Review of Player Personality Classifications to Inform Game Design

Bourke, Philip; Murphy, David; O’Mullane, John; Marshall, Kevin; Howell, Stephen

doi:10.1109/gem.2018.8516513

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Through acquiring any opponent's risk propensity, the AI system may infer his/her following adopted actions. This customization of game mechanics based on players' personalities can improve users' gameplay experience, making games more enjoyable (Nagle et al, 2016;Bourke et al, 2018;Soares et al, 2018).…”

Section: The Possible Implications Of Game-based Assessmentmentioning

confidence: 99%

Predicting Risk Propensity Through Player Behavior in DOTA 2: A Cross-Sectional Study

et al. 2022

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As traditional methods such as questionnaires for measuring risk propensity are not applicable in some scenarios, a nonintrusive method that could automatically identify individuals' risk propensity could be valuable. This study utilized Defense of the Ancients 2 (DOTA 2) single match data and historical statistics to train predictive models to identify risk propensity by machine learning methods. Self-reported risk propensity scores from 218 DOTA 2 players were paired with their behavioral metrics. The best-performing model occurred with Gaussian process regression. The root mean square error of this model was 1.10, the correlation between predicted scores and self-reported questionnaire scores was 0.44, the R-squared was 0.17, and the test–retest reliability was 0.67. We discussed how selected behavioral features could contribute to predicting risk propensity and how the approach could be of potential value in the application of perceiving individuals' risk propensities. Moreover, the limitations of our study were discussed, and recommendations were made for future studies in this field.

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Section: The Possible Implications Of Game-based Assessmentmentioning

confidence: 99%

Predicting Risk Propensity Through Player Behavior in DOTA 2: A Cross-Sectional Study

et al. 2022

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“…That is, the success of communication largely determines the gain/ loss, which motivates participants to improve their communication skills. However, the player reaction to game mechanics and reward system greatly depends on the player psychological types [17] and personality [18].…”

Section: Introductionmentioning

confidence: 99%

Validation of Student Psychological Player Types for Game-Based Learning in University Math Lectures

Sidekerskienė

Damaševičius

Maskeliūnas

2021

Communications in Computer and Information Science

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Game-based learning can make educational activities more manageable and planned, and therefore contribute to the achievement of a more productive educational result. To enable adaptive and personalized learning process, the knowledge of learning game player psychological types is required. Player type can be established using a questionnaire such as HEXAD. We present the analysis of the student survey results using statistical analysis, correlation analysis, Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA). The simplification of the questionnaire is suggested. We shortened the 30-item HEXAD survey to a 12-item survey (named sHEXAD) using a factor analysis on responses from university course students. Testing demonstrated that the items of the sHEXAD survey correlated and well agreed with the original HEXAD survey items and could be used to derive the same outcomes.

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