An Object-Oriented Course Framework for Developing Adaptive Learning Systems
ARTICLE
Shian-Shyong Tseng, Jun-Ming Su, Gwo-Jen Hwang, Gwo-Haur Hwang, Chin-Chung Tsai, Chang-Jiun Tsai
Journal of Educational Technology & Society Volume 11, Number 2, ISSN 1176-3647 e-ISSN 1176-3647
Abstract
The popularity of web-based learning systems has encouraged researchers to pay attention to several new issues. One of the most important issues is the development of new techniques to provide personalized teaching materials. Although several frameworks or methods have been proposed, it remains a challenging issue to design an easy-to-realize framework for developing adaptive learning systems that benefit student learning performance. In this paper, we propose a modular framework that can segment and transform teaching materials into modular learning objects based on the SCORM standard such that subject contents can be composed dynamically according to the profile and portfolio of individual students. An adaptive learning system has been developed based on this innovative approach. Based on the experimental results of a college computer course, we conclude that the proposed framework can be used to develop adaptive learning systems that benefit the students' learning achievements. (Contains 5 tables and 20 figures.)
Citation
Tseng, S.S., Su, J.M., Hwang, G.J., Hwang, G.H., Tsai, C.C. & Tsai, C.J. (2008). An Object-Oriented Course Framework for Developing Adaptive Learning Systems. Journal of Educational Technology & Society, 11(2), 171-191. Retrieved March 19, 2024 from https://www.learntechlib.org/p/75370/.
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Keywords
- College Instruction
- College Students
- Computer Assisted Instruction
- computer science education
- Computer Software
- Computer Software Evaluation
- Computer System Design
- educational technology
- electronic learning
- Foreign Countries
- Individualized Instruction
- instructional design
- intelligent tutoring systems
- internet
- Matched Groups
- models
- Pretests Posttests
- programming
- standards
Cited By
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SVM and PCA Based Learning Feature Classification Approaches for E-Learning System
Aditya Khamparia & Babita Pandey, Lovely Professional University, Jalandhar, India
International Journal of Web-Based Learning and Teaching Technologies Vol. 13, No. 2 (April 2018) pp. 32–45
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