A personalized recommendation system for teaching resources in sports using fuzzy C-means clustering technique
Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education insti...
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| Vydáno v: | Soft computing (Berlin, Germany) Ročník 28; číslo 1; s. 703 - 720 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2024
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy
C
-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching. |
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| AbstractList | Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy
C
-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching. |
| Author | Chen, Jiayong Zhong, Yize Zhou, Guangzhen |
| Author_xml | – sequence: 1 givenname: Jiayong surname: Chen fullname: Chen, Jiayong email: palermo16@163.com organization: School of Physical Education and Sports, Central China Normal University – sequence: 2 givenname: Guangzhen surname: Zhou fullname: Zhou, Guangzhen organization: School of Physical Education and Sports, Central China Normal University – sequence: 3 givenname: Yize surname: Zhong fullname: Zhong, Yize organization: School of Physical Education and Sports, Central China Normal University |
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| Cites_doi | 10.1007/s11071-018-4732-x 10.3390/app12084059 10.1007/s10639-019-10027-z 10.1049/cth2.12136 10.1007/s11042-023-16852-2 10.23919/CCC50068.2020.9188843 10.33365/jeltl.v3i1.1694 10.3233/IDA-173442 10.1007/s00500-023-09037-4 10.3390/app12073416 10.1002/asjc.2762 10.1016/j.edurev.2021.100394 10.3389/fpsyg.2021.616059 10.1002/rnc.4839 10.23919/ChiCC.2019.8866334 10.1117/12.2540362 10.1007/s00500-023-09164-y 10.7717/peerj-cs.908 10.1088/1742-6596/1437/1/012024 10.1007/s00500-023-09278-3 10.21839/jaar.2018.v3iS1.165 10.23919/ChiCC.2017.8028015 10.1049/iet-cta.2018.5469 10.1111/coin.12086 10.1007/s00521-018-3510-5 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Personalized teaching Fuzzy clustering algorithm Data mining Recommendation of teaching resources |
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| References | ChenGChenPHuangWZhaiJContinuance intention mechanism of middle school student users on online learning platform based on qualitative comparative analysis methodMath Probl Eng20222022112 AslamMSDaiXHouJLiQUllahRNiZLiuYReliable control design for composite-driven scheme based on delay networked T–S fuzzy systemInt J Robust Nonlinear Control202030416221642408539310.1002/rnc.4839 XuHSunZCaoYA data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of ThingsSoft Comput202310.1007/s00500-023-09037-4 CastroMDBTumibayGMA literature review: efficacy of online learning courses for higher education institution using meta-analysisEduc Inf Technol2021261367138510.1007/s10639-019-10027-z LiFChenYJDesign and simulation of intelligent matching algorithm for online mathematics courses based on fuzzy clusteringModern Electron Technol20214416125128 UllahRDaiXShengAEvent-triggered scheme for fault detection and isolation of non-linear system with time-varying delayIET Control Theory Appl2020141624292438441797310.1049/iet-cta.2018.5469 WuQLiXWangKRegional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehiclesSoft Comput202327181951821310.1007/s00500-023-09278-3 ZhangJLiCXZhuRHMassive video teaching resource management system based on cloud platformModern Electron Technol20214321151155 OdeeshJSalihSThe role of green human resource management functions in promoting blue ocean strategyHum J202081129145 Yao W, Guo Y, Wu Y, Guo J (2017 July) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese control conference (CCC). IEEE, pp 4192–4197. https://doi.org/10.23919/ChiCC.2017.8028015 YangFZhangJTraditional Chinese sports under China’s health strategyJ Environ Public Health202220221 ZhengWTianXYangBLiuSDingYTianJYinLA few shot classification methods