Abstract:

The incorporation of technologies has brought the possibility of applying data analysis to learning processes. In this sense, the information contained in these data can be used to provide feedback to students about their learning process. Learning Analytics (LA) according to The Society for Learning Analytics Research, are defined as "measurement, collection, analysis, and presentation of data on students, their contexts, and the interactions that are generated, to understand the learning process that is taking place and optimize the environments in which it occurs" (Siemens & Gasevic, 2012, p.1). Usually, LA is showed by reporting systems that are known as Learning Analytics Dashboards (LADs).

Winne (2017) upholds that LA could improve students’ self-regulation processes by proving information that they found significative. However, previous experiences have been aimed, mostly, at institutional decisions or at providing information to teachers on which activities to maintain and which to review or eliminate, or what changes to make in the curricular design.

In this context, there is still scarce evidence of how they can be aimed at students to support the process of self-regulation of learning (Bodily & Vebert, 2017). The current limitation is that there are very few studies that analyse how LA can be used properly to develop interventions to promote feedback at the process level (Pardo et al.,2017) and to provide quality feedback (Matcha et al., 2020). In this vein, Sedrakyan et al. (2020) uphold that LADs could contribute to improve learning processes; but for that it is fundamental to consider the mechanisms underlaying the learning regulation.

With all this, it is necessary to analyse which analytics are useful to support teachers and students (Knight & Shum, 2017) and to support the self-regulated learning process. As Rets et al. (2021) conclude, to improve the usefulness of LADs, accessible information should be provided. In this sense, it is essential to investigate which indicators are relevant for LA to be a source of information so that students can reflect on and analyse their own learning process, developing their autonomy and their active role.

As part of a research and innovation project “Analysis of the effects of feedback supported by digital monitoring technologies on generic competencies” (e-FeedSkill), we developed a learner dashboard prototype with the logs that we collected from a specific didactical sequence settled in Moodle. This LAD was implemented in several courses from different faculties (Biology, Economy & Business, Education, Geography and History, Pharmacy & Food Sciences, Mathematics and Computer Science, Law and, Medicine & Health Sciences).

In this paper, we will present the results and findings from the analysis of the students’ responses to an ad-hoc questionnaire for measuring the satisfaction and learning perception of students after using the learning analytics dashboard (LAD). IBM SPSS Statistics, version 23 was used to perform data analysis. Quantitative data analysis consisted of descriptive, correlational and reliability statistics. Our preliminary results indicate a positive relation between the perception of how helpful the information provided to learn is and the overall satisfaction with the digital tool. The findings could be relevant for the design, development and implementation of tools based in learning analytics.

Keywords:

Higher Education, Learning Analytics, dashboard, self-regulated learning.