Effective learning strategies based on principles like personalization,
retrieval practice, and spaced repetition are often challenging to implement
due to practical constraints. Here we explore the integration of AI tutors to
complement learning programs in accordance with learning sciences. A
semester-long study was conducted at UniDistance Suisse, where an AI tutor app
was provided to psychology students taking a neuroscience course (N=51). After
automatically generating microlearning questions from existing course materials
using GPT-3, the AI tutor developed a dynamic neural-network model of each
student's grasp of key concepts. This enabled the implementation of distributed
retrieval practice, personalized to each student's individual level and
abilities. The results indicate that students who actively engaged with the AI
tutor achieved significantly higher grades. Moreover, active engagement led to
an average improvement of up to 15 percentile points compared to a parallel
course without AI tutor. Additionally, the grasp strongly correlated with the
exam grade, thus validating the relevance of neural-network predictions. This
research demonstrates the ability of personal AI tutors to model human learning
processes and effectively enhance academic performance. By integrating AI
tutors into their programs, educators can offer students personalized learning
experiences grounded in the principles of learning sciences, thereby addressing
the challenges associated with implementing effective learning strategies.
These findings contribute to the growing body of knowledge on the
transformative potential of AI in education.

整合 AI 导师来补充学习程序，基于学习科学，个性化学习策略，神经网络模型和学术表现等主题为学生提供个性化学习体验。

使用个人 AI 导师实施学习原则的案例研究

Implementing Learning Principles with a Personal AI Tutor: A Case Study

Temporal networks serve as abstractions of many real-world dynamic systems.
These networks typically evolve according to certain laws, such as the law of
triadic closure, which is universal in social networks. Inductive
representation learning of temporal networks should be able to capture such
laws and further be applied to systems that follow the same laws but have not
been unseen during the training stage. Previous works in this area depend on
either network node identities or rich edge attributes and typically fail to
extract these laws. Here, we propose Causal Anonymous Walks (CAWs) to
inductively represent a temporal network. CAWs are extracted by temporal random
walks and work as automatic retrieval of temporal network motifs to represent
network dynamics while avoiding the time-consuming selection and counting of
those motifs. CAWs adopt a novel anonymization strategy that replaces node
identities with the hitting counts of the nodes based on a set of sampled walks
to keep the method inductive, and simultaneously establish the correlation
between motifs. We further propose a neural-network model CAW-N to encode CAWs,
and pair it with a CAW sampling strategy with constant memory and time cost to
support online training and inference. CAW-N is evaluated to predict links over
6 real temporal networks and uniformly outperforms previous SOTA methods by
averaged 10% AUC gain in the inductive setting. CAW-N also outperforms previous
methods in 4 out of the 6 networks in the transductive setting.

本文介绍了一种基于 Causal Anonymous Walks (CAWs) 的神经网络模型 CAW-N，用于利用匿名化的时空随机游走来表示动态网络，支持联机训练和推理，并在链接预测方面表现出优秀的性能。

通过因果匿名漫步进行时态网络中的归纳表征学习

Inductive Representation Learning in Temporal Networks via Causal  Anonymous Walks

This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the multilingual STS task which measures similarity across English, Spanish,
and Arabic.

本文介绍了一种基于注意力机制的循环神经网络模型，该模型在 2017 年 SemEval 跨语言语义文本相似度（STS）任务中取得了竞争性表现（前六名），并描述了我们参加跨语言 STS 任务的情况，该任务涉及英语、西班牙语和阿拉伯语的相似度比较。