College of Foreign Language, Pingdingshan University, Pingdingshan, Henan 467000, China.
Comput Intell Neurosci. 2022 May 21;2022:6866134. doi: 10.1155/2022/6866134. eCollection 2022.
In recent years, the amount of educational data in colleges and universities has increased rapidly. Each university has set up multiple courses to recruit talents. Students cannot choose courses. The emergence of data mining technology and its application in college teaching and curriculum has been preferred particularly to streamline these activities. When analyzing the correlation of courses based on data mining technology, we usually use the correlation between the scores of various subjects to analyze the correlation between courses. Correlation among various courses that are offered at colleges or universities is reflected through many different aspects such as factors or metrics, which are affecting course interest, course content, course arrangement, etc. In this article, we have thoroughly analyzed various factors that are affecting students' interest in literature courses with the help of association rules of data mining technology. Through the collected original data, this article uses Apriori algorithm to screen the association rules affecting students' interest in literature courses and combines them with the current teaching situation to complete the rule analysis. The results of rule analysis show that the most relevant factors affecting students' interest in literature curriculum mainly include the space-time dimension of textbook selection and compilation, the processing method of selected reading, and the evaluation method. The reading content is effectively processed by using the counterpoint reading method, and the literature curriculum textbooks are compiled from the perspective of cross-cultural communication, to enhance students' interest in the literature curriculum.
近年来，高校教育数据量迅速增加。各高校开设了多门课程来招揽人才，学生无法选课。数据挖掘技术的出现及其在高校教学和课程中的应用，特别是在简化这些活动方面，受到了特别的青睐。在基于数据挖掘技术分析课程相关性时，我们通常使用各学科成绩之间的相关性来分析课程之间的相关性。高校开设的各课程之间的相关性体现在课程兴趣、课程内容、课程安排等诸多方面的影响因素或指标上。在本文中，我们利用数据挖掘技术的关联规则，深入分析了影响文学课程兴趣的各种因素。通过收集的原始数据，本文使用 Apriori 算法筛选出影响学生文学课程兴趣的关联规则，并结合当前教学情况完成规则分析。规则分析的结果表明，影响学生文学课程兴趣的最相关因素主要包括教材选择和编写的时空维度、选读处理方法和评价方法。通过使用对比阅读法对阅读内容进行有效处理，并从跨文化交际的角度编写文学课程教材，可以提高学生对文学课程的兴趣。
