Emotional Modelling to Enhance Learning with Games (AMELIA)
This research project (ID#: 101105874) deals with measuring and modeling emotions as nonlinear dynamical systems that manifest before, during, and after learners’ interact with specific game mechanics within game-based learning environments called Antidote COVID-19 and MediaWatch.
To collect emotions, a mixed-multimodal methods approach is utilized, capturing a range of data channels, including video recordings of facial expressions of emotions and posture, audio recordings of emote- and think-alouds, and computer-screen recordings of learner’s interactions with game-based mechanics, neuro-imaging data (NIRS and EEG), eye tracking, electrodermal signals, and heart rate during game-based learning (GBL).
Two experiments will take place in this project. One at Tampere University (TAU) in Finland and the other at the University of Graz in Austria.
- TAU: A sample of 70 participants was collected using a quasiexperimental with a pre/post design. Participants played Antidote Covid-19 on an iPad and multiple measures were used to capture their cognitive and affective processes.
- GRAZ: To be conducted in Fall 2024; A sample of 60 participants will be collected using a 2x3 repeated measures with a pre/post design. Participants will play MediaWatch and we will assess relationships between two types of feedback (GBL scaffolds) and its relation to affective responses, cognition, and learning outcomes. Multiple measures will be utilized including Near-infrared spectroscopy (NIRS).
These data will be leveraged to study how multiple affective dimensions manifest during GBL, including expressive, affective, motivational, neurophysiological, and cognitive, to assess their relation to cognitive processes and learning outcomes. The implications of this work will guide how to design adaptive GBL mechanics that best support affective and cognitive facets of learning.
- Data management plan aligned with FAIR principles and the Academy of Finland Data Management guidelines can be found here
- Project visibility here
- Data and Scripts will be Forthcoming.