based on multiscale relational networksAppl Sci202212405910.3390/app12084059 DouHLiuYChenSA hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highwaysSoft Comput202327163731638810.1007/s00500-023-09164-y Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceedings of the SPIE 11198, 4th international workshop on pattern recognition, 111980T (31 July 2019). https://doi.org/10.1117/12.2540362 ZhangHWLDA based personalized teaching resource recommendation systemModern Inf Technol20182101820 EscrivaboulleyGTessierDNtoumanisNNeed-supportive professional development in elementary school physical education: effects of a cluster-randomized control trial on teachers' motivating style and student physical activitySport Exerc Perform Psychol201872218234 ZhengWZhouYLiuSTianJYangBYinLA deep fusion matching network semantic reasoning modelAppl Sci202212341610.3390/app12073416 YanLQiLResearch on Big Data mining technology of mobile learning system based on android platformModern Electron Technol20174019142144 ShamroozMLiQHouJFault detection for asynchronous T–S fuzzy networked Markov jump systems with new event-triggered schemeIET Control Theory Appl2021151114611473458335110.1049/cth2.12136 LiTFanYLiYTarkomaSHuiPUnderstanding the long-term evolution of mobile app usageIEEE Trans Mobile Comput202120211 HuTDesign of English teaching resource management system based on collaborative recommendationAutom Technol Appl2019389158161 MitchellAPetterSHarrisALLearning by doing: twenty successful active learning exercises for information systems coursesJ Inf Technol Educ Innov Pract20171612146 ThejaswiniNAdityaCRSmart E-commerce recommendation system for handling limited resource and cold start problemInt J Comput Sci Eng201975961964 Yin B, Khan J, Wang L, Zhang J, Kumar A (2019 July) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese control conference (CCC). IEEE, pp 6772–6777. https://doi.org/10.23919/ChiCC.2019.8866334 ChengYXuBResearch on key technologies of personalized education resource recommendation system based on big data environmentJ Phys Conf Ser20201437101202410.1088/1742-6596/1437/1/012024 MahboobVAJalaliMJahanMVSwallow: resource and tag recommender system based on heat diffusion algorithm in social annotation systemsComput Intell201733199118361529010.1111/coin.12086 OgunleyeAAnyaegbunaBEAn assessment of physics laboratory teaching and learning resources in two Nigerian universitiesCypriot J Educ Sci20183713114 WangGSYuanHLHuangXJNetwork learning resource recommendation algorithm based on improved collaborative filteringMinicomput Syst2021425940945 ChenZObserver-based dissipative output feedback control for network T–S fuzzy systems under time delays with mismatch premiseNonlinear Dyn2019952923294110.1007/s11071-018-4732-x ErlanggaDTStudent problems in online learning: solutions to keep education going onJ Engl Lang Teach Learn202231212610.33365/jeltl.v3i1.1694 KrishnanGKParthasarathyMSasidharDEmotion detection and music recommendation system using machine learningInt J Pure Appl Math20181191514871497 García-MoralesVJGarrido-MorenoAMartín-RojasRThe transformation of higher education after the COVID disruption: emerging challenges in an online learning scenarioFront Psychol20211261605910.3389/fpsyg.2021.616059 MüllerCMildenbergerTFacilitating flexible learning by replacing classroom time with an online learning environment: a systematic review of blended learning in higher educationEduc Res Rev20213410039410.1016/j.edurev.2021.100394 AliMYinBBilalHAdvanced efficient strategy for detection of dark objects based on spiking network with multi-box detectionMultimed Tools Appl202310.1007/s11042-023-16852-2 AshrafMAYangMZhangYDendenMTliliALiuJHuangRBurgosDA systematic review of systematic reviews on blended learning: trends, gaps, and future directionsPsychol Res Behav Manag20211525–15412021 RajaRNagasubramaniPCImpact of modern technology in educationJ Appl