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

职业：在线学习环境中的时间感知多目标推荐

• 批准号: 2047500
• 负责人: Sherry Sahebi
• 金额: $ 54.77万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047500&HistoricalAwards=false
• 关键词: CAREER; Time; Aware; Multi; Objective

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.

在线教育在劳动力培训、技能发展和终身学习方面发挥着越来越重要的作用。鉴于在线学习系统的规模及其对学生自我调节的依赖，自动推荐和教学工具对于学生在在线教育中的成功至关重要。理想情况下，这些工具应该为学生提供个性化的指导，使他们能够使用最有效的学习材料（例如，要解决的问题或要观看的视频讲座），以有效地完成他们的个人学习目标（例如，以满足他们的兴趣或在最短的时间内学习一个主题）。然而，目前的解决方案集中于指导一种类型的学习活动，忽视了学习时间间隔的重要性，并且只满足所有学生的一个目标。该项目将研究新一代的教育推荐系统，通过自动检测和平衡不同的、可能相互冲突的学习目标，实现学生的长期目标。该项目将开发计算模型和算法，可以为学生建议各种类型的个性化学习活动和最佳选择的学习时间。由此产生的解决方案将适用于机器学习和数据挖掘领域，特别是在健康和健身等领域的长期效用和时间敏感的推荐系统。这项研究的成果将改善在线教育的可及性，以更好地为代表性不足的学习者服务。研究结果可应用于教育领域，以改善学生的学习。该项目包括一个综合的教学计划，有利于培养下一代跨学科的本科生和研究生在计算机科学和教育领域的融合。本项目旨在研究具有时间感知的、多目标的、多类型的、个性化的教育推荐模型和算法。本计画所设计的推荐系统将针对学生的长期学习兴趣、行为偏好与学习目标进行最佳化。他们将能够向学生推荐评估和非评估类型的学习材料，并为他们建立最佳学习时间间隔。该项目的贡献通过三个研究重点实现。在第一个推力，个性化的知识追踪模型，学生选择模型，和行为偏好模型的设计和集成模型的长期奖励，促进多目标的建议。在第二个推力中，该项目将研究多类型的知识模型和算法，以建议评估（例如，问题）和未评估（例如，视频讲座）学习材料，同时考虑到他们的多个目标。第三个推力集中在一个新的模式的教育推荐系统，通过将点过程建模检测不同活动之间的连续时间的影响，并找到最佳的个性化返回到学习时间。这些研究重点建立在教育数据挖掘，推荐系统，时间过程建模和强化学习文献的基础上，并扩展了其中的每一个，同时为学生的学习过程提供了可操作的见解。为了评估该项目的结果，纳入了一个全面的评估计划，其中包括模拟，对真实世界数据集的离线评估，与实时系统的在线集成以及用户研究。该项目的成果将通过开源软件和出版物传播到更广泛的机器学习、人工智能和教育数据挖掘社区。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

MORS 2022: The Second Workshop on Multi-Objective Recommender Systems

MORS 2022：第二届多目标推荐系统研讨会

• DOI: 10.1145/3523227.3547410
• 发表时间: 2022
• 期刊: RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
• 作者: Abdollahpouri, Himan;Sahebi, Shaghayegh;Elahi, Mehdi;Mansoury, Masoud;Loni, Babak;Nazari, Zahra;Dimakopoulou, Maria
• 通讯作者: Dimakopoulou, Maria

Proximity-Based Educational Recommendations: A Multi-Objective Framework

• 发表时间: 2022
• 作者: Chunpai Wang;Shaghayegh Sherry Sahebi;Peter Brusilovsky

STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

• DOI: 10.1145/3486622.3493967
• 发表时间: 2021-12
• 期刊: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
• 作者: Chunpai Wang;Shaghayegh Sherry Sahebi;Helma Torkamaan

Transition-Aware Multi-Activity Knowledge Tracing

• DOI: 10.1109/bigdata55660.2022.10020617
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Siqian Zhao;Chunpai Wang;Shaghayegh Sherry Sahebi

Graph-enhanced multi-activity knowledge tracing

图增强的多活动知识追踪

• 发表时间: 2022
• 期刊: European Conference on Machine Learning and Principles and Practice of Knowledge
• 作者: Zhao, Siqian;Sahebi, Shaghayegh
• 通讯作者: Sahebi, Shaghayegh