From understanding learners’ adaptive motivation and emotion to designing social learning companions

Photo by stem.T4L on Unsplash

Principal Investigators:

Rebecca Lazarides
Verena Hafner
Niels Pinkwart

Team members:

Johann Chevalère (Postdoctoral researcher)
Haeseon Yun (Doctoral researcher)
Heiko Hübert (External collaborator)

Developing an approach integrating game- and agent-based Intelligent Tutoring Systems (ITS) and computational models to help optimize social learning situations

Research Unit 1, SCIoI Project 06

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.


Related Publications

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., Fortenbacher, A., Geißler, S., & Heumos, T. (2020). Towards External Regulation of Emotions Using Sensors: Tow Case Studies. INTED2020, 9313–9320.
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.
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., 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).
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.
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.
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.
Yun, H. S., & Fortenbacher, A. (2019). Listen to your body: making learners aware of their cognitive and affective state. ICER2019.
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.
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.
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.
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.
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.
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., 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.
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.