Open Corpus Personalized Learning

Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.

This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.

The Project Team

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  Peter Brusilovsky
  Director

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  Daqing He
  Co-director

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  Yun Huang

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  Shuguang Han

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  Rui Meng

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  Sanqiang Zhao

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  Jordan Barria-Pineda

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  Hung Chau

Learner Modeling

Dynamic Knowledge Modeling in Textbook Reading

We have developed CUMULATE, a centralized user modeling server built for the ADAPT2 architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. The explanation of the communication between the interface and learner model can be found in Aggregate. We have proposed and implemented different learner models over past years, including asymptotic assessment of user knowledge which is the main one currently deployed in our systems, and Feature-Aware Student knowledge Tracing (FAST) which is our new learner model proposed in 2014 with state-of-the-art predictive performance. We have also explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis. More about Learner Modeling More about CUMULATE More about asymptotic assessment of user knowledge More about Feature-Aware Student knowledge Tracing (FAST)