# Prompt Tuning for Multi-Label Text Classification: How to Link Exercises to Knowledge Concepts?
## 1 Introduction  
In recent decades, personalized learning has become a mainstream solution to enhance students' learning interest, and experience in intelligent education systems [1, 2, 3]. One of the fundamental and key tasks in personalized learning is knowledge tracing [4, 5], which aims to evaluate the students' learning state of knowledge concepts. Exercises have played an important role in the knowledge tracing tasks, which is one of the evaluation metrics of whether students have mastered specific knowledge concepts [6, 7]. Students in intelligent education systems choose the right exercises according to their own needs and acquire specific knowledge concepts during exercise. In turn, we can track changes in students' acquisition of knowledge concepts during their exercising process. From this perspective, knowledge tracing should consist of a students-exercises-knowledge concepts hierarchy [8]. However, most existing methods of knowledge tracing approaches [9, 10, 11] are partially modeled among the hierarchy (i.e., students-exercises or students-concepts). This is because, in some intelligent systems, there is a lack of connection between exercises and knowledge concepts. To this end, we take the automatic linking of exercises to knowledge concepts into consideration for knowledge tracing tasks. In essence, linking exercises to knowledge concepts is a multi-label text classification (MLTC) problem. As shown in Figure 1, the relationship between exercises and knowledge concepts is one-to-one or one-to-many, which aims to assign one or more concepts to each input exercise in the dataset. Moreover, Figure 1 shows that the semantics between exercises and knowledge concepts are highly correlated. Recently, deep-learning-based methods have achieved fairly good performance in MLTC for the superiority of feature representation learning. For example, [PERSON] et al. (2020) utilized the strengths of the existing convolutional neural network and took multi-label co-occurrence patterns into account in the optimization objective to produce good results in MLTC. [PERSON] et al. (2020) proposed a graph attention network-based model to capture the attentive dependency structure among the labels. [PERSON] et al. (2020) fine-tuned the BERT language model ([PERSON] et al., 2020) to capture the contextual relations between input text and the induced label clusters. However, these deep-learning-based methods in MLTC tasks require large amounts of training data for model optimization, which is usually time-consuming and labour-intensive in real-world scenarios. Unfortunately, linking exercises to knowledge concepts usually lacks training data because some knowledge concepts corresponding to a few exercises or new courses may contain a paucity of labelled data. To address these problems, we propose a Prompt Tuning method for Multi-Label Text classification (PTMLTC for short). First, the prompt tuning model with a unified template predicts the relevance scores of exercises and knowledge concepts. Then, the multiple associated knowledge concepts are picked with a threshold. In order to verify the effectiveness of our proposed method, an Exercises-Concepts dataset of the Data Structure course is constructed. Extensive experimental results confirm our method outperforms other state-of-the-art methods by up to 32.53% and 41.78% in Micro and Macro F1, respectively. The contribution of our paper can be summarized as follows:  
(1) To the best of our knowledge, this is the first attempt to automatically link exercises to knowledge concepts. We built an Exercises-Concepts dataset of the Data Structure course and reconstructed the few-shot dataset. (2) We propose a prompt tuning method for multi-label text classification to link exercises to knowledge concepts. Large amounts of labelled or unlabeled training data are not required. (3) Extensive experimental results confirm that our proposed method outperforms other state-of-the-art deep-learning-based methods. Figure 1: Examples of exercises linking to knowledge concepts from dataset.