Contributions to Learning Analytics Focused on Assessment and Self-Regulated Learning

Liz-Domínguez, Martín

doi:10.1109/siie53363.2021.9583644

Cited by 1 publication

(1 citation statement)

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“…In recent academic years, the overall success rate in this subject ranged between 30% and 40% [44], and thus, students that retake the subject make up a significant fraction of each year's enrollments. Nevertheless, and for the sake of analyzing a subset of students with similar backgrounds and characteristics, we will only be considering students who were enrolled in the subject for the first time during the 2020/2021 academic year.…”

Section: Experiments Description a Educational Contextmentioning

confidence: 99%

Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

et al. 2022

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The ability to regulate one's own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students' efficacy when studying and academic performance, for better or worse. However, neither students or instructors generally have proper understanding of what selfregulated learning is, the impact that it has or how to assess it. This paper has the purpose of showing how learning analytics can be used in order to generate simple metrics related to several areas of students' selfregulation, in the context of a first-year university course. These metrics are based on data obtained from a learning management system, complemented by more specific assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation profiles are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.

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Section: Experiments Description a Educational Contextmentioning

confidence: 99%