A framework for smart academic guidance using educational data mining

The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic guidance recommendation by predicting students' performance which involves analyzing data of students' records, socio-economic data and of course the student's motivation. The proposed model was analyzed and tested using student's data collected from the preparatory classes for Grandes Ecoles Reda Slaoui (CPGE) - Morocco. More specifically, it proposes the use of three models that were applied on real data: Decision tree, Naive Bayes, and Neural networks. The data include the classes period (2012-2014 and 2013-2015) of 330 students in specialty the grade Mathematical Physics (MP) and Engineering Sciences (MPSI). The performance results indicate that our framework can make more accurate predictions of students' performance.