The Readiness of Universities to Use Predictive Learning Analytics for Students’ Retention and Academic Success

Dorina Dumitra Zlota

Logos Universalitate Mentalitate Educatie Noutate - Sectiunea Stiinte Sociale/ Logos Universality Mentality Education Novelty - Section: Social Sciences, 2020, vol. 9, issue 1, 155-171

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.

Keywords: learning analytics; students retention; processual perspective of learning; qualitative variables; learning predictors; “big data”/”small data”; academic success; holistic approach (search for similar items in EconPapers)
JEL-codes: A23 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:lum:rev17s:v:9:y:2020:i:1:p:155-171

DOI: 10.18662/lumenss/9.1/40

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