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Mobile Learning Strategy Based on Principal Component Analysis

Author

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  • Qiongjie Kou

    (Xuchang University, China)

  • Quanyou Zhang

    (Xuchang University, China)

  • Laiqun Xu

    (Xuchang University, China)

  • Yaohui Li

    (Xuchang University, China)

  • Yong Feng

    (Chongqing University, China)

  • Huiting Wei

    (Xuchang University, China)

Abstract

Mobile learning is a kind of learning mode by using mobile devices, and it is an indispensable way of learning strategy in colleges and universities. The authors conducted the interviews and questionnaires about the teaching situation, learning strategies, using of network resources, and so on. Next, the authors checked and verified carefully the feedback data from classroom teaching. In the process of investigation, the students were divided into two groups. The authors analyzed the mean and standard deviation of the two groups of data tables. According to the data reliability analysis, exploratory factor analysis, significance analysis, the authors propose the teaching mode of “one heart, two sides and six links(OHTSSL)” based on mobile learning strategy. In order to construct new cognitive content and train students' innovation ability, teacher and students must implement the mobile learning strategy in classroom teaching. Teacher and students execute teaching process of six links based on OHTSSL teaching mode.

Suggested Citation

  • Qiongjie Kou & Quanyou Zhang & Laiqun Xu & Yaohui Li & Yong Feng & Huiting Wei, 2022. "Mobile Learning Strategy Based on Principal Component Analysis," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 14(3), pages 1-12, July.
    • Handle: RePEc:igg:jisss0:v:14:y:2022:i:3:p:1-12
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    2. Chen, Hao & Yang, Ni & Song, Xuanhua & Lu, Chunhua & Lu, Menglan & Chen, Tan & Deng, Shulin, 2025. "A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning," Agricultural Water Management, Elsevier, vol. 308(C).
    3. Nawin Raj & Jaishukh Murali & Lila Singh-Peterson & Nathan Downs, 2024. "Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia," Mathematics, MDPI, vol. 12(15), pages 1-25, July.
    4. Abugabr Elhag, Husam Eldin E. & Baleela, Rania M.H., 2025. "Preliminary investigation and analysis of the impact of the Sudan War on higher education and scientific research sectors," International Journal of Educational Development, Elsevier, vol. 112(C).
    5. Ning Guo & Li Xu & Wei Gao & Hongwei Xia & Min Xie & Xiaohan Ren, 2024. "Progress in the Application of Laser-Induced Breakdown Spectroscopy in Coal Quality Analysis," Energies, MDPI, vol. 17(14), pages 1-36, July.
    6. Neus Ortega Albero & Sara Vallejo Sardon & Ioan Lupuţ & Monica Boscaiu & Maria P. Donat-Torres & Ana Fita & Sara González-Orenga, 2024. "Sarcocornia fruticosa , a Potential Candidate for Saline Agriculture: Antioxidant Levels in Relation to Environmental Conditions in the Eastern Iberian Peninsula," Agriculture, MDPI, vol. 14(9), pages 1-15, September.
    7. Zhang, Xianggang & Ma, Shenggui & Wang, Xuya & He, Zhen & Chang, Yulong & Jiang, Xia, 2025. "Energy-efficient design of cyclone separators: Machine learning prediction of particle self-rotation velocities," Energy, Elsevier, vol. 316(C).
    8. Dua Weraikat & Kristina Šorič & Martin Žagar & Mateo Sokač, 2024. "Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices," Sustainability, MDPI, vol. 16(17), pages 1-12, August.
    9. Celli, Fabio, 2022. "Feature Engineering for Quantitative Analysis of Cultural Evolution," SocArXiv aj8xk, Center for Open Science.
    10. Katarzyna Ragin-Skorecka & Łukasz Hadaś, 2024. "Sustainable E-Procurement: Key Factors Influencing User Satisfaction and Dissatisfaction," Sustainability, MDPI, vol. 16(13), pages 1-13, July.
    11. Xu, Fanfan & Shao, Yihe & Zhang, Yanpeng & Zong, Peijie & Tian, Yuanyu & Wang, Jingxian & Qiao, Yingyun, 2025. "Parametric investigation of the effects on waste tire pyrolysis oil in a downdraft tube reactor," Energy, Elsevier, vol. 314(C).

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