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Predicting academic success by using context variables and probabilistic classification
Author(s) -
Bahritidinov Bakhtiyor,
Sánchez Eduardo
Publication year - 2017
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12195
Subject(s) - computer science , identification (biology) , context (archaeology) , probabilistic logic , machine learning , variable (mathematics) , artificial intelligence , statistical model , data mining , mathematics , paleontology , mathematical analysis , botany , biology
Abstract The aim of the study reported here was to predict students' grades based on context and personal state variables. Motivation for the study derives from the need to provide accurate recommendations about both educational resources and activities that match students' requirements and expectations. The proposed prediction method takes advantage of information associated with the context variables of the students, proposes the identification of clusters that group students with similar attributes, and estimates the final grade based on a probabilistic model. The findings show that the proposed model outperforms other existing models in terms of error accuracy.