From understanding learners’ adaptive motivation and emotion to designing social learning companions
Developing an approach integrating game- and agent-based Intelligent Tutoring Systems (ITS) and computational models to help optimize social learning situations
©SCIoI
In this threefold project, we will firstly examine how novel user modelling approaches and feedback strategies in Intelligent Tutoring Systems (ITS) incorporating virtual agents can enhance positive emotions and motivation (self-regulation, goal orientations) and reduce negative emotions in social learning situations and how these approaches can be used to prevent inequalities in education. We will also be exploring the (moderating and mediating) processes that underlie the relations between pedagogical agents’ ‘behaviors’ and learners’ performance by investigating psychological factors that strengthen or reduce the effects of ITS on learners’ motivation and emotions. As a third and final objective, we intend to create a robotic learning companion that keeps an updated model/simulation of the learner and their current knowledge, motivational, and emotional state, acting accordingly.
Yun, H. S., Taliaronak, V., Kirtay, M., Chevelère, J., Hübert, H., Hafner, V. V., Pinkwart, N., & Lazarides, R. (2022). Challenges in Designing Teacher Robots with Motivation Based Gestures. 17th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022). https://arxiv.org/abs/2302.03942
Yun, H. S., Hübert, H., Taliaronak, V., Mayet, R., Kirtay, M., Hafner, V. V., & Pinkwart, N. (2022). AI-based Open-Source Gesture Retargeting to a Humanoid Teaching Robot. AIED 2022: The 23rd International Conference on Artificial Intelligence in Education. https://link.springer.com/chapter/10.1007/978-3-031-11647-6_51
Yun, H. S., Hübert, H., Sardogan, A., Pinkwart, N., Hafner, V., & Lazarides, R. (2023). Humanoid Robot as a Debate Partner. 25th International Conference on Human-Computer Interaction. https://doi.org/10.1007/978-3-031-36004-6_74
Yun, H. S., Hübert, H., Taliarona, V., & Sardogan, A. (2022). Utilizing Machine Learning based Gesture Recognition Software, Mediapipe, in the Context of Education and Health. AI Innovation Summit 2022.
Yun, H. S., Hübert, H., Chevalere, J., Pinkwart, N., Hafner, V., & Lazarides, R. (2023). Analyzing Learners’ Emotion from an HRI experiment using Facial Expression Recognition Systems. 25th International Conference on Human-Computer Interaction. https://doi.org/10.1007/978-3-031-34550-0_29
Yun, H. S., Fortenbacher, A., Geißler, S., & Heumos, T. (2020). Towards External Regulation of Emotions Using Sensors: Tow Case Studies. INTED2020, 9313–9320. https://doi.org/10.21125/inted.2020.2576
Yun, H. S., Chevalère, J., Karl, M., & Pinkwart, N. (2021). A comparative study on how social robots support learners’ motivation and learning. 14th Annual International Conference of Education, Research and Innovation, 2845–2850. https://doi.org/10.21125/iceri.2021.0708
Yun, H. S., Karl, M., & Fortenbacher, A. (2020). Designing an interactive second language learning scenario: a case study of Cozmo. Proceedings of HCI Korea, 384–387.
Yun, H. S., & Fortenbacher, A. (2019). Listen to your body: making learners aware of their cognitive and affective state. ICER2019. https://docs.google.com/document/d/1D3XlYPBKg-Z7UoR1SunXZrAJy7oojV9S8N_OgjQQ5ck/edit
Spatola, N., Chevalère, J., & Lazarides, R. (2021). Human vs computer: What effect does the source of information have on cognitive performance and achievement goal orientation? Paladyn, Journal of Behavioral Robotics, 12(1), 175–186. https://doi.org/10.1515/pjbr-2021-0012
Spatola, N., & Wudarczyk, O. (2021). Ascribing emotions to robots: Explicit and implicit attribution of emotions and perceived robot anthropomorphism. Computers in Human Behavior, 124, 106934. https://doi.org/10.1016/j.chb.2021.106934
Lazarides, R., & Raufelder, D. (2021). Control-value theory in the context of teaching: does teaching quality moderate relations between academic self-concept and achievement emotions? British Journal of Educational Psychology, 91(1), 127–147. https://doi.org/10.1111/bjep.12352
Lazarides, R., & Chevalère, J. (2021). Artificial intelligence and education: Addressing the variability in learners’ emotion and motivation with adaptive teaching assistants. Bildung Und Erziehung, 74(3), 264–279. https://doi.org/10.13109/buer.2021.74.3.264
Kirtay, M., Chevalère, J., Lazarides, R., & Hafner, V. V. (2021). Learning in Social Interaction: Perspectives from Psychology and Robotics. 2021 IEEE International Conference on Development and Learning (ICDL), 1–8. https://doi.org/10.1109/ICDL49984.2021.9515648
Hübert, H., & Yun, H. S. (2024). Sobotify: A Framework for Turning Robots into Social Robots. Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA.
Chevalère, J., Lazarides, R., Yun, H. S., Henke, A., Lazarides, C., Pinkwart, N., & Hafner, V. (2023). Do instructional strategies considering activity emotions reduce students’ boredom in a computerized open-ended learning environment? Computers & Education, 196. https://doi.org/10.1016/j.compedu.2023.104741
Chevalère, J., Kirtay, M., Hafner, V., & Lazarides, R. (2022). Who to Observe and Imitate in Humans and Robots: The Importance of Motivational Factors. International Journal of Social Robotics. https://doi.org/10.1007/s12369-022-00923-9
