Learning analytics in higher education: Assessing learning outcomes Conference Paper uri icon


  • Students’ retention and dropout from degree courses before their completion continue to represent issues which challenge researchers, teachers and higher education institutions to seek solutions. Higher education institutions have been attempting different strategies to promote their students’ success, among which we highlight the implementation and use of virtual learning environments (VLEs). Through the use of learning analytics in the analysis of data regarding the frequency of access to a VLE as well as the learning outcomes of a sample of 2,636 undergraduates, we aimed to identify indicators which contribute to the possible prediction of student retention and dropout situations. The methodology followed is essentially quantitative and the desk review was the main data collection tool. The data was treated by using the statistic software SPSS for the organisation and descriptive statistic presentation, as well as inferential statistics through appropriate statistic tests. The outcomes of this study resulted in contributions which enable the prediction of undergraduates’ retention or dropout since we were able to prove that the lower the frequency of access to the institution’s virtual learning environment, the lower the attendance to on-site lessons and the lower the number of course units in which students obtain a passing mark. Absenteeism and the lack of course units passed represent indicators of school dropout and retention. This research work reveals to be of great importance since, through a framework associated with learning analytics, it provides indicators supported by a large amount of recent and validated data, which enables us to rethink the connection between undergraduates’ access to a virtual learning environment and its influence on educational attainment.

publication date

  • January 1, 2017