ICTACT Journal on Soft Computing

Nikhil Mitra ¹, Nilanjana Goel ², S. Chakraverty ², Gurmeet Singh ²

Affiliations
1 Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, IN
2 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN

Abstract

The world-wide-web offers a posse of textual information sources which are ready to be utilized for several applications. In fact, given the rapidly evolving nature of online data, there is a real risk of information overload unless we continue to develop and refine techniques to meaningfully segregate these information sources. Specifically, there is a dearth of content-oriented and intelligent techniques which can learn from past search experiences and also adapt to a user's specific requirements during her current search. In this paper, we tackle the core issue of prioritizing textual information sources on the basis of the relevance of their content to the central theme that a user is currently exploring. We propose a new Source Prioritization Algorithm that adopts an iterative learning approach to assess the proclivity of given information sources towards a set of user-defined seed words in order to prioritise them. The final priorities obtained serve as initial priorities for the next search request. This serves a dual purpose. Firstly, the system learns incrementally from several users' cumulative search experiences and re-adjusts the source priorities to reflect the acquired knowledge. Secondly, the refreshed source priorities are utilized to direct a user's current search towards more relevant sources while adapting also to the new set of keywords acquired from that user. Experimental results show that the proposed algorithm progressively improves the system's ability to discern between different sources, even in the presence of several random sources. Further, it is able to scale well to identify the augmented information source when a new enriched information source is generated by clubbing existing ones.

Keywords

Textual Information Source Prioritization, Search Engines, Domain Specificity, Term-Source Matrix, Text Information Density.

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Sushma Hans ¹, S. Chakraverty ¹, Aditya Bindal ¹

Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN

Abstract

The e-learning paradigm is now a well-established vehicle of modern education. It caters to a wide spectrum of students with diverse backgrounds who enroll with their own learning aims. A core challenge under this scenario is to generate personalized learning paths so that each student can achieve her learning aim most effectively. Prior works used static attributes such as prior knowledge level, learning ability, browsing preferences, learning style etc. to generate personalized learning paths. In this paper, we take an entirely new route by taking into account the continuous improvement of a learner in the light of her own learning aim, to redefine her learning path at each level of the course. We introduce the concept of personalized examination system that systematically evaluates the dynamic learning ability of every student according to her pre-set goals. The proposed intelligent e-learning system uses Ant Colony Optimization to iteratively optimize the forward learning paths. Experimental results reveal that the system is able to tap a student's improved learning ability to choose more difficult paths that contribute highly towards her own aims. We demonstrate that the overall learning success of weaker students doubles as compared to statically generated paths while there is considerable improvement of 50% in the learning success for average students as well. This clearly indicates that our approach gives realistic benefits to initially weak students who gradually evolve as the course progresses.

Keywords

Personalized e-Learning, Learning Aims, Ant Colony Optimization, Dynamic Learning Ability, Learning Success.

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