Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

Abstract

Adaptive Learning System (ALS) is a supportive environment, which dynamically provides learners with services that can satisfy their demand for personalized learning in accordance with the differentiation of their individual traits. At present, study on ALS is still in the exploratory stage, and there are still many fields that deserve to be studied thoroughly. User characteristic model is the foundation and core of ALS and the key to the implementation of intelligent and personalized recommendation service. Based on this, this paper intends to analyze learners’ characteristics in ALS through several dimensions, such as basic information, interest, preference, cognitive level and learning style, through which learners’ user characteristic model is established. In the end, ALS, which supports the function of personalized recommendation, is implemented based on this model. It is suggested by the result of the simulation experiment that ALS, which is developed through this model, demonstrates a satisfying effect in recommendation, and it can dynamically recommend appropriate learning resources in accordance with learners’ personalized demands through which learners’ quality and efficiency of learning can be effectively enhanced to a certain extent.

A study on the personalized recommendation of intelligent learning system based on user feature model analysis

“…Therefore, in order to build a more scientific user feature model, we need to think from multiple dimensions as comprehensively as possible. Based on the analysis of relevant research results [8,9], this paper will mainly analyze and build a user feature model from four dimensions of users' basic information, learning style, cognitive level and operational behavior. The specific characterization method is as follows.…”