Stable Parallel Algorithms for Interdisciplinary Computer-Based Online Education with Real Problem Scenarios for STEM Education

In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdis...

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Published in:Complexity (New York, N.Y.) Vol. 2021; no. 1
Main Authors: Jiang, Liangfu, Yuan, Haoran
Format: Journal Article
Language:English
Published: Hoboken Hindawi 2021
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
CAI
Communication skills
Computer assisted instruction
Creative ability
Curricula
Education
Energy
Junior high school students
Knowledge
Learning
Mathematics education
Neural networks
Online education
Overflow
Problem solving
Product reviews
Project design
Recommender systems
Robotics
Science education
Secondary schools
Sparsity
STEM education
Students
Teaching methods
Technical education
Technology education
Training
ISSN:1076-2787, 1099-0526
Online Access:Get full text
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Abstract In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdisciplinary knowledge points related to the theme are determined. Based on the project theme, the teaching design model of the STEM-based robotics project was constructed. The analysis of the results showed that the STEM-based middle-school robotics project not only increased students’ interest in robotics but also promoted their creativity, problem-solving, teamwork, and communication skills, as well as their learning of other subjects. In this paper, an adaptive overflow-aware loss amplification strategy is proposed, which effectively alleviates the training nonconvergence problem caused by gradient overflow in mixed accuracy training. Also, this paper demonstrates that data-parallel training under multiple machines and multiple cards should use local batch normalization, which is particularly effective in accelerating neural networks containing more batch normalization layers.
AbstractList In this paper, we analyse and study the interdisciplinary style of stable parallel algorithms for online computer education and real problem scenarios for STEM education. Under the guidance of the STEM concept, the project theme is designed based on the principles of project design, and the interdisciplinary knowledge points related to the theme are determined. Based on the project theme, the teaching design model of the STEM-based robotics project was constructed. The analysis of the results showed that the STEM-based middle-school robotics project not only increased students’ interest in robotics but also promoted their creativity, problem-solving, teamwork, and communication skills, as well as their learning of other subjects. In this paper, an adaptive overflow-aware loss amplification strategy is proposed, which effectively alleviates the training nonconvergence problem caused by gradient overflow in mixed accuracy training. Also, this paper demonstrates that data-parallel training under multiple machines and multiple cards should use local batch normalization, which is particularly effective in accelerating neural networks containing more batch normalization layers.
Audience Academic
Author Yuan, Haoran
Jiang, Liangfu
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Cites_doi 10.1007/s40333-018-0091-1
10.1080/17450101.2020.1806509
10.1016/j.tips.2019.06.004
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10.1007/s00146-020-01033-8
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ContentType Journal Article
Copyright Copyright © 2021 Liangfu Jiang and Haoran Yuan.
COPYRIGHT 2021 John Wiley & Sons, Inc.
Copyright © 2021 Liangfu Jiang and Haoran Yuan. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms Algorithms
CAI
Communication skills
Computer assisted instruction
Creative ability
Curricula
Education
Energy
Junior high school students
Knowledge
Learning
Mathematics education
Neural networks
Online education
Overflow
Problem solving
Product reviews
Project design
Recommender systems
Robotics
Science education
Secondary schools
Sparsity
STEM education
Students
Teaching methods
Technical education
Technology education
Training
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Title Stable Parallel Algorithms for Interdisciplinary Computer-Based Online Education with Real Problem Scenarios for STEM Education
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