Adv Res201831333510.21839/jaar.2018.v3iS1.165 Ali M, Yin B, Kumar A, Sheikh AM et al (2020 July) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese control conference (CCC). IEEE, pp 7406–7411. https://doi.org/10.23919/CCC50068.2020.9188843 HanYConstruction of online educational resources recommendation model based on subject wordsAutom Technol Appl2019389170173 LiHLiHZhangSZhongZChengJIntelligent learning system based on personalized recommendation technologyNeural Comput Appl2019314455446210.1007/s00521-018-3510-5 LeninMWilliamEMaitrayiMPersonalized news recommendation using graph-based approachIntell Data Anal201822488190910.3233/IDA-173442 QaisarMIMajidAShamroozSAdaptive event-triggered robust H∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time-varying delayAsian J Control2023251213228456232910.1002/asjc.2762 ZhengWYinLCharacterization inference based on joint-optimization of multi-layer semantics and deep fusion matching networkPeerJ Comput Sci20228e90810.7717/peerj-cs.908 GråsténAWattAA motivational model of physical education and links to enjoyment, knowledge, performance, total physical activity and body mass indexJ Sports Sci Med2017163318327 MingQYAnalysis and design of video teaching recommendation system based on personalizationMicrocomput Appl20193593336 M Ali (9421_CR2) 2023 A Mitchell (9421_CR23) 2017; 16 Z Chen (9421_CR6) 2019; 95 H Li (9421_CR19) 2019; 31 W Zheng (9421_CR43) 2022; 12 G Escrivaboulley (9421_CR11) 2018; 7 R Ullah (9421_CR31) 2020; 14 F Li (9421_CR18) 2021; 44 C Müller (9421_CR24) 2021; 34 T Hu (9421_CR15) 2019; 38 H Xu (9421_CR35) 2023 T Li (9421_CR20) 2021; 2021 M Lenin (9421_CR17) 2018; 22 MS Aslam (9421_CR4) 2020; 30 HW Zhang (9421_CR40) 2018; 2 Y Han (9421_CR14) 2019; 38 MDB Castro (9421_CR5) 2021; 26 Y Cheng (9421_CR8) 2020; 1437 MI Qaisar (9421_CR27) 2023; 25 MA Ashraf (9421_CR3) 2021; 1525–1541 W Zheng (9421_CR44) 2022; 12 Q Wu (9421_CR34) 2023; 27 W Zheng (9421_CR42) 2022; 8 N Thejaswini (9421_CR30) 2019; 7 9421_CR1 DT Erlangga (9421_CR10) 2022; 3 R Raja (9421_CR28) 2018; 3 A Ogunleye (9421_CR26) 2018; 37 A Gråstén (9421_CR13) 2017; 16 L Yan (9421_CR36) 2017; 40 F Yang (9421_CR37) 2022; 2022 9421_CR38 VJ García-Morales (9421_CR12) 2021; 12 9421_CR39 GS Wang (9421_CR33) 2021; 42 J Zhang (9421_CR41) 2021; 43 G Chen (9421_CR7) 2022; 2022 H Dou (9421_CR9) 2023; 27 GK Krishnan (9421_CR16) 2018; 119 J Odeesh (9421_CR25) 2020; 8 9421_CR32 VA Mahboob (9421_CR21) 2017; 33 QY Ming (9421_CR22) 2019; 35 M Shamrooz (9421_CR29) 2021; 15 |
| References_xml | – reference: EscrivaboulleyGTessierDNtoumanisNNeed-supportive professional development in elementary school physical education: effects of a cluster-randomized control trial on teachers' motivating style and student physical activitySport Exerc Perform Psychol201872218234 – reference: ZhangJLiCXZhuRHMassive video teaching resource management system based on cloud platformModern Electron Technol20214321151155 – reference: GråsténAWattAA motivational model of physical education and links to enjoyment, knowledge, performance, total physical activity and body mass indexJ Sports Sci Med2017163318327 – reference: MitchellAPetterSHarrisALLearning by doing: twenty successful active learning exercises for information systems coursesJ Inf Technol Educ Innov Pract20171612146 – reference: WuQLiXWangKRegional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehiclesSoft Comput202327181951821310.1007/s00500-023-09278-3 – reference: Ali M, Yin B, Kumar A, Sheikh AM et al (2020 July) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese control conference (CCC). IEEE, pp 7406–7411. https://doi.org/10.23919/CCC50068.2020.9188843 – reference: XuHSunZCaoYA data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of ThingsSoft Comput202310.1007/s00500-023-09037-4 – reference: KrishnanGKParthasarathyMSasidharDEmotion detection and music recommendation system using machine