The six core competencies of mathematics in vocational schools are becoming increasingly important in mathematics learning. The reverse teaching design of vocational school mathematics, which focuses on core competencies, precisely grasps the internal logic of knowledge from a holistic perspective, and designs it on a unit by unit basis. The design process is to infer the starting point from the endpoint. Therefore, how to use reverse thinking in teaching design research in vocational school mathematics teaching under the background of core competencies will be the main content of this article.

Performance Management is a major concern to various stakeholders in Education System, it is considered to be key driver to improve school effectiveness and learning quality. However, the complexity of education Systems, has made it challenging to apply an effective PM model. This study paper introduces a maturity model with six dimensions, fifteen Capability Areas and forty-two Best-Practices to assess education systems’ organizational capacity for performance management. It provides deep insights into their structural and functional characteristics and serves as a framework for decision-makers to identify and implement missing practices while enhancing existing ones. The maturity model was developed following the Design Science Research methodology to ensure both rigor and relevance. A bottom-up approach guided its design, integrating insights from extensive literature reviews and lessons learned from benchmark countries. The evaluation process employed a qualitative approach, using focus groups with a carefully selected cohort of academics, experts, and practitioners. The Moroccan case study serves as part of the “Reflection and Learning” phase, providing an initial test for the model and paving the way for further empirical research. Future studies will aim to test, refine, and extend the model, facilitating its application across diverse educational contexts.

Prefabricated decoration is an efficient construction mode in the current construction field, with the main purpose of quickly improving the efficiency and quality of decoration through the effective application of modular decoration technology. Therefore, there is a high demand for efficient prefabricated technical talents in various construction units or enterprises in the construction industry. How to cultivate efficient prefabricated technical talents is a problem that relevant professional teachers in universities must pay attention to at present. This paper mainly analyzes the research and practice of the training mode of prefabricated technical talents, summarizes the connotation of prefabricated building and the importance of prefabricated building talent training, analyzes the key points and requirements of prefabricated building teaching, summarizes the problems existing in the training process of prefabricated building talents and puts forward corresponding optimization countermeasures, so as to lay a solid foundation for the optimization of the training mode of prefabricated talents in the next stage and the promotion of talent training level.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.