The growing volumes of data continue to outpace human capabilities to extract and discover valuable information capable of supporting sound decision-making and plausible prediction in any business or industry this can only be addressed using automated methods such as data mining. Data Mining Technology (DMT) is a robust technology that can help organizations to make optimal informed decisions-instead of guesswork decisions 
, in addressing contemporary challenges facing higher education. Although data mining technology is progressing, its use leaves much to be desired. its adoption and implementation is expensive and any failure in implementation causes not only financial loss but also dissatisfaction among users, which makes it imperative to study the acceptance and adoption of this technology by its potential users. previous studies about data mining have always focused either on the technical aspect or the development of DMT application algorithms without considering users‘ perception of the technology 
. Little effort has been used to encourage users to appreciate DMT‘s capabilities and let's DMT obtain the users‘ acknowledgement; as this could help minimizing underutilization or eventual abandonment despite the prevalent benefit.
This study aims to promote and encourage the use of data mining technologies by its potential users within higher education context:
• This paper aims to contribute to the conceptual and theoretical understanding of Data mining within higher education.
• It introduces the notion of Data mining and outlines its relevance to higher education. From a perspective of e- learning practitioners and data mining practitioners
To achieve these goals, this study aims to design a web portal that targets two types of potential users, e-learning practitioners and data mining practitioners and that can serve as a one-stop knowledge base. That allows to explore the different participants in the education system and how each of these players can benefit from the data mining system in a virtuous cycle. And allows data mining practitioners to thoroughly understand the data mining philosophy and apprehended the practice of its process by focusing on data preparation and three key areas in the modeling process; criteria to consider in order to optimize the chances of extracting useful information and to make the right choice of data mining algorithms, Model construction and model evaluation.