Related papers: Automatic Sensor-free Affect Detection: A Systematic Literature Review

Automatic Sensor-free Affect Detection: A Systematic Literature Review

URL: http://arxiv.org/abs/2310.13711v1
Date: Wed, 11 Oct 2023 13:24:27 GMT
Title: Automatic Sensor-free Affect Detection: A Systematic Literature Review
Authors: Felipe de Morais, Di\'ogines Goldoni, Tiago Kautzmann, Rodrigo da Silva, Patricia A. Jaques
Abstract summary: This paper provides a comprehensive literature review on sensor-free affect detection. Despite the field's evident maturity, demonstrated by the consistent performance of the models, there is ample scope for future research. There is also a need to refine model development practices and methods.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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.