In this research we propose a comprehensive set of knowledge indicators aimed to enhance learners’ selfreflection and awareness in the learning and testing process. Since examined intelligent tutoring systems do not include additional messaging features, the introduction of common set of knowledge indicators differentiates our approach from the previous studies. In order to investigate the relation between proposed knowledge indicators and learner performance, the correlation and regression analysis were performed for 3 different courses and each examined intelligent tutoring system. The results of correlation and regression analysis, as well as learners’ feedback, guided us in discussion about the introduction of knowledge indicators in dashboard-like visualizations of integrated intelligent tutoring system.
The classification of nonstationary signals in a noisy environment is a difficult task. In this paper a modified version of S-Transform technique has been proposed for classification of power signal disturbances. The S-Transform is a signal processing technique which is used for visual localization, detection, pattern classification. S-Transform has good ability in gathering high frequency signals and suppressing the lower frequency signal. The S-Transform has been used to extract features from the nonstationary power disturbance signals. The extracted features are fed as the input support vector machine classifier for power signal disturbance pattern classification. To enhance the pattern classification accuracy the extreme learning
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