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Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interventions to mitigate these challenges. However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior. To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. Unlike regular point processes that assume a constant external triggering effect from the environment, we model three dynamic types of external stimuli, according to assignment availabilities, assignment deadlines, and each student’s time management habits. Our experiments on two synthetic datasets and two real-world datasets show a superior performance of future activity prediction, comparing with state-of-the-art models. Moreover, we show that our model achieves a flexible and accurate parameterization of activity intensities in students. 

Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis. Traditionally, these models parameterize each process independently and assume that the history of each point process can be fully observed. Such models could however be inefficient or even prohibited in certain real-world applications, such as in the field of education, where such assumptions are violated. Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. Our experiments on both synthetic and real-world education datasets show that RCHawkes-Gamma can effectively recover student clusters and their temporal procrastination dynamics, resulting in better predictive performance of future student activities. Our further analyses of the learned parameters and their association with student delays show that the discovered student clusters unveil meaningful representations of various procrastination behaviors in students. 

Procrastination, as an act of voluntarily delaying tasks, is particularly pronounced among students. Recent research has proposed several solutions to modeling student behaviors with the goal of procrastination modeling. Particularly, temporal and sequential models, such as Hawkes processes, have proven to be successful in capturing students’ behavioral dynamics as a representation of procrastination. However, these discovered dynamics are yet to be validated with psychological measures of procrastination through student self-reports and surveys. In this work, we fill this gap by discovering associations between temporal procrastination modeling in students with students’ chronic and academic procrastination levels and their goal achievement. Our analysis reveals meaningful relationships between the learning dynamics discovered by Hawkes processes with student procrastination and goal achievement based on student self-reported data. Most importantly, it shows that students who exhibit inconsistent and less regular learning activities, driven by the goal to outperform or perform not worse than other students, also reported a higher degree of procrastination. 

Human service organizations (HSOs) operate in an environment considered to be prohibitive of collaboration. To understand how HSOs come together to address the grand challenges associated with meeting human needs, we attempted to automatically construct the network of HSOs based on the information publicly available through each organization's website-the medium that people use to find relevant information to access services. Our analysis of the the complex system of relationships among HSOs in Albany, New York suggests that the network of HSOs in this area exhibits a multipolar structure with few super connectors, and strong relations between organizations that serve similar functions. We quantitatively evaluate the quality of the constructed HSOs' network from Web data based on structured, in-person interviews we conducted with HSOs. 

Human service providers play a critical role in improving well–being in the United States. However, little is know about (i) how service seekers find the services they are looking for by navigating among available service providers, and (ii) how such organizations collaborate to meet human needs. In this paper, we report the first outcomes of our ongoing project. Specifically, we first describe a data acquisition engine, designed around the particular challenges of capturing, maintaining, and updating data pertaining to human service organizations from semistructured Web sources. We then proceed to illustrate the potential of the resulting comprehensive repository of human service providers through a case study showcasing a mobile app prototype designed to provide a one–stop shop for human service seekers. 

Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processing methods are necessary to maintain citizens' satisfaction with their local governments. Herein, an optimal stopping framework is proposed to process urban issue requests quickly and accurately. The optimal classification and stopping rules are derived, and significant reduction in time-to-decision without sacrificing accuracy is demonstrated on a real-world dataset from SeeClickFix. 

Civic engagement platforms such as SeeClickFix and FixMyStreet have revolutionized the way citizens interact with local governments to report and resolve urban issues. However, recognizing which urban issues are important to the community in an accurate and timely manner is essential for authorities to prioritize important issues, allocate resources and maintain citizens' satisfaction with local governments. To this end, a novel formulation based on optimal stopping theory is devised to infer urban issues importance from ambiguous textual, time and location information. The goal is to optimize recognition accuracy, while minimizing the time to reach a decision. The optimal classification and stopping rules are derived. Furthermore, a near-real-time urban issue reports processing method to infer the importance of incoming issues is proposed. The effectiveness of the proposed method is illustrated on a real-word dataset from SeeClick-Fix, where significant reduction in time-to-decision without sacrificing accuracy is observed.