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

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Computers & Education Volume 42, Number 3, ISSN 0360-1315 Publisher: Elsevier Ltd


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.


Hwang, W.Y., Chang, C.B. & Chen, G.J. (2004). The relationship of learning traits, motivation and performance-learning response dynamics. Computers & Education, 42(3), 267-287. Elsevier Ltd. Retrieved March 20, 2023 from .

This record was imported from Computers & Education on January 30, 2019. Computers & Education is a publication of Elsevier.

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