Delgado Kloos, CarlosDimitriadis, YannisHernández Leo, DaviniaAlario-Hoyos, CarlosMartínez-Monés, AlejandraSantos Rodríguez, PatríciaMuñoz-Merino, Pedro J.Asensio-Pérez, Juan I.Vicent Safont, Lluís2023-04-032023-04-032022Delgado Kloos C, Dimitriadis Y, Hernández-Leo D, Alario-Hoyos C, Martínez-Monés A, Santos P, Muñoz-Merino PJ, Asensio-Pérez JI, Vicent Safont L. H2O learn: hybrid and human-oriented learning: trustworthy and human-centered learning analytics (TaHCLA) for hybrid education. In: Jemni M, Kallel I, Akkari A, editors. Proceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON); 2022 Mar 28-31; Tunis, Tunisia. [Piscataway]: IEEE; 2022. p. 94-101. DOI: 10.1109/EDUCON52537.2022.97667702165-9559http://hdl.handle.net/10230/56400Comunicació presentada a 2022 IEEE Global Engineering Education Conference (EDUCON), celebrat del 28 al 31 de març de 2022 a Tunis, Tunísia.This paper presents the H2O Learn (Hybrid and Human-Oriented Learning) project, a coordinated research project funded by the Spanish Research Agency, which just started in 2021 and will last for three years. The main goal of the H2O Learn project is to build Trustworthy and Human-Centered Learning Analytics (TaHCLA) solutions to support human stakeholders when designing, orchestrating and (self-,socially- or co-) regulating learning in Hybrid Learning (HL). The contributions of H2O Learn consider key requirements for trustworthy Artificial Intelligence (AI), as defined by the European Commission: 1) fostering human (i.e., teachers, learners…) agency; 2) enabling transparency of the Learning Analytics (LA) systems; 3) seeking socio-emotional and inclusive wellbeing; and 4) promoting accountability by enabling the assessment of algorithms and design processes.application/pdfeng© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/EDUCON52537.2022.9766770info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/EDUCON52537.2022.9766770Learning AnalyticsArtificial IntelligenceTrustworthinessTransparencyWell-beingHuman AgencyAccountabilityinfo:eu-repo/semantics/openAccess