EDUCATIONAL DATA SETS AND TECHNIQUES OF RECOMMENDER SYSTEMS: A SURVEY

Due to growth of World Wide Web, enormous data are created. To get the information out of available data it is necessary to store these data in a particular format. These formatted data are called datasets. These datasets are important for extracting information in such a way so that decision can be taken to recommend the trend embedded in the datasets. In addition, they can be used to test and train many information processing applications. A general practice to use available datasets obtained from different application environments is to evaluate developed recommendation techniques. Such techniques, in turn, are used as benchmarks to develop new recommendation techniques and compare them with other techniques under same applications. In this paper, we explored available public datasets collected for educational applications. These data sets can be used to evaluate and compare the performance of different recommendation techniques for learning. From basic techniques to the state-of-the-art, this paper also attempts to explore recommendation techniques, which can be served as a roadmap for research and practice in this area.