Creating a well-structured lesson plan is essential for improving classroom efficiency, yet it is often a labor-intensive process. Recently, many studies have leveraged large language models (LLMs) to generate lesson plans automatically. However, existing methods heavily rely on LLMs that are pre-trained on large-scale universal corpora, which often lack critical educational theory and textbook-specific information. This can lead to inconsistencies and misalignments with textbook content. To address these challenges, we propose CE-LessonPlan, a novel compress-expand framework to generate lesson plans by effectively combining external lesson plan references and textbook information. The framework consists of two key components: a compressor, which synthesizes multiple retrieved references into a cohesive document, and an expander, which integrates textbook-specific information with the parametric knowledge of LLMs to produce another enriched lesson plan. The outputs of the compressor and expander are then seamlessly integrated to create a comprehensive golden context, further enhancing the lesson plan generation process with LLMs. We conduct extensive experiments to demonstrate that CE-LessonPlan outperforms existing methods for generating lesson plans.

Knowledge tracing (KT) is a prediction task that aims to predict students’ future performance based on their past learning data. The rapid progress in attention mechanisms has led to the emergence of various high-performing attention based KT models. However, in online or personalized education settings, students’ varying learning paths result in different lengths of student interaction sequences, which poses a significant challenge for attention based KT models as their context window sizes are fixed during both training and prediction stages. We refer to this as the length extrapolation of KT model. In this paper, we propose extraKT to facilitate better extrapolation that learn from student interactions with a short context window and continue to perform well across various longer context window sizes at prediction stage. Specifically, we negatively bias attention scores with linearly decreasing penalties that are proportional to query-key distance, which efficiently represents short-term forgetting characteristics of student knowledge states. We conduct comprehensive and rigorous experiments on three real-world educational datasets. The results show that our extraKT model exhibits robust length extrapolation capability and outperforms state-of-the-art baseline models in terms of AUC and accuracy. To encourage reproducible research, we merge our data and code to the publicly available pyKT benchmark at https://github.com/pykt-team/pykt-toolkit.