Optimizing pattern weights with a genetic algorithm to improve automatic working memory capacity identification

Abstract

Cognitive load theory states that improper cognitive loads may negatively affect learning. By identifying students’ working memory capacity (WMC), personalized scaffolding techniques can be used, either by teachers or adaptive systems to offer students individual recommendations of learning activities based on their individual cognitive load. WMC has been identified traditionally by dedicated tests. However, these tests have certain drawbacks (e.g., students have to spend additional time on them, etc.). Therefore, recent research aims at automatically detecting WMC from students’ behavior in learning systems. This paper introduces an automatic approach to identify WMC in learning systems using a genetic algorithm. An evaluation of this approach using data from 63 students shows it outperforms the existing leading approach with an accuracy of 85.1%. By increasing the accuracy of automatic WMC identification, more accurate interventions can be made to better support students and ensure that their working memory is balanced properly while learning.