A case study of learning analytics within a statistics course for undergraduate students in economics

Higher education institutions globally face a continuous expansion of their enrolment in which learner success constitutes a major challenge. Therefore, there is growing interest in the analysis of data linked to student learning engagement. Indeed, large amounts of learning-related student data are currently not being fully exploited, while their aggregation and quantitative analysis would definitely be elements valuable to support teachers and students, to optimize students’ learning experience. In this global context, we have applied, in a public university without any academic filter for enrolment, such analysis to virtually tutor first-year undergraduate students in a statistics course. By supporting them in the form of voluntary online self-assessing tests, we examined what were the personal profiles of the students who were using available tests and how they exploited this help. Finally, using econometric models we tried to determine if there was a link between student success and the use of this help.