Adaptive Recommendation System Using Machine Learning Algorithms for Predicting Student's Best Academic Program

Autor/inn/en	Ezz, Mohamed; Elshenawy, Ayman
Titel	Adaptive Recommendation System Using Machine Learning Algorithms for Predicting Student's Best Academic Program
Quelle	In: Education and Information Technologies, 25 (2020) 4, S.2733-2746 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Elshenawy, Ayman)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-019-10049-7
Schlagwörter	Educational Technology; Artificial Intelligence; Computation; Mathematics; Academic Achievement; Prediction; College Students; Engineering Education; Foreign Countries; Departments; Accuracy; Egypt (Cairo) + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Künstliche Intelligenz; Mathematik; Schulleistung; Vorhersage; Collegestudent; Ingenieurausbildung; Ausland; Department; Abteilung
Abstract	Some of the educational organizations have multi-education paths such as engineering and medicine collages. In such colleges, the behavior of the student in the preparatory year determines which education path the student will join in the future. In this paper, an adaptive recommendation system is proposed for predicting a suitable education path(s) for a student in college preparatory year. The adaptability is achieved by automatically applying different data mining techniques for extracting relevant features and building a tailor-made model for each education path. The problem formulated as a multi-label multi-class binary classification problem and the dataset automatically translated into one-versus-all (for binary classification). As a case study, the proposed model is applied to predict student's academic performance in the faculty of engineering at AL-Azhar University. It recommends a suitable engineering department among seven engineering departments for each student based on his academic performance. The data of each department (i.e. educational program) is fed to the recommendation system. Then, the relevant set of features for each department is selected and a machine learning algorithm with the best performance is selected for the recommendation process of each department. The obtained results showed that the proposed model recommends the best machine learning algorithm (i.e. model) for each faculty department, find the relevant data that are important in the recommendation process and recommend the student with the suitable engineering department(s) with high accuracy. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01