Abstract:
Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved predi...Show MoreMetadata
Abstract:
Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying and understanding these factors remain a challenge. Thus, this article proposes the temporal fuzzy cognitive map (tFCM) model, which combines the prediction power of DNNs with the interpretability of fuzzy cognitive maps. In the proposed tFCM model, cognitive states are modeled as fuzzy, multidimensional, and interrelated vectors, which are input to a long short-term memory network for prediction. This integration allows the proposed model to combine the exceptional ability of DNNs to uncover latent factors with the distinct benefits of fuzzy cognitive maps' ability to reveal potential correlations. A comparative experiment was designed and conducted on a large-scale dataset to assess the predictive performance and interpretability of the proposed tFCM model. The results demonstrate tFCM's superior performance and interpretability compared to existing models. The findings of this study contribute to the development of a multidimensional quantitative model to represent cognitive states and an interpretable model architecture for state prediction.
Published in: IEEE Transactions on Learning Technologies ( Volume: 17)
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- IEEE Keywords
- Index Terms
- Cognitive Status ,
- Fuzzy Cognitive Maps ,
- Neural Network ,
- Predictive Performance ,
- Deep Neural Network ,
- Large-scale Datasets ,
- Human Learning ,
- Changes In Conditions ,
- Cognitive Processes ,
- Learning Process ,
- Education Research ,
- Student Learning ,
- Matrix Factorization ,
- Individual Learning ,
- Historical Information ,
- Membership Function ,
- Historical Conditions ,
- Fuzzy Logic ,
- Fuzzy Set ,
- Educational Technology ,
- LSTM Model ,
- Fuzzy Membership ,
- Fuzzy Control ,
- Automatic Control System ,
- Bottom Of Page ,
- Adaptive Learning ,
- ReLU Activation Function ,
- Psychological Factors ,
- Nonlinear Function ,
- Control System
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Cognitive Status ,
- Fuzzy Cognitive Maps ,
- Neural Network ,
- Predictive Performance ,
- Deep Neural Network ,
- Large-scale Datasets ,
- Human Learning ,
- Changes In Conditions ,
- Cognitive Processes ,
- Learning Process ,
- Education Research ,
- Student Learning ,
- Matrix Factorization ,
- Individual Learning ,
- Historical Information ,
- Membership Function ,
- Historical Conditions ,
- Fuzzy Logic ,
- Fuzzy Set ,
- Educational Technology ,
- LSTM Model ,
- Fuzzy Membership ,
- Fuzzy Control ,
- Automatic Control System ,
- Bottom Of Page ,
- Adaptive Learning ,
- ReLU Activation Function ,
- Psychological Factors ,
- Nonlinear Function ,
- Control System
- Author Keywords