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<p>Digital information technology provides an innovative way for the obsolete and inefficient C language classroom. This paper applies computational thinking to the design of C language hybrid teaching and builds a task-driven model for teaching C language using a driven teaching approach. Digital information technology is applied to TDT teaching; test questions are dynamically extracted in the process of students’ tests, intelligently analyzed according to the student’s answers to the test questions, and a genetic algorithm is used to adjust the difficulty, differentiation and knowledge points of the group paper. Based on the hybrid similarity model, we optimize the mutation operation in the genetic algorithm and verify the effect of similarity calculation through the practice of grouping papers for C language teaching. Hybrid similarity yields weighted average similarity scores of test questions that are 0.14 and 0.16 higher than edit distance and cosine similarity, respectively. On the basis of similarity calculation, the time taken to improve the GA to generate the test paper is no more than 1 second, and the difficulty coefficient obtained does not differ from the expected coefficient by more than 0.05. The intelligent paper grouping method can maximize the coverage of the needs of more students and improve the investigation of C language teaching. The way of teaching C language.</p>
</abstract>

Digital information technology provides an innovative way for the obsolete and inefficient C language classroom. This paper applies computational thinking to the design of C language hybrid teaching and builds a task-driven model for teaching C language using a driven teaching approach. Digital information technology is applied to TDT teaching; test questions are dynamically extracted in the process of students’ tests, intelligently analyzed according to the student’s answers to the test questions, and a genetic algorithm is used to adjust the difficulty, differentiation and knowledge points of the group paper. Based on the hybrid similarity model, we optimize the mutation operation in the genetic algorithm and verify the effect of similarity calculation through the practice of grouping papers for C language teaching. Hybrid similarity yields weighted average similarity scores of test questions that are 0.14 and 0.16 higher than edit distance and cosine similarity, respectively. On the basis of similarity calculation, the time taken to improve the GA to generate the test paper is no more than 1 second, and the difficulty coefficient obtained does not differ from the expected coefficient by more than 0.05. The intelligent paper grouping method can maximize the coverage of the needs of more students and improve the investigation of C language teaching. The way of teaching C language.

Digital Information Technology Enabling Computational Thinking C Language Blended Teaching

Applied Mathematics and Nonlinear Sciences

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

Digital information technology provides an innovative way for the obsolete and inefficient C language classroom. This paper applies computational thinking to...

Digital Information Technology Enabling Computational Thinking C Language Blended Teaching

Data publikacji: 23 gru 2023

Zakres stron: -

Otrzymano: 11 sty 2023

Przyjęty: 03 lip 2023

DOI: https://doi.org/10.2478/amns.2023.2.01585

Słowa kluczowe
Genetic algorithm, Hybrid similarity, Intelligent grouping, Constraints, C language teaching

© 2023 Jingxin Cao, published by Sciendo

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

Digital Information Technology Enabling Computational Thinking C Language Blended Teaching

Data publikacji: 23 gru 2023

Zakres stron: -

Otrzymano: 11 sty 2023

Przyjęty: 03 lip 2023

DOI: https://doi.org/10.2478/amns.2023.2.01585

Słowa kluczoweGenetic algorithm, Hybrid similarity, Intelligent grouping, Constraints, C language teaching

© 2023 Jingxin Cao, published by Sciendo

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

Słowa kluczowe
Genetic algorithm, Hybrid similarity, Intelligent grouping, Constraints, C language teaching