Research on the Influencing Factors of Problem-Driven Children’s Deep Learning

Zhang, Xiaohong; Li, Chunyan

doi:10.3389/fpsyg.2022.764121

Cited by 2 publications

(1 citation statement)

References 14 publications

(16 reference statements)

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“…In the process of model prediction, there may be some connection between the predicted outcome and the predicted purpose, but since both the predicted outcome and the purpose are indicators of the predicted learning status of students, it is not possible to determine the relationship between them diagnostically [18]. erefore, it is necessary to build a prediction model for the neural network horizon and analyze the actual relationship between the prediction outcome, purpose, and the various stages of prediction [19,20]. (3) If there are difficulties in the process of predicting the collection of items by educators, this aspect can be solved when entering the prediction model [21].…”

Section: Model Characteristicsmentioning

confidence: 99%

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

Shao

Liu

et al. 2022

Mathematical Problems in Engineering

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Since the 3rd technological revolution, electronic information has developed at an increasingly rapid pace and has been widely used in various aspects of people’s lives and social production. With the advent of the information society, information security is particularly important and is related to the security of various industries and fields, such as transportation, national defense, and economy. If there is a problem in information, it means that there is a problem in information countermeasures, which leads to the emergence of information countermeasures technology. At present, with the accelerated progress of socialization, the requirements for information technology talents have been raised accordingly, so schools are required to pay attention to the education and training of talents in this area to meet the needs of society and speed up the development of society. Moreover, more and more scholars are recognizing the significance of data analysis technology for education development, and some scholars have constructed learning prediction models from different educational environments and perspectives. However, some of the models have their own limitations and thus can hinder the parameter setting. Therefore, educational researchers need to combine the characteristics of their own educational environment to build a widely adaptable predictive model to provide a good foundation for the development of education. Also, on that basis, information confrontation technology should be applied to explore diverse teaching courses, improve the traditional teaching philosophy, improve the management and evaluation mechanism, provide students with diverse learning styles, emphasize the practical nature of learning, and continuously improve students’ independent problem-solving ability.

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Section: Model Characteristicsmentioning

confidence: 99%

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

Shao

Liu

et al. 2022

Mathematical Problems in Engineering

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Analysis of Heavy Metal Pollution in Soil along the Shuimo River by the Grey Relational Method and Factor Analysis

Zhang

Wang

Zang

et al. 2023

Metals

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Soil samples were collected from the upstream, midstream and downstream areas of the Shuimo River in three layers of 0–20, 20–40 and 40–60 cm, and each group of sample points was located 5 m, 1 km and 2 km away from the river bank, respectively. The analysis was carried out. Based on the investigation and research, six indicators, including As, Pb, Zn, Cu, Ni and Cr, were designated as evaluation factors in combination with the results of the sample collection with low or no detectable values of Cd and Hg. The mean values of the samples measured in the upper, middle and downstream layers were taken, and the degree and source of pollution were evaluated and jointly analyzed using the gray correlation analysis and factor analysis methods. By using the gray correlation analysis, it was found that the evaluation results of the upstream and middle reaches of the soil along the Shuimo River were both level 3, with slight pollution, and the evaluation results of the downstream areas were level 2, with good soil quality. There are two main sources of pollution obtained through the factor analysis; source 1 is mainly heavy metals such as Zn, Cu, Cr, Pb and Ni, while source 2 is mainly heavy metals such as As, Pb and Ni. The amount of pollution sources is inferred from the heavy metal types of each source and the soil environment along the Shuimo River as industrial and human sources of pollution. From the analysis results, the combination of the gray correlation analysis model and factor analysis model is convenient and fast and can accurately quantify the source contribution of various pollution sources. Not only can it reflect the actual situation more objectively and realistically in the evaluation of soil heavy metal pollution and pollution sources, but also the calculation is simple and easily applied with low data requirements.

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Research on the Influencing Factors of Problem-Driven Children’s Deep Learning

Cited by 2 publications

References 14 publications

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

Analysis of Heavy Metal Pollution in Soil along the Shuimo River by the Grey Relational Method and Factor Analysis

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