Analysis of Player Engagement with Eye Tracking in Game-Based Training

Human factor engineering (HFE) concentrates on designs that take human
characteristics into account, aiming at providing leisure and reaction, maximizing human capabilities, improving human’s quality of life and so on. In this field, game-based training is an essential method. Combining passive content with interactive games, users can stay engaged with certain level, which is significantly affected by game difficulty level. Hence, it is crucial to establish the relationship between game difficulty level and player engagement level. To this end, in this paper, an example hooking game is
developed based on Field Programmable Gate Array (FPGA) and simulated successfully on an online simulation platform, which makes it possible to
construct an offline embedded system for the purpose of investigating player engagement. Eye-tracking methodology was adopted as an objective way to track participants’ reaction, which is hard to capture by typical observation method. User performance and eye-tracking heatmaps of the user interface are analyzed, which reveals that game difficulty is a key factor of player engagement level.

Using GazePointer application as the eye-tracker, this research performed real time tracking of users’ eye gaze with webcam. Three example eye-tracking heatmaps are generated from the recording of real-time eye gaze. Red arrows indicate the fish moving trajectories before successfully fishing. Orange shadows show the users’ eye gaze. Blue rectangles mark the current fish distribution. Eye-tracking data is an intuitive representation of engagement level. Data obtained from eye-tracking approach is also examined in two viewpoints: varying game level and varying fish number. Participants show more concentration when remaining fish is less than three, with gaze drifted with the targeted fish for a relatively long time. The engagement level keeps stale with slight increasing for the first four levels, however, when level
number exceeds 5, participants stop gazing at a single fish and the hooking process becomes guesswork. This situation results from the high moving speed of fish increases the randomness, and the predict accuracy descents dramatically.

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