skip to main content


Title: Curb Your Procrastination: A Study of Academic Procrastination Behaviors vs. A Planning and Time Management App
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.  more » « less
Award ID(s):
1917949
NSF-PAR ID:
10436875
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
Page Range / eLocation ID:
124 to 134
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. 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. 
    more » « less
  3. 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. 
    more » « less
  4. Abstract

    Our career-forward approach to general chemistry laboratory for engineers involves the use of design challenges (DCs), an innovation that employs authentic professional context and practice to transform traditional tasks into developmentally appropriate career experiences. These challenges are scaled-down engineering problems related to the US National Academy of Engineering’s Grand Challenges that engage students in collaborative problem solving via the modeling process. With task features aligned with professional engineering practice, DCs are hypothesized to support student motivation for the task as well as for the profession. As an evaluation of our curriculum design process, we use expectancy–value theory to test our hypotheses by investigating the association between students’ task value beliefs and self-confidence with their user experience, gender and URM status. Using stepwise multiple regression analysis, the results reveal that students find value in completing a DC (F(5,2430) = 534.96,p < .001) and are self-confident (F(8,2427) = 154.86,p < .001) when they feel like an engineer, are satisfied, perceive collaboration, are provided help from a teaching assistant, and the tasks are not too difficult. We highlight that although female and URM students felt less self-confidence in completing a DC, these feelings were moderated by their perceptions of feeling like an engineer and collaboration in the learning process (F(10,2425) = 127.06,p < .001). When female students felt like they were engineers (gender x feel like an engineer), their self-confidence increased (β = .288) and when URM students perceived tasks as collaborative (URM status x collaboration), their self-confidence increased (β = .302). Given the lack of representation for certain groups in engineering, this study suggests that providing an opportunity for collaboration and promoting a sense of professional identity afford a more inclusive learning experience.

     
    more » « less
  5. POSTER. Presented at the Symposium (9/12/2019) Abstract: The Academy of Engineering Success (AcES) employs literature-based, best practices to support and retain underrepresented students in engineering through graduation with the ultimate goal of diversifying the engineering workforce. AcES was established in 2012 and has been supported via NSF S-STEM award number 1644119 since 2016. The 2016, 2017, and 2018 cohorts consist of 12, 20, and 22 students, respectively. Five S-STEM supported scholarships were awarded to the 2016 cohort, seven scholarships were awarded to students from the 2017 cohort, and six scholarships were awarded to students from the 2018 cohort. AcES students participate in a one-week summer bridge experience, a common fall semester course focused on professional development, and a common spring semester course emphasizing the role of engineers in societal development. Starting with the summer bridge experience, and continuing until graduation, students are immersed in curricular and co-curricular activities with the goals of fostering feelings of institutional inclusion and belonging in engineering, providing academic support and student success skills, and professional development. The aforementioned goals are achieved by providing (1) opportunities for faculty-student, student-student, and industry mentor-student interaction, (2) academic support, and student success education in areas such as time management and study skills, and (3) facilitated career and major exploration. Four research questions are being examined, (1) What is the relationship between participation in the AcES program and participants’ academic success?, (2) What aspects of the AcES program most significantly impact participants’ success in engineering, (3) How do AcES students seek to overcome challenges in studying engineering, and (4) What is the longitudinal impact of the AcES program in terms of motivation, perceptions, feelings of inclusion, outcome expectations of the participants and retention? Students enrolled in the AcES program participate in the GRIT, LAESE, and MSLQ surveys, focus groups, and one-on-one interviews at the start and end of each fall semester and at the end of the spring semester. The surveys provide a measure of students’ GRIT, general self-efficacy, engineering self-efficacy, test anxiety, math outcome efficacy, intrinsic value of learning, inclusion, career expectations, and coping efficacy. Focus group and interview responses are analyzed in order to answer research questions 2, 3, and 4. Survey responses are analyzed to answer research question 4, and institutional data such as GPA is used to answer research question 1. An analysis of the 2017 AcES cohort survey responses produced a surprising result. When the responses of AcES students who retained were compared to the responses of AcES students who left engineering, those who left engineering had higher baseline values of GRIT, career expectations, engineering self-efficacy, and math outcome efficacy than those students who retained. A preliminary analysis of the 2016, 2017, and 2018 focus group and one-on-one interview responses indicates that the Engineering Learning Center, tutors, organized out of class experiences, first-year seminar, the AcES cohort, the AcES summer bridge, the AcES program, AcES Faculty/Staff, AcES guest lecturers, and FEP faculty/Staff are viewed as valuable by students and cited with contributing to their success in engineering. It is also evident that AcES students seek help from peers, seek help from tutors, use online resources, and attend office hours to overcome their challenges in studying engineering. 
    more » « less