Abstract
In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.
Highlights
The development of Internet technology allows learners to receive the highest quality education anytime and anywhere, and allows the dissemination of knowledge to be no longer limited to books
The text preprocessing process is improved by combining the characteristics of text-based primary and secondary school teaching resources
Aiming at the problems of traditional KNN algorithm in the classification of teaching resources in primary and middle schools, an improved KNN algorithm based on density tailoring scheme is proposed
Summary
The development of Internet technology allows learners to receive the highest quality education anytime and anywhere, and allows the dissemination of knowledge to be no longer limited to books. Various types of educational resources are abundant and large. Massive educational resources are still growing at geometric multiples, and the types are complex. Among the various types of resources, including video, audio, pictures, and text, the number of text-based resources is the largest. In this case, how to effectively classify the teaching resources in the network is an important problem that needs to be solved urgently. With the continuous increase of the number of resources, the problem of low manual classification efficiency and the decrease of classification accuracy with the increase of working hours has become increasingly prominent
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