Zhai Yixi, Chu Liqing, Liu Yanlan, Wang Dandan, Wu Yufei
School of Foreign Studies, Tangshan Normal University, Tangshan City, China.
PeerJ Comput Sci. 2024 Mar 29;10:e1844. doi: 10.7717/peerj-cs.1844. eCollection 2024.
With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves into the application of AI-generated electronic images in the training and instruction of middle school educators, subjecting it to thorough analysis. Emphasis is placed on elucidating the pivotal role played by AI electronic images in elevating the proficiency of middle school teachers. Subsequently, an integrated intelligent device serves as the foundation for establishing a model that applies intelligent classification and algorithms based on the Structure of the Observed Learning Outcome (SOLO). This model is designed to assess the cognitive literacy and teaching efficacy of middle school educators, and its performance is juxtaposed with classification algorithms such as support vector machine (SVM) and decision trees. The findings reveal that, following 600 iterations of the model, the SVM algorithm achieves a 77% accuracy rate in recognizing teacher literacy, whereas the SOLO algorithm attains 80%. Concurrently, the spatial complexities of the SVM-based and SOLO-based intelligent literacy improvement models are determined to be 45 and 22, respectively. Notably, it is discerned that, with escalating iterations, the SOLO algorithm exhibits higher accuracy and reduced spatial complexity in evaluating teachers' pedagogical literacy. Consequently, the utilization of AI methodologies proves highly efficacious in advancing electronic imaging technology and enhancing the efficacy of image recognition in educational instruction.
随着社会信息化的快速发展，电子教育资源已成为现代教育不可或缺的组成部分。针对中学教师面临的日益严峻的挑战，本研究致力于分析和探索人工智能（AI）方法在提升其认知素养方面的应用。首先，本文深入探讨了人工智能生成的电子图像在中学教育工作者培训和教学中的应用，并进行了全面分析。重点阐述了人工智能电子图像在提高中学教师教学水平方面所起的关键作用。随后，以一种集成智能设备为基础，建立了一个基于观察学习成果结构（SOLO）应用智能分类和算法的模型。该模型旨在评估中学教育工作者的认知素养和教学效果，并将其性能与支持向量机（SVM）和决策树等分类算法进行对比。研究结果表明，在该模型进行600次迭代后，支持向量机算法在识别教师素养方面的准确率达到77%，而SOLO算法达到80%。同时，基于支持向量机和基于SOLO的智能素养提升模型的空间复杂度分别确定为45和22。值得注意的是，可以看出，随着迭代次数的增加，SOLO算法在评估教师教学素养方面表现出更高的准确率和更低的空间复杂度。因此，人工智能方法的应用在推进电子成像技术和提高教育教学中的图像识别效果方面被证明是非常有效的。
