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Detection Of Overly Intensive Student Interactions Using Weblog Of Course Website
Hadas Levi- Gamieli, Anat Cohen and Rafi Nachmias

The aim of this study is to identify online learning behavior that is excessively intense as reflected in a student’s overly frequent interaction with the instructor through various communication channels. Then, this study aims to use learning analytics methodologies to discover whether a student with the identified behavior also displays the same overly frequent behavior with respect to online usage of a course website. Furthermore, this research aims to determine whether there is a correlation between the learning intensity characteristic, gender and student achievement. This study found that the behavior through the direct communication channels was similar to the behavior in interacting with the website and thus it is possible to identify the intensive students by using learning analytics. Additionally, highly intense interaction with the instructor or intense website content usage may indicate inefficient learning. The over-intensity in learning processes was found to be a high predictor of student grades. Different gender behavior was found with regard to the effectiveness of different means of communication with the instructor. Furthermore, among women, highly intensive interaction with the instructor led to higher achievement compared to high frequency website usage, while for men there was no difference between the contributions to achievement from the two modalities.

Keywords: Online learning, web-usage mining, learning analytics, big data, higher education, overly intensive learning

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