An implementation of personalized e-learning in Moodle aiming at data and text mining integration

Pao Yue-kong Library Electronic Theses Database

An implementation of personalized e-learning in Moodle aiming at data and text mining integration

 

Author: Shu, Chang
Title: An implementation of personalized e-learning in Moodle aiming at data and text mining integration
Degree: M.Sc.
Year: 2013
Subject: Educational technology.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: vii, 112 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2578657
URI: http://theses.lib.polyu.edu.hk/handle/200/6902
Abstract: The research in applying data mining to education became very intensive, and web-based education continuously proliferated. Between learners and the information content, the interaction adaptation is the focus of a lot of studies, and then the framework of personalized e-learning is introduced and used in various application domains as a generic term. Many models of e-learning personalization are developed to collect the experiences generated in the learning process and analyze the characteristics based on the combined information of learners. Experiments are conducted by using statistical methods, data mining and semantic web mining to analyze LMS data, and the results prove the effectiveness in real-world practice. The concepts of learner profile and learning objects content are introduced for better utilizing any technologies methods to do the modeling. The network and repository of learning objects are designed to center the learner and monitor all interactions. At last, personalized services are adapted with optimal operations, content and sequence according to the well-modeled profile. This paper proposes a personalized e-learning system model which integrates data and text mining techniques for educational services personalization into the popular LMS software called Moodle. A number of mature technologies and methods are used with some customizations to provide the necessary functionalities and solve the integration problem of system. The model is implemented by developing plug-in and background programs of data collection, processing, data mining algorithms, and web user interfaces. At last, the solution system with real and simulated data is tested, and an assessment of student classification is conducted.

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