## 1 Introduction  
With the widespread of online courses, intelligent tutoring systems (ITS) [1] have gradually become a mainstream method in university education. ITS builds models by assessing learners' status and prior knowledge. However, students learning in MOOCs face the issues of lacking face-to-face interaction and immediate feedback. The course content and difficulty may not be suitable for every learner. Therefore, numerous recent studies indicate a tendency for early dropout on online learning platforms. The learning time of a learner using ITS is the foundation for the performance of ITS. ITS can better understand the learner's needs and learning states. Early dropout means less learning time, which makes it harder for ITS to give personalized predictions. [PERSON] et al. [2] introduced course dropout prediction (CDP), which aims to analyze dropout situations in online learning and provide early intervention for students. As the demand for more flexible learning environments grows, universities are gradually shifting from purely online courses to blended courses. As a result, predicting dropout for a single learning session is more relevant in blended courses. Following the terminology in [3], this task is referred to as learning session dropout prediction (LSDP). The goal is to predict the probability that a learner will drop out of the current session based on their ongoing actions. If the probability exceeds a certain threshold, the ITS can intervene in real time. In the current research domain of smart education, there is a lack of studies on the LSDP task. This indicates a significant gap in research progress between LSDP and CDP. Currently, the LSDP task faces the following challenges:  
* Direct application of the SOTA model in CDP suffers from a lack of accuracy and real-time performance. * The real-time generated student action sequences lack panel data required for model training. * Compared to CDP, the higher frequency of session dropout in LSDP leads to the fragmentation of behavioral sequences. In recent years, tasks such as knowledge tracing and educational resource recommendation have achieved success using fine-tuning methods based on pre-trained language models (PLMs). Since LSDP belongs to the domain of learning sequence analysis, similar methods can be applied. The pre-training and fine-tuning paradigm with PLMs has shown groundbreaking results in natural language processing. First, pre-training tasks are designed to learn implicit relationships within contextual data, providing a foundation for downstream tasks. Second, this paradigm enables more accurate predictions during fine-tuning. Thus, using PLMs to capture and understand the contextual associations within learning behaviors is important for accurate prediction. However, due to limited and imbalanced learning behavior data in real-world scenarios, it is challenging to make full use of the pre-training and fine-tuning paradigm. In situations with limited data, fine-tuning methods often lead to model overfitting. Few-shot learning addresses this issue by updating only a small portion of the parameters or adding a limited number of parameters to adapt to new tasks. Common approaches to few-shot learning include adapter modules, prompt tuning, sparse update, and prefix tuning. These methods enable the model to adapt to new tasks without significantly increasing computational costs. Among the four potential few-shot learning methods, adapter modules require inserting small fully connected networks (adapters) after each sub-layer of the transformer model. This may change the original model structure and increase the number of parameters. Additionally, adapter modules may add architectural complexity, making them about 5% slower than full fine-tuning when generating results. This is unacceptable for LSDP tasks that require real-time performance. Sparse update methods can entirely ignore the model structure and selectively update specific parameters.