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Reseach Article

Implementation Paper on Visual Education using Data Mining and Innovative Visualization on Cloud

by Suraj Gaikwad, Kunal Pokale, Anirban Dutta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 5
Year of Publication: 2015
Authors: Suraj Gaikwad, Kunal Pokale, Anirban Dutta
10.5120/20332-2053

Suraj Gaikwad, Kunal Pokale, Anirban Dutta . Implementation Paper on Visual Education using Data Mining and Innovative Visualization on Cloud. International Journal of Computer Applications. 116, 5 ( April 2015), 19-21. DOI=10.5120/20332-2053

@article{ 10.5120/20332-2053,
author = { Suraj Gaikwad, Kunal Pokale, Anirban Dutta },
title = { Implementation Paper on Visual Education using Data Mining and Innovative Visualization on Cloud },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number5/20332-2053/ },
doi = { 10.5120/20332-2053 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:14.433092+05:30
%A Suraj Gaikwad
%A Kunal Pokale
%A Anirban Dutta
%T Implementation Paper on Visual Education using Data Mining and Innovative Visualization on Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 5
%P 19-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Kinesthetic, Auditory, Reading / Writing - Preference Learning and Visual Learning are the 4 categories of learning types as per the widely used Fleming VAK / VARK model. Visual Learning represents concepts visually. By representing the information visually students are able to concentrate on meaning, acknowledge and group similar ideas easily making better use of visual memory. Visual learning improves student performance in the areas of Retention, Reading Comprehension, Student Achievement and Critical Learning skills. this system focuses on semi-automatically converting textual learning resources related to important world events into visual information. Few examples include Terrorist Activities in India, 26/11 Mumbai bomb blasts and Earth Quakes in India. This systems scrapes the unstructured text from textual learning resources, applies natural language processing to extract named entities in the text, uses location api's and innovative visualizations (e. g. Google Maps) to visualize the world event information in an intuitive manner. Human intervention is needed in case of incomplete information in the learning resource and verification of generated information. This system makes it easy to identify human intervention need via data health reports.

References
  1. Building Domain Ontologies from Text for Educational Purposes Amal Zouaq and Roger Nkambou, Member, IEEE
  2. Visualizing Dynamic Data with Maps Daisuke Mashima, Stephen G. Kobourov, and Yifan Hu
  3. http://www2. research. att. com/yifanhu/TrendMap/, 2012. Reconstructing the Structure of the World-Wide Music Scene withlast. fm. http://sixdegrees. hu/last. fm/index. html, 2012.
  4. U. Brandes and S. R. Corman, "Visual Unrolling of Network Evolution and the Analysis of Dynamic Discourse," Proc. IEEE Symp. Information Visualization (INFOVIS '02), pp. 145-151,2002
  5. Y. Agichtein and S. Gravano, "Snowball: Extracting Relations from Large Plain-Text Collections,"
  6. H. Alani and C. Brewster, "Metrics for Ranking Ontologies,"
Index Terms

Computer Science
Information Sciences

Keywords

Web scraping rule engine curate engine NNP.