Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke Correction

Qing Lang, Caifeng Zhang, Hengnian Qi (), Yaqin Du, Xiaorong Zhu, Chu Zhang and Mizhen Li
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Qing Lang: Huzhou University Library, Huzhou University, Huzhou 313000, China
Caifeng Zhang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Hengnian Qi: School of Information Engineering, Huzhou University, Huzhou 313000, China
Yaqin Du: School of Information Engineering, Huzhou University, Huzhou 313000, China
Xiaorong Zhu: School of Information Engineering, Huzhou University, Huzhou 313000, China
Chu Zhang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Mizhen Li: Department of Training, China Language & Culture Press, Beijing 100010, China

Sustainability, 2023, vol. 15, issue 3, 1-13

Abstract: Using AI technology to improve teaching and learning is an important goal of educational sustainability. By mining the correlation between knowledge points, the discrete knowledge points can be integrated to improve the knowledge density and reduce the learning task. In addition, the successful experiences of similar learners can be shared, thus shortening the learning path of new learners. To change the common situation of irregular writing stroke order, to teach and correct stroke order effectively, this study uses association rules to explore the potential correlation between error-prone Chinese characters based on a large number of learners’ writing records, and then summarizes and sorts out a set of error-prone Chinese characters based on this. Every Chinese character contained in an error-prone category has a common error-prone feature. By correcting this error, it can be extended to every Chinese character of this category, and the learning efficiency of Chinese character strokes can be improved tens of times. In the training and testing system with a Chinese character error-prone character set, combined with the improved collaborative filtering algorithm, a learner-based personalized error-prone Chinese character recommendation model was proposed. Experimental results showed that the Apriori algorithm with lift measure can excavate effective strong association rules and provide an important reference for the character set table. The improved collaborative filtering algorithm can make use of the similarity between learners, share successful learning experiences, provide a personalized recommendation service for error-prone Chinese characters, and the recommendation performance is higher than that of the traditional collaborative filtering model. In the test of different types of learning groups, there are obvious differences between the independent pre-test and the post-training test, which effectively corrects the irregular writing habits, and further indicates that the excavation of knowledge correlation and the combination of learners’ similarity can effectively improve the efficiency and effect of teaching and learning.

Keywords: educational sustainability; Chinese character stroke order; association rules; collaborative filtering; personalized learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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