A Fast Multidimensional Analysis Scheme to Analyze Linear Learning Sequence of Learning Activities

Abstract

Learning activity (LA), a linear sequence structure consisting of several learning steps, is a popular method to integrate the learning content with the pedagogical strategies for facilitating efficient learning. However, how to efficiently analyze the linear learning sequence of a LA in terms of the Grade at each step (G), the learning attempt Count (C), and the learning Time (T) is an issue because it can be regarded as the multidimensional data analysis (MDA) problem. In order to efficiently understand the learning status, how to propose a fast method using the fuzzy pattern matching technique to efficiently analyze the multidimensional learning data in terms of G, C, T dimensions is our main concern. Therefore, in this study, a Fast Multidimensional Learning Activity Data Analysis (FaMiLaDa) scheme is proposed to solve the defined Multidimensional Learning Data Analysis for a LA (MLDA-LA) problem. According to the experimental analysis, FaMiLaDa scheme is able to fast analyze the learning data concerning the G, C, T dimensions in constant time complexity and perform the learning query in real time. Consequently, teachers are able to efficiently understand the learning status of students.