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<p>This paper establishes a comprehensive framework for the in-class practical teaching curriculum tailored to majors in Chinese Language and Literature. It comprises four core components: a system of practical teaching content, specified practical teaching requirements, practical training modules, and a target index system for practical teaching outcomes. Through the application of single-factor and two-factor analyses, this study investigates the correlation among course outcomes to refine the scheduling arrangements and the model itself. It introduces a scheduling process designed using a genetic algorithm, which is also employed to allocate instructors and courses within the practical course curriculum, calculating the average teaching hours per instructor. Additionally, the research examines the impact of students’ gender and their varying levels of comprehension regarding professional practice abilities on their practical skills in the field. Specifically focusing on the discipline-related practical activities in Chinese language and literature, the study identifies key courses within the major. Notably, it found a significant correlation coefficient of 0.691 between literary writing and literary appreciation courses. Moreover, the overall average for career development capabilities, based on different levels of professional understanding, is 3.9975, indicating that a deeper comprehension of career development in this major positively influences students’ practical skills in Chinese Language and Literature.</p>
</abstract>

This paper establishes a comprehensive framework for the in-class practical teaching curriculum tailored to majors in Chinese Language and Literature. It comprises four core components: a system of practical teaching content, specified practical teaching requirements, practical training modules, and a target index system for practical teaching outcomes. Through the application of single-factor and two-factor analyses, this study investigates the correlation among course outcomes to refine the scheduling arrangements and the model itself. It introduces a scheduling process designed using a genetic algorithm, which is also employed to allocate instructors and courses within the practical course curriculum, calculating the average teaching hours per instructor. Additionally, the research examines the impact of students’ gender and their varying levels of comprehension regarding professional practice abilities on their practical skills in the field. Specifically focusing on the discipline-related practical activities in Chinese language and literature, the study identifies key courses within the major. Notably, it found a significant correlation coefficient of 0.691 between literary writing and literary appreciation courses. Moreover, the overall average for career development capabilities, based on different levels of professional understanding, is 3.9975, indicating that a deeper comprehension of career development in this major positively influences students’ practical skills in Chinese Language and Literature.

Exploring the Teaching of In-Class Practice Sessions in the Context of Big Data--Taking Chinese Language and Literature Major as an Example

Applied Mathematics and Nonlinear Sciences

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

This paper establishes a comprehensive framework for the in-class practical teaching curriculum tailored to majors in Chinese Language and Literature. It...

Exploring the Teaching of In-Class Practice Sessions in the Context of Big Data--Taking Chinese Language and Literature Major as an Example

Publié en ligne: 30 mai 2024

Pages: -

Reçu: 06 févr. 2024

Accepté: 18 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1214

Mots clés
Genetic algorithm, Scheduling model, Practical teaching, Chinese language and literature

© 2024 Xue Li et al., published by Sciendo

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

Exploring the Teaching of In-Class Practice Sessions in the Context of Big Data--Taking Chinese Language and Literature Major as an Example

Publié en ligne: 30 mai 2024

Pages: -

Reçu: 06 févr. 2024

Accepté: 18 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1214

Mots clésGenetic algorithm, Scheduling model, Practical teaching, Chinese language and literature

© 2024 Xue Li et al., published by Sciendo

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

Mots clés
Genetic algorithm, Scheduling model, Practical teaching, Chinese language and literature