Abstract:
Nowadays, Higher Learning Institutions (HLIs) store a large amount of students’ data.
However, those data are not widely used to solve the academic problems of the
students that are available at the HLIs such as poor performance of the students in
some of the courses. Educational Data Mining (EDM) is the technology that can be
applied to predict the performance of the students on the available dataset at the HLIs.
This study intended to solve the problem of poor performance in Mathematics for
degree management students at HLIs using EDM techniques. The purpose of the study
was to predict Management degree Students’ performance in Mathematics using EDM
taking Mzumbe University (MU) as a case study. The quantitative research approach
was applied in this study basing on the design science steps. Secondary data were
collected to create the dataset through document review from examination office (final
examination (FE), course work (CW) and Remarks), admission office (age, gender,
entry category and ordinary level mathematics grades), accounts office (sponsorship
details), department of mathematics and statistics (number of instructors) and
accommodation office (living location) at MU including Main campus Morogoro and
Mbeya Campus. Different Machine Learning (ML) algorithms were applied on
training set (60%) such as K-Nearest Neighbor (K-NN), Random Forest (RF),
Decision Tree (DT), Support Vector Classification (SVC) and Multilayer Perceptron
(MLP). ML algorithms were validated using 10-fold cross-validation and validation
dataset (20%) and the best algorithms were RF, DT and K-NN. During the evaluation
of the three best ML algorithms using 20% of the dataset, RF ML algorithm was found
to be the best for model development in mathematics performance prediction in this
study with the accuracy of 99% and F1-scores of 99% and 100% for fail and pass class
respectively. Moreover, DT was able to generate rules that were applied to recommend
the minimum grade of D for ordinary level mathematics in admission to degree
management students to reduce the failure rate at HLIs.
