An Approach for Detecting Students' Working Memory Capacity from Their Behavior in Learning Systems

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.