learningInt J Pure Appl Math20181191514871497 – reference: HuTDesign of English teaching resource management system based on collaborative recommendationAutom Technol Appl2019389158161 – reference: ZhengWYinLCharacterization inference based on joint-optimization of multi-layer semantics and deep fusion matching networkPeerJ Comput Sci20228e90810.7717/peerj-cs.908 – reference: AliMYinBBilalHAdvanced efficient strategy for detection of dark objects based on spiking network with multi-box detectionMultimed Tools Appl202310.1007/s11042-023-16852-2 – reference: MingQYAnalysis and design of video teaching recommendation system based on personalizationMicrocomput Appl20193593336 – reference: ErlanggaDTStudent problems in online learning: solutions to keep education going onJ Engl Lang Teach Learn202231212610.33365/jeltl.v3i1.1694 – reference: García-MoralesVJGarrido-MorenoAMartín-RojasRThe transformation of higher education after the COVID disruption: emerging challenges in an online learning scenarioFront Psychol20211261605910.3389/fpsyg.2021.616059 – reference: LiHLiHZhangSZhongZChengJIntelligent learning system based on personalized recommendation technologyNeural Comput Appl2019314455446210.1007/s00521-018-3510-5 – reference: OdeeshJSalihSThe role of green human resource management functions in promoting blue ocean strategyHum J202081129145 – reference: MüllerCMildenbergerTFacilitating flexible learning by replacing classroom time with an online learning environment: a systematic review of blended learning in higher educationEduc Res Rev20213410039410.1016/j.edurev.2021.100394 – reference: OgunleyeAAnyaegbunaBEAn assessment of physics laboratory teaching and learning resources in two Nigerian universitiesCypriot J Educ Sci20183713114 – reference: ShamroozMLiQHouJFault detection for asynchronous T–S fuzzy networked Markov jump systems with new event-triggered schemeIET Control Theory Appl2021151114611473458335110.1049/cth2.12136 – reference: MahboobVAJalaliMJahanMVSwallow: resource and tag recommender system based on heat diffusion algorithm in social annotation systemsComput Intell201733199118361529010.1111/coin.12086 – reference: RajaRNagasubramaniPCImpact of modern technology in educationJ Appl Adv Res201831333510.21839/jaar.2018.v3iS1.165 – reference: CastroMDBTumibayGMA literature review: efficacy of online learning courses for higher education institution using meta-analysisEduc Inf Technol2021261367138510.1007/s10639-019-10027-z – reference: LeninMWilliamEMaitrayiMPersonalized news recommendation using graph-based approachIntell Data Anal201822488190910.3233/IDA-173442 – reference: YanLQiLResearch on Big Data mining technology of mobile learning system based on android platformModern Electron Technol20174019142144 – reference: YangFZhangJTraditional Chinese sports under China’s health strategyJ Environ Public Health202220221 – reference: AshrafMAYangMZhangYDendenMTliliALiuJHuangRBurgosDA systematic review of systematic reviews on blended learning: trends, gaps, and future directionsPsychol Res Behav Manag20211525–15412021 – reference: ChenGChenPHuangWZhaiJContinuance intention mechanism of middle school student users on online learning platform based on qualitative comparative analysis methodMath Probl Eng20222022112 – reference: UllahRDaiXShengAEvent-triggered scheme for fault detection and isolation of non-linear system with time-varying delayIET Control Theory Appl2020141624292438441797310.1049/iet-cta.2018.5469 – reference: HanYConstruction of online educational resources recommendation model based on subject wordsAutom Technol Appl2019389170173 – reference: Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. 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| Title | A personalized recommendation system for teaching resources in sports using fuzzy C-means clustering technique |
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