Cyberlearning: Detecting and Predicting Procrastination in Online and Social Learning
Shaghayegh (Sherry) Sahebi (Co-PI) and Reza Feyzi-Behnagh (PI) are collaborating on research designed to detect procrastination in online learners and find ways to improve these learners' self-regulation. The project, “Cyberlearning: Detecting and Predicting Procrastination in Online and Social Learning“ is sponsored by NSF with a grant of $749,766.
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 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.
Sahebi and Feyzi-Behnagh are assistant professors of Computer Science and Educational Theory and Practice, respectively. Sahebi, who has a Ph.D. in Intelligent Systems, brings her expertise in machine learning and educational data mining to this interdisciplinary project. Feyzi-Behnagh, who has a Ph.D. in Educational Psychology, Learning Sciences, brings his expertise in how students learn and self-regulate their learning in computer-based learning environments.
Visit Dr. Sahebi’s lab page, to find more about her current research projects and opportunities for qualified student researchers.