Trackability of learning and teaching activities
Search for features: studies that identify and explain data features that are useful for analyzing, understanding, and optimizing teaching and learning.
Learning metrics: studies that evaluate the progress of learning through the measurement and analysis of student actions or artifacts.
Data storage and exchange: proposals of technical and methodological procedures for storing, sharing, and preserving the traces of learning.
Understanding learning and teaching
Data-driven learning theories: proposals of new theories of learning/teaching or revisions/reinterpretations of existing theories based on data analysis at a grand scale.
Information on particular learning processes: studies seeking to understand particular aspects of a learning/teaching process through the use of data science techniques.
Modeling: creation of mathematical, statistical, or computational models of a learning/teaching process, including its actors and context.
Improving Learning and Teaching
Feedback and decision-making support systems: studies that evaluate the impact of systems of feedback or decision-making support, based on the analysis of learning (dashboards, early warning systems, automated messages, etc.).
Empirical evaluation of learning analytics: empirical evidence of the effectiveness of learning analytics implementations or educational initiatives guided by the analysis of learning.
Personalized and adaptable learning: studies that evaluate the impact and effectiveness of (semi) automated adaptive technologies based on the analysis of learning.
Adoption and Sustainability of Learning Analytics
Values, ethics, and law: analysis of problems and approaches for the capture and legal and ethical use of educational data; addressing involuntary bias and value judgements in the selection of data and algorithms; perspectives and methods for a participatory design that is sensitive to the value that empowers interested parties.
Adoption: discussion and evaluation of strategies for promoting and incorporating initiatives for the analysis of learning in educational institutions and learning organizations.
Scalability: discussion and evaluation of strategies to scale the capture and analysis of information at the program, institution, or national level; critical reflection on the organizational structures that promote analytical innovation and the impact of an institution.
Other topics that authors consider relevant to the discussion of learning analytics for education in the region can also be included. All articles submitted will be reviewed and weighed based on their relevance, originality, coherence, and clarity by at least two reviewers from the same area. The program will only include works with at least one author signed up for the conference before the submission deadline for the final version. Accepted and presented articles will be indexed and published in CEUR under ISBN.