The relationship of learning traits, motivation and performance-learning response dynamics

被引:19
作者
Hwang, WY [1 ]
Chang, CB
Chen, GJ
机构
[1] Natl Cent Univ, Grad Sch Network Learning Technol, Taoyuan 300, Taiwan
[2] Natl Kaohsiung Normal Univ, Kaohsiung 802, Taiwan
关键词
data mining; learning portfolios; learning dynamics; Newton's mechanics; learning curve;
D O I
10.1016/j.compedu.2003.08.004
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian mechanics in some way; therefore, this model is named Learning Response Dynamics. First, in this paper, the relationship between learning dynamics and learning speed has been investigated in a learning system, and then the changes of learning energy are inferred from the relationships obtained. The learning effect is estimated according to the changes of the learning energy. Based on the learning portfolios of the learners, the model is designed to investigate the changes of learning, speed over time. Various dynamics will influence the learning speed. These dynamics include the traits of the learners, the traits of the learning materials, and the stimulation of the learning activities. How to use different dynamics to motivate the learners is crucial to the success of learning. This model converts the factors in a learning system to quantified and comprehensible data, deducing the relationships between those factors. It makes the study of the learning system more efficient and scientific. With the experience of the two-year ongoing experiments on distance learning, and with the learning information discovered from the web-based-distance-class learners' learning portfolios by means of data mining techniques, the learning model mentioned above is inferred, tested and verified. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码:267 / 287
页数:21
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