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

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.