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1. Curb Your Procrastination: A Study of Academic Procrastination Behaviors vs. A Planning and Time Management App

   https://doi.org/10.1145/3565472.3592953  

   Zhao, Siqian; Sahebi, Shaghayegh; Feyzi Behnagh, Reza (January 2023, Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization)

   Procrastination is a major issue faced by students which can lead to negative impacts on their academic performance and mental health. Productivity tools aim to help individuals to alleviate this behavior by providing self-regulatory support. However, the processes of how these applications help students conquer academic procrastination are under-explored. Particularly, it is essential to understand what aspects of these applications help which kinds of students in accomplishing their academic tasks. In this paper, we address this gap by presenting an academic planning and time management app (Proccoli) and a study designed to understand the association between student procrastination modeling, in-app behaviors, and perceived performance with app evaluation. As the core of our study, we analyze student perceptions of Proccoli and its impact on their study tasks and time management skills. Then, we model student procrastination behaviors by Hawkes process mining, assess student in-app behaviors by specifying planning and performance-related measures and evaluate the relationship between student behaviors and the evaluation survey results. Our study shows a need for personalized self-regulation support in Proccoli, as students with different in-app studying behaviors are found to have different perceptions of the app functionalities and the association between the prompts for social accountability students received by using Proccoli and their procrastination behavior is significant. 

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2. Temporal Processes Associating with Procrastination Dynamics

   https://doi.org/10.1007/978-3-030-78292-4_37  

   Yao, Mengfan; Sahebi, Shaghayegh; Feyzi Behnagh, Reza; Bursali, Semih; Zhao, Siqian (January 2021, Artificial Intelligence in Education: 22nd International Conference)

   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. 

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3. Relaxed clustered Hawkes process for procrastination modeling in MOOCs

   Yao, Mengfan; Zhao, Siqian; Sahebi, Shaghayegh; Feyzi Behnagh, Reza (January 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   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. 

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4. NSF RIEF: Influence of Self-Efficacy and Social Support on Persistence and Achievement in Chemical Engineering Sophomores: Measuring the Impact of an Intervention

   Cicciarelli, Bradley A.; Sherer, Eric A.; Reeves, Timothy C.; Belk, Catherine H.; Orr, Marisa K. (June 2022, 2022 ASEE Annual Conference & Exposition)

   As part of our study examining the factors that influence the academic performance and persistence of second-year chemical engineering students, we are assessing the impact of an intervention (a two-day voluntary workshop) on the specific factors of self-efficacy and social support. This workshop, called the “ChemE Camp”, is held just before the start of fall classes and includes team-building exercises, presentations from faculty about upcoming classes, a lab tour, presentations from upper-level students and alumni about their experiences in the curriculum and in industry, information about academic advising and the career fair, and some recreational games. Students who attend the camp can learn more about chemical engineering courses and the profession and also have the opportunity to meet peers and interact with faculty and upper-level students. It was hypothesized that the activities included in the camp would have a positive impact on students’ self-efficacy and social integration, factors which have been shown in other studies to significantly influence student experience and student success. To assess the effect of the intervention, surveys were administered to students at the start of the camp. These surveys included published subscales used in the study of self-efficacy and social and academic integration. These same surveys were also administered to all second-year chemical engineering students at the beginning of the academic year (three days after the beginning of the camp) and the end of the academic year (approximately eight months later). Data collected from the previous two academic years indicate a statistically significant increase in the chemical engineering self-efficacy, coping self-efficacy, and social and academic integration ratings for students who attend the camp. These effects appear to largely be maintained throughout the sophomore year and are distinct from the results observed for nonattendees. 

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5. Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling

   https://doi.org/10.1145/3442381.3450104  

   Yao, Mengfan; Zhao, Siqian; Sahebi, Shaghayegh; Feyzi Behnagh, Reza (April 2021, Proceedings of the Web Conference 2021)

   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. 

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