This study aims to identify the conceptual structure and the thematic progress in Learning Analytics (evolution) and to elaborate on backbone/emerging topics in the field (maturity) from 2011 to September 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the Learning Analytics community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 459 papers from the proceedings of the Learning Analytics and Knowledge (LAK) conference and 168 articles published in the Journal of Learning Analytics (JLA), and the respective 3092 author-assigned keywords and 4051 machineextracted key-phrases, were included in the analyses. The results indicate that the community has significantly focused in areas like Massive Open Online Courses and visualizations; Learning Management Systems, assessment and self-regulated learning are also basic topics, yet topics like natural language processing and orchestration are emerging. The analysis highlights the shift of the research interest throughout the past decade, and the rise of new topics, comprising evidence that the field is expanding. Limitations of the approach and future work plans conclude the paper.