skip to main content
10.1145/3660043.3660082acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicieaiConference Proceedingsconference-collections
research-article

Data-Driven Online-Merge-Offline Teaching for New Employee Training of State Grid

Published: 30 May 2024 Publication History

Abstract

In the era of digital transformation, organizations are seeking innovative approaches to enhance employee training effectiveness. This study proposes a data-driven online-merge-offline teaching method for new employee training at State Grid, aiming to optimize the learning experience and accelerate knowledge retention. The proposed approach combines digital platforms and traditional offline training methods to create a comprehensive training program. Leveraging advanced data analytics techniques, learner data is collected and analyzed to identify individual strengths and weaknesses, enabling customized training content delivery. Online modules provide employees with interactive multimedia resources, including video lectures, simulations, and quizzes, fostering self-paced learning and engagement. Real-time monitoring enables trainers to track learners' progress and provide timely feedback and support. Offline components, such as workshops and group discussions, facilitate collaboration, problem-solving, and practical application of knowledge. Through a seamless combination of online and offline activities, employees can reinforce their learning and acquire hands-on skills. Moreover, the data-driven approach allows trainers to continually evaluate training effectiveness and make necessary adjustments based on learner performance analysis. This iterative process ensures the optimization of training outcomes. Preliminary results indicate that the data-driven online-merge-offline teaching method has significantly improved the efficiency and effectiveness of new employee training at State Grid. Enhanced engagement, personalized learning experiences, and practical skill development contribute to a skilled workforce. Future research could explore the scalability and adaptability of this approach in other industries and organizations.

References

[1]
Xiao J, Sun-Lin H Z, Cheng H C. A framework of online-merge-offline (OMO) classroom for open education: A preliminary study [J]. Asian Association of Open Universities Journal, 2019, 14(2): 134-146.
[2]
Huang R, Tlili A, Wang H, Shi Y, Bonk C J, Yang J, Burgos D. Emergence of the Online-Merge-Offline (OMO) learning wave in the post-COVID-19 era: a pilot study [J]. Sustainability, 2021, 13, 3512.
[3]
Yu H, Shi G, Li J, Yang J. Analyzing the differences of interaction and engagement in a smart classroom and a traditional classroom [J]. Sustainability, 2022, 14, 8184.
[4]
Chatti M A, Dyckhoff A L, Schroeder U, Hendrik Thüs. A reference model for learning analytics [J]. International Journal of Technology Enhanced Learning, 2012, 4(5/6): 318-331.
[5]
Felder R M, Silverman L K. Learning and teaching styles in engineering education [J]. Journal of Engineering Education, 1998, (7): 674-681.
[6]
Fredricks J A, Blumenfeld P C, Paris A H. School engagement: potential of the concept, state of the evidence [J]. Review of Educational Research, 2004, 74(1): 59-109.
[7]
Hubel D H, Wiesel T N. Receptive fields and functional architecture of monkey striate cortex [J]. The Journal of Physiology, 1968, 195(1): 215-243.
[8]
Kim Y. Convolutional neural networks for sentence classification [C]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014.
[9]
Zhang X, Sun L. Research on the influencing factors of open education students online learning satisfaction based on interpretive structure model [J]. Adult Education, 2022, 42(03): 48-57.
[10]
Kuo Y C, Walker A E, Schroder K E, E Belland B R. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses [J]. Internet & Higher Education, 2014, 20, 35-50.

Index Terms

  1. Data-Driven Online-Merge-Offline Teaching for New Employee Training of State Grid

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
        December 2023
        1132 pages
        ISBN:9798400716157
        DOI:10.1145/3660043
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 30 May 2024

        Permissions

        Request permissions for this article.

        Check for updates

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        ICIEAI 2023

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 22
          Total Downloads
        • Downloads (Last 12 months)22
        • Downloads (Last 6 weeks)6
        Reflects downloads up to 20 Feb 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media