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

• S. Hashemifar and S. Sahebi, “Personalized student knowledge modeling for future learning resource prediction,” in The 26th International Conference on Artificial Intelligence in Education (AIED), 2025.

• S. Zhao and S. Sahebi, “Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling,” in The 39th Annual AAAI Conference on Artificial Intelligence (AAAI), 2025, pp. 13374–13382. paper code slides

• S. Sahebi, M. Yao, S. Zhao, and R. Feyzi Behnagh “MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling”, ACM Transactions on Knowledge Discovery from Data (TKDD), 2024. paper code

• S. Zhao and S. Sahebi, “Multi-Task Modeling of Student Knowledge and Behavior,” in the 33rd ACM International Conference on Information and Knowledge (CIKM), 2024, pp.3363-3373. paper code slides

• S. Zhao and S. Sahebi, “Discerning Canonical User Representation for Cross-Domain Recommendation,” in the 18th ACM Conference on Recommender Systems (RecSys), 2024, pp.318-328. paper code slides

• S. Zhao and S. Sahebi, “Exploring Simultaneous Knowledge and Behavior Tracing,” in the 17th International Conference on Educational Data Mining (EDM), 2024, pp. 927-932. paper code

• C. Wang, S. Sahebi, “Continuous personalized knowledge tracing: Modeling long-term learning in online environments,” 2023, pp. 2616-2625. paper code slides

• S. Zhao and S. Sahebi, “Graph-enhanced multi-activity knowledge tracing,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2023, pp. 529–546. paper code slides

• S. Zhao, C. Wang, and S. Sahebi, “Transition-aware multi-activity knowledge tracing,” in The 2022 IEEE International Conference on Big Data, 2022. paper code slides video

• 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.