Learning Emotion Assessment Method Based on Belief Rule Base and Evidential Reasoning

Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertaint...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Mathematics (Basel) 2023-03, Vol.11 (5), p.1152
Hauptverfasser: Chen, Haobing, Zhou, Guohui, Zhang, Xin, Zhu, Hailong, He, Wei
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Learning emotion assessment is a non-negligible step in analyzing learners’ cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertainty in the process of data collection and model parameter errors. Given the above problems, a new learning emotion assessment model based on evidence reasoning and a belief rule base (E-BRB) is proposed in this paper. First, the transformation matrix is introduced to transform multiple emotional indicators into the same standard framework and integrate them, which keeps the consistency of information transformation. Second, the relationship between emotional indicators and learning emotion states is modeled by E-BRB in conjunction with expert knowledge. In addition, we employ the projection covariance matrix adaptation evolution strategy (P-CMA-ES) to optimize the model parameters and improve the model’s accuracy. Finally, to demonstrate the effectiveness of the proposed model, it is applied to emotion assessment in science learning. The experimental results show that the model has better accuracy than data-driven models such as neural networks.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11051152