Postdoctoral Fellow: Detecting and Predicting Procrastination in Online and Social Learning

Website ualbany University at Albany

We are looking for a postdoctoral fellow, for an NSF-Funded project (see details below), who is interested in developing predictive models to identify and detect procrastination processes in students while studying in an online learning environment. The postdoctoral fellow will work in the Computer Science Department, as part of an interdisciplinary team with an expertise in machine learning and education, at the State University of New York – Albany, Albany, USA. The research has an emphasis on temporal and social modeling of student trace, survey, and interaction data, using sequential and process models. Requirements:

  • PhD (or near completion of PhD) in machine learning, or computer science with a focus of educational data mining, recommender systems, or social network modeling;
  • A strong research record, documented by recent publications in the past 3 years;
  • Good communication skills and fluency in English;
  • Being highly motivated and creative, enjoying working in a collaborative research environment.

The position is provided for up to 2 years with competitive salary. The starting dates are flexible (available starting August 1st, 2019). Applications will be considered until the position is filled. Please send your detailed CV (including the contact information of two references) and a one-page research statement, discussing how your background fits the requirements and topic to Dr. Sherry Sahebi at and Dr. Reza Feyzi Behnagh at with the subject “Postdoc Application”.

Project: Detecting and Predicting Procrastination in Online and Social Learning

As online education becomes increasingly available and trusted by both employers and students, many workers are turning to online courses to advance their education and job prospects. However, online courses demand effective time management skills, as students are required to plan and set goals, manage their time, and work by themselves (or in a group), often with less structure than an in-person course. This increases the risks of procrastination, a key challenge to time management and success in both work and education contexts. To address those risks, this project will use computational algorithms to model students’ procrastination behaviors, identify indicators of likely future procrastination, and detect it early on in both individual and in group work. The algorithms will learn to predict procrastination according to learners’ studying behavior captured by a time management application and their performance in courses. The findings of this project can be used to enhance students’ learning by helping them to set goals and plan their work, monitor their progress, and keep track of what they need to do to successfully accomplish their assignments on time. These findings can be applied to related areas such as workforce development, and the data collection tools and algorithms developed will be made available to other researchers who want to work on related questions at the intersection of behavior and learning.

This project examines individual and group procrastination behavior by developing computational models using data on students’ self-reported cognitive, metacognitive, motivational, and affective processes. Current theories of procrastination will be studied and extended based on cross-sectional self-report survey data asking for student self-ratings of procrastination related to academic tasks, and time-stamped trace data of studying and interaction behavior generated by a mobile app used by students during their courses. The cyberlearning advancements of this study are (1) a novel model of individual and individual-in-group (social) procrastination, to detect procrastination based on both self-report and trace data; (2) a novel model to predict student performance based on their procrastination, previous task accomplishment behavior, and previous performance; and (3) exploration of the most parsimonious combination of self-report and trace data to produce effective procrastination model. These goals will be accomplished by (a) developing and updating an application for data collection and survey administration, (b) deploying the app in several graduate online courses, (c) analyzing data to understand underlying procrastination processes, and (d) developing machine learning algorithms to model and detect procrastination. The project will result in the dissemination of findings and developed algorithms to the broader field of sequential data science.

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