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

<p>This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data quality, redundant data in the dataset is eliminated. To provide better learning recommendations and personalized services, prediction accuracy is assessed by calculating the deviation between predicted and actual ratings. Teachers and students can use the analysis results of feature weights, correct word cut scores, and other indicators obtained from data mining as key learning references. In the data mining analysis of students’ English learning data, the feature weight is 0.921, which helps to assess students’ knowledge mastery and learning effect more accurately.</p>
</abstract>

This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data quality, redundant data in the dataset is eliminated. To provide better learning recommendations and personalized services, prediction accuracy is assessed by calculating the deviation between predicted and actual ratings. Teachers and students can use the analysis results of feature weights, correct word cut scores, and other indicators obtained from data mining as key learning references. In the data mining analysis of students’ English learning data, the feature weight is 0.921, which helps to assess students’ knowledge mastery and learning effect more accurately.

A reform path for English learning based on data mining algorithms

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"A reform path for English learning based on data mining algorithms"}

This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data...

A reform path for English learning based on data mining algorithms

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 13. Dez. 2022

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01112

Schlüsselwörter
English learning, Predicting target users, Data mining, Feature weights, Word slicing

© 2023 Haiping Zhou et al., published by Sciendo

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

A reform path for English learning based on data mining algorithms

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 13. Dez. 2022

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01112

SchlüsselwörterEnglish learning, Predicting target users, Data mining, Feature weights, Word slicing

© 2023 Haiping Zhou et al., published by Sciendo

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

Schlüsselwörter
English learning, Predicting target users, Data mining, Feature weights, Word slicing