Open conversations are one of the most engaging forms of teaching. However,
creating those conversations in educational software is a complex endeavor,
especially if we want to address the needs of different audiences. While
language models hold great promise for educational applications, there are
substantial challenges in training them to engage in meaningful and effective
conversational teaching, especially when considering the diverse needs of
various audiences. No official data sets exist for this task to facilitate the
training of language models for conversational teaching, considering the
diverse needs of various audiences. This paper presents a novel source for
facilitating conversational teaching of scientific concepts at various
difficulty levels (from preschooler to expert), namely dialogues taken from
video transcripts. We analyse this data source in various ways to show that it
offers a diverse array of examples that can be used to generate contextually
appropriate and natural responses to scientific topics for specific target
audiences. It is a freely available valuable resource for training and
evaluating conversation models, encompassing organically occurring dialogues.
While the raw data is available online, we provide additional metadata for
conversational analysis of dialogues at each level in all available videos.

该论文介绍了一种用于各类难度级别的科学概念对话教学的新方法，并通过对视频转录的对话进行分析，展示了其中多样化的例子可以用于为特定目标受众生成感性合适和自然的回应。这是一个对话模型的有价值资源，可以用于训练和评估，涵盖了有机发生的对话。

以对话为基础在不同教育层次教授科学概念

Conversations as a Source for Teaching Scientific Concepts at Different  Education Levels

The ThEdu series pursues the smooth transition from an intuitive way of doing
mathematics at secondary school to a more formal approach to the subject in
STEM education, while favouring software support for this transition by
exploiting the power of theorem-proving technologies. What follows is a brief
description of how the present volume contributes to this enterprise. The 11th
International Workshop on Theorem Proving Components for Educational Software
(ThEdu'22), was a satellite event of the 8th Federated Logic Conference (FLoC
2022), July 31-August 12, 2022, Haifa, Israel ThEdu'22 was a vibrant workshop,
with two invited talk by Thierry Dana-Picard (Jerusalem College of Technology,
Jerusalem, Israel) and Yoni Zohar (Bar Ilan University, Tel Aviv, Israel) and
four contributions. An open call for papers was then issued, and attracted
seven submissions. Those submissions have been accepted by our reviewers, who
jointly produced at least three careful reports on each of the contributions.
The resulting revised papers are collected in the present volume. The
contributions in this volume are a faithful representation of the wide spectrum
of ThEdu, ranging from those more focused on the automated deduction research,
not losing track of the possible applications in an educational setting, to
those focused on the applications, in educational settings, of automated
deduction tools and methods. We, the volume editors, hope that this collection
of papers will further promote the development of theorem-proving based
software, and that it will allow to improve the mutual understanding between
computer scientists, mathematicians and stakeholders in education. While this
volume goes to press, the next edition of the ThEdu workshop is being prepared:
ThEdu'23 will be a satellite event of the 29th international Conference on
Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy.

本文介绍了 ThEdu 系列以及本卷在 STEM 教育中应用定理证明技术的自动演绎和应用于教育软件的方法和工具在教育方面的应用。

第 11 届定理证明教育软件组件国际研讨会

Proceedings 11th International Workshop on Theorem Proving Components for Educational Software

In the study of human learning, there is broad evidence that our ability to
retain information improves with repeated exposure and decays with delay since
last exposure. This plays a crucial role in the design of educational software,
leading to a trade-off between teaching new material and reviewing what has
already been taught. A common way to balance this trade-off is spaced
repetition, which uses periodic review of content to improve long-term
retention. Though spaced repetition is widely used in practice, e.g., in
electronic flashcard software, there is little formal understanding of the
design of these systems. Our paper addresses this gap in three ways. First, we
mine log data from spaced repetition software to establish the functional
dependence of retention on reinforcement and delay. Second, we use this memory
model to develop a stochastic model for spaced repetition systems. We propose a
queueing network model of the Leitner system for reviewing flashcards, along
with a heuristic approximation that admits a tractable optimization problem for
review scheduling. Finally, we empirically evaluate our queueing model through
a Mechanical Turk experiment, verifying a key qualitative prediction of our
model: the existence of a sharp phase transition in learning outcomes upon
increasing the rate of new item introductions.

使用间隔重复系统的日志数据，针对人类学习中重复学习的作用，在 Leitner 系统中优化复习进度，通过机械土耳其实验验证新物品引入的速率对学习结果的影响等方面进行研究。