A Structural Equation Model of Predictors for Effective Online Learning

Abstract

In studying online learning, researchers should examine three critical interactions: instructor-student, student-student, and student-content. Studentcontent interaction may include a wide variety of pedagogical tools (e.g., streaming media, PowerPoint, and hyperlinking). Other factors that can affect the perceived quality of online learning include distance education advantages (e.g., work and family flexibility) and antecedent personal characteristics (e.g., experience and gender). The study indicated that instructor-student interaction is most important, twice that of student-student interaction; that some student-content interaction is significantly related to perceived learning; that antecedent variables are not significant; and that distance education advantages/flexibility, although significant, are less important than other interactions.

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

“…In line with academic retention or academic analytics literature (Marks et al, 2005;Richardson, 2012), several demographic factors are known to influence performance. A main advantage of this type of data is that institutions can relatively easily extract this information from student admission, and are therefore logical factors to include in learning analytics models.…”

“…A vast body of research on student retention (Credé & Niehorster, 2012;Marks, Sibley, & Arbaugh, 2005;Richardson, 2012) indicates that academic performance can be reasonably well predicted by a range of demographic, academic integration, social integration, psycho-emotional and social factors, although most predictive models can explain only up to 30% of variance. Recent studies in learning analytics (Agudo-Peregrina et al, 2014;Author A, 2013a;Macfadyen & Dawson, 2010;Wolff et al, 2013) seem to indicate that adding LMS user behaviour to these models can substantially improve the explained variance of academic performance.…”

“…While an increasing body of research is becoming available how students' usage and behaviour in LMS influences academic performance (e.g., Arbaugh, 2014;Macfadyen & Dawson, 2010;Marks et al, 2005;Wolff et al, 2013), how the use of e-tutorials or other formats of blended learning effects performance (e.g., Lajoie & Azevedo, 2006), and how feedback based on learning dispositions stimulates learning (Buckingham Shum and Deakin Crick (2012), to the best of our knowledge no study has looked at how all these factors can be combined into one research context, and what the relative contributions of LMSs, formative testing, e-tutorials, and applying dispositional learning analytics to student performance are. In our empirical contribution focusing on a large scale module in introductory mathematics and statistics followed by 922 students, we aim to provide a practical application of such an infrastructure based on combining longitudinal learning data from our LMS, the two e-tutorials, and (self-reported) learner data.…”

The Roles of Extrinsic Factors in a Community of Inquiry Model of E-Learning

“…Studies show conflicting results in the relationship between participant age and elearning experience. While some researchers find no significant relationship between age and elearning experience (Keller & Cernerud, 2002;Marks et al, 2005), others contend that older students tend to perform better via e-learning (Alstete & Beutell, 2004). However, rather than being a direct determinant of e-learning experience, we propose that participant age indirectly determines e-learning experience by moderating the relationship between social presence and elearning experience.…”

“…A fundamental difference between teacher-directed and student-centred learning is whether teachers play a dominant role in imparting knowledge to students in a top-down approach, or whether students bear the responsibilities and control over their own learning process, with teachers as facilitators (Ramsden, 2003). Marks et al (2005) argue that when instructors facilitated student learning by encouraging feedback and discussions, by debating case studies, and by addressing students by first names in emails, students perceived better learning and satisfaction with online courses. The authors contend that while teacher-student interactions may be the strongest determinant of learning and satisfaction, such interactions are only possible, or at least are more predominant, when instructors adopt a student-centred rather than a teacher-directed approach.…”