Information Technology Reference
In-Depth Information
TaBLe 17.1
Summary of the Proposed Conceptual Frameworks
How People Learn
Framework
Affordances of the Current Web
Affordances of the Semantic Web
Learner centered
Capacity to support
individualized and community
centered learning activities
Content that changes in response
to individualized and group
learner models
Knowledge
centered
Direct access to vast libraries of
content and learning activities
organized from a variety of
discipline perspectives
Agents for selecting, personalizing,
and reusing content
Community
centered
Asynchronous and synchronous;
collaborative and individual
interactions in varied formats
Agents for translating,
reformatting, time shifting,
monitoring, and summarizing
community interactions
Assessment
centered
Multiple time and place shifted
opportunities for formative and
summative assessment by self,
peers, and teachers
Agents for assessing, critiquing,
and providing “just in time
feedback”
17.6 Conclusion
In the modern world, e-learning has become a fact of life. No one disputes
whether e-learning should be applied or not, the remaining questions are
how and when it should be applied. In response to the different needs and
characteristics of a growing number of e-learning users, the major tech-
nologies and strategies for a personalized learning environment have been
examined. In the future, more and more learners will not only go to schools
for courses, learning experiences will come to learners in response to their
strengths and prior learning, interests, and aspirations.
References
1. Aleven, V. and Keodinger, K. 2000. Limitations of student control: Do students
know when they need help? In Intelligent Tutoring Systems, Proceedings of the 5th
International Conference, ITS, Montreal, Canada. Lecture Notes in Computer Science ,
1839, eds. G. Gauthier, C. Frason, and K. VanLehn, Berlin: Springer, 292-303.
2. Anderson, T. and Elloumi, F. 2004. Toward a Theory of Online Learning, Theory and
Practice of Online Learning . Edmonton: Athabasca University, 33-60.
3. Ankush, M., Kanishka, R., and Sung, W. K. 2003. Content-based retrieval sys-
tems for personalization of educational videos. Artificial Intelligence in Education,
Sydney: IOS Press, 289-96.
 
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