A Survey on Educational Data Mining [2014-2019]

Nowadays Data Mining is used in many application areas enabling large data streams and algorithms for analysis and extraction of powerful data. On their side, the Computer Environments for Human Learning (EIAH) offer TEL devices (Technology-enhanced learning) such as simulators, serious games, MOOCs (massive online open courses), or educational platforms. These devices provide data that are traces of the activities of students or teachers. The data produced are cognitive information of very fine levels (student knowledge, skills, and errors) and require specific analysis and processing tools, we talk here about educational data mining methods, Educational data processing (EDM) is rising as a notion of research and analysis with a set of machine and psychological ways and research approaches for understanding however students learn. EDM uses machine approaches to research instructional knowledge so as to review instructional queries. For this knowledge exploration, several tools were used like personal learning environments, recommender systems, Context learning, and Course management systems. These tools offer numerous edges for instructional data processing. In this survey, we have a tendency to focus and supply numerous tools of analysis trends exploitation EDM Tools to explore data and knowledge, and explaining the process of EDM application, the goal is not only to transform the data into knowledge but also to filter the extracted knowledge to know how to modify the educational environment to improve learners’ learning. This paper surveys the foremost relevant studies administrated during this field up to date.