• Korean Journal of Childcare and Education
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• v.16 no.6
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• pp.155-183
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• 2020

Objective: In this study, we tried to verify the effects of program by constructing an effective maker education program that can cultivate the maker's capabilities through voluntary production activities by utilizing various technologies and tools. Methods: First, prior research on maker education and the station teaching strategy was considered, and interviews and surveys were conducted on prospective early childhood teachers in order to find out the degree of demand for maker education. The final program was finalized through verification of the contents validity. Results: The developed program was applied to a total of 49 prospective early childhood teachers (24 in the experimental group, 25 in the comparative group) attending U College, and it was found to be effective in enhancing convergence talent, education knowledge of early childhood teachers' technology, and self-directed learning skills. Conclusion/Implications: These findings show that the preliminary early childhood teacher maker education program using station teaching strategy has educational value that can be used as an effective teaching method in early childhood education.

• Smart Structures and Systems
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• v.32 no.1
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.