Typically, when someone talks about “mining” it involves people wearing helmets<br />with lamps attached to them, digging underground for natural resources. And while<br />it could be funny picturing guys in tunnels mining for batches of zeroes and ones,<br />that doesn't exactly answer “what is data mining.”<br />Data mining (DM) is a process of extracting (discovering) and understanding<br />hidden patterns and useful information in huge data [1][2][3]. Data mining is like<br />actual mining because, in both cases, the miners are sifting through mountains of<br />material to find valuable resources and elements.<br />DM methods have been used in several fields such as medical, business,<br />marketing, and education [4]. Recently, research interests have doubled regarding<br />the use of data mining methods in higher education and there is a clear indication<br />of the direction of researchers towards educational datasets. This led to the<br />emergence of a field called Educational Data Mining (EDM) [5][6].<br />Currently, different educational modes are available such as traditional learning<br />(Face to Face), e-learning, blended learning, and online learning. However, online<br />learning becomes popular in contemporary education [7][8]. It takes many forms<br />such as massive open online courses (MOOCs), learning management systems<br />(LMSs), and virtual learning environment (VLE) [12][8]. On the other hand,<br />literatures shows that a high number of students either withdraw or fail to achieve<br />good scores in online learning environments [9][7]. Moreover, the number of<br />dropouts in online learning environments is higher than the traditional learning<br />mode [10][12].<br />Based on the above-discussed disadvantages, it is essential to analyze learners' data<br />to obtain a better understanding of students' online achievement. Because of the<br />growth of the size of data, researchers need sophisticated methods to analyze it.<br /><br />Assistant Lecturer Miami Abdul Aziz