In search of higher persistence rates in distance education online programs

By Alfred P. Rovai. Internet and Higher Education, 2003

744 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e64de08b9d2f271500fa165a69bb9d7d50b69db4

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Citations in the corpus

Four key challenges to the design of blended learning: A systematic literature review (2017, background): “can facilitate the formation of informal study groups, and help students to become part of the social life of the school (Rovai, 2003; Workman & Stenard, 1996).”
The making of an exemplary online educator (2011, background): “Rovai ( 2003 ) cited that a heightened sense of self‐esteem as well as the need for meaningful interpersonal relationships with their instructors are critical for student’s academic and personal well‐being.”
The instructor’s face in video instruction: Evidence from two large-scale field studies. (2015, background): “Identifying factors associated with attrition has been the focus of a substantial amount of research (e.g., Packham, Jones, Miller, & Thomas, 2004; Rovai, 2003; Willging & Johnson, 2004).”
Investigating factors that influence social presence and learning outcomes in distance higher education (2011, background): “The retention rate of overall online courses is still low compared to the one of face-to-face instruction ( [Doherty, 2006] and [Simpson, 2004] ), which directly indicates ‘academic non-success’ (Diaz, 2002) and ineffectiveness of school programs (Rovai, 2003).”
Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning (2009, background): “Rovai (2003) proposed a persistence model to explain factors affecting a learner's decision to drop out of online learning.”
AN EXPLORATION OF THE RELATIONSHIP BETWEEN INDICATORS OF THE COMMUNITY OF INQUIRY FRAMEWORK AND RETENTION IN ONLINE PROGRAMS (2019, background): “68 Journal of Asynchronous Learning Networks, Volume 13: Issue 3 [3, 4, 5, 6].”
Utilising learning analytics to support study success in higher education: a systematic review (2020, background): “Moreover, there are several theoretical perspectives and models of student success in higher education (Bean and Metzner 1985; Rovai 2003; Tinto 1982), and many share common factors, even though their emphasis varies.”
E‐learning and retention: key factors influencing student withdrawal (2004, background): “suggest students over the age of 24 whilst Rovai (2003)”
Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom (2017, background): “When the literature is reviewed, it is seen that the e-learning readiness comprises of computer such elements as self-efficacy, internet self-efficacy, online communication self-efficacy, self-directed learning, learner control and motivation towards e-learning (Demir, 2015; Hao, 2016a; Hung et al.,”
Comprehensive Assessment of Student Retention in Online Learning Environments (2011, background): “…of retention remains problematic for online programs, with several studies and anecdotal evidence indicating attrition rates for online courses frequently being much higher than for their campus-based counterpart (Bos & Shami, 2006; Diaz & Cartnal, 2006; Rovai, 2003; Willging & Johnson, 2004).”
Factors Influencing a Learner's Decision to Drop-Out or Persist in Higher Education Distance Learning (2010, background): “Persistence, defined as continuation of one’s studies in spite of obstacles, is often considered a measure of program effectiveness by higher education institutions (Rovai, 2002).”
Retention in Online Courses (2016, background): “As a result, such students felt incompatible with the institution’s social system and were more likely to drop out (Evans, 2009; Rovai, 2003; Summers, 2003; Tinto, 2006-2007).”
Blended learning effectiveness: the relationship between student characteristics, design features and outcomes (2017, background): “Rovai, (2003) noted that learners’ computer literacy and time management are crucial in distance learning contexts and concluded that such factors are meaningful in online classes. This is supported by Selim (2007) that learners need to posses time management skills and computer skills necessary for”
Dropout Prediction in MOOCs using Learner Activity Features (2014, background): “The amount of time a student can allocate the course depends on what other activities the student is involved with in her life (Rovai (2003), Tinto (2006)).”