用游戏行为预测程序员适配度，准确率达94%。
MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approaches
- 设计多类型游戏收集玩家行为数据，通过机器学习建模预测适配度。
- 模型最高达97%精确率和94%准确率，优于传统问卷。
- 适合招聘、职业规划领域，尤其关注减少主观偏差的场景。
职业指导与人才选拔中的性格评估传统依赖自评问卷，易受反应偏差、疲劳和故意扭曲影响。基于游戏的评估通过捕捉游戏过程中的隐式行为信号提供了可行替代方案。本研究提出一种结合多类型严肃游戏与机器学习技术的框架，用于预测软件开发岗位的适配性。通过文献综述与对专业程序员的实证研究，确定了开发者相关的性格与行为特质，并设计了一款定制化移动端游戏，以诱发问题解决、规划、适应性、坚持力、时间管理及信息获取等行为。收集并分析细粒度的游戏事件数据，采用两阶段建模策略，仅基于游戏行为特征预测适配性。结果表明，模型在预测中达到最高97%的精确率与94%的准确率。行为分析显示，合适候选人表现出更频繁通关益智类关卡、完成更多支线任务、更常操作菜单，且暂停、重试与放弃次数更少。这些发现表明，游戏过程中捕获的隐式行为痕迹在无需显式性格测试的情况下，有效预测软件开发适配性，支持严肃游戏作为可扩展、具吸引力且偏差更小的职业评估替代方案。
原文摘要 · Abstract (English)
Personality assessment in career guidance and personnel selection traditionally relies on self-report questionnaires, which are susceptible to response bias, fatigue, and intentional distortion. Game-based assessment offers a promising alternative by capturing implicit behavioral signals during gameplay. This study proposes a multi-genre serious-game framework combined with machine-learning techniques to predict suitability for software development roles. Developer-relevant personality and behavioral traits were identified through a systematic literature review and an empirical study of professional software engineers. A custom mobile game was designed to elicit behaviors related to problem solving, planning, adaptability, persistence, time management, and information seeking. Fine-grained gameplay event data were collected and analyzed using a two-phase modeling strategy where suitability was predicted exclusively from gameplay-derived behavioral features. Results show that our model achieved up to 97% precision and 94% accuracy. Behavioral analysis revealed that proper candidates exhibited distinct gameplay patterns, such as more wins in puzzle-based games, more side challenges, navigating menus more frequently, and exhibiting fewer pauses, retries, and surrender actions. These findings demonstrate that implicit behavioral traces captured during gameplay is promising in predicting software-development suitability without explicit personality testing, supporting serious games as a scalable, engaging, and less biased alternative for career assessment.
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