Abstract:

Advancements in technology and data analysis enable more sophisticated use of learning analytics (LA) in education to assess Learning Management Systems (LMS) effectiveness and students' progress. Widespread technology adoption has changed study habits, emphasizing self-regulation learning (SRL) and including progress monitoring.

One continuously growing issue in higher education, especially in the field of engineering, is high dropout rates. Previous studies (Macfadyen, Dawson, Pardo & Gašević, 2014) have shown that academic performance is associated with better SRL skills.

Learning analytics combines education, data analysis, and technology to improve educational practices by collecting and interpreting learners' data. For learners, it enables personalized learning experiences tailored to their individual needs and preferences, facilitating SRL, identifying areas of improvement, and enhancing academic performance. Educators gain real-time visibility, identify at-risk students, and provide timely support using LA.

This research explores the relationship between learning analytics, SRL, and academic performance. The broader objective of this article is to identify opportunities to enhance students' SRL skills and reduce dropout rates using LA tools. To achieve this goal, the following research objectives were set: to map whether and how students monitor their progress and how it differs across courses; to assess whether the use of LA tools is associated with better self-regulation and academic progress; to determine which aspects of self-regulation examined are most closely related to predicting academic progress; and to describe the expectations of different target groups regarding LA.

The study took place at TTK University of Applied Sciences, Estonia's largest applied higher education institution. TTK has 2,704 students in the 2022/2023 academic year. Reducing dropout rates is a strategic goal at the school, using LA tools to enhance academic performance and motivation.
In this study, four anonymous and self-reported questionnaires were administered (N=233), with items measuring different aspects of SRL: goal focus, time management, psychological flexibility and procrastination. The study examined the relationship between the questionnaires' results, academic performance, and progress monitoring using different LA tools.

Findings showed a moderate inverse relationship between academic procrastination and goal focus, time management, progress monitoring, and academic performance. Regular progress monitoring correlated with lower procrastination levels.
Regarding Moodle activities for monitoring studies, participants reported using various actions. The most commonly used were the Grader report (26.38%), curriculum implementation monitoring (23.54%), calendar (20.85%), average grade viewing (17.69%), completion progress (10.58%), and the Learning Analytics Dashboard (0.95%).

Keywords:

Learning analytics, learning management system, self-regulation skills, academic performance.