  Choosing the right and effective way to assess students is one of the most
important tasks of higher education. Many studies have shown that students tend
to receive higher scores during their studies when assessed by different study
methods which include units that are fully assessed by varying the duration of
study or a combination of courses and exams than by exams alone. Many
Educational Data Mining studies process data in advance through traditional
data extraction, including the data preparation process. In this paper, we
propose a different data preparation process by investigating more than 230000
student records for the preparation of scores. The data have been processed
through diverse stages in order to extract a categorical factor through which
students module marks are refined during the data preparation stage. The
results of this work show that students final marks should not be isolated from
the nature of the enrolled module assessment methods. They must rather be
investigated thoroughly and considered during EDM data preprocessing stage.
More generally, educational data should not be prepared in the same way normal
data are due to the differences in data sources, applications, and error types.
The effect of Module Assessment Index on the prediction process using Random
Forest and Naive Bayes classification techniques were investigated. It was
shown that considering MAI as attribute increases the accuracy of predicting
students second year averages based on their first year averages.
