A Study on the Teaching Design of a Hybrid Civics Course Based on the Improved Attention Mechanism

Wenwu Miao

doi:10.3390/app12031243

Abstract

As an important vehicle for moral education, the moral indicators of civics and political science textbooks are naturally some of the most important criteria for revising textbooks. However, the textbook text dataset has too much textual information, ambiguous features, unbalanced sample distributions, etc. To address these problems, this paper combines a novel data enhancement method to obtain classification results based on word vectors. Additionally, for the problem of unbalanced sample sizes, this paper proposes a network model based on the attention mechanism, which combines the ideas of SMOTE and EDA, and uses a self-built stop word list and synonym word forest to conduct synonym queries, achieve a few categories of oversampling, and randomly disrupt the sentence order and intra-sentence word order to build a balanced dataset. The experimental results also show that the data augmentation method used in this paper’s model can effectively improve the performance of the model, resulting in a higher boost in the F1-measure of the model. The model incorporating the attention mechanism has better model generalization compared to the one without the attention mechanism, as well as a significant advantage compared to the reference model in other settings. The experimental results show that, compared with the original text classifier, the scheme of this paper effectively improves the evaluation effect and the reliability design for teaching a civics course.

Highlights

China’s Ministry of Education has proposed “eliminating ‘useless courses’ and creating ‘useful courses’”, with “one degree of gender” as a guide to effectively improve teaching quality
To address the problem of the unbalanced text datasets of college textbooks, this paper combines the ideas of synthetic minority class oversampling technique (SMOTE) and EDA to propose a new data enhancement method, i.e., using self-built deactivated word lists and synonym word forests to conduct synonym queries, achieving oversampling on categories with fewer indicators, and randomly disrupting the order of words within sentences as well as the order of samples
The dataset of this paper was derived from the text of college civics textbooks, with a total of 33,360 data, containing 16 categories and 23,083 terms

Summary

Introduction

China’s Ministry of Education has proposed “eliminating ‘useless courses’ and creating ‘useful courses’”, with “one degree of gender (high order, innovation and challenge)” as a guide to effectively improve teaching quality. The “innovation” of the Golden Course requires the modernization of the content of civic science courses, with an advanced teaching form and the personalization of the learning results [1]. With the new “Internet + teaching” information, in a student-centered education context, this is necessary in order to overcome the misconception of pure “technology worship” and to ensure students’ roles as active learners and independent constructors through the effective teaching design of civics and political science courses [3]. The Internet, artificial intelligence, and information technology tools are constantly changing and reshaping modern education, fundamentally promoting the development of new forms and modes of teaching civics courses, as well as driving a change in learning styles [5]

Methods

Results

Conclusion