DFAM: A DISTRIBUTED FEEDBACK ANALYSIS MECHANISM FOR KNOWLEDGE BASED EDUCATIONAL BIG DATA

Authors

  • Rashidah F. Olanrewaju Department of Electrical & Computer Engineering, Kulliyyah of Engineering, International Islamic University, Malaysia
  • Burhan Ul Islam Khan Department of Electrical & Computer Engineering, Kulliyyah of Engineering, International Islamic University, Malaysia
  • Roohie Naaz Mir Department of Computer Science & Engineering, National Institute of Technology, Kashmir
  • Asifa Mehraj Baba Department of Electronics & Communication Engineering, Islamic University of Science and Technology, Kashmir
  • Farhat Anwar Department of Electrical & Computer Engineering, Kulliyyah of Engineering, International Islamic University, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.10020

Keywords:

Big data, data analytics, data mining, educational data, knowledge discovery

Abstract

There is almost digitization of the entire educational system, as there is an abundant of digital materials available. The educational system is not left out from the global standardization as well as authorities are imposing certain standard at local and global level. As a result, these data which are getting generated in the context of educational also complies all the basic characteristics of the Big Data such as volume in terms of size, and others velocity, variety etc. In order to store, search and process an open source project, Apache Hadoop has been conceptualized, whereas it lags the application specific needs especially in the field of education to enhance the teaching and learning processes. In this paper, an architectural model is illustrated to demonstrate the existing eco-system and a proposed model for provisioning the enhanced teaching -learning mechanism, so that it can be adopted to enhance the intelligence into mechanism of educational framework.

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Published

2016-12-15

How to Cite

DFAM: A DISTRIBUTED FEEDBACK ANALYSIS MECHANISM FOR KNOWLEDGE BASED EDUCATIONAL BIG DATA. (2016). Jurnal Teknologi, 78(12-3). https://doi.org/10.11113/jt.v78.10020