Adaptive and personalized learning based on students’ cognitive characteristics

Working memory capacity (WMC) is a cognitive characteristic that affects students’ learning behaviors to perform complex cognitive tasks. However, WMC is very limited and can be easily overloaded in learning activities. Considering students’ WMC through personalized learning materials and activities helps in avoiding cognitive overload and therefore positively affects students’ learning. However, in order to consider students’ WMC in the learning process, an approach is needed to identify students’ WMC without any additional efforts from students. To address this problem, we introduce a general approach to automatically identify WMC from students’ behavior in a learning system. Our approach is generic and designed to work with different learning systems. Furthermore, by knowing students’ WMC, a learning system can provide teachers meaningful recommendations to support students with low and high WMC. Accordingly, we created a recommendation mechanism that provides recommendations based on the guidelines of cognitive load theory. These recommendations are intended to assist in presentation of information in order to reduce working memory overload. Information about WMC is also the basis for designing adaptive systems that can automatically provide students with individualized support based on their WMC.