Intelligent Tutoring Systems (ITSs) have significantly enhanced adult
literacy training, a key factor for societal participation, employment
opportunities, and lifelong learning. Our study investigates the application of
advanced AI models, including Large Language Models (LLMs) like GPT-4, for
predicting learning performance in adult literacy programs in ITSs. This
research is motivated by the potential of LLMs to predict learning performance
based on its inherent reasoning and computational capabilities. By using
reading comprehension datasets from the ITS, AutoTutor, we evaluate the
predictive capabilities of GPT-4 versus traditional machine learning methods in
predicting learning performance through five-fold cross-validation techniques.
Our findings show that the GPT-4 presents the competitive predictive abilities
with traditional machine learning methods such as Bayesian Knowledge Tracing,
Performance Factor Analysis, Sparse Factor Analysis Lite (SPARFA-Lite), tensor
factorization and eXtreme Gradient Boosting (XGBoost). While XGBoost (trained
on local machine) outperforms GPT-4 in predictive accuracy, GPT-4-selected
XGBoost and its subsequent tuning on the GPT-4 platform demonstrates superior
performance compared to local machine execution. Moreover, our investigation
into hyper-parameter tuning by GPT-4 versus grid-search suggests comparable
performance, albeit with less stability in the automated approach, using
XGBoost as the case study. Our study contributes to the field by highlighting
the potential of integrating LLMs with traditional machine learning models to
enhance predictive accuracy and personalize adult literacy education, setting a
foundation for future research in applying LLMs within ITSs.

应用大型语言模型，如 GPT-4，预测智能辅导系统中成人识字课程的学习表现。研究表明，GPT-4 与传统机器学习方法在预测学习表现方面具有竞争力，而 GPT-4 与 XGBoost 的结合在性能上优于本地机器执行。此外，研究发现 GPT-4 与网格搜索相比具有可比性表现，并且将大型语言模型与传统机器学习模型整合可提高预测准确性，个性化成人识字教育。

基于大型语言模型的学习表现预测：一个成年识字能力的研究

Predicting Learning Performance with Large Language Models: A Study in  Adult Literacy

Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive
abilities by exploiting graph structural and temporal dynamics. However, the
existing DyGNNs fail to handle distribution shifts, which naturally exist in
dynamic graphs, mainly because the patterns exploited by DyGNNs may be variant
with respect to labels under distribution shifts. In this paper, we propose
Disentangled Intervention-based Dynamic graph Attention networks with
Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in
dynamic graphs by discovering and utilizing invariant patterns, i.e.,
structures and features whose predictive abilities are stable across
distribution shifts. Specifically, we first propose a disentangled
spatio-temporal attention network to capture the variant and invariant
patterns. By utilizing the disentangled patterns, we design a spatio-temporal
intervention mechanism to create multiple interventional distributions and an
environment inference module to infer the latent spatio-temporal environments,
and minimize the variance of predictions among these intervened distributions
and environments, so that our model can make predictions based on invariant
patterns with stable predictive abilities under distribution shifts. Extensive
experiments demonstrate the superiority of our method over state-of-the-art
baselines under distribution shifts. Our work is the first study of
spatio-temporal distribution shifts in dynamic graphs, to the best of our
knowledge.

本文提出了基于解缠干预的动态图注意力网络的方法来处理动态图中的时空分布变化，并发现和利用稳定的具有预测能力的不变模式。实验证明我们的方法在分布变化下优于现有基线模型。

具有解缠干预和不变性促进的超越分布的广义动态图神经网络

Out-of-Distribution Generalized Dynamic Graph Neural Network with  Disentangled Intervention and Invariance Promotion

In modern business processes, the amount of data collected has increased
substantially in recent years. Because this data can potentially yield valuable
insights, automated knowledge extraction based on process mining has been
proposed, among other techniques, to provide users with intuitive access to the
information contained therein. At present, the majority of technologies aim to
reconstruct explicit business process models. These are directly interpretable
but limited concerning the integration of diverse and real-valued information
sources. On the other hand, Machine Learning (ML) benefits from the vast amount
of data available and can deal with high-dimensional sources, yet it has rarely
been applied to being used in processes. In this contribution, we evaluate the
capability of modern Transformer architectures as well as more classical ML
technologies of modeling process regularities, as can be quantitatively
evaluated by their prediction capability. In addition, we demonstrate the
capability of attentional properties and feature relevance determination by
highlighting features that are crucial to the processes' predictive abilities.
We demonstrate the efficacy of our approach using five benchmark datasets and
show that the ML models are capable of predicting critical outcomes and that
the attention mechanisms or XAI components offer new insights into the
underlying processes.

通过使用现代 Transformer 架构和传统机器学习技术，评估模拟过程规律的能力，并突出关键特征，提高模型预测能力。证明了应用机器学习模型预测关键业务流程的有效性以及 XAI 组件提供的新见解。