An enhanced bayesian network model for prediction of students' academic performance in engineering programs

Predicting students' academic performance (SAP) provides invaluable information for educational institutes' authorities. This information offers numerous opportunities for instructors and decision makers to improve their quality of services and consequently help the students to succeed in their education. In this paper, we introduce a prediction model to forecast the SAP of the Engineering students. The model is based on the Bayesian networks framework. The model is constructed using a database of the undergraduate engineering students at University of Illinois at Chicago (UIC). The specific objective of this model is to predict the students' grades in three major courses which most of the students take in their second semester. The grades in these courses have major impact on students' retention rates as many students receive low grades in them. Therefore, predicting students' grades in these courses can be used to identify the students who might receive low grades and hence need extra help from the educational authorities. The proposed model has been tested against the conventional models which have been proposed in the literature and it is proven to outperform them in grade prediction.

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