Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

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

“…Although we added self-reported data from a range of learning disposition instruments to get a more fine-grained, nuanced understanding of the data, several studies indicate that students increasingly use informal networks (Agudo-Peregrina et al, 2014;Hommes et al, 2012) and learning tools (e.g., Facebook, twitter, texts) to share knowledge and learn together. For example, Hommes et al (2012) found that informal social learning links primarily predicted academic performance amongst 300 medical students.…”

“…In particular, the number of clicks in an LMS just before the next assessment significantly predicted continuation of studies (Wolff et al, 2013). Similarly, in a study of six online and two blended courses, Agudo-Peregrina et al (2014) found that interactions with assessment tools, followed by interactions with peers and teachers, and active participation significantly predicted academic performance in the six online courses. However, no clear paths of learning analytics data were found for the two blended courses.…”

“…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. However, according to Agudo-Peregrina et al (2014) there is no consensus in the learning analytics community on which user behaviour and interactions data are appropriate to measure, understand and model learning processes and academic performance.…”

“…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. However, according to Agudo-Peregrina et al (2014) there is no consensus in the learning analytics community on which user behaviour and interactions data are appropriate to measure, understand and model learning processes and academic performance. Clow (2013, p. 692) argues that "as a field, learning analytics is data-driven and is often atheoretical, or more precisely, is not explicit about its theoretical basis".…”

“…Many learning analytics applications use data generated from learner activities, such as the number of clicks (Siemens, 2013;Wolff, Zdrahal, Nikolov, & Pantucek, 2013), learner participation in discussion forums (Agudo-Peregrina, Iglesias-Pradas, Conde-González, & Hernández-García, 2014;Macfadyen & Dawson, 2010), or (continuous) computer-assisted formative assessments (Author A, , 2012bWolff et al, 2013). User behaviour data are frequently supplemented with background data retrieved from learning management systems (LMS) (Macfadyen & Dawson, 2010) and other student admission systems, such as accounts of prior education (Arbaugh, 2014;Richardson, 2012).…”