TY  - JOUR
T1  - Prediction of Student’s Academic Performance using k-Means Clustering and
Multiple Linear Regressions
AU - Tinuke Omolewa, Oladele AU - Roseline Oluwaseun, Ogundokun AU - Adekanmi Adeyinka, Adegun AU - Taye Oladele, Aro 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 22
SP  - 8254
EP  - 8260
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8254.8260
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8254.8260
KW  - academic
performance
KW  -academic performance
KW  -multiple linear regression
KW  -cluster
KW  -Data mining
KW  -k-means
AB  - In today’s educational system, performances of students are mainly based on tests, assignments,
attendance, quizzes and final examination. It is at the end of this exercise that a minimum mark is determined on
which promotion will be based. There is need to identify factors that lead to a student’s success or failure. This
will allow the teacher to provide appropriate counselling and focus more on such factors. Hence, a model for
forecasting student’s performance academically is of a pronounced significance, therefore, data mining
techniques in classifying and forecasting the academic performance of students was put into application in this
research study. k-means clustering and Multiple Linear Regression (MLR) were used for assessing student’s
performance. The results showed that student’s test scores, quiz and assignment were the major factors that
could be used in predicting academic performance of students. Also, two clusters were derived with the use
of elbow method to group all the students into clusters.
ER  -