Predicting and analyzing the performance of students is essential to design helpful guidance process that allows good success rates and raises the institution's ranking as one of the criteria for a high-quality university. However the lack of adequate support and personalized guidance increases students failure rate. Nowadays, there are many research findings that propose predictive models based on machine learning methods to do many kinds of tasks. Also, machine learning methods have been applied with success in many domains. The aim of this work is to evaluate the possibility of improving the students guidance system by using machine leaning modeling.We have developed a model with objective of predicting the chance of success of students of the Unit of Training and Research in Science and Technology (UFR-ST) of the University Norbert Zongo (UNZ). The approach used in the design of this model was to estimate students success probability when they make their pathway choice among Mathematics, Physics, Chemistry and Computer science after the semester 3. Several Machine learning algorithms (Adaboost, Random Forest, SVM and KNN) were used to fit model with students of academic years 2017-2018 and 2018-2019 achievements data. The results obtained on the test data reveal a score of above 70% for the best algorithm (Random Forest).
Guidance is a complex and multidisciplinary field where the main goal is to help students find their suitable training pathways. The emergence of artificial intelligence has boosted many area of research. Machine learning tools have been used to improve both educational and vocational guidance system. Since 2018, the university guidance system has evolved with the establishment of an online platform named CAMPUSFASO. This platform, presented as an innovation for the guidance, has been strongly criticized. Firstly, we present the academic achievements of the first year students of Université Norbert Zongo after they are guided by CAMPUSFASO. These academic achievements show that more the student is guided at his preferential training path more he succeed. Secondly, we present a machine learning model for the guidance. Unlike CAMPUSFASO, our model uses of high school grades of the student to find the suitable training path. The model reaches auspicious results with simulated data.
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