[论文解读] MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approaches

Soroush Elyasi, Arya VarastehNezhad+1 more|arXiv (Cornell University)|Jan 3, 2026

Educational Games and Gamification被引用 0

一句话总结

本论文提出一个基于游戏的框架，使用多类型的严肃游戏与机器学习，从游戏行为特征中预测软件开发者的适配性，达到高达97%的精确率和94%的准确率。

ABSTRACT

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.

研究动机与目标

• 从文献与专业人士的实证研究中识别与开发者相关的性格与行为特征。
• 设计一个多类型的严肃游戏以引出与软件开发相关的特征。
• 开发一个使用游戏行为特征来推断软件开发适配性的预测型机器学习模型。

提出的方法

• 将MBTI的Thinking维度视为与软件开发核心相关，辅以相关行为特征。
• 开发一个基于Unity的移动端严肃游戏，跨多种类型的分段以获取认知与行为信号。
• 搭建后端（ASP.NET Core、SQL Server），在获得同意的前提下记录细粒度游戏事件的数据。
• 对高维游戏数据进行预处理、选择信息量高的特征，并采用两阶段建模以利用有标签与无标签数据。
• 使用经过验证的软件开发专家的带标签示例来训练和评估模型，利用预测框架推断无标签数据。

实验结果

研究问题

• RQ1 游戏来源的行为特征是否能在超越自我报告性格测量的情况下预测软件开发适配性？
• RQ2 哪些开发者相关特征与游戏模式最能区分合适的候选人？
• RQ3 两阶段建模方法（先用人格信息完成标签再进行全面预测）在标签有限的情况下是否能提高准确率？
• RQ4 MBTI Thinking类型及相关行为在基于游戏的评估中作为预测指标的有效性如何？
• RQ5 将游戏化评估用于可扩展、偏见较少的职业指导有哪些实际意义？

主要发现

• 模型在从游戏特征预测软件开发适配性方面达到高达97%的精确率和94%的准确率。
• 显著的游戏模式特征区分了合适候选人，如更多解谜游戏胜利、更多额外挑战、频繁的菜单导航、较少的暂停、重试和放弃行为。
• 两阶段建模策略通过先以人格信息完成标签再用游戏特征预测适配性，在有标签数据有限的情况下实现预测。
• 该方法表明在游戏过程中隐性行为轨迹可以成为可扩展、具参与性且较少偏见的职业评估替代Explicit性格测试的方案。