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

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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.