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.
Software programs for storage and statistical analysis of MMIC measurement data are developed. Programs are included in INDESYS-MS system for measurement automation. Information structure in data base and statistical analysis tools are described.
The paper describes a module developed for purpose of measurement process automation, storage organization and statistical analysis of MMIC components characteristics, on the basis of the Indesys-MS software system. Implemented tools of statistical analysis are a histogram and a space distribution parameter over a wafer. Statistical analysis of measured S-parameters of the field heterostructured microwave transistors on a semiconductor wafer is considered as an example.
In this poster we introduce Col*Fusion – a novel architecture for large-scale data integration, fusion and preservation based on crowdsourcing. Col*Fusion is implemented as easy-to-use web application and provides uniform data submit and integration interface. It provides all functionality expected from professional data archival repository, but also solves two main problems of current approaches – repository and dataset isolation – by involving users into active participation of both data submission and integration processes.
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.