The attainment of White and ethnic minority students in distance education

Abstract

At UK institutions of higher education, the academic attainment of White students tends to be higher than that of students from other ethnic groups. The present study confirmed this trend in students who were taking courses by distance learning with the UK's Open University. It was apparent in students of all ages apart from the youngest, and it was largely independent of their gender, academic background and socioeconomic circumstances, or the particular course being taken. Students' interactions with teachers and other students are attenuated in distance education, and so the results suggest that the explanation for the attainment gap in ethnic minority students must be sought elsewhere than in the nature of their relationships with teachers and other students.

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

“…However, more predictors do not imply better prediction: mathematics performance is much better predicted than statistics performance, with overall performance in an intermediate position, due to lack of coverage in so many high school programs. In other words, in line with previous research (Author A, 2013a;Marks et al, 2005;Richardson, 2012) prior education seems to be a useful factor to include in learning analytics modelling.…”

“…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.…”

“…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). For example, in one of the first learning analytics studies focused on 118 biology students, Macfadyen and Dawson (2010) found that some (# of discussion messages posted, # assessments finished, # mail messages sent) LMS variables but not all (e.g., time spent in the LMS) were useful predictors of student retention and academic performance.…”

The effect of work placements on the academic performance of Chinese students in UK higher education

“…Similar to UK students, international students are not a homogeneous group in terms of country of origin, ability, attitude, level of prior qualification and so forth (Morrison et al 2005). Research on UK students shows that a number of individual factors such as age, gender, ethnicity, prior academic achievement, discipline of study and mode of study influence academic performance to various degrees (Smith and Naylor 2001, Richardson and Woodley 2003, Richardson 2008, Richardson 2012, Cassidy 2012and Sheard 2009). …”