The use of e-learning systems has a long tradition, where students can study
online helped by a system. In this context, the use of recommender systems is
relatively new. In our research project, we investigated various ways to create
a recommender system. They all aim at facilitating the learning and
understanding of a student. We present a common concept of the learning path
and its learning indicators and embed 5 different recommenders in this context.

通过调查研究项目的方式，我们提出了一个学习路径的概念，并在这一背景下嵌入了五种不同的推荐系统，以促进学生的学习和理解。

关于概念化和电子学习中学习路径推荐系统的概述

On conceptualisation and an overview of learning path recommender  systems in e-learning

This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a
scalable approach for creating adaptive learning paths within E-learning
systems. The ALPN system employs an attention-based Knowledge Tracing (AKT)
model to evaluate students' knowledge states and a decision-making model using
Proximal Policy Optimization (PPO) to suggest customized learning materials.
The proposed system accommodates students' needs by considering personalization
parameters such as learning objectives, time constraints, and knowledge
backgrounds. Through an iterative process of recommendation and knowledge state
updating, the ALPN system produces highly adaptive learning paths. Experimental
results reveal the outstanding performance of the proposed system, providing
good insights into the future development of E-learning systems.

本文介绍了自适应学习路径导航（ALPN）系统，它使用基于注意力的知识跟踪（AKT）模型评估学生的知识状态，使用近端策略优化（PPO）的决策模型提供定制的学习材料，通过迭代的推荐和知识状态更新产生高度自适应的学习路径，实验结果表明，该系统的性能优异，为未来 E-learning 系统的发展提供了良好的启示。

基于知识追踪和强化学习的自适应学习路径导航

Adaptive Learning Path Navigation Based on Knowledge Tracing and  Reinforcement Learning

With the boom of digital educational materials and scalable e-learning
systems, the potential for realising AI-assisted personalised learning has
skyrocketed. In this landscape, the automatic generation of educational
questions will play a key role, enabling scalable self-assessment when a global
population is manoeuvring their personalised learning journeys. We develop
EduQG, a novel educational question generation model built by adapting a large
language model. Our initial experiments demonstrate that EduQG can produce
superior educational questions by pre-training on scientific text.

本研究基于大型语言模型开发了 EduQG，该模型能够自动生成出优质的教育问题，以实现在数字化教育材料和可扩展的 e-learning 系统背景下的 AI 协助个性化学习。

用科学文本进行预训练，提高教育问题生成质量

Pre-Training With Scientific Text Improves Educational Question Generation

As e-learning systems become more prevalent, there is a growing need for them
to accommodate individual differences between students. This paper addresses
the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational
approach to this problem called MAPLE (Multi-Armed Bandits based
Personalization for Learning Environments) that combines difficulty ranking
with multi-armed bandits. Given a set of target questions MAPLE estimates the
expected learning gains for each question and uses an exploration-exploitation
strategy to choose the next question to pose to the student. It maintains a
personalized ranking over the difficulties of question in the target set which
is used in two ways: First, to obtain initial estimates over the learning gains
for the set of questions. Second, to update the estimates over time based on
the students responses. We show in simulations that MAPLE was able to improve
students' learning gains compared to approaches that sequence questions in
increasing level of difficulty, or rely on content experts. When implemented in
a live e-learning system in the wild, MAPLE showed promising results. This work
demonstrates the efficacy of using stochastic approaches to the sequencing
problem when augmented with information about question difficulty.

本文介绍了一种名为 MAPLE（基于多臂赌博机的学习环境个性化）的计算方法，通过结合难度排序和多臂赌博机，解决了如何个性化教育内容以最大化其学习收益的问题，并展示了该方法的有效性。