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<p>Early childhood development (ECD) is an essential foundation for children’s future development and a building block and driving force for society’s future development. Traditional evaluation models of early childhood development focus on children’s comprehensive evaluation, ignoring the importance of each evaluation data. In addition, the previous evaluation model mainly relies on the expert’s experience, which is highly dependent and has a strengthened subjectivity. Therefore, this paper combines the random forest algorithm to construct an early childhood development evaluation model and builds a multilayer evaluation index system. Test experiments show that the evaluation results obtained after the model’s training have a lower error than the traditional evaluation model, and the results are closer to the expected results. Application experiments show that the model can effectively present the evaluation results of children’s abilities and intuitively present comprehensive evaluation results, so that parents and teachers can view the corresponding evaluation data according to the needs of children’s development. At the same time, the evaluation results are consistent with the actual development of children, which can provide parents and teachers with effective and reliable evaluation data to improve children’s development program.</p>
</abstract>

Early childhood development (ECD) is an essential foundation for children’s future development and a building block and driving force for society’s future development. Traditional evaluation models of early childhood development focus on children’s comprehensive evaluation, ignoring the importance of each evaluation data. In addition, the previous evaluation model mainly relies on the expert’s experience, which is highly dependent and has a strengthened subjectivity. Therefore, this paper combines the random forest algorithm to construct an early childhood development evaluation model and builds a multilayer evaluation index system. Test experiments show that the evaluation results obtained after the model’s training have a lower error than the traditional evaluation model, and the results are closer to the expected results. Application experiments show that the model can effectively present the evaluation results of children’s abilities and intuitively present comprehensive evaluation results, so that parents and teachers can view the corresponding evaluation data according to the needs of children’s development. At the same time, the evaluation results are consistent with the actual development of children, which can provide parents and teachers with effective and reliable evaluation data to improve children’s development program.

Construction and Application of Random Forest (RF)-Based Early Childhood Development Assessment Models

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Early childhood development (ECD) is an essential foundation for children’s future development and a building block and driving force for society’s future...

Construction and Application of Random Forest (RF)-Based Early Childhood Development Assessment Models

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0340

Parole chiave
Random forest (RF), Early childhood development, Developmental assessment, Assessment modeling

© 2024 Shengwei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction and Application of Random Forest (RF)-Based Early Childhood Development Assessment Models

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 02 gen 2024

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0340

Parole chiaveRandom forest (RF), Early childhood development, Developmental assessment, Assessment modeling

© 2024 Shengwei Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Random forest (RF), Early childhood development, Developmental assessment, Assessment modeling