A large-scale On-line Social Network supporting efficient adaptive learning methodologies. The project utilizes crowdsourcing techniques, collaborative filtering and collective intelligence, dynamic data warehousing, data analysis and data mining techniques.
That allows gaining educational knowledge via adaptive learning through teaching and adaptively converging to the most productive learning pathways with respect to a particular group of students and their performance profiles. Users of the system contribute concise and clear lessons focused on a very specific topic. Each lesson has four mandatory components:
Fast development of computers, mobile devices, internet, web and social networks changed the way people share information on-line. Each user may produce and consume a substantial amount of data. However, the ease and convenience of sharing data poses new problems; systems for sharing data become decentralized and loosely coordinated, which makes the search for a piece of information by a particular user difficult.
This project addresses the problem of adaptive information processing in a large scale decentralized and loosely coordinated systems based on crowdsourcing and social computing. Such systems can be thought of as complex adaptive systems (CAS). Two very interesting properties of the CAS, emergence and self-organization, allow global structure or patterns to appear in a system without a central authority or external element imposing it through planning. However, there is question whether such systems can produce any useful information, self-organize and adapt for users. Perhaps emergence and self-organization will not converge to produce desired properties.
As an example of CAS, we propose to build a complex adaptive information system as an on-line social network to improve the process of learning. Therefore, in addition to the research contribution, our project will bring a solid practical result to engage students in an active learning process. It will help more students (especially those who are underprepared) excel in various courses by utilizing their growing familiarity with social media.
We introduce a novel social learning paradigm that adapts to needs of individual users based on their collective learning experiences. The proposed approach combines concepts of Crowdsourcing, Online Social Networks and Complex Adaptive Systems to engage users in efficient learning through teaching. The process of adaptive convergence is facilitated through dynamic data analysis techniques that we implemented in the SALT (Self-Adaptive Learning through Teaching) system. Our results show that collective learning experiences can be efficiently utilized in adaptive social learning.
Project page last time updated on Jan. 21, 2014