ArticleFrontiers in psychology2022
An artificial intelligence method for comprehensive evaluation of preschool education quality.
Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Role of higher education system in promoting law abiding behavior among students.Frontiers in psychology · 2022Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolution in the quality of teaching for preschool education is worth studying. In this article, we solved the qualitative problems in the comprehensive quality evaluation by suggesting a method of quantitative combination and establishing a set of indicators suitable for the comprehensive quality evaluation of students in the kindergarten. According to the experience summed up by previous scholars, the weight of each index is obtained by an analytic hierarchy process. This study analyzed the defects and causes of fuzzy comprehensive evaluation and the neural network model in the construction of early childhood and preschool education's comprehensive quality evaluation model and propose a Feedforward Neural Network (FNN) model. FNN combined with neural network (NN) and fuzzy logic characteristics introduces fuzzy concepts and fuzzy inference rules into neural networks of neurons, the connection power, and network learning. It improves the learning ability of NN and fuzzy evaluation of the power of expression and effectively exerts the advantages of fuzzy logic and neural network to make up for their shortcomings. However, the convergence speed is very slow. To solve this problem, the similarity measure was used to improve the number of hidden layer nodes of the network. The effectiveness and feasibility of the FNN improved hidden layer nodes are verified by an example so as to realize the automation of comprehensive quality evaluation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.