Abstract
Society and learning change with the development of technology. In order to keep up the latest development of technology in education, this study focused on a remedial English e-learning course in a university. Hopefully, by using a chance building model, few but important elements would be acquired. The chance building was based on the text mining theory and KeyGraph technology to present a visualized scenario. The participants were graduate school students who took the e-learning course. A questionnaire is composed of the ARCS model and preference selection with material topics and types. The results indicated that from the interaction between students’ characters and teaching characters some attributes tended to create innovated scenarios. With the nodes and links, different innovated scenarios would be interpreted. As a result, the chance points also appeared in the graph. More studies in chance building model and in empirical experiment are needed.
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Hsu, Cl., Wang, Al., Lin, Yc. (2010). Instructional Design for Remedial English e-Learning. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_39
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DOI: https://doi.org/10.1007/978-3-642-16732-4_39
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