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

Better-supervised models might have better performance. In this paper, we
first clarify what makes for good supervision for a classification problem, and
then explain two existing label refining methods, label smoothing and knowledge
distillation, in terms of our proposed criterion. To further answer why and how
better supervision emerges, we observe the learning path, i.e., the trajectory
of the model's predictions during training, for each training sample. We find
that the model can spontaneously refine "bad" labels through a "zig-zag"
learning path, which occurs on both toy and real datasets. Observing the
learning path not only provides a new perspective for understanding knowledge
distillation, overfitting, and learning dynamics, but also reveals that the
supervisory signal of a teacher network can be very unstable near the best
points in training on real tasks. Inspired by this, we propose a new knowledge
distillation scheme, Filter-KD, which improves downstream classification
performance in various settings.

本文提出了一种新的知识蒸馏方案，Filter-KD，通过观察模型的学习路径来解释现有的标签平滑和知识蒸馏方法，并发现模型可以通过 “锯齿形” 学习路径自发地改善 “糟糕” 的标签，从而提高下游分类性能。