<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on the accurate application of precision teaching can enable students to conduct efficient learning and achieve the educational goal of all-round development. In this paper, a big data precision teaching model is constructed to stage the teaching process, set the data corresponding to each stage as random variables, and apply the AIC criterion to reflect the complexity of the model. Then the evaluation model of the precision teaching algorithm is constructed by calculating the test set error based on cross-validation to find the precision teaching solution under random variables. In the last stage, it is known from the observation data of the students that the optimal decision function is derived from the variable selection, according to which the targeted development ideas of precision teaching are obtained. The experimental results showed that the practice subjects were divided into control and experimental groups, follow-up tests were conducted, and the percentage of failures in the control group was 4%, while the percentage of failures in the experimental group was 9%. The scores of the experimental class were higher when analyzed by the comprehensive attainment degree. It shows that the development and application of the platform under precision teaching can not only make up for some problems and shortcomings of the traditional classroom but also enable students to get better learning results in this precision teaching process.</p>
</abstract>

Based on the accurate application of precision teaching can enable students to conduct efficient learning and achieve the educational goal of all-round development. In this paper, a big data precision teaching model is constructed to stage the teaching process, set the data corresponding to each stage as random variables, and apply the AIC criterion to reflect the complexity of the model. Then the evaluation model of the precision teaching algorithm is constructed by calculating the test set error based on cross-validation to find the precision teaching solution under random variables. In the last stage, it is known from the observation data of the students that the optimal decision function is derived from the variable selection, according to which the targeted development ideas of precision teaching are obtained. The experimental results showed that the practice subjects were divided into control and experimental groups, follow-up tests were conducted, and the percentage of failures in the control group was 4%, while the percentage of failures in the experimental group was 9%. The scores of the experimental class were higher when analyzed by the comprehensive attainment degree. It shows that the development and application of the platform under precision teaching can not only make up for some problems and shortcomings of the traditional classroom but also enable students to get better learning results in this precision teaching process.

New ideas of precision teaching development based on the background of big data

Guangdong University of Science & Technology

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"New ideas of precision teaching development based on the background of big data"}

Based on the accurate application of precision teaching can enable students to conduct efficient learning and achieve the educational goal of all-round...

New ideas of precision teaching development based on the background of big data

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 18 mag 2022

Accettato: 05 ott 2022

DOI: https://doi.org/10.2478/amns.2023.1.00123

Parole chiave
Cross-validation, Precision teaching model, Random variables, AIC criterion, Optimal decision function

© 2023 Juju Zhou, published by Sciendo

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

New ideas of precision teaching development based on the background of big data

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 18 mag 2022

Accettato: 05 ott 2022

DOI: https://doi.org/10.2478/amns.2023.1.00123

Parole chiaveCross-validation, Precision teaching model, Random variables, AIC criterion, Optimal decision function

© 2023 Juju Zhou, published by Sciendo

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

Parole chiave
Cross-validation, Precision teaching model, Random variables, AIC criterion, Optimal decision function