TY - GEN
T1 - Optimizing pattern weights with a genetic algorithm to improve automatic working memory capacity identification
AU - Bernard, Jason
AU - Chang, Ting Wen
AU - Popescu, Elvira
AU - Graf, Sabine
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Genetic algorithm
KW - Student modeling
KW - Working memory capacity
UR - http://www.scopus.com/inward/record.url?scp=84976640307&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-39583-8_38
DO - 10.1007/978-3-319-39583-8_38
M3 - Published Conference contribution
AN - SCOPUS:84976640307
SN - 9783319395821
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 334
EP - 340
BT - Intelligent Tutoring Systems - 13th International Conference, ITS 2016, Proceedings
A2 - Stamper, John
A2 - Micarelli, Alessandro
A2 - Panourgia, Kitty
T2 - 13th International Conference on Intelligent Tutoring Systems, ITS 2016
Y2 - 7 June 2016 through 10 June 2016
ER -