CAREER: Time-Aware Multi-Objective Recommendation in Online Learning Environments

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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. This project aims to research time-aware, multi-objective, multi-type, personalized educational recommender models and algorithms. The recommender systems designed in this project will be optimizing for students’ long-term learning interests, behavioral preferences, and learning goals. They will be capable of recommending both assessed and non-assessed types of learning materials to students and modeling the best study time intervals for them. The contributions of this project are realized via three research thrusts. In the first thrust, personalized knowledge tracing models, student choice models, and behavioral preference models are designed and integrated to model long-term rewards that facilitate multi-objective recommendations. In the second thrust, the project will investigate multi-type knowledge models and algorithms to suggest both assessed (e.g., problems) and non-assessed (e.g., video lectures) learning materials to students while considering their multiple objectives. The third thrust focuses on a new modality of educational recommender systems by incorporating point process modeling to detect the continuous-time influence between different activities and find the optimal personalized return-to-study time. These research thrusts build upon educational data mining, recommender systems, temporal process modeling, and reinforcement learning literature and extend each of them while contributing actionable insights in student learning processes. To assess the results of this project, a comprehensive evaluation plan is incorporated that includes simulations, offline evaluation on real-world datasets, online integration with live systems, and user studies. The results of this project will be disseminated to the broader Machine Learning, Artificial Intelligence, and Educational Data Mining communities via open-source software and publications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.