Data miming (DM) is the technique employed in extracting relevant knowledge from data. While Educational data mining (EDM) is concerns with developing methods that will learn from the extracted knowledge that come from educational environment. The main objective of this paper is to design and develop programs and give recommendations based on the outcomes to help the University management take proactive measures on the causes of students’ poor performance based on the discoveries made. We extracted seven hundred and forty-three (743) records collected from four sampled schools: School of Physical Sciences, (SPS), School of Environmental Studies (SES), School of Technology and Science Education (STSE), School of Agriculture and Agricultural Technology (SAAT) all from the Modibbo Adama University of Technology, Yola Nigeria. The Human Learning (HL) system in our model ‘Framework for Evaluating Academic Performance (FEAP) to come up in our paper titled’ in London Journal of Research in Computer Science and Technology, was made use of in this paper for the DM task which charts: Performance (in percentage) below/above average departmentally, Performance (in percentage) by class of degree departmentally, Performance of students (in percentage) by Mode of Entry departmentally. It made use of SQL server for the extraction of knowledge and Visual studio for charting the extracted knowledge for human visualisation. We were able to improve on how WEKA visualise data; from displaying single query charts to displaying multiple queries charts for a better performance evaluation in an inter phase that enable a user to select either WEKA GUI or HL GUI.