As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness.

通过集成 SGNN 和 LLM 嵌入的创新策略来增强学生问答数据的噪声韧性，从而提高基于学习者提供的数据的预测准确性。在冷启动场景下表现出优异的预测准确性，尤其有利于解决问题缺乏学习者反馈的情况。经过对 5 个实际数据集的验证，方法胜过基准方法，展示了增强的预测准确性和鲁棒性。

提高学生性能预测中基于学习者提供问题的 SGNN-LLM 协同效应

Enhancing Student Performance Prediction on Learnersourced Questions  with SGNN-LLM Synergy

Learnersourcing involves students generating and sharing learning resources
with their peers. When learnersourcing multiple-choice questions, creating
explanations for the generated questions is a crucial step as it facilitates a
deeper understanding of the related concepts. However, it is often difficult
for students to craft effective explanations due to limited subject
understanding and a tendency to merely restate the question stem, distractors,
and correct answer. To help scaffold this task, in this work we propose a
self-reinforcement large-language-model framework, with the goal of generating
and evaluating explanations automatically. Comprising three modules, the
framework generates student-aligned explanations, evaluates these explanations
to ensure their quality and iteratively enhances the explanations. If an
explanation's evaluation score falls below a defined threshold, the framework
iteratively refines and reassesses the explanation. Importantly, our framework
emulates the manner in which students compose explanations at the relevant
grade level. For evaluation, we had a human subject-matter expert compare the
explanations generated by students with the explanations created by the
open-source large language model Vicuna-13B, a version of Vicuna-13B that had
been fine-tuned using our method, and by GPT-4. We observed that, when compared
to other large language models, GPT-4 exhibited a higher level of creativity in
generating explanations. We also found that explanations generated by GPT-4
were ranked higher by the human expert than both those created by the other
models and the original student-created explanations. Our findings represent a
significant advancement in enriching the learnersourcing experience for
students and enhancing the capabilities of large language models in educational
applications.

通过使用自我增强的大型语言模型框架，本研究提出了一种在学生辅助下生成和评估解释的方法，以改善学生生成多项选择题解释的质量，进一步丰富学生资源共享体验，并提升大型语言模型在教育应用中的能力。

利用大型语言模型探索自我强化提升学习者生成的多项选择题解释

Exploring Self-Reinforcement for Improving Learnersourced  Multiple-Choice Question Explanations with Large Language Models

Engaging students in creating novel content, also referred to as
learnersourcing, is increasingly recognised as an effective approach to
promoting higher-order learning, deeply engaging students with course material
and developing large repositories of content suitable for personalized
learning. Despite these benefits, some common concerns and criticisms are
associated with learnersourcing (e.g., the quality of resources created by
students, challenges in incentivising engagement and lack of availability of
reliable learnersourcing systems), which have limited its adoption. This paper
presents a framework that considers the existing learnersourcing literature,
the latest insights from the learning sciences and advances in AI to offer
promising future directions for developing learnersourcing systems. The
framework is designed around important questions and human-AI partnerships
relating to four key aspects: (1) creating novel content, (2) evaluating the
quality of the created content, (3) utilising learnersourced contributions of
students and (4) enabling instructors to support students in the
learnersourcing process. We then present two comprehensive case studies that
illustrate the application of the proposed framework in relation to two
existing popular learnersourcing systems.

本文提出了一个框架，考虑到现有的学习资源文献、最新的学习科学见解和 AI 的最新进展，以提供未来发展学生资源的方向。该框架围绕着四个关键方面的重要问题和人工智能合作伙伴展开，以创建新的内容、评估已创造的内容的质量、利用学生的贡献以及使教师能够支持学生参与学习资源创作流程。文章还提出了两个综合案例，展示了在两个现有的流行的学习资源系统中应用所提出的框架的应用情况。