Abstract
The article proposes an analysis of the readiness level of universities for using the predictions available through systems of learning analytics, in order to ensure the academic success of students, especially for those at risk of dropout.The proposed approach aims to highlight the need for connections to be established between the determinant factors of the success of the studies, as it turns out from the categories of already collected data through Learning Management Systems, versus information derived from recent studies conducted by using the students' perspective. They show fewer links with measures based on meritocracy or employment prospects in the labour market, than with qualitative indicators connected with the development of the skills that students consider relevant, in particular, the orientation towards embodied and emotional success, the formation of critical and reflective thinking that conditions the satisfaction of completing the studies seen as a "continuous challenge", especially to make authentic choices for the professional future. Based on an improved inventory of indicators, including qualitative ones, which also value students’ opinions, data collection through early warning systems can be significantly improved so that intervention strategies can be configured to support students in situations "of risk".Beginning with this premise, we conclude by highlighting some challenges for the management of human resources necessary for the implementation of the predictions of learning analytics in universities, in order to increase the retention rate of students, devoting special attention to stimulating teachers’ metacognitions in order to identify the areas of intervention at the level of the didactic activities.
Highlights
IntroductionThe article focuses on the orientation towards the involvement of the teachers and students in order to determine the academic success, based on an approach built "from bottom up", using "small data" benchmarks, derived from the analysis of processual perspective of learning and the learning analytics built within the ―big data‖ systems available in the university environment
In line with the intention to determine a holistic construction of a "bottom up" approach, we propose to analyse two necessary elements: the need to consider qualitative variables within the "learning analytics", by referring to the life cycle of the studies and the degree of preparation of the effective implementation of an approach based on "small data", through roles assigned to the teachers from the universities, in order to retain students and ensure their academic success
Some reflections with applicability at the university level or what does preliminary readiness mean for the use of learning analytics to determine the students studies success?
Summary
The article focuses on the orientation towards the involvement of the teachers and students in order to determine the academic success, based on an approach built "from bottom up", using "small data" benchmarks, derived from the analysis of processual perspective of learning and the learning analytics built within the ―big data‖ systems available in the university environment. In line with the intention to determine a holistic construction of a "bottom up" approach, we propose to analyse two necessary elements: the need to consider qualitative variables within the "learning analytics", by referring to the life cycle of the studies and the degree of preparation of the effective implementation of an approach based on "small data", through roles assigned to the teachers from the universities, in order to retain students and ensure their academic success. The interest of the universities towards ensuring the students‘ academic success is easy to understand in the context in which the investment costs with the initial training of students can be seriously affected, by an alarming increase of the phenomenon of early leaving of the
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