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

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

Marianne Kogut Eliasquevici (Federal University of Pará, Belém, Brazil), Marcos César da Rocha Seruffo (Federal University of Pará, Belém, Brazil) and Sônia Nazaré Fernandes Resque (Federal University of Pará, Belém, Brazil)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/IJDET.2017100104
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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.
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1. Introduction

With the growth of distance education initiatives in Brazil and in other parts of the world, there has been more and more interest in understanding student behavior and learning process in this new format. This interest is spurred even further by concerns that learning is not possible outside the traditional, classroom-based setting.

Beyond learning as the main focus of the whole academic process, it is also necessary to offer students conditions to help them persist and complete courses at a distance. This requires desirable characteristics not only at the individual level (autonomy, written communication skills, team work, etc.) but also from the educational institution (management commitment; a suitable pedagogical approach; adequate résumés, teaching materials and learning practices; physical and technological infrastructure; support and counseling; and multidisciplinary teams, among others).

In distance courses, there is often a considerable gap between the number of students enrolling and those who persist until graduation. Rovai (2003) defines persistence as the behavior of continuing action despite the presence of obstacles; it is an important measure of higher education program effectiveness. In this sense, finding ways to predict factors that contribute to student persistence in distance courses may be vital for educational managers who aim to minimize attrition and to maximize retention and graduation rates.

In the Brazilian higher education system, student attrition represents a crucial waste of social, academic and economic efforts, directly affecting all the persons concerned. In distance education, it is considered the greatest obstacle to course development (Censo EAD Brasil, 2014), which in itself justifies further studies.

Similar concerns have led the Distance Education Division (AEDI) at Federal University of Pará (UFPA) – a public higher education institution in the Northern region of Brazil – not only to evaluate their distance undergraduate courses but also to invest in better understanding the phenomenon of distance learning student attrition. Traditionally a campus-based institution, it was one the first Brazilian universities to be accredited by the Ministry of Education to offer distance undergraduate programs.

Aiming to understand the variables related to student persistence, defined by Rovai (2003) as a phenomenon opposite to attrition, the researchers chose to focus on factors that lead students to graduate despite the difficulties encountered. In 2013 we initiated a research project called “Distance undergraduate programs at UFPA: reasons for student persistence” as a way to give positive feedback on course quality to the academic community and to the federal system that financially supports them.

The preliminary results of the research with graduates from the courses of Literature and Mathematics showed that the most important factor for persisting until graduation was the student’s personal commitment. Other factors that demand improvement were also mentioned, such as: libraries with insufficient collections to meet course requirements, problems with the internet connection, difficulty understanding complex contents, and lack of personal study discipline, among others.

Preliminary data analyses prompted the researchers to dig deeper into the study to identify which variables will mostly help student retention in online academic programs, as well as to measure their degree of influence based on their probability of occurrence. A literature review in search of a methodological procedure to support this investigation led to the choice of the Bayesian Networks (BNs) approach. This is a probabilistic method to represent knowledge through the correlations of different variables in a common domain. The degree of influence exerted by each variable on the others is measured and propositions are expressed in a graphical form, which is easy to grasp.

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