To adapt the PKT model for dynamic changes in student knowledge over time, including forgetting and knowledge transfer between skills, several strategies can be implemented:
Temporal Knowledge Representation: Incorporating a temporal component into the student representation can help the model track changes in knowledge over time. This can be achieved by using recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks that are specifically designed to capture temporal dependencies in sequential data. By maintaining a memory of past interactions, the model can better understand how a student's knowledge evolves.
Forgetting Mechanisms: Implementing forgetting mechanisms can allow the model to simulate the natural decline of knowledge over time. This can be done by introducing decay factors that reduce the influence of older interactions on the current knowledge state. For instance, the model can apply exponential decay to the weights of past interactions, thereby emphasizing more recent performance data.
Knowledge Transfer Modeling: To account for knowledge transfer between skills, the model can be enhanced with a multi-task learning framework. This approach allows the model to learn shared representations across related skills, enabling it to leverage knowledge from one skill to improve predictions for another. For example, if a student learns a new skill that is related to previously mastered skills, the model can adjust its predictions based on this transfer of knowledge.
Adaptive Learning Rate: Implementing an adaptive learning rate can help the model adjust to changes in student performance. For instance, if a student shows a sudden drop in performance, the model can increase the learning rate to quickly adapt to this change, allowing for more responsive predictions.
Feedback Loops: Incorporating feedback loops where the model continuously updates its understanding of a student's knowledge state based on new interactions can enhance adaptability. This can involve retraining the model periodically with the latest data to ensure that it reflects the most current knowledge state of the student.
Contextual Factors: Including contextual factors, such as the time since the last interaction or the difficulty of the questions, can provide additional insights into a student's knowledge state. This information can help the model make more informed predictions about a student's ability to recall or apply knowledge.
By implementing these strategies, the PKT model can effectively adapt to the dynamic nature of student knowledge, providing more accurate and personalized predictions that reflect changes in learning over time.