The explosive growth of online education environments is generating a massive
volume of data, specially in text format from forums, chats, social networks,
assessments, essays, among others. It produces exciting challenges on how to
mine text data in order to find useful knowledge for educational stakeholders.
Despite the increasing number of educational applications of text mining
published recently, we have not found any paper surveying them. In this line,
this work presents a systematic overview of the current status of the
Educational Text Mining field. Our final goal is to answer three main research
questions: Which are the text mining techniques most used in educational
environments? Which are the most used educational resources? And which are the
main applications or educational goals? Finally, we outline the conclusions and
the more interesting future trends.

在线教育环境的爆炸性增长正在产生大量的数据，尤其是来自论坛、聊天、社交网络、评估和论文等以文本格式呈现的数据，如何挖掘文本数据以找到对教育相关人士有用的知识是一个令人振奋的挑战。本文系统概述了当前教育文本挖掘领域的现状，回答了三个主要的研究问题：在教育环境中最常用的文本挖掘技术是哪些？最常用的教育资源有哪些？主要的应用或教育目标是什么？最后，我们概述了结论和更有趣的未来趋势。

教育中的文本挖掘

Text mining in education

The influence of artificial intelligence (AI) within the field of nuclear
medicine has been rapidly growing. Many researchers and clinicians are seeking
to apply AI within PET, and clinicians will soon find themselves engaging with
AI-based applications all along the chain of molecular imaging, from image
reconstruction to enhanced reporting. This expanding presence of AI in PET
imaging will result in greater demand for educational resources for those
unfamiliar with AI. The objective of this article to is provide an illustrated
guide to the core principles of modern AI, with specific focus on aspects that
are most likely to be encountered in PET imaging. We describe convolutional
neural networks, algorithm training, and explain the components of the commonly
used U-Net for segmentation and image synthesis.

人工智能在核医学领域的影响正在迅速增长，研究人员和临床医生力图将人工智能应用到 PET，临床医生将很快发现自己参与到从图像重建到增强报告的分子成像链的人工智能应用中，这种人工智能在 PET 成像中的不断扩大的存在将导致对与人工智能不熟悉的教育资源的更大需求。本文的目标是为那些不熟悉人工智能的人提供现代人工智能核心原理的图文指南，重点关注在 PET 成像中最有可能遇到的方面。我们描述了卷积神经网络、算法训练，并解释了常用的 U-Net 在分割和图像合成中的组件。

正电子发射计算机断层扫描中人工智能的解剖学和生理学

Anatomy and Physiology of Artificial Intelligence in PET Imaging

Large Language Models (LLMs), such as ChatGPT, are quickly advancing AI to
the frontiers of practical consumer use and leading industries to re-evaluate
how they allocate resources for content production. Authoring of open
educational resources and hint content within adaptive tutoring systems is
labor intensive. Should LLMs like ChatGPT produce educational content on par
with human-authored content, the implications would be significant for further
scaling of computer tutoring system approaches. In this paper, we conduct the
first learning gain evaluation of ChatGPT by comparing the efficacy of its
hints with hints authored by human tutors with 77 participants across two
algebra topic areas, Elementary Algebra and Intermediate Algebra. We find that
70% of hints produced by ChatGPT passed our manual quality checks and that both
human and ChatGPT conditions produced positive learning gains. However, gains
were only statistically significant for human tutor created hints. Learning
gains from human-created hints were substantially and statistically
significantly higher than ChatGPT hints in both topic areas, though ChatGPT
participants in the Intermediate Algebra experiment were near ceiling and not
even with the control at pre-test. We discuss the limitations of our study and
suggest several future directions for the field. Problem and hint content used
in the experiment is provided for replicability.

本文评估了 ChatGPT 的学习增益，比较其提示的功效，发现 ChatGPT 提示的质量虽比人工创作的提示部分高，但人工创作的提示所得的学习增益明显高于 ChatGPT，暗示其未来在教育领域中的应用潜力与局限性。