Abstract

MENDOZA JURADO, Helmer Fellman. Web 4.0-oriented application model for student academic performance in higher education. Edu. Sup. Rev. Cient. Cepies [online]. 2021, vol.8, n.2, pp.39-48. ISSN 2518-8283.

Abstract The objective of this paper is to propose an application model based on Web 4. 0 (Intelligent Web), which underlies  an intrinsic relationship between an artificial intelligence model based on association rules and a decision tree algorithm that structures a predictive model for early warning in the pedagogical development of the student, which is reflected in the grades that quantify the degree of learning in different subjects, mainly from the potential of the Apriori algorithm that achieves a low efficiency of frequent paths of sets of elements, this model uses mainly  the FP- growth model to search frequent sets of elements by means of association rules and decision trees. However, there are clear dependencies between subjects, levels, social and cultural environment, leading to a rational analysis and early warning of the learning process of each subject, like with the evaluated student. The proposed model provides a precise academic orientation, which can effectively improve the quality in the management of people’s learning, being of great importance for the development and orientation of the students themselves. Besides that, it aims to help understand the situation of students in all aspects and improve the overall level of students, being more effective compared to other machine learning algorithms (Machine Learning), which characterize the Web 4.0

Keywords : Web 4.0; Academic Performance; Association Rules; Decision Trees.

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