  Various studies have shown that students tend to get higher marks when
assessed through coursework based assessment methods which include either
modules that are fully assessed through coursework or a mixture of coursework
and examinations than assessed by examination alone. There are a large number
of educational data mining studies that preprocess data through conventional
data mining processes including data preparation process, but they are using
transcript data as they stand without looking at examination and coursework
results weighting which could affect prediction accuracy. This paper proposes a
different data preparation process through investigating more than 230000
student records in order to prepare students marks based on the assessment
methods of enrolled modules. The data have been processed through different
stages in order to extract a categorical factor through which students module
marks are refined during the data preparation process. The results of this work
show that students final marks should not be isolated from the nature of the
enrolled modules assessment methods. They must rather be investigated
thoroughly and considered during EDMs data preprocessing phases. More
generally, it is concluded that educational data should not be prepared in the
same way as other data types due to differences as data sources, applications,
and types of errors in them. Therefore, an attribute, coursework assessment
ratio, is proposed to be used in order to take the different modules assessment
methods into account while preparing student transcript data. The effect of CAR
on prediction process using the random forest classification technique has been
investigated. It is shown that considering CAR as an attribute increases the
accuracy of predicting students second year averages based on their first year
results.
