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Providing early predictions of academic performance is necessary for identifying at-risk students and subsequently providing them with timely intervention for critical factors affecting their academic performance. Although e-book systems are often used to provide students with teaching/learning materials in university courses, seldom has research made the early prediction based on their online reading behaviours by implementing machine learning classifiers. This study explored to what extent university students' academic achievement can be predicted, based on their reading behaviours in an e-book supported course, using the classifiers. It further investigated which of the features extracted from the reading logs influence the predictions. The participants were 100 first-year undergraduates enrolled in a compulsory course at a university in Taiwan. The results suggest that logistic regression supports vector classification, decision trees, and random forests, and neural networks achieved moderate prediction performance with accuracy, precision, and recall metrics. The Bayes classifier identified almost all at-risk students. Additionally, student online reading behaviours affecting the prediction models included: turning pages, going back to previous pages and jumping to other pages, adding/deleting markers, and editing/removing memos. These behaviours were significantly positively correlated to academic achievement and should be encouraged during courses supported by e-books.

Abstract: The objective of this paper is to present two modal identification methods that extract dynamic characteristics from output-only data sets collected by a low-cost and rapid-to-deploy wireless structural monitoring system installed upon a long-span cable-stayed bridge. Specifically, an extensive program of full-scale ambient vibration testing has been conducted to measure the dynamic response of the 240 m Gi-Lu cable-stayed bridge located in Nantou County, Taiwan. Two different output-only identification methods are used to analyze the set of ambient vibration data: the stochastic subspace identification method (SSI) and the frequency domain decomposition method (FDD). A total of 10 modal frequencies and their associated mode shapes are identified from the dynamic interaction between the bridge’s cables and deck vibrations within the frequency range of 0–7 Hz. The majority of the modal frequencies observed from recording cable vibrations are also found to be associated with the deck vibrations, implying considerable interaction between the deck and cables. [Copyright &y& Elsevier]