I-portrait: A Multidimensional Student Portrait System for Learning Situation Analysis

Abstract

Learning situation analysis systems provide personalized learning diagnostic services for students by mining learning data to improve learning efficiency. However, most of the existing systems only focus on partial data from a single learning situation, unable to meet the analysis of changeable and complicated states of students. To alleviate the problem, we propose a novel system I-portrait, which is based on the analysis of multidimensional learning data to provide students with comprehensive portrait services. I-portrait is composed of four modules, cognitive level, subject ability, classroom behavior and emotional attitude. Specifically, we first divide student learning data into static data and dynamic data by concepts and data sources. Then, in each module, I-portrait uses corresponding intelligence artificial technologies to smartly analyze multidimensional student data. Finally, I-portrait integrates analysis results and offers students personalized intelligent learning recommendations, promoting efficient study.