Study on the Personalized Learning Model of Learner-Learning Resource Matching
Lijuan Zhou, Feifei Zhang, Shudong Zhang, Min Xu
Abstract
With the development of service integration technology, online learning platforms have gathered a large number of learning resources, causing learners to get lost in a variety of course information and it is difficult to obtain learning resources that match their own needs. The proposal of personalized learning gives the problem a direction to solve. However, current personalized learning resource recommendation services facing problems such as excessive candidate resources, sparse history and cold starts. In addition, the learning resources provided also show problems of "difficult or easy, uneven quality". For this article researches the personalized learning recommendation model of learner-learning resource matching. The main content includes three parts: First, build a demand model based on learner registration information, learning behavior and other data. Second, analyze the access behavior of learning resources and assess their quality. Third, calculate the matching degree between learners and learning resources based on the demand model and the quality information of the learning resources, and recommend them.