Contextual Data Mining for Higher Educational Institutions
Subhashini Sailesh Bhaskaran1, Mansoor Al Aali2, Kevin Lu3

1Dr. Subhashini Sailesh, Department of MIS,  Ahlia University College of Business, Ahlia University, Bahrain.
2Prof. Mansoor Al Aali, President, Ahlia University, Bahrain.
3Dr. Kevin Lu, Brunel University, London.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 167-178 | Volume-8 Issue-5, June 2019 | Retrieval Number: D6066048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Context-awareness research has been carried out by many researchers. However, little has been done in building a context-aware data mining methodology which supports decision-making in HEIs. The usefulness of Knowledge discovery data mining process (KDDM process) in HEIs were investigated to discover hidden knowledge that is contextualized, resident in student datasets and use them in decision making. It was experimented and found that not any of the KDDM processes include a contextual factor mining stage that is essential to take out hidden knowledge from datasets described by contextual factors. Therefore a new process was introduced in KDDM process that uncovered contextual data to be used to support business goal and produced a dataset at the preparation stage which generated data mining model that was contextual leading to the unearthing of course taking patterns that are contextualized. This discovery has enabled forecasting of optimum CGPA and time-to-degree.
Keywords: Heis, Data Mining, KDDM, Time To Degree, Student Performance, Context-Awareness.

Scope of the Article: Data Mining