TY - JOUR
T1 - Educational data mining
T2 - An intelligent system to predict student graduation AGPA
AU - Shana, Zuhrieh
AU - Abdulla, Shubair
N1 - Publisher Copyright:
© 2015, Praise Worthy Prize S.r.l. - All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Educational Data Mining (EDM) is an emerging research area, in which techniques are applied for exploring data educational systems. Since Accumulated Grade Point Average (AGPA) is crucial in students’ professional lives, having data-driven profiles for the students who are likely to graduate with low AGPA is an interesting and challenging problem. Identifying these kinds of students accurately enables educational institutions to improve the students’ level by providing them with special academic guidance and tutoring. In this paper, using a large and feature-rich dataset of student marks in the foundation stage, we developed a model to predict the graduation AGPA of students of Al Ain University of Science and Technology (AAU). The prediction process is done through employing neuro-fuzzy inference systems. The dataset used to determine the model quality and validity consists of 200 students’ records from two colleges, Law and Business Administration. The model was trained with 150 training samples and tested with 50 samples, which were not included within the training period. The experimental results showed a high level of accuracy of 97%. This accuracy revealed the suitability of neuro-fuzzy inference systems in predicting the students’ AGPA.
AB - Educational Data Mining (EDM) is an emerging research area, in which techniques are applied for exploring data educational systems. Since Accumulated Grade Point Average (AGPA) is crucial in students’ professional lives, having data-driven profiles for the students who are likely to graduate with low AGPA is an interesting and challenging problem. Identifying these kinds of students accurately enables educational institutions to improve the students’ level by providing them with special academic guidance and tutoring. In this paper, using a large and feature-rich dataset of student marks in the foundation stage, we developed a model to predict the graduation AGPA of students of Al Ain University of Science and Technology (AAU). The prediction process is done through employing neuro-fuzzy inference systems. The dataset used to determine the model quality and validity consists of 200 students’ records from two colleges, Law and Business Administration. The model was trained with 150 training samples and tested with 50 samples, which were not included within the training period. The experimental results showed a high level of accuracy of 97%. This accuracy revealed the suitability of neuro-fuzzy inference systems in predicting the students’ AGPA.
KW - ANFIS
KW - Educational data mining
KW - Neuro-fuzzy inference
KW - Prediction
KW - Student AGPA
UR - http://www.scopus.com/inward/record.url?scp=84938516423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938516423&partnerID=8YFLogxK
U2 - 10.15866/irecos.v10i6.6488
DO - 10.15866/irecos.v10i6.6488
M3 - Article
AN - SCOPUS:84938516423
SN - 1828-6003
VL - 10
SP - 593
EP - 601
JO - International Review on Computers and Software
JF - International Review on Computers and Software
IS - 6
ER -