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Personalized Context Aware Assignment Recommendations in E-Learning System

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
M. Venu Gopalachari, P. Sammulal

Venu M Gopalachari and P Sammulal. Article: Personalized Context Aware Assignment Recommendations in E-Learning System. International Journal of Computer Applications 135(4):1-5, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {M. Venu Gopalachari and P. Sammulal},
	title = {Article: Personalized Context Aware Assignment Recommendations in E-Learning System},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {135},
	number = {4},
	pages = {1-5},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


E-Learning is the technology that targets the community seeks learning through various means such as material, information, courses to facilitate the ease of access. Online delivery of educational instruction provides the opportunity to bring colleges and universities new energy, students, and revenues. However e-learning system has rapid development in making activities of learning online and providing a vast set of resources for the material and online assignments to complete. Although personalized e-learning systems developed and provides services they limited to focus their recommendations of the material is only about the student’s level interest of surfing on the learning material but never considered the level of understanding of the learning material. This system developed a model that aim to recommend the assignments and material of the course to the student based on the level of understanding by analyzing the performance of the student in past. Experimental results on the proposed recommender system exhibited significant results than the traditional e-learning system. This shown the impact of personalized assignment recommendations in improving the student’s interest towards the course.


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Classification, e-learning, Recommender System, Web Mining.