The Application of Artificial Intelligence Technology in Improving the Quality of Teaching and Learning in Computer Education

Abstract

The personalized recommendation of courses and exercises can realize more accurate, tailor-made teaching, significantly improve teaching efficiency, and stimulate students’ learning interest and motivation. In this paper, we first obtain the knowledge point data of computer specialties and then extract the course knowledge point entities using the TF-IDF algorithm. We then use artificial rules to extract the relationships between the entities, design an estimation method for students’ mastery of the knowledge points, combine the knowledge graph to generate different learning sequences for students, and recommend course resources for them. Then, we use a simple Bayesian classification algorithm to classify and characterize the text of exercises and recommend personalized exercises to learners by combining their mastery of knowledge points, degree of difficulty, and other characteristics. The system in this paper is applied to a computer program at a university in Guangzhou to compare the teaching effect. It was found that the average grade of the experimental class was 11.25 points higher than the average grade of the control class, an improvement of 11.92 points compared to the pre-test, and the distribution of the scores clearly progressed from the 75-80 point range to the 85-90 point range. In the three dimensions of perceived usefulness, perceived ease of use, and intention to use, the vast majority of the respondents chose to agree or strongly agree, and the average score of the survey was 4.31, 4.24, and 4.18, respectively, indicating that the system is easy to operate and has a reasonable functional design and that the results of the recommended course resources and exercises basically conform to the learners’ psychological expectations and practical needs. This study proposes a feasible path for the integration of artificial intelligence technology into computer teaching, which will improve the quality and efficiency of computer education and teaching.

Keywords

• tf-idf
• knowledge graph
• text feature representation
• bayesian classification
• computer teaching
• 97m50