Distribution shift is a common situation in machine learning tasks, where the
data used for training a model is different from the data the model is applied
to in the real world. This issue arises across multiple technical settings:
from standard prediction tasks, to time-series forecasting, and to more recent
applications of large language models (LLMs). This mismatch can lead to
performance reductions, and can be related to a multiplicity of factors:
sampling issues and non-representative data, changes in the environment or
policies, or the emergence of previously unseen scenarios. This brief focuses
on the definition and detection of distribution shifts in educational settings.
We focus on standard prediction problems, where the task is to learn a model
that takes in a series of input (predictors) $X=(x_1,x_2,...,x_m)$ and produces
an output $Y=f(X)$.

在机器学习任务中，分布偏移是一种常见情况，其表示训练模型使用的数据与实际应用模型的数据不同。本文旨在定义和检测教育环境中的分布偏移，关注标准预测问题，即学习一个以输入序列为输入（预测变量）X=(x_1,x_2,...,x_m) 并生成输出 Y=f (X) 的模型。