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<p>This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges>211 colleges and universities, and general undergraduate schools>211 colleges and universities.</p>
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This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges>211 colleges and universities, and general undergraduate schools>211 colleges and universities.

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Applied Mathematics and Nonlinear Sciences

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

This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose...

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Publié en ligne: 23 déc. 2023

Pages: -

Reçu: 19 févr. 2023

Accepté: 03 juil. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01593

Mots clés
Genetic algorithm, Personalized recommendation, One-way ANOVA, Teaching English and American literature

© 2023 Xiaoguang Zhu, published by Sciendo

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

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Publié en ligne: 23 déc. 2023

Pages: -

Reçu: 19 févr. 2023

Accepté: 03 juil. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01593

Mots clésGenetic algorithm, Personalized recommendation, One-way ANOVA, Teaching English and American literature

© 2023 Xiaoguang Zhu, published by Sciendo

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

Mots clés
Genetic algorithm, Personalized recommendation, One-way ANOVA, Teaching English and American literature