Design of network English autonomous learning education system based on human-computer interaction

The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive f...
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Gespeichert in:

Autor*in:	Xin Wang [verfasserIn] Simon Smith [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	human-computer interaction (HCI) autonomous English learning system (AELS) Optimized Deep Learning Network (ODNN) independent learning education system

Übergeordnetes Werk:	In: Frontiers in Psychology - Frontiers Media S.A., 2010, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fpsyg.2022.989884

Katalog-ID:	DOAJ034145672

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spelling	10.3389/fpsyg.2022.989884 doi (DE-627)DOAJ034145672 (DE-599)DOAJ922a917f05ad47e6a1490874b63003d8 DE-627 ger DE-627 rakwb eng BF1-990 Xin Wang verfasserin aut Design of network English autonomous learning education system based on human-computer interaction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning. human-computer interaction (HCI) autonomous English learning system (AELS) Optimized Deep Learning Network (ODNN) independent learning education system Psychology Simon Smith verfasserin aut In Frontiers in Psychology Frontiers Media S.A., 2010 13(2022) (DE-627)631495711 (DE-600)2563826-9 16641078 nnns volume:13 year:2022 https://doi.org/10.3389/fpsyg.2022.989884 kostenfrei https://doaj.org/article/922a917f05ad47e6a1490874b63003d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyg.2022.989884/full kostenfrei https://doaj.org/toc/1664-1078 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
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allfieldsSound	10.3389/fpsyg.2022.989884 doi (DE-627)DOAJ034145672 (DE-599)DOAJ922a917f05ad47e6a1490874b63003d8 DE-627 ger DE-627 rakwb eng BF1-990 Xin Wang verfasserin aut Design of network English autonomous learning education system based on human-computer interaction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning. human-computer interaction (HCI) autonomous English learning system (AELS) Optimized Deep Learning Network (ODNN) independent learning education system Psychology Simon Smith verfasserin aut In Frontiers in Psychology Frontiers Media S.A., 2010 13(2022) (DE-627)631495711 (DE-600)2563826-9 16641078 nnns volume:13 year:2022 https://doi.org/10.3389/fpsyg.2022.989884 kostenfrei https://doaj.org/article/922a917f05ad47e6a1490874b63003d8 kostenfrei https://www.frontiersin.org/articles/10.3389/fpsyg.2022.989884/full kostenfrei https://doaj.org/toc/1664-1078 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
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callnumber-first	B - Philosophy, Psychology, Religion
author	Xin Wang
spellingShingle	Xin Wang misc BF1-990 misc human-computer interaction (HCI) misc autonomous English learning system (AELS) misc Optimized Deep Learning Network (ODNN) misc independent learning misc education system misc Psychology Design of network English autonomous learning education system based on human-computer interaction
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topic_title	BF1-990 Design of network English autonomous learning education system based on human-computer interaction human-computer interaction (HCI) autonomous English learning system (AELS) Optimized Deep Learning Network (ODNN) independent learning education system
topic	misc BF1-990 misc human-computer interaction (HCI) misc autonomous English learning system (AELS) misc Optimized Deep Learning Network (ODNN) misc independent learning misc education system misc Psychology
topic_unstemmed	misc BF1-990 misc human-computer interaction (HCI) misc autonomous English learning system (AELS) misc Optimized Deep Learning Network (ODNN) misc independent learning misc education system misc Psychology
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title	Design of network English autonomous learning education system based on human-computer interaction
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title_full	Design of network English autonomous learning education system based on human-computer interaction
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title_sort	design of network english autonomous learning education system based on human-computer interaction
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title_auth	Design of network English autonomous learning education system based on human-computer interaction
abstract	The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning.
abstractGer	The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning.
abstract_unstemmed	The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning.
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title_short	Design of network English autonomous learning education system based on human-computer interaction
url	https://doi.org/10.3389/fpsyg.2022.989884 https://doaj.org/article/922a917f05ad47e6a1490874b63003d8 https://www.frontiersin.org/articles/10.3389/fpsyg.2022.989884/full https://doaj.org/toc/1664-1078
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up_date	2024-07-03T21:40:33.394Z
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Design of network English autonomous learning education system based on human-computer interaction

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