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A successful flipped classroom relies heavily on student engagement in pre-class learning and their active participation in classroom activities. However, much to students' disappointment, their efforts in the learning process often go unnoticed in traditional assessment approaches (such as term papers and final examinations) that are more product-oriented. As one of the important goals of the flipped classroom approach is to improve students' learning behaviors and attitudes, we argue that more attention should be shifted to assessing the learning process in order to align with such goal. In this paper, we advocate the use of learning analytics as one of the assessment components in flipped classrooms. We show how this change in assessment can be beneficial to both student learning and teacher reflection. We also indicate some limitations on current learning management systems (LMS) in providing the learning analytics for assessment purposes and suggest several revisions on LMS for better facilitating implementation of the flipped classroom approach. [ABSTRACT FROM AUTHOR]

Learning Style (LS) is an important parameter in the learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments. Consequently, an important capability of an e-learning system could be the automatic determination of a student's learning style. In this paper, a set of features which are important in extracting the learning style automatically from students' behavior has been determined. These features, which are recognized based on Myers-Briggs Type Indicator's (MBTI), play a key role in predicting learning styles in an online course. The features are determined and ranked using pattern recognition techniques, such as K-means clustering algorithm, to show which features can be better to separate learning style dimensions. The results show several features can be used to predict learning styles with high precision. [ABSTRACT FROM AUTHOR]