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Comunicació presentada a: EC-TEL 2019 celebrat a Delft, Països Baixos, del 16 al 19 de setembre de 2019. Certain affective states are less conducive to learning than others. Moreover, results from studies suggest that a classroom’s social-emotional climate affects student motivation and performance; and that moods can be automatically transferred among individuals in a group. The ClassMood App is an online classroom orchestration tool for social emotional learning that identifies the aggregate mood of a class and suggests classroom activities for educators to help shift the class mood to one that is more conducive to learning. Suggested activities are categorized based on how they aim to impact students’ internal state of arousal. The application aims to facilitate learner and educator development of self-awareness and self-management competencies consistent with the CASEL framework for systemic social and emotional learning. Preliminary results, conducted as part of an iterative designed-based research process, suggest that the tool is perceived as being easy-to-use for both educators and undergraduate students. This work has been co-funded by the European Union, Project Number 2018-1-ES01-KA201-050646 (Spotlighters) under the Erasmus + programme; and partially supported by FEDER, the National Research Agency of the Spanish Ministry of Science MDM-2015-0502, TIN2014-53199-C3-3-R, TIN2017-85179-C3-3-R. DHL is a Serra Húnter Fellow. Note: The material in this paper reflects only the authors’ views and the European Commission cannot be held responsible for any use that may be made of the information it contains. The authors want to thank Pablo Abenia for the technical implementation of the application, and Minna Huotilainen for her advice regarding the student graphical interface.

Comunicació presentada a: EC-TEL 2019 celebrat a Delft, Països Baixos, del 16 al 19 de setembre de 2019. In this paper, we describe an exploratory study where we investigate the possibilities of motion capture system as an instrument to consider in multi-modal analyses of face-to-face collaborative learning scenarios. The goal is to understand to what extent motion capture can facilitate certain measurements leading to collaborative learning indicators that are currently time-consuming to achieve with other instruments. We focus on the simultaneous measurement of known physical collaboration indicators such as gaze direction, the distance be-tween learners and the speed of movement/reactions. The study considers a lab setting simulating a classroom scenario based on the Jigsaw collaborative learn-ing flow pattern, which proposes a sequence of activities with changes in group size and formation. Preliminary results indicate a high degree of applicability of the system in measuring these indicators, with certain limitations for gaze direc-tion measurements. With appropriate marker position on the participants, the sys-tem is able to automatically provide desired measurements with satisfactory pre-cision. Additionally, with a small number of additional markers, we were able to determine the way students used working surfaces (shared desks). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673. Milica Vujovic has received financial support through the “la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence, “la Caixa” Banking Foundation, Barcelona, Spain. This work has been partially supported by the National Research Agency of the Spanish Ministry of Science, Innovations and Universities MDM-2015-0502, TIN2014- 53199-C3-3-R, TIN2017-85179-C3-3-R.