Abstract
The growth of the internet has given rise to a number of open online learning platforms, enabling access to learning materials by millions of individuals regardless of age or educational background (Zawilska et al., Social collective intelligence. Springer International Publishing, pp. 187–202, 2014) [1]. These massive open online courses (MOOC) have replaced traditional institutional learning environments, such as physical attendance of lectures, with globally accessible learning via the Web. Despite the large number of students who enrol in MOOCs, however, the percentage of student completion these course remains low. The current work investigates methods for predicting learner behaviour and analysis of overall student performance, will help service providers identify students at risk of dropout so that they can take preventive measures. Results show that students who attended lectures and engaged and interacted with course material achieved the highest grades. The galaxy schema is used to show the recommended queries for the students based primarily on their seek.
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Suresh Kumar, S., Mallikarjuna Shastry, P.M. (2019). Analysis of Student Engagement and Course Completion in Massive Open Online Courses. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_46
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