Abstract-Massive Open Online Courses (MOOC) are an emerging technology for online teaching and learning at a larger scale. Therefore getting an overall view of student behavior and performance is quite challenging. We intend to provide a solution by identifying the learner behavior using click stream interaction analysis.In MOOCs, videos provide the most informative content of learner behavior because majority of the students gather knowledge from videos whereas a relatively fewer number of students participate in assignments and forum activities. In this research the click stream data from the video interactions were analyzed to understand the learner behavior. The interactions such as play, pause, seeks and speed changes were aggregated to calculate features which indicate frequency and temporal dynamics of the behavior of the students. Two courses Engineering CS101 and Humanities and Statistical Learning from a dataset of the edX platform were analyzed by using unsupervised learning. The findings from this research can be used to understand how the learner behavior in MOOC videos differs in two different courses.Index Terms-E-learning, learner behavior, unsupervised learning.
Providing security and assurance to information systems and communications is one of the highest national priorities .It is our task to prepare computer information system security professionals with current security and assurance information to achieve this objective. To meet this urgent need, the faculty must have the knowledge and skills to be taught in the classroom. For several semesters, we have been developing information system security curriculum with a computer forensics course for the students. Active participation in real life security problems will be a great opportunity to learn the subject matter. This paper describes one of our positive experiences.
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