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Sequential Rule Mining in M-Learning Domain
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 3 |
| Year of Publication: 2016 |
| Authors: Shivesh Tiwari, Lokendra Kumar Tiwari |
10.5120/ijca2016907873
|
Shivesh Tiwari, Lokendra Kumar Tiwari . Sequential Rule Mining in M-Learning Domain. International Journal of Computer Applications. 134, 3 ( January 2016), 23-29. DOI=10.5120/ijca2016907873
Abstract
Use of Sequential Rule mining is becoming an important tool in m-learning domain to convert the data into information. It is commonly used in a wide series of profiling practices, such as marketing, fraud detection and scientific discovery. Sequential Rule mining is the specialized technique using which we can extract some patterns from given data. These rules can be used to uncover patterns in data but is often performed only on given sample of data. The mining process will be ineffective if the samples are not good representation of the larger body of the data. The discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the method is the verification and validation of patterns on other samples of data. Since almost half of the world’s population are mobile phone holders; M-learning appears to be a promising field since it’s empowering learners with the ability to learn anytime and anywhere, access learning resources, communicate and interact with other learners all over the globe, participate in creating learning resources, and access the World Wide Web; thus many Open Universities are coming up with a new concept of the web which is mobile learning (m-learning); which is internet for small screen that would be suitable and viewable on a mobile devices which allow quick access to web content; Sequential Rule mining is a emerging research area in the field of M-Learning; this concept of data mining in m-learning is still in its growing stage; thus this paper aims to present an approach for building a m-learning architecture, keeping in mind the server based content adaptation approach of backend database. Learners are made to create their profiles and their contextual information is stored at server end. This information is updated by learner, whenever learner makes an attempt to access learning content sequential role mining comes into major role to help extracting some behavioral patterns from the database.
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