we propose a clustering-based framework that seeks to extract, model, and characterize the control actions of air traffic controllers when managing aircraft within the immediate vicinity of an airport. Toward achieving our goal, we begin by developing and applying a hidden Markov model to identify and extract heading changes within aircraft trajectories. Identification of these heading changes allows for translating spatially defined trajectories into variable-length strings describing executed lateral maneuvers resulting from the control actions of air traffic controllers. The trajectory strings are then compared and clustered using edit-distance metrics in combination with the [ ]-medoids clustering algorithm. Taken together, each step forms a consistent framework for modeling and understanding trajectories in the context of control actions of air traffic controllers. The proposed modeling and clustering framework also enables identification of anomalous and potentially disruptive decisions when managing aircraft. Through application on a set of historical trajectories at Washington National Airport, we are able to demonstrate that the proposed framework is able to successfully overcome shortfalls associated with traditional clustering techniques.

Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While prior research has established full-time students maintain better outcomes then their part-time counterparts, limited study has examined the impact of enrollment patterns or strategies on academic outcomes. In this paper, we applying a Hidden Markov Model to identify and cluster students' enrollment strategies into three different categorizes: full-time, part-time, and mixed-enrollment strategies. Based the enrollment strategies we investigate and compare the academic performance outcomes of each group, taking into account differences between first-time-in-college students and transfer students. Analysis of data collected from the University of Central Florida from 2008 to 2017 indicates that first-time-in-college students that apply a mixed enrollment strategy are closer in performance to full-time students, as compared to part-time students. More importantly, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Similarly, analysis of transfer students shows that a mixed-enrollment strategy is correlated a similar graduation rates as the full-time enrollment strategy, and more than double the graduation rate associated with part-time enrollment. Such a finding suggests that increased engagement through the occasional full-time enrollment leads to better overall outcomes.