Scenario based DCMS for e-Learning Environment

Bharati A Patil, Ashmita Kanojia, Nisha Kapse, Mayuri Shinde


In learning process of classroom concentration is one of the key factor Due to various distraction ,it is difficult      to maintain the concentration in classroom during teaching method. In e-learning environment ,where various distraction exist, it is much harder to maintain concentration. In this  project for maintaining the learners concentration scenario based Dynamic Content Management System(DCMS) is used. DCMS  is a highly effective tool which is employed by existing edutain- ment(Education with Entertainment)providers. But the existing Content Management System does not have Scenarios based content selection and the result has not been up to expectation. To defeat these problems and to make dynamic changes for valid results, scenario which assures that the alteration of contents is used. The proposed method shows how to implement the scenario and Dynamic Content Management is the approach of providing the personalized curriculum to learners. With this method the efficiency of e-learning can be increased. The method guides the content modification by providing entertainment in scheme of story and learning elements. The learning elements are classified related to the difficulty level. This level is used to make the learners customize curriculum.

Full Text:



Scenario based Dynamic Content Management System for e-Learning Environment. 30th International Conference on Advanced Information Networking and Applications Workshops(2016 ).

A. Kozko, Structural and Semantic Analysis of Messages for Construction of Adaptive Educational Forums, in Proc. The World of Scientific Discoveries, 2015, vol. 4.2, pp. 831-843.

A. Kozko, Semantic Similarity of Educational Forums Messages, in Proc. of the 1th International Workshop on DSPTech, 2015.

S. Newman, Building Microservices, O’Reilly Media, Inc, 2015

moderation in online forums, in Proc. Government Information Quarterly, 2014, 31(2), pp. 317-326.

G. Simarpreet, Exploring the benefits of tagging forum posts based on a hierarchical domain model of the course content in online forums, Ph.D. Dissertation, 2014.

J. Kim, Influence of group size on students’ participation in online discussion forums, Computers Education, 62, pp. 1239, 2013

A. Panchenko, Similarity measures for semantic relation extraction, Ph.D. Dissertation, Louvain-la-Neuve, Belgium, 2013.

9L. Peng, W. Bin, S. Zhiwei, C. Yachao, L. Hengxun, Tag-TextRank: A Webpage Keyword Extraction Method Based on Tags, Journal of Computer Research and Development, 11, 014, 2012.

P.Y. Wang, H.C. Yang, Using collaborative filtering to support college students use of online forum for English learning, Computers Education, 59(2), pp. 628-637, 2012.

Chi, P.-Y., Lieberman, L. Raconteur: from Intent to Stories. In: 15th International Conference on Intelligent User Interfaces, pp. 301304 (2010)

Watanabe, T., Arasawa, R. Computer-supported novel composition, based on externalization. In: 18th Annual Conference on Knowledge- based and Intelligent Information Engineering Systems, KES2014, pp. 16621671 (2010).

Witten, D. Milne, An effective, low-cost measure of semantic relatedness obtained from Wikipedia links, in Proc. AI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, AAAI Press, Chicago, USA, 2008, pp. 25-30.

Si, M., Marsella, S. C., Riedl, M. O. Interactive drama authoring with plot and chapter: An Intelligent System that Fosters Creativity. In: AAAI 2008 Spring Symposium on Creative Intelligent Systems (2008).

R. Mihalcea, A. Csomai, Wikify!: linking documents to encyclopedic knowledge in Proc. Information and knowledge management, New York, USA, 2007, pp. 233-242.

E. Gabrilovich, S. Markovitch, Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis, in Proc. IJCAI, 2007, vol. 7, pp. 1606-1611.

J.W. Reed, Y. Jiao, T.E. Potok, B.A. Klump, M.T. Elmore, A.R. Hurson, TF-ICF: A new term weighting scheme for clustering dynamic data streams, in Proc. Machine Learning and Applications, 2006, pp. 258-263.

Marta R. L., Ana F.V. A Model for Personalized Learning Through IDTV. In: Adaptive Hypermedia and Adaptive Web-Based Systems.

Springer. Berlin. 2006

Martin W., The content management handbook Facet Publishing, 2005.

A. Aizawa, An information-theoretic perspective of tfidf measures, Information Processing Management, 39(1), pp. 45- 65, 2003.

ENGEL J.F., BLACKWELL R.D., MINIARD P.W. Consumer Behavior, 8th, Chicago: the Dryden Press, 1995

Csikszentmihalyi, M. Flow: The psychology of Optimal Experience.NY:

Harper Row, 1990

C. Lampe, P. Zube, J. Lee, C.H. Park, E. Johnston, Crowdsourcing civility: A natural experiment examining the effects of distributed.

M. Strube, S.P. Ponzetto, WikiRelate! Computing semantic relatedness using Wikipedia, in Proc. AAAI, vol. 6, pp. 1419-1424.

Pomodoro Technique :

Joomla :

Drupal :

Concrete5 :

TinyCMS :


  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology