A self-adjusting e-course generation process for personalized learning

doi:10.1016/j.eswa.2011.09.009

2012-02-01

PRE

Jianwei Li

Yi-Chun Chang *

DOI：10.1016/j.eswa.2011.09.009

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摘要

En 中文

This paper proposes a self-adjusting e-course generation process, which support to provide a truly personalized learning environment. The proposed process is divided into four steps: (1) determining learning concept structure, (2) adjusting the difficulty of the e-learning material, (3) analyzing a learner's ability and learning goals, and (4) composing personalized e-courses. Meanwhile, this paper applies the collaborative voting approach to determine the difficulty of the e-learning material, and the maximum likelihood estimation (MLE) to analyze a learner's ability and her/his learning goals. Since evolutionary algorithms (EAs) have been developed to find close optimal solutions, this paper adopts them to compose personalized e-courses that meet individual learners' demands. Once a learner learns one or more learning concepts covered in a personalized e-course, the feedback information from the learner must be returned to: (I) self-adjust the difficulty of the e-learning material for step 2, and (II) update the learners' ability and learning goals for step 3. Furthermore, to find appropriate EAs for personalized e-course composition, this paper devises some experiments to compare two widely applied EAs, Genetic algorithms (GA) and Particle Swarm Optimization (PSO). When the number of e-learning materials is less than 300, the experimented results indicate that the executing effectiveness of PSO is better than that of GA. Besides, to validate the practicability of the proposed process, an e-course authoring tool based on the proposed process is developed to generate personalized e-courses. The generated personalized e-courses have been provided to 103 actual learners who participate in an Introduction to Computer curriculum. The investigation results indicate that the proposed process adapts to learners by utilizing the feedback from many learners. In other words, learning experiences of one organization/class can benefit to another organization/class's learners in the same curriculum. (C) 2011 Elsevier Ltd. All rights reserved.

Keyword:

Collaborative voting approach

e-Learning

Evolutionary algorithms (EAs)

Maximum likelihood estimation (MLE)

Personalized e-course