Difference between revisions of "Open Corpus Personalized Learning"
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|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]] | |valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]] | ||
| − | |valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance | + | |valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. |
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis. | Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis. | ||
Revision as of 16:34, 4 December 2016
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.
Contents
The Project Team
Peter Brusilovsky
Director
Daqing He
Co-director
Knowledge Extraction
Learner Modeling
| We have recently proposed a data-driven framework for dynamic knowledge modeling in textbook-based learning (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying Feature-Aware Student knowledge Tracing (FAST) which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment.
Over past years, our lab has developed CUMULATE, a centralized user modeling server built for the ADAPT2 architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including asymptotic assessment of user knowledge. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis. |
