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<p>This paper proposes a course knowledge resource extraction method based on the improved Hidden Markov Model and combines the method with hierarchical classification ideas. The improved Hidden Markov Model is used in the study of the text classification process of course knowledge resources, the expected cross entropy selects the feature words, and the semantic space composition is obtained by using the implicit semantic indexing method. When setting the observation output probability matrix for the HMM classifier model, the improved TFIDF method is introduced to reflect the semantic relations between feature words. An evaluation criterion such as macro-micro mean is used to analyze the performance of the whole text set classification and gradually improve the HMM model. Finally, the HMM resource information extraction model has been improved to mine the elements of civics in digital electronics technology courses. It is found that in the compulsory textbook knowledge of digital electronics technology, the trigger knowledge has the greatest integration degree with the Civic-Political elements with a percentage of 0.3382, followed by the analog-digital and digital-analog converter knowledge with a percentage of 0.2818. In the textbook column on digital electronics technology, the extended knowledge has the greatest integration degree with the Civic-Political elements with a percentage of 0.5043, followed by the strengthened category column with a percentage of 0.1712. The analysis of the students’ different Civic-Political dimensions after the instruction has been done. In the political identity category, national sentiment, scientific spirit and ecological environment topics improved by 7.23, 2.01, 3.2 and 2.14 points, respectively. It shows that students can improve their cultural knowledge in digital electronics and establish a correct worldview, life view, and values.</p>
</abstract>

This paper proposes a course knowledge resource extraction method based on the improved Hidden Markov Model and combines the method with hierarchical classification ideas. The improved Hidden Markov Model is used in the study of the text classification process of course knowledge resources, the expected cross entropy selects the feature words, and the semantic space composition is obtained by using the implicit semantic indexing method. When setting the observation output probability matrix for the HMM classifier model, the improved TFIDF method is introduced to reflect the semantic relations between feature words. An evaluation criterion such as macro-micro mean is used to analyze the performance of the whole text set classification and gradually improve the HMM model. Finally, the HMM resource information extraction model has been improved to mine the elements of civics in digital electronics technology courses. It is found that in the compulsory textbook knowledge of digital electronics technology, the trigger knowledge has the greatest integration degree with the Civic-Political elements with a percentage of 0.3382, followed by the analog-digital and digital-analog converter knowledge with a percentage of 0.2818. In the textbook column on digital electronics technology, the extended knowledge has the greatest integration degree with the Civic-Political elements with a percentage of 0.5043, followed by the strengthened category column with a percentage of 0.1712. The analysis of the students’ different Civic-Political dimensions after the instruction has been done. In the political identity category, national sentiment, scientific spirit and ecological environment topics improved by 7.23, 2.01, 3.2 and 2.14 points, respectively. It shows that students can improve their cultural knowledge in digital electronics and establish a correct worldview, life view, and values.

Mining and Teaching Design of Civic and Political Elements in Digital Electronics Technology Course Based on Markov Modeling

Applied Mathematics and Nonlinear Sciences

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

This paper proposes a course knowledge resource extraction method based on the improved Hidden Markov Model and combines the method with hierarchical...

Mining and Teaching Design of Civic and Political Elements in Digital Electronics Technology Course Based on Markov Modeling

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 30 gru 2022

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01536

Słowa kluczowe
Hidden Markov model, Cross entropy, Implicit semantic indexing method, TFIDF, Civic elements

© 2023 Jie Zhang, published by Sciendo

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

Mining and Teaching Design of Civic and Political Elements in Digital Electronics Technology Course Based on Markov Modeling

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 30 gru 2022

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01536

Słowa kluczoweHidden Markov model, Cross entropy, Implicit semantic indexing method, TFIDF, Civic elements

© 2023 Jie Zhang, published by Sciendo

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

Słowa kluczowe
Hidden Markov model, Cross entropy, Implicit semantic indexing method, TFIDF, Civic elements