DESIGN AND IMPLEMENTATION OF LEARNING ANALYTICS IN HIGHER EDUCATION. A PILOT CASE STUDY

Learning analytics, an emerging field in educational science, leverages data-driven insights to significantly enhance the teaching and learning experience in the context of higher education. It acts as a transformative catalyst, reshaping how educators and institutions perceive and enrich students' learning journeys. By harnessing data from diverse sources, including learning management systems, student interactions, and assessments, learning analytics provides profound insights into student performance, engagement, and behavior. These, in turn, help educators make well-informed decisions to optimize teaching methods, personalize instruction, and ultimately improve overall student achievement. This pilot case study describes the deployment and impact of learning analytics tools at a higher education institution in a master's degree program primarily focused on improving teaching practices. Its purpose is to highlight the benefits and challenges for both instructors and students associated with integrating learning analytics in higher education. The key question addressed by this study is: how does learning analytics influence student success, teaching methodologies, and institutional effectiveness in higher education, and how can its implementation be fine-tuned to unlock its full potential? The assessment encompasses different aspects, including course design, student engagement levels, and instructor-student interactions, to measure the tangible effects of using learning analytics. A section of the paper will describe the technical infrastructure and processes used to create the dataset from which the metrics were extracted. The findings from this case study demonstrate the capacity of learning analytics to provide valuable insights into student performance, engagement, and learning behaviors. Professors involved in this pilot case study observed a significant improvement in their teaching practices through the early identification of low-performing or at-risk students and the subsequent timely adoption of appropriate educational measures to meet their needs. We believe that this work will help strengthen our university’s existing strategy to further promote the use of learning analytics in optimizing resource allocation, designing curricula, identifying at-risk students, improving the quality of education, and providing students with tools to enhance their educational journey and performance. The overarching goal is the establishment of a data architecture capable of generating role- and context-based dashboards for our key stakeholders, including students, faculty, and management. Our broader goal is to foster a data culture and promote data-driven decision-making within our institution.