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IJSTR >> Volume 9 - Issue 3, March 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Current Trends In E-Learning

[Full Text]



Raj Kumar, Dr. Shaveta Bhatia



Data Mining, E-learning, Machine Learning and Sentiment Score



E-learning is the buzzword of today’s era and a large number of e-learning resources are available in online and offline mode. However, to derive useful pattern from this abundant pool of e-learning resources is a very tedious task. Various data mining approach can be used to generate interesting patterns from this enormous repository. The data analytics helps in analyzing the information access pattern of the users. The information access pattern can be helpful in identifying the learning behavior traits of an individual. Moreover, machine learning along with data mining has opened up new avenues. The combination of data analytics and machine learning may be used to generate targeted recommendations.



[1] Joeran Beel, Bela Gipp, Stefan Langer, and Corinna Breitinger. “Research Paper Recommender Systems: A Literature Survey.” International Journal on Digital Libraries (2015):1–34. doi:10.1007/s00799-015-0156-0.
[2] Amer Al-Badarenah, jamal Alsakaran, “An automated recommender system for course selection.” (IJACSA) International , Journal of Advanced Computer Science and Applications, Vol. 7, No. 3, 2016
[3] Aleksandra Klasnja-Milicevic, Mirjana Ivanovic, Zoran Budimac,” Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques.” (2017) Springer, https://creativecommons.org/licenses/by/4.0/
[4] Rakesh Kumar Arora Dr. Dharmendra Badal, “Mining Association Rules to Improve Academic Performance.” IJCSMC, Vol. 3, Issue. 1, January 2014, pg.428 – 433 Available Online at www.ijcsmc.com
[5] Fatiha Bousbahi, Henda Chorfi, “MOOC-Rec: A Case based Recommender System for MOOcs.” (2015) Science Direct Available Online at https://creativecommons.org/licenses/by-nc-nd/4.0/
[6] Xia Jing, Jie Tang, “Guess you like: Course Recommendation in MOOCs.” 2017 ACM, Available online at https://xeutangx.com
[7] Gine Georg, Anisha M. Lal, “Review of ontology-based recommender systems in e-learning.” 2019, Elsevier https://www.sciencedirect.com/science/article/abs/pii/S0360131519301952?via%3Dihub
[8] Sh. Asadi, S.M. Jafari and Z. Shokrollahi, “Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules.” DOI 10.22044/JADM 2018.6260.1739 2019 Journal of AI and Data Mining.
[9] F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh “Recommendation systems: Principles, methods and evaluation.” 2015 www.elsevier.com/locate/eijwww.sciencedirect.com
[10] Bhumika Bhatt, Premal J Patel, Hetal Gaudani, “A review paper on machine learning based recommendation system.” 2014 International journal of engineering development and research. https://www.ijedr.org/
[11] K. Mohankumar, B. Srinivasan “Formation of Similar Users group by using Support Vector Machine with Facebook Posts.” 2019 International Journal of Computer Sciences and Engineering DOI: https://doi.org/10.26438/ijcse/v7i2.158163 Available online at: www.ijcseonline.org
[12] Walaa Medhat, Ahmed Hassan, Hoda Korashy “Sentiment analysis algorithms and applications: A survey.” 2014 www.elsevier.com/locate/asej
[13] Sowmya Kamath S, “A personalized recommender system using Machine Learning based Sentiment Analysis over social data.” 2016 https://www.researchgate.net/publication/305675255
[14] Ha, T., & Lee, S. “Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network.” Information Processing &Management, 53(5), 1171-1184. https://doi.org/10.1016/j.ipm.2017.05.003.
[15] Harrathi, M., Touzani, N., & Braham, R. “A hybrid knowlegde-based approach for recommending massive learning activities.” In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)(pp. 49-54). IEEE. 10.1109/AICCSA.2017.150.
[16] G. Shani and A. Gunawardana, “Evaluating recommendation systems,” Recommender systems handbook, Springer, 2011, pp. 257–297.