Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

Frustration is a common response during game interactions, typically decreasing a user's engagement and leading to game failure. Artificially intelligent methods capable to automatically detect a user's level of frustration at an early stage are hence of great interest for game designers, since this would enable optimisation of a player's experience in real-time. Nevertheless, research in this context is still in its infancy, mainly relying on the use of pre-trained models and fine-tuning tailored to a specific dataset. Furthermore, this lack in research is due to the limited data available and to the ambiguous labelling of frustration, which leads to outcomes which are not generalisable in the real-world. Meanwhile, contrastive loss has been considered instead of the traditional cross-entropy loss in a variety of machine learning applications, showing to be more robust for system stability alternative in self-supervised learning. Following this trend, we hypothesise that using a supervised contrastive loss might overcome the limitations of the cross-entropy loss yielded by the labels' ambiguity. In fact, our experiments demonstrate that using the supervised contrastive method as a loss function, results improve for the automatic recognition (binary frustration vs nofrustration) of game-induced frustration from speech with an Unweighted Average Recall increase from 86.4 % to 89.9 %.

show abstract

Supervised Contrastive Learning for Game-Play Frustration Detection from Speech

Song

Parada-Cabaleiro

Liu

et al. 2021

Universal Access in Human-Computer Interaction. Design Methods and User Experience

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

Cited by 1 publication

References 39 publications

Supervised Contrastive Learning for Game-Play Frustration Detection from Speech

Supervised Contrastive Learning for Game-Play Frustration Detection from Speech

Contact Info

Product

Resources

About