In order to successfully attain the goals of "peak carbon emissions and carbon neutrality," a comprehensive and transformative shift is imperative. The green and sustainable development of organizations is progressively recognized as a vital strategic priority for achieving the "dual carbon" objectives and driving high-quality development. Within the present context, the importance of incorporating green human resource management (GHRM) practices into organizational frameworks to foster understanding and implementation of sustainable practices becomes increasingly crucial. Consequently, this study seeks to fill information gaps by studying the relationship between GHRM practices and their impact on long-term development outcomes. Depend on the social exchange theory, an empirical approach using a questionnaire survey was employed, and statistical analysis techniques were applied to test the hypotheses. The findings demonstrate the positive influence of GHRM on organizational sustainable development performance by emphasizing the mediating function of employee green behavior and identifying the positive moderating effect of green self-efficacy. This research enhances our understanding of the relationship between GHRM and organizational sustainable development and has important implications for organizations aiming to improve sustainability practices and performance outcomes.
KEYWORDS: Machine learning, Online learning, Data modeling, Decision trees, Performance modeling, Evolutionary algorithms, Data mining, Education and training
With the reform of information-based education, online learning has become a regular way of learning. In order to better help online learners to learn and promote the improvement of teachers' online teaching quality. In this study, the machine learning algorithm was used to mine the data such as the online live class learning behavior of university students on the Super Star Learning platform through Python software, and the online teaching effect prediction model was constructed to better predict the learning effect evaluation of online learners.
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