Personalization of e-Learning Using Data Mining.

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Bibliographic Details
Title:	Personalization of e-Learning Using Data Mining.
Authors:	Christudas, Beulah Christalin Latha¹, Paul, Sujni¹, Ezra, Kirubakaran², Venkatraman, Saravanan³
Source:	International Journal of Learning. 2010, Vol. 17 Issue 4, p585-594. 10p.
Subject Terms:	Internet in education, Online education, World Wide Web, Education research, *Teaching, Data mining, Web personalization, Pattern recognition systems, Databases
Abstract:	The development of the World Wide Web has opened new avenues in the arena of educational research. E-learning is a process in which education is imparted through digital means. E-learning mainly focuses on learner-centric training rather than teacher-centric training, which has been in practice in traditional teaching. One of the crucial aspects of learner-centric training is the personalization of the learning process. The learner is directed to learn from the web-based contents. Online examinations are conducted to assess the learner . The scores obtained by the learner are analyzed with the contents traversed by the learner which forms the basis for personalization. This paper proposes a novel method of personalizing the web-based learning process using data mining. The links and the contents traversed by the learner are identified and analyzed using data mining. The score obtained is also analyzed in order to provide a guideline for the new learners to improve themselves by traversing through the contents through which the successful candidates had traversed. Prediction, an interesting problem in e-learning is applied in this work, although it must be borne in mind that it can easily overlap with classification and regression problems. The forecasting of students' behavior and performance when using e-learning systems bears the potential of facilitating the improvement of virtual courses as well as e-learning environments in general. Course log-files stored in databases could be mined by trainers using evolutionary algorithms to discover important relationships and patterns, with the target of discovering relationships between learners' knowledge levels, e-learning system usage times and learners' scores. This paper proposes a novel method of identifying the interesting patterns by analyzing the learning path taken by the learner along with their interests and goals and the scores obtained with the help of data mining and thereby paving a way to provide an effective learner-centric training. The Data Mining techniques are used for personalizing e-Learning and the results are shown graphically. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	The development of the World Wide Web has opened new avenues in the arena of educational research. E-learning is a process in which education is imparted through digital means. E-learning mainly focuses on learner-centric training rather than teacher-centric training, which has been in practice in traditional teaching. One of the crucial aspects of learner-centric training is the personalization of the learning process. The learner is directed to learn from the web-based contents. Online examinations are conducted to assess the learner . The scores obtained by the learner are analyzed with the contents traversed by the learner which forms the basis for personalization. This paper proposes a novel method of personalizing the web-based learning process using data mining. The links and the contents traversed by the learner are identified and analyzed using data mining. The score obtained is also analyzed in order to provide a guideline for the new learners to improve themselves by traversing through the contents through which the successful candidates had traversed. Prediction, an interesting problem in e-learning is applied in this work, although it must be borne in mind that it can easily overlap with classification and regression problems. The forecasting of students' behavior and performance when using e-learning systems bears the potential of facilitating the improvement of virtual courses as well as e-learning environments in general. Course log-files stored in databases could be mined by trainers using evolutionary algorithms to discover important relationships and patterns, with the target of discovering relationships between learners' knowledge levels, e-learning system usage times and learners' scores. This paper proposes a novel method of identifying the interesting patterns by analyzing the learning path taken by the learner along with their interests and goals and the scores obtained with the help of data mining and thereby paving a way to provide an effective learner-centric training. The Data Mining techniques are used for personalizing e-Learning and the results are shown graphically. [ABSTRACT FROM AUTHOR]
DOI:	10.18848/1447-9494/CGP/v17i04/46972