CRII: III: Modeling Student Knowledge and Improving Performance when Learning from Multiple Types of Materials

CRII：III：从多种类型的材料中学习时对学生知识进行建模并提高表现

• 批准号: 1755910
• 负责人: Sherry Sahebi
• 金额: $ 17.47万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755910&HistoricalAwards=false
• 关键词: CRII; III; Modeling; Student; Knowledge

As the national interest in higher and professional education has been increasing, interest in online learning systems has also grown rapidly. Online learning systems, such as Massive Open Online Courses and Intelligent Tutoring Systems, aim to contribute to the society by providing high quality, affordable, and accessible education, at scale. They highly impact advancement of the national prosperity by preparing skillful professionals for high-demand jobs. Delivering such high-impact goals requires automatic tools that can help us understand students' learning process and answer questions such as what knowledge is gained by watching a video lecture (domain knowledge modeling), what is a student's state of knowledge (student knowledge modeling), and how a specific student would perform on a test (predicting student performance). Ideally, these tools should model student's learning from various learning material types (such as problems, readings, and video lectures) and capture the knowledge span offered by combinations of gradable and non-gradable learning resources. However, the current tools are limited to a single type of learning material (typically, "problems"), ignoring the heterogeneity of learning materials from which students may learn. This project aims to achieve a better understanding of students' learning process in online educational systems by presenting an integrated research and education plan (1) to model student interactions with both gradable and non-gradable learning material types, (2) to integrate the proposed models with learning material content, and (3) to evaluate the proposed models by experimenting with real-world online educational datasets. The project will provide learning and research opportunities to graduate and undergraduate students.To achieve the goal of improving students' learning process in online educational systems, the researchers develop multi-view machine learning algorithms that minimize the error of student performance prediction while maximizing the correlations among multiple views of the learning data. In the first year of this project, using activity sequences of students a model will be built that can capture a shared latent knowledge space among sets of gradable learning material and non-gradable ones. During the second year of this project, content information of learning materials, including expert labels, will be included in the learning model in order to improve it. This model aims to discover the relationship between content information and the shared latent knowledge space. The project results are evaluated using the task of predicting student performance. This project is at the intersection of domain adaptation, sequence modeling, and educational data mining. The model is inspired by Canonical Correlation Analysis as an approach for transferring information and adapting various views to student activity data, while modeling student learning process as a sequence of knowledge acquisitions. This is a novel treatment of the student modeling problem, with a sequential domain-adaptation view, that facilitates future research directions, such as personalized education and improved student retention in online learning environments. This work contributes novel sequential and content-aware domain adaptation and multi-variate analysis models that combine information from multiple sequential data sources and time-invariant content resources at the same time. While motivated by the task of student knowledge modeling, the models are general and can be applied to a broad spectrum of research including domain adaptation problems and recommender systems. The developed solutions will be presented in journals and conference venues, and the project website will provide access to the results, with references to code for the developed and evaluated models that will be available at GitHub.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.

随着国家对高等教育和职业教育的兴趣不断增加，对在线学习系统的兴趣也迅速增长。在线学习系统，如大规模开放在线课程和智能辅导系统，旨在通过提供高质量、负担得起和可获得的大规模教育，为社会做出贡献。他们通过为高需求的工作岗位培养熟练的专业人才，对国家繁荣的发展产生了很大的影响。实现这种高影响力的目标需要自动化工具，这些工具可以帮助我们了解学生的学习过程，并回答诸如通过观看视频讲座获得什么知识（领域知识建模），学生的知识状态是什么（学生知识建模）以及特定学生在考试中的表现如何（预测学生的表现）等问题。理想情况下，这些工具应该为学生从各种学习材料类型（如问题、阅读和视频讲座）中学习建模，并捕获可分级和不可分级学习资源组合提供的知识范围。然而，目前的工具仅限于单一类型的学习材料（通常是“问题”），忽略了学生可能学习的学习材料的异质性。该项目旨在通过提出一个集成的研究和教育计划(1)来模拟学生与可分级和不可分级学习材料类型的互动，(2)将提出的模型与学习材料内容集成，以及(3)通过对现实世界在线教育数据集的实验来评估提出的模型，从而更好地理解在线教育系统中学生的学习过程。该项目将为研究生和本科生提供学习和研究的机会。为了实现改善在线教育系统中学生学习过程的目标，研究人员开发了多视图机器学习算法，以最大限度地减少学生成绩预测的误差，同时最大限度地提高学习数据的多个视图之间的相关性。在这个项目的第一年，将使用学生的活动序列建立一个模型，该模型可以捕获可分级学习材料和不可分级学习材料之间共享的潜在知识空间。在这个项目的第二年，将学习材料的内容信息，包括专家标签，纳入到学习模型中，以改进学习模型。该模型旨在发现内容信息与共享的潜在知识空间之间的关系。通过预测学生表现的任务来评估项目结果。这个项目是领域适应、序列建模和教育数据挖掘的交集。该模型受到典型相关分析的启发，作为一种传递信息和适应学生活动数据的各种观点的方法，同时将学生的学习过程建模为一系列知识获取。这是对学生建模问题的一种新颖处理方法，具有顺序域适应视图，有助于未来的研究方向，例如个性化教育和提高在线学习环境中的学生保留率。这项工作提供了新颖的顺序和内容感知领域自适应和多变量分析模型，该模型同时结合了来自多个顺序数据源和时不变内容资源的信息。虽然受学生知识建模任务的激励，但这些模型是通用的，可以应用于广泛的研究，包括领域适应问题和推荐系统。开发的解决方案将在期刊和会议场所展示，项目网站将提供对结果的访问，并参考开发和评估模型的代码，这些代码将在GitHub上提供。该奖项反映了美国国家科学基金会的法定使命，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

Modeling Knowledge Acquisition from Multiple Learning Resource Types

对多种学习资源类型的知识获取进行建模

• 发表时间: 2020
• 期刊: Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020
• 作者: Zhao, S.;Wang, C.;Sahebi, S.
• 通讯作者: Sahebi, S.

Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors

• DOI: 10.1109/wiiat50758.2020.00041
• 发表时间: 2020-12
• 期刊: 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
• 作者: M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky

Detecting Trait versus Performance Student Behavioral Patterns Using Discriminative Non-Negative Matrix Factorization

• 发表时间: 2020
• 作者: M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky

Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

• 发表时间: 2019
• 作者: Thanh-Nam Doan;Shaghayegh Sherry Sahebi

Rank-Based Tensor Factorization for Student Performance Prediction

用于学生表现预测的基于排名的张量分解

• 发表时间: 2019
• 期刊: 12th International Conference on Educational Data Mining (EDM
• 作者: Doan, T.N.;Sahebi, S.
• 通讯作者: Sahebi, S.