A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

Scholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educators have pointed out the need to take learners’ affective status into account. Therefore, this study proposed an expert system approach by taking into account both the affective and cognitive status of individual learners. An adaptive learning system was implemented based on the proposed approach. In addition, an experiment was conducted in a fifth-grade Mathematics course to compare the learning performances and perceptions of the students who learned with the adaptive learning system with affective and cognitive status analysis, a cognitive-status-based adaptive learning system, and a conventional learning system. The ANCOVA results revealed that the adaptive learning model with the affective and cognitive performance analysis mechanism outperformed the other two approaches in terms of improving the students’ learning achievement (F ​= ​3.12, p ​< ​0.05) and reducing their mathematical anxiety (F ​= ​5.59, p ​< ​0.01). In addition, it was found that the proposed approach helped the low achievers successfully complete the learning tasks, while those learning with the conventional cognitive factor-based approach were more likely to give up some learning tasks, and were more reliant on the detailed version of the instructional materials.