CAREER: Time-Aware Multi-Objective Recommendation in Online Learning Environments
NSF Grant No. 2047500
PI: Shaghayegh (Sherry) Sahebi
Email: ssahebi [at] albany [dot] edu
Project Duration: 08/1/2021 - 07/31/2026
Online education is playing an increasingly essential role in workforce training, skill development, and life-long learning. Given the scale of online learning systems and their dependence on student self-regulation, automatic recommendation and instructional tools are crucial for students’ success in online education. Ideally, these tools should provide personalized guidance to students to work with the most effective type of learning material (e.g., a problem to solve or a video lecture to watch), at the right time, to efficiently accomplish their personal study goals (e.g., to fulfill their interests or to learn a topic in the shortest time). Current solutions, however, focus on instructing one type of learning activity, ignoring the importance of studying time intervals, and only satisfying one goal for all students. This project will research a new generation of educational recommender systems towards achieving students’ long-term goals by automatically detecting and balancing between their different, potentially conflicting study goals. This project will develop computational models and algorithms that can suggest various types of personalized learning activities and optimally selected study times for students. The resulting solutions will be applicable to machine learning and data mining fields, especially, to long-term utility and time-sensitive recommender systems in domains such as health and fitness. The products of this research will improve the accessibility of online education to better serve underrepresented learners. The findings can be used in the Education domain to improve students’ learning. This project includes an integrated teaching plan that facilitates training the next generation of interdisciplinary undergraduate and graduate students in the convergence of Computer Science and Education fields.
Related Publications
- C. Wang, S. Sahebi, and P. Brusilovsky “Proximity-based educational recommendations: A multi-objective framework”, The 2nd Workshop on Multi-Objective Recommender Systems (MORS’22), 2022.
- C. Wang, S. Sahebi, and H. Torkamaan “STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data”, The 2021 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology (WI-IAT), 2021. code
- C. Wang, S. Sahebi, and P. Brusilovsky “MOCHI: an Offline Evaluation Framework for Educational Recommendations”, The Workshop on Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES’21), 2021. code
This material is based upon work supported by the National Science Foundation under Grant No. 204750.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.