A total of 29 features were defined and implemented to be automatically extracted and analysed in the context of NeuroK, a learning platform within the neurodidactics paradigm. The objective is to analyse if the defined features are able to predict students’ performance under different machine learning approaches. All details from the implementation and the experiments can be found in the open access paper, that is requested for citation:Pérez-Sánchez, C.J., Calle-Alonso, F. & Vega-Rodríguez, M.A. Learning analytics to predict students’ performance: A case study of a neurodidactics-based collaborative learning platform. Education and Information Technologies (2022). https://doi.org/10.1007/s10639-022-11128-yThe features are: -ID. Subject identification.-Evaluation. Results of the evaluation (Passed, Failed).-v1. Number of logins. -v2. Graded centrality. -v3. Closeness centrality. -v4. Betweenness centrality.-v5. Influence. -v6. Communications. -v7. Contacts. -v8. Concordance. -v9. Number of words used. -v10. Participation score.-v11. Average rating. -v12. Learning activities rating.-v13. Comments sent. - v14. Comments received. -v15. Favourites sent.-v16. Favourites received. -v17. Mentions sent.-v18. Mentions received. -v19. Ratings sent. -v20. Ratings received. -v21. Documents. -v22. Videos. -v23. References.-v24. Posts. -v25. Submissions. -v26. Published content.-v27. Discussions. -v28. Learning activities completed.-v29. Peer reviews performed.

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