Difference between revisions of "Topics"

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= Systems =
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= System Types =
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Our group explores several kinds of information systems focused mostly on personalized systems (such as adaptive learning and recommener systems) and various kinds of systems that support human navigation in information space (such as adaptive hypermedia and social navigation)
  
 
== Personalized Learning Systems ==
 
== Personalized Learning Systems ==
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* [[ProgressorPlus]]
 
* [[ProgressorPlus]]
  
== Adaptive Information Access Systems ==
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== Adaptive Information Retrieval Systems ==
 
Systems:
 
Systems:
* [[Proactive]]
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* [[TaskSieve]]
 
* [[TaskSieve]]
 
* [[YourNews]]
 
* [[YourNews]]
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== Recommender Systems ==
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* [[Proactive]]
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* [[CourseAgent]]
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* [[Cross-Domain Recommender Systems]]
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* [[Social Recommender Systems]]
  
 
== Social Information Access Systems ==
 
== Social Information Access Systems ==

Revision as of 15:29, 8 December 2017

System Types

Our group explores several kinds of information systems focused mostly on personalized systems (such as adaptive learning and recommener systems) and various kinds of systems that support human navigation in information space (such as adaptive hypermedia and social navigation)

Personalized Learning Systems

Personalized learning technologies provide an alternative to the dominant “one-size-fits-all” approach to treating diverse student audiences. While having a relatively long history, this research direction moved to the forefront only recently when modern information technologies opened new learning opportunities for a wide range of students. Nowadays, personalized learning is considered to be a top priority research direction by many experts. For example, advanced personalized learning was named among 14 Grand Challenges for Engineering along with preventing nuclear terror and making solar energy economical. It has also been listed among the highest funding priorities in Communications of the ACM.

Personalized learning technologies enable e-learning systems to maintain a model of the goals, preferences and knowledge of each student and apply this model to adapt the system performance to the student making the learning process more efficient and enjoyable. In so doing, various kinds of personalized e-learning systems demonstrated their ability to help students acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and increase student engagement. Our team is interested a range of personalized learning technologies, focusing on modeling learner knowledge of the subject. Individual models of learner knowledge that our systems maintain are used to guide learners to the most appropriate learning content using course sequencing and adaptive navigation support technologies. Some systems also use the models to deliver adaptive visualization. Below is the list of personalized learning systems developed by our group. Most of these systems are open for anyone to use and explore online.

More at Personalized Learning Systems

Systems:

Adaptive Information Retrieval Systems

Systems:

Recommender Systems

Social Information Access Systems

Systems:

Personalized Social Systems for Local Communities

Systems:

Models

Domain Modeling

Learner Modeling


Technologies

Architectures for Personalized E-Learning

ADAPT2 (read adapt-square) - Advanced Distributed Architecture for Personalized Teaching and Training - is a framework targeted at providing personalization and adaptation services for developers of content that lacks personalization. more

Adaptive Electronic Textbooks

==> more

Adaptive Information Visualization

Adaptive Navigation Support

Adaptive Program Visualization

Adaptive Program Visualization more

Cross-Domain Recommendation

Navigation in Video Lectures

Open Corpus Adaptive Hypermedia

Open Social Learner Modeling

Social Navigation

Worked-out Examples in Programming

User-Controlled Personalization