Why Learning and Teaching Learning Analytics is Hard: An Experience from a Real-Life LA Course Using LA Methods

Abstract

Learning analytics emerged more than a decade ago to harness the power of data to understand and optimize learning, learners’ behavior, and learning environments. Ever since, the field has grown to encompass a diverse range of methods, research strands and traditions. Recent literature reviews tell us that most common applications of learning analytics include predictive analytics, social network analysis, sequence and process analysis, visualizations, and dashboards to mention a few. In the same vein, the research field has attracted several interdisciplinary researchers and practitioners from computer science, education, data science, engineering, administration, and from the education technology industry. Whereas such diverse backgrounds and perspectives bring a wealth of different perspectives to the field, it makes teaching and learning analytics hard to narrow down in a single course. This study reports on the analysis of students’ approach to learning learning analytics, reflects on the insights that learning analytics offers, and makes recommendations for future researchers who are teaching or investigating similar courses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.