The aim of this paper is to categorize and describe different types of
learners in massive open online courses (MOOCs) by means of a subgroup
discovery approach based on MapReduce. The final objective is to discover
IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery
approach, which is an extension of the well-known FP-Growth algorithm,
considers emerging parallel methodologies like MapReduce to be able to cope
with extremely large datasets. As an additional feature, the proposal includes
a threshold value to denote the number of courses that each discovered rule
should satisfy. A post-processing step is also included so redundant subgroups
can be removed. The experimental stage is carried out by considering
de-identified data from the first year of 16 MITx and HarvardX courses on the
edX platform. Experimental results demonstrate that the proposed MapReduce
approach outperforms traditional sequential subgroup discovery approaches,
achieving a runtime that is almost constant for different courses.
Additionally, thanks to the final post-processing step, only interesting and
not-redundant rules are discovered, hence reducing the number of subgroups in
one or two orders of magnitude. Finally, the discovered subgroups are easily
used by courses' instructors not only for descriptive purposes but also for
additional tasks such as recommendation or personalization.

本文采用基于 MapReduce 的子群发现方法对海量在线公开课（MOOC）中不同类型的学习者进行分类和描述，旨在发现出现在不同 MOOC 中的 IF-THEN 规则。实验结果表明，该方法优于传统的顺序子群发现方法，运行时几乎保持不变，发现的子群也减少了一到两个数量级。此外，发现的子群可供课程教师用于描述以及推荐或个性化任务。

MOOC 中的子群发现：描述不同类型学习者的大数据应用

Subgroup Discovery in MOOCs: A Big Data Application for Describing  Different Types of Learners

We introduce a novel category of GC-agents capable of functioning as both
teachers and learners. Leveraging action-based demonstrations and
language-based instructions, these agents enhance communication efficiency. We
investigate the incorporation of pedagogy and pragmatism, essential elements in
human communication and goal achievement, enhancing the agents' teaching and
learning capabilities. Furthermore, we explore the impact of combining
communication modes (action and language) on learning outcomes, highlighting
the benefits of a multi-modal approach.

介绍了一种新型的 GC 代理，能够同时作为教师和学习者，通过基于行动的演示和基于语言的指令增强通信效率，并研究了教育学和实用主义在人类沟通和目标实现中的重要性，以提高代理的教学和学习能力。此外，还探讨了结合行动和语言通信模式对学习结果的影响，强调多模态方法的益处。

通过行动和语言增强代理通信和学习

Enhancing Agent Communication and Learning through Action and Language

Rapid progress in machine learning for natural language processing has the
potential to transform debates about how humans learn language. However, the
learning environments and biases of current artificial learners and humans
diverge in ways that weaken the impact of the evidence obtained from learning
simulations. For example, today's most effective neural language models are
trained on roughly one thousand times the amount of linguistic data available
to a typical child. To increase the relevance of learnability results from
computational models, we need to train model learners without significant
advantages over humans. If an appropriate model successfully acquires some
target linguistic knowledge, it can provide a proof of concept that the target
is learnable in a hypothesized human learning scenario. Plausible model
learners will enable us to carry out experimental manipulations to make causal
inferences about variables in the learning environment, and to rigorously test
poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in
humans on the basis of speculations about learnability. Comparable experiments
will never be possible with human subjects due to practical and ethical
considerations, making model learners an indispensable resource. So far,
attempts to deprive current models of unfair advantages obtain sub-human
results for key grammatical behaviors such as acceptability judgments. But
before we can justifiably conclude that language learning requires more prior
domain-specific knowledge than current models possess, we must first explore
non-linguistic inputs in the form of multimodal stimuli and multi-agent
interaction as ways to make our learners more efficient at learning from
limited linguistic input.

通过使用机器学习及自然语言处理技术，研究语言习得过程，探讨如何让模型学习在有限的语言输入下获取语言知识，以验证关于人类语言学习本质的假说。