Personalized adaptive gamification has the potential to improve individuals’ motivation and performance. Current methods aim to predict the perceived affective state (i.e., emotion) of an individual in order to improve their motivation and performance by tailoring an application. However, existing methods may struggle to predict the state of an individual that it has not been trained for. Moreover, the affective state that correlates to good performance may vary based on individuals and task characteristics. Given these limitations, this paper presents a machine learning method that uses task information and an individual's facial expression data to predict his/her performance on a gamified task. The training data used to generate the adaptive-individual-task model is updated every time new data from an individual is acquired. This approach helps to improve the model's prediction accuracy and account for variations in facial expressions across individuals. A case study is presented that demonstrates the feasibility and performance of the model. The results indicate that the model is able to predict the performance of individuals, before completing a task, with an accuracy of 0.768. The findings support the use of adaptive models that dynamically update their training data set and consider task information and individuals’ facial expression data.

中文翻译：

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走向个性化自适应游戏化：一种预测性能的机器学习模型

个性化的自适应游戏化具有提高个人动机和绩效的潜力。当前的方法旨在预测个人的感知情感状态（即情绪），以便通过定制应用程序来提高他们的动机和表现。然而，现有方法可能难以预测未经训练的个体的状态。此外，与良好表现相关的情感状态可能因个人和任务特征而异。鉴于这些限制，本文提出了一种机器学习方法，该方法使用任务信息和个人的面部表情数据来预测他/她在游戏化任务中的表现。每次从个人获取新数据时，都会更新用于生成自适应个人任务模型的训练数据。这种方法有助于提高模型的预测准确性，并考虑到个体面部表情的变化。一个案例研究展示了该模型的可行性和性能。结果表明，该模型能够在完成任务之前预测个人的表现，准确率为 0.768。研究结果支持使用自适应模型动态更新其训练数据集并考虑任务信息和个人面部表情数据。精度为 0.768。研究结果支持使用自适应模型动态更新其训练数据集并考虑任务信息和个人面部表情数据。精度为 0.768。研究结果支持使用自适应模型动态更新其训练数据集并考虑任务信息和个人面部表情数据。