First-person shooter (FPS) games are a genre in which players immerse themselves in a virtual wor... more First-person shooter (FPS) games are a genre in which players immerse themselves in a virtual world from a first-person perspective and compete against others. While both mouse and gamepad are commonly used as input devices in FPS games, the analog stick on gamepads provides velocity-based control, making precise adjustments more difficult compared to the position-based input of a mouse. To address this, gamepads often incorporate assist functions such as aim assist, but the degree of such assists is a subject of debate from the perspectives of user experience (UX) and difficulty balancing. This study compares three alternative input methods for gamepad viewpoint control-gamepad, touchpad, and trackball-with the aim of achieving intuitive operation without sacrificing portability. The three devices are evaluated in terms of usability scores and perceived ease of use. Based on the findings, we propose guidelines for improving the UX of gamepads. The results of this research are expected to provide a foundation for enhancing the UX of conventional gamepads and offering a more comfortable gaming experience to a wider range of players.
First-person shooter (FPS) games are a genre in which players immerse themselves in a virtual wor... more First-person shooter (FPS) games are a genre in which players immerse themselves in a virtual world from a first-person perspective and compete against others. While both mouse and gamepad are commonly used as input devices in FPS games, the analog stick on gamepads provides velocity-based control, making precise adjustments more difficult compared to the position-based input of a mouse. To address this, gamepads often incorporate assist functions such as aim assist, but the degree of such assists is a subject of debate from the perspectives of user experience (UX) and difficulty balancing. This study compares three alternative input methods for gamepad viewpoint control-gamepad, touchpad, and trackball-with the aim of achieving intuitive operation without sacrificing portability. The three devices are evaluated in terms of usability scores and perceived ease of use. Based on the findings, we propose guidelines for improving the UX of gamepads. The results of this research are expected to provide a foundation for enhancing the UX of conventional gamepads and offering a more comfortable gaming experience to a wider range of players. This paper is still a work in progress, but it is a study in the field of HCI (Human-Computer Interaction). I have been exploring ways to improve the FPS gaming experience with gamepads, and I received valuable advice during the writing of this paper.
The advice was that, when dividing participants into groups, it is important to avoid overly broad classifications and to define the groups appropriately according to the research objectives. Specifically, even among experienced FPS players, there are tendencies for preferred input methods to differ between those who use touchpads and those who use mice. Therefore, I was advised that more careful grouping is necessary. For example, mouse users tend to highly evaluate trackballs, which have similar pointer input methods, and through interviews, it may be possible to draw out more detailed information such as differences in aiming operations.
From this perspective, I thought it would be appropriate to divide participants into three groups: “beginners,” “experienced FPS players (gamepad users),” and “experienced FPS players (mouse users).” However, dividing into three groups presents the challenge of requiring a larger number of participants. Initially, I planned to conduct a pretest with 6 participants per group (for a total of 12), and if sufficient data could be obtained, to add another 6 participants per group, resulting in a total of 24 participants for the main experiment. However, if I divide into three groups, a total of 36 participants would be needed, which is realistically difficult due to budget and resource constraints. Also, simply reducing the number of participants per group would make it difficult to achieve statistical significance, so that is not an easy solution either.
Faced with these issues, I am currently struggling to optimize the research design. I would greatly appreciate any advice or suggestions you may have regarding this matter.
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This paper is still a work in progress, but it is a study in the field of HCI (Human-Computer Interaction).
I have been exploring ways to improve the FPS gaming experience with gamepads, and I received valuable advice during the writing of this paper.
The advice was that, when dividing participants into groups, it is important to avoid overly broad classifications and to define the groups appropriately according to the research objectives.
Specifically, even among experienced FPS players, there are tendencies for preferred input methods to differ between those who use touchpads and those who use mice. Therefore, I was advised that more careful grouping is necessary.
For example, mouse users tend to highly evaluate trackballs, which have similar pointer input methods, and through interviews, it may be possible to draw out more detailed information such as differences in aiming operations.
From this perspective, I thought it would be appropriate to divide participants into three groups: “beginners,” “experienced FPS players (gamepad users),” and “experienced FPS players (mouse users).”
However, dividing into three groups presents the challenge of requiring a larger number of participants.
Initially, I planned to conduct a pretest with 6 participants per group (for a total of 12), and if sufficient data could be obtained, to add another 6 participants per group, resulting in a total of 24 participants for the main experiment.
However, if I divide into three groups, a total of 36 participants would be needed, which is realistically difficult due to budget and resource constraints.
Also, simply reducing the number of participants per group would make it difficult to achieve statistical significance, so that is not an easy solution either.
Faced with these issues, I am currently struggling to optimize the research design.
I would greatly appreciate any advice or suggestions you may have regarding this matter.