An approach for detecting students' working memory capacity from their behavior in learning systems

Ting Wen Chang, Moushir M. El-Bishouty, Sabine Graf, Kinshuk

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

11 Citations (Scopus)

Abstract

Working memory capacity (WMC) is a cognitive trait that affects students' learning behaviors while performing complex cognitive tasks. Knowing students' WMC can positively enhance students' learning in many ways, for example, by providing them with adaptive content and activities to suit their individual WMC. This paper presents an approach for identifying students' WMC from their learning behaviors in learning systems. The approach as well as its implementation into an existing detection tool are introduced in this paper. The following six learning behaviors, extracted from the literature, are modeled to infer students' WMC: linear navigation, constant reverse navigation, performing simultaneous tasks, recalling learned material, revisiting passed learning objects, and corresponding learning styles.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Pages82-86
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013 - Beijing, China
Duration: 15 Jul. 201318 Jul. 2013

Publication series

NameProceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013

Conference

Conference2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Country/TerritoryChina
CityBeijing
Period15/07/1318/07/13

Keywords

  • Learning System
  • Student Modeling
  • Working Memory Capacity

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