Informational architectures of asynchronous and synchronous learning models by Lina Jarmuškaitė, Regina Kulvietienė, Irma Šileikienė

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Assessment, creativity and learning: A personal perspective by Richard Hickman

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Deep Learning in Nuclear Industry: A Survey by Chenwei Tang, Caiyang Yu, Yi Gao, Jianming Chen, Jiaming Yang, Jiuling Lang, Chuan Liu, Ling Zhong, Zhenan He, Jiancheng Lv

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Spatial Reasoning of Students With Mathematical Learning Difficulties by Alfin Khoiro Amalia, Mohammad Faizal Amir

“…Meanwhile, primary school students often experience mathematical learning difficulties (MLD) using spatial reasoning. …”
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Collaborative Online Learning: Plurilingual and Pluricultural Development by O. Polyakova, I. N. Goryacheva, R. Galstyan-Sargsyan

Subjects: “…collaborative online international learning (coil)…”
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Student participation rate in their learning self-assessment by Liliam Barrios Herrero, Eduardo de Jesús Pomares Bory, Dayamí Maza Ares

“…<p><strong>Foundation:</strong> self-assessment contributes to the self-regulation of learning by students, so it is convenient to monitor this activity. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

Subjects: “…online learning…”
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Motor-Skill Learning Is Dependent on Astrocytic Activity by Ragunathan Padmashri, Anand Suresh, Michael D. Boska, Anna Dunaevsky

“…Motor-skill learning induces changes in synaptic structure and function in the primary motor cortex through the involvement of a long-term potentiation- (LTP-) like mechanism. …”
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Big Data Deep Learning: Challenges and Perspectives by Xue-Wen Chen, Xiaotong Lin

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Editorial: Mental health, epidemiology and machine learning by Marcos DelPozo-Banos, Robert Stewart, Robert Stewart, Ann John

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LOGICAL AND COMPETENCY APPROACH IN LEARNING SPORTS DISCIPLINES by Yury D. Ovchinnikov

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An Active Learning Approach with Uncertainty, Representativeness, and Diversity by Tianxu He, Shukui Zhang, Jie Xin, Pengpeng Zhao, Jian Wu, Xuefeng Xian, Chunhua Li, Zhiming Cui

“…Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. …”
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Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

“…This paper explores the applications of machine learning in the prediction of anemia, highlighting its potential to revolutionize clinical diagnosis and management. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

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Storytelling: recontextualizing language in literacy for student learning by Luciana Backes, Vera Lúcia Felicetti, Fabiane Aparecida Parcianello de Almeida

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Preliminary study on the reform of machine learning teaching by Nan WEI, Lihua YIN, Hong NING, Binxing FANG

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Learning Spaces as Urban Places: Issues and Perspectives by Cristina Renzoni, Paola Savoldi

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Serial reversal learning in nectar-feeding bats by Shambhavi Chidambaram, Sabine Wintergerst, Alex Kacelnik, Vladislav Nachev, York Winter

Subjects: “…Serial reversal learning…”
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Enhanced Learning Behaviors and Ability Knowledge Tracing by Fanglan Ma, Changsheng Zhu, Peng Lei, Peiwen Yuan

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Auto-learning TSN: improvements on TSN usability by Tongtong WANG, Jiayi ZHANG, Lihao CHEN, Xuesong GENG

“…., industrial control) require networks to provide deterministic transmission services, and traditional statistical multiplexing networks can not meet the needs of this type of applications.IEEE 802.1 TSN standard and related research can provide deterministic networking services over Ethernet.The basic principles of TSN were described briefly, the key queuing mechanisms of TSN by using network calculus were analyzed quantitatively, and an auto-learning TSN mechanism was proposed to adaptively support non-TSN device connections by network intelligence.Together with delay guarantee algorithms, a E2E TSN solution can be formed and used for the further promotion and application of TSN technology.…”
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