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Predicting student success based on interactions with Virtual Learning Environment.
Online learning can be called the millennial sister of classroom learning; tech savvy, always connected, and flexible. These features offer a convenient alternative to students with constraints and working professionals to learn on demand. According to National Center for Education Statistics, over 5 million students are currently enrolled in distance education courses. The growing trend and popularity of MOOCs (Massive Open Online Courses) and distance learning makes it an interesting area of research. We plan to work on OULA (Open University Learning Analytics) dataset. Learning analytics provides many insights on the learning pattern of students and on module assessments. These insights may be researched to enhance participants’ learning experience. In this paper, we predict students’ success in an online course using regression, clustering and classification methods. We have a mix of categorical and numeric inputs present in the OULA datasets that are in csv file formats and contain information for more than 30,000 students pertaining to 7 distance learning courses, student demographics, course assessments and student interaction with virtual learning environment. We have merged tables together using unique identifiers. We will first explore the merged data using SAS® to generate insights and then build appropriate predictive models.