PREDICTIVE MODELLING OF ACADEMIC PERFORMANCE BY MEANS OF BAYESIAN NETWORKS

Predicting academic performance is an often-required task in Higher Education field. Development of data mining, especially educational data mining (EDM) provided algorithms for effective data analysis with the aim to improve quality of the educational processes. In this paper, probability based app...

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Bibliographic Details
Main Authors: Oreski, Dijana, Konecki, Mario, Pihir, Igor
Format: Conference Proceeding
Language:English
Subjects:
Academic achievement
Algorithms
Artificial intelligence
Bayesian analysis
Computer engineering
Computers
Data analysis
Data mining
Data processing
Datasets
Demographics
Education
Feature selection
Higher education
Learning algorithms
Machine learning
Mathematical models
Motivation
Networks
Performance prediction
Prediction models
Probability learning
Questionnaires
Sensitivity analysis
Student retention
Students
Success
Variables
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Summary:Predicting academic performance is an often-required task in Higher Education field. Development of data mining, especially educational data mining (EDM) provided algorithms for effective data analysis with the aim to improve quality of the educational processes. In this paper, probability based approach to machine learning (Bayesian networks) is applied in order to predict academic performance of IT students based on data about their socio-demographic characteristics, attitudes, motivation and behavior. Main aim of presented research was twofold: (i) to predict students' academic performance and to identify most significant predictors of students' success, (ii) to investigate possibilities of probability based machine learning approach for developing predictive models in educational domain. Research results indicated high level of potential for Bayesian networks application on educational datasets.
ISSN:1849-6903
1849-6903