| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1110899 | Procedia - Social and Behavioral Sciences | 2015 | 8 Pages |
With the advancement in internet technology all over the world, the demand for online education is growing. Many educational institutions are offering various types of online courses and e-content. The analytical models from data mining and computer science heuristics help in analysis and visualization of data, predicting student performance, generating recommendations for students as well as teachers, providing feedback to students, identifying related courses, e-content and books, detecting undesirable student behaviours, developing course contents and in planning various other educational activities. Today many educational institutions are using data analytics for improving the services they provide. The data access patterns about students, logged and collected from online educational learning systems could be explored to find informative relationships in the educational world. But a major concern is that the data are exploding, as numbers of students and courses are increasing day by day all over the world. The usage of Big Data platforms and parallel programming models like MapReduce may accelerate the analysis of exploding educational data and computational pattern finding capability. The paper focuses on trial of educational modelling based on Big Data techniques.
