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

Abstract

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.

Factors Contributing to Student Retention in Online Learning and Recommended Strategies for Improvement: A Systematic Literature Review

“…Unlike in a traditional learning environment, online environments did not exhibit obvious spoken and visual cues of communication (Alman, Frey, & Tomer, 2012). As a result, online students felt isolated and unsupported by their peers (Aversa & MacCall, 2013;Hammond & Shoemaker, 2014;Pinchbeck & Heaney, 2017), mainly when there was an expectation for equivalent interaction like in the traditional learning environment (Eliasquevici, Seruffo, & Resque, 2017). Ironically, this negative feeling was also linked to a low sense of community and poor student integration (Aversa & MacCall, 2013), which posed a barrier in breaking the ice among students and consequently influenced their decision in continuing.…”

“…Facilitation of student engagement and promotion of a sense of belonging Absent verbal and visual cues (Alman, Frey, & Tomer, 2012) Isolated and unsupported students (Aversa & MacCall, 2013;Hammond & Shoemaker, 2014;Pinchbeck & Heaney, 2017) Expectation of equivalent engagement as in traditional environment (Eliasquevici, Seruffo, & Resque, 2017).…”

“…While women might be worse in online courses in STEM fields (Wladis et al, 2015), there also existed a likelihood of men withdrawing from online courses (Cochran et al, 2014). Therefore, Eliasquevici et al (2017) believed that genders were not necessarily associated with student retention in online learning environments.…”

“…Resources are aimed to assist students who pose signs of struggling (Shah & Cheng, 2018), such as freshmen (Wladis et al, 2015), those who have withdrawn from an online class (Cochran et al, 2014), and those enrolling in course(s) with high attrition rates (Wladis & Hachey, 2017;Wladis et al, 2014). Another early intervention is applicable through the development of entrance orientations covering topics in regard to college readiness, learning strategies, self-discipline, time management, self-efficacy, technological skills and expectation pertaining to online learning (Eliasquevici et al, 2017;Gaytan, 2015;Hachey et al, 2013;Ice et al, 2011;Wuellner, 2013). As aforementioned, these student characteristics were found to be positively linked to the retention rates.…”

“…As aforementioned, these student characteristics were found to be positively linked to the retention rates. RECOMMENDED STRATEGIES DETAILED STRATEGIES Early interventions targeting students Early assessment of students' prior knowledge and behavioral characteristics to inform decision making in deploying interventions and ongoing progress monitoring Nistor & Neubauer, 2010) Use of early alert system (Shaw et al, 2016) Mining and analyzing pre-college and early semester data (Raju & Schumacker, 2015;Colorado & Eberle, 2010) Establishing policies, procedures, practices, and resources for maximizing student success (Boston et al, 2011;Cochran et al, 2014;Hachey et al, 2013;Haydarov, Moxley, & Anderson, 2013;Xu & Jaggars, 2011b) Focusing on at-risk students (Shah & Cheng, 2018) such as freshmen (Wladis et al, 2015), those who previously withdrew from online courses (Cochran et al, 2014), and those enrolled in courses with high attrition rates (Wladis & Hachey, 2017;Wladis et al, 2014) Entrance orientations on online learning readiness, learning behaviors, and technological skills (Eliasquevici Technology support (Aversa & MacCall, 2013;Blau et al, 2016;Eliasquevici et al, 2017) Tutorial, counseling or advising services, and remedial programs (Boston et al, 2011;Eliasquevici et al, 2017;Gaytan, 2015;Heyman, 2010) Ongoing improvement of student services and policies and procedures (Moore & Greenland, 2017;Nichols, 2010) Support for faculty Professional development, training, and workshops to inform faculty practices associated with online learning theories, student engagement, students' needs, dynamic dialogue, high quality feedback, appropriate delivery methods and technology (Alman et al, 2012;Blau, Mittal, Schirmer, & Ozkan, 2017;Boston et al, 2011;Gaytan, 2015;…”