Persistence in Distance Education: A Study Case Using Bayesian Network to Understand Retention

This article presents a study on the variables promoting student retention in distance undergraduate courses at Federal University of Pará, aiming to help school managers minimize student attrition and maximize retention until graduation. The theoretical background is based on Rovai's Composite...

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Bibliographic Details
Published in:International journal of distance education technologies 2017-10, Vol.15 (4), p.61-78
Main Authors: Eliasquevici, Marianne Kogut, Seruffo, Marcos César da Rocha, Resque, Sônia Nazaré Fernandes
Format: Article
Language:English
Subjects:
Academic Persistence
Analysis
Bayesian analysis
Bayesian Statistics
Case Studies
College campuses
College dropouts
Colleges & universities
Conditional probability
Distance Education
Distance learning
Educational materials
Educational Technology
Equipment and supplies
Feedback
Foreign Countries
Higher education
Instructional Materials
Mixed Methods Research
Networks
Online Courses
Online education
Pedagogy
Probability
Questionnaires
Student Attitudes
Student Attrition
Student retention
Teaching
Teaching Methods
Technology Uses in Education
Tutoring
Undergraduate Students
Variables
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Summary:This article presents a study on the variables promoting student retention in distance undergraduate courses at Federal University of Pará, aiming to help school managers minimize student attrition and maximize retention until graduation. The theoretical background is based on Rovai's Composite Model and the methodological approach is conditional probability analysis using the Bayesian Networks graphical model. Network modeling has shown that among internal factors after admission to the course (as defined in the Composite Model) face-to-face tutorial sessions need to be better planned and executed, learning materials are still not adequate to online course specificities and the support structure needs to be remodeled.
ISSN:1539-3100
1539-3119
DOI:10.4018/IJDET.2017100104