Advances in Web-Based Learning – ICWL 2013: 12th by Nick Rushby (auth.), Jhing-Fa Wang, Rynson Lau (eds.)

By Nick Rushby (auth.), Jhing-Fa Wang, Rynson Lau (eds.)

This publication constitutes the refereed complaints of the twelfth overseas convention on Web-Based studying, ICWL 2013, held in Kenting, Taiwan, in October 2013.
The 34 revised complete papers awarded have been conscientiously reviewed and chosen from approximately 117 submissions. The papers are equipped in topical sections on interactive studying environments, layout, version and framework of e-learning structures, customized and adaptive studying, internet 2.0 and social studying environments, clever instruments for visible studying, semantic internet and ontologies for e-learning, and Web-based studying for languages learning.

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Extra info for Advances in Web-Based Learning – ICWL 2013: 12th International Conference, Kenting, Taiwan, October 6-9, 2013. Proceedings

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The methods using image processing for detecting moving objects contain (1) Temporal Differencing [11-13] and (2)Background Subtraction [14-16]. Background Subtraction is more popular than Temporal differencing, as it could acquire the complete shape of an object, show better tolerance on noise, and reduce the effects of shadow resulted from the moving object. A background Iback is acquired from the environment as the base, and the acquired image Inow is compared with the base Iback for the changing areas.

Using a personal response system in economics teaching. International Review of Economics Education 1(1), 80–86 (2003) 5. : e-ducation. CACM 46(9), 134–140 (2003) 6. : Anonymity and in class learning: The case for electronic response systems. AJET 22(4), 568–580 (2006) 7. nl: integrating backchannels with physical space. In: CHI 2008: CHI 2008 Extended Abstracts on Human Factors in Computing Systems, pp. 2751–2756. ACM, New York (2008) 8. : The use of clickers in the classroom: Teaching innovation or merely an amusing novelty?

Overall, it shows that the rating mechanism is effective in discriminating spam. g. “Please like Christo’s page on FB” 45 dislikes, 5 likes, or “Let’s all go to the swimming pool” 24 dislikes, 12 likes) get the most ratings and the positive ratings are high. Students seem to try to game the system by upvoting spam, but this is suppressed effectively by the ‘wisdom of the crowd’. g. “Can we have the slides before the lecture” 10 dislikes, 65 likes, or “Close the blinds, we can’t see anything”, 6 dislikes, 43 likes) are very popular and get many positive ratings.

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