Emotions and other affective states play a pivotal role in cognition and,
consequently, the learning process. It is well-established that computer-based
learning environments (CBLEs) that can detect and adapt to students' affective
states can enhance learning outcomes. However, practical constraints often pose
challenges to the deployment of sensor-based affect detection in CBLEs,
particularly for large-scale or long-term applications. As a result,
sensor-free affect detection, which exclusively relies on logs of students'
interactions with CBLEs, emerges as a compelling alternative. This paper
provides a comprehensive literature review on sensor-free affect detection. It
delves into the most frequently identified affective states, the methodologies
and techniques employed for sensor development, the defining attributes of
CBLEs and data samples, as well as key research trends. Despite the field's
evident maturity, demonstrated by the consistent performance of the models and
the application of advanced machine learning techniques, there is ample scope
for future research. Potential areas for further exploration include enhancing
the performance of sensor-free detection models, amassing more samples of
underrepresented emotions, and identifying additional emotions. There is also a
need to refine model development practices and methods. This could involve
comparing the accuracy of various data collection techniques, determining the
optimal granularity of duration, establishing a shared database of action logs
and emotion labels, and making the source code of these models publicly
accessible. Future research should also prioritize the integration of models
into CBLEs for real-time detection, the provision of meaningful interventions
based on detected emotions, and a deeper understanding of the impact of
emotions on learning.

该研究综述了基于非传感器的情感检测，探讨了最常见的情感状态、传感器发展的方法和技术、CBLEs 和数据样本的定义属性以及关键的研究趋势。尽管该领域已取得了明显的成熟度，但未来的研究仍有很大空间，包括提升非传感器检测模型的性能、积累更多样本来表示少数情感以及识别额外的情感。此外，还需改进模型开发实践和方法，包括比较不同数据采集技术的准确性、确定最佳的持续时间粒度、建立共享的行为记录和情感标签数据库，并公开发布这些模型的源代码。未来的研究还应将模型整合到 CBLEs 中进行实时检测、基于检测到的情感提供有意义的干预措施，并深入了解情感对学习的影响。