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I-Mouse: A Framework for Player Assistance in Adaptive Serious Games

 
: Lalwani, Riya; Chouhan, Ashish; John, Varun; Sonar, Prashant; Mahajan, Aakash; Pendyala, Naresh; Streicher, Alexander; Prabhune, Ajinkya

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Roll, Ido (Ed.):
Artificial Intelligence in Education. 22nd International Conference, AIED 2021. Proceedings. Pt.II : Utrecht, The Netherlands, June 14-18, 2021
Cham: Springer Nature, 2021 (Lecture Notes in Artificial Intelligence 12749)
ISBN: 978-3-030-78269-6 (Print)
ISBN: 978-3-030-78270-2 (Online)
pp.234-238
International Conference on Artificial Intelligence in Education (AIED) <22, 2021, Online>
English
Conference Paper
Fraunhofer IOSB ()
serious games; adaptivity; eye and mouse tracking

Abstract
A serious game is an educational digital game created to entertain and achieve characterizing goal to promote learning. However, a serious game’s major challenge is capturing and sustaining player attention and motivation, thus restricting learning abilities. Adaptive frameworks in serious games (Adaptive serious games) tackle the challenge by automatically assisting players in balancing boredom and frustration. The current state-of-the-art in Adaptive serious games targets modeling a player’s cognitive states by considering eye-tracking characteristics like gaze, fixation, pupil diameter, or mouse tracking characteristics such as mouse positions. However, a combination of eye and mouse tracking characteristics has seldom been used. Hence, we present I-Mouse, a framework for predicting the need for player assistance in educational serious games through a combination of eye and mouse-tracking data. I-Mouse framework comprises four steps: (a) Feature generation for identifying cognitive states, (b) Partition clustering for player state modeling, (c) Data balancing of the clustered data, and (d) Classification to predict the need for assistance. We evaluate the framework using a real game data set to predict the need for assistance, and Random Forest is the best performing model with an accuracy of 99% amongst the trained classification models.

: http://publica.fraunhofer.de/documents/N-636241.html