## 6 Conclusions  
This paper proposes a learning session dropout prediction model (FAP-Prefix) based on a lightweight prefix-tuning method. In the transformer structure, FAP-Prefix combines adjustable prefix vectors with key-value pairs to explore the implicit contextual relationships between individual learning behavior features and continuous learning behaviors. Firstly, the model freezes the pre-trained weight parameters. Then, it fine-tunes the prefix parameters with session dropout supervision signals. Finally, FAP-Prefix adjusts the vector representations using continuously trainable prefix vectors to generate the probability of a student exiting the current session. The experimental results indicate that FAP-Prefix outperforms SOTA models in the LSDP field on three different datasets and performs well with data-limited scenarios. Despite its strong performance in the LSDP task, there is still room for improvement in tuning methods and prefix vector initialization. Additionally, FAP-Prefix provides finer-grained and real-time results, which has great potential for delivering timely learning analytics interventions to learners.  
In terms of limitations, we notice that static information such as user information and course information has not been fully utilized. This leads to insufficient differentiation between different users and courses. These static features can be combined with the user's historical logs, potentially improving prediction accuracy in the future. Moreover, FAP  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Auc**} & \multicolumn{4}{c}{**Acc**} & \multicolumn{4}{c}{**F1 Score**} \\ \cline{2-11}  & **50\%** & **30\%** & **10\%** & **1\%** & **50\%** & **30\%** & **10\%** & **1\%** & **50\%** & **30\%** & **10\%** & **1\%** \\ \hline LSTM & 0.612 & 0.624 & 0.649 & 0.622 & 0.590 & 0.597 & 0.612 & 0.596 & 0.590 & 0.597 & 0.612 & 0.596 \\ GRU & 0.669 & 0.662 & 0.657 & 0.602 & 0.605 & 0.604 & 0.603 & 0.595 & 0.603 & 0.623 & 0.601 & 0.615 \\ SVR & 0.772 & 0.789 & 0.772 & 0.764 & 0.742 & 0.736 & 0.711 & 0.695 & 0.757 & 0.743 & 0.722 & 0.710 \\ M3 PP & 0.802 & 0.783 & 0.782 & 0.767 & 0.734 & 0.732 & 0.731 & 0.683 & 0.771 & 0.759 & 0.727 & 0.720 \\ DAS & 0.817 & 0.825 & 0.784 & 0.762 & 0.735 & 0.721 & 0.707 & 0.697 & 0.775 & 0.771 & 0.743 & 0.725 \\ MLP & 0.825 & 0.812 & 0.794 & 0.785 & 0.812 & 0.807 & 0.726 & 0.749 & 0.812 & 0.807 & 0.726 & 0.749 \\ FAP-Prefix & 0.892 & 0.887 & 0.859 & 0.870 & 0.845 & 0.840 & 0.829 & 0.830 & 0.842 & 0.839 & 0.803 & 0.815 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiments of SOTA models and FAP-Prefix on limited datasets.  
Prefix can be applied to other different scenarios (e.g., gaming, live streaming, etc.). A key focus of future work will be how to improve the model's generalization ability and robustness across different scenarios.  
Methodology, Y.L.; Software, Y.L.; Writing--original draft, Y.L.; Writing--review & editing, Z.W.; Supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Smart Education Platform Solution Key Technology Consulting Services, Shanghai Xinyan Information Technology Co., Ltd.] grant number [H300-72308] and [Relying on Enterprises to Implement Professional Integration: Construction and Practice of the _Artificial Intelligence_ Course Dell-Ministry of Education Industry-Academia Collaboration Project] grant number [H300-42211].  
Not applicable.  
The data that support the findings of this study are available from the author [PERSON], upon reasonable request.  
The authors are grateful to [PERSON] for his help.  
The authors declare no conflicts of interest.