# Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
## 2 Related Work  
### Deep Learning-Based Knowledge Tracing  
At present, DLKT models have received considerable attention from scholars [1; 6]. These models have achieved significant predictive performance compared to traditional knowledge tracing models such as Bayesian knowledge tracing [21], item response theory [22], and performance factor analysis [23]. This is due to the powerful feature extraction and function fitting capabilities of deep learning. On the one hand, several innovations have been made in the structure of knowledge tracing models. For example, the Dynamic Key-Value Memory Network (DKVMN) model utilizes dynamic key-value pairs and enhanced memory networks to better capture student knowledge states [8]. The Self-Attentive Knowledge Tracing (SAKT) model leverages the self-attention mechanism to improve the model's ability to focus on relevant past interactions [24]. Additionally, the Separated Self-Attentive Neural Knowledge Tracing (SAINT) model adopts a Transformer-based architecture [25], where exercises are embedded in the encoder and student responses are predicted in the decoder, further advancing the capability of knowledge tracing models to handle complex learning sequences.  
On the other hand, mining and integrating learning-related factors is also a major trend adopted to improve the performance of DKT models. For example, scholars proposed an extensible representation learning approach for DKT to extract and integrate representations of these four types of factors, including exercise and skill, the attributes of exercise, the historical performance of the learners, and the forgetting behavior of the learners in the learning process [26]. Furthermore, a new method of contrastive deep knowledge tracing was proposed [27]. This model adopts the items with the same concepts as positive samples and the items with different concepts as negative samples, and it obtains new features from students as the input for long short-term memory networks. The models above use SKQ ID information and generate a one-hot coding vector of corresponding length according to its length, which limits the use of this model in a real environment [13]. For example, when a new SKQ appears, the original one-hot coding will no longer be applicable to the new SKQ, so the original KT model needs to be retrained. This is what this study refers to as the vocabulary gap in KT, and it is also an urgent problem that needs to be solved in the current field of KT [1].  
To address the problem of the vocabulary gap, this study proposes a new methodological approach. We advocate for a focused effort on extracting shared feature representations across various datasets. These shared features, known as side information, include but are not limited to elements such as question difficulty, student ability, and the complexity of knowledge, and do not involve any specific ID information. By this means, we can capture more universal and essential data characteristics, thereby facilitating the effective transfer and application of the DLKT model in new and unknown datasets. Additionally, this method aims to enhance the generalizability of the model, enabling it to be more robust and effective across different educational environments and scenarios.