Design of Resource Recommendation Model for Personalized Learning in the Era of Big Data

Abstract

This paper proposes a personalized learning resource recommendation model based on big data. The design of the model consists of data storage, data analysis, resource matching, and the resource recommendation. In order to provide a suitable resource, data analysis is a more critical procedure that involves the analyses of basic information, learning style, learning status, learning behavior, and learning interest, which can be successfully analyzed by means of kafka and flume. Through an experiment, it shows that personalized resource recommendation platform really plays a positive role in improving students learning.