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

基本信息

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

项目摘要

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中起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，自动建议和教学工具对于学生在在线教育方面的成功至关重要。理想情况下，这些工具应在适当的时候为学生提供个性化的指导，以便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的问题），以有效地实现其个人学习目标（例如，满足他们的兴趣或在最短时间学习主题））。但是，当前的解决方案专注于指导一种类型的学习活动，忽略了学习时间间隔的重要性，而仅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，以通过自动检测和平衡其不同的，潜在的潜在研究目标来实现学生的长期目标。该项目将开发计算模型和算法，这些模型和算法可以提出各种类型的个性化学习活动，并为学生提供最佳选择的学习时间。最终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中的长期效用和时间敏感的推荐系统。这项研究的产品将提高在线教育的可访问性，以更好地为代表性不足的学习者提供服务。这些发现可用于教育领域来改善学生的学习。该项目包括一项综合教学计划，促进培训下一代计算机科学和教育领域的跨学科本科和研究生。该项目旨在研究时间感知，多目标，多类型，个性化的教育推荐模型和算法。该项目设计的推荐系统将针对学生的长期学习兴趣，行为偏好和学习目标进行优化。他们将能够向学生推荐评估和未评估的学习材料类型，并为他们建模最佳的学习时间间隔。该项目的贡献是通过三个研究推力来实现的。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到建模长期奖励，以促进多目标建议。在第二个推力中，该项目将调查多类知识模型和算法，以建议评估（例如问题）和未评估的（例如，视频讲座）学习材料，同时考虑他们的多个目标。第三个推力通过合并点过程建模来检测不同活动之间的连续时间影响并找到最佳的个性化重返研究时间，从而着重于教育推荐系统的新方式。这些研究推力基于教育数据挖掘，推荐系统，临时过程建模和强化学习文献的发展，并扩展了每个研究，同时在学生学习过程中提供了可行的见解。为了评估该项目的结果，制定了一项全面的评估计划，其中包括模拟，现实数据集的脱机评估，与实时系统的在线集成以及用户研究。该项目的结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的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准通过评估来评估的。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Peter Brusilovsky
通讯作者：
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
影响因子：
0
作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Helma Torkamaan
通讯作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Helma Torkamaan

MORS 2021: 1st Workshop on Multi-Objective Recommender Systems

MORS 2021：第一届多目标推荐系统研讨会

DOI：
10.1145/3460231.3470936
发表时间：
2021
期刊：
RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems
影响因子：
0
作者：
Abdollahpouri, Himan;Elahi, Mehdi;Mansoury, Masoud;Sahebi, Shaghayegh;Nazari, Zahra;Chaney, Allison;Loni, Babak
通讯作者：
Loni, Babak

Transition-Aware Multi-Activity Knowledge Tracing