Fuzzy cognitive mapping of LMS users' Quality of Interaction within higher education blended-learning environment

Learning Management Systems (LMSs) under blended (b-) learning modality can efficiently support online learning environments (OLEs) at Higher Education Institutions (HEIs). Mining of LMS users’ data, involving artificial intelligence and incertitude modeling, e.g., via fuzzy logic, is a fundamental challenge. This study addresses the hypothesis that the structural characteristics of a Fuzzy Cognitive Map (FCM) can efficiently model the way LMS users interact with it, by estimating their Quality of Interaction (QoI) within a blearning context. This study introduces the FCM-QoI model (combined with a model visualizer) consisting of 14 input-one output concepts, dependences and trends, considering one academic year of the LMS use from 75 professors and 1037 students at a HEI. The experimental findings have shown that the proposed FCM-QoI model can provide concepts interconnection and causal dependencies representation of Moodle LMS users’ QoI, revealing perspectives of its evolution both at a micro and macro analysis level. Moreover, the FCM-QoI model significantly adds to the evaluation and analysis of the QoI influential concepts’ contribution to self-sustained cycles (static analysis) and their alterations, when the time period of the LMS use is considered (dynamic analysis), showing potential to increase the flexibility and adaptivity of the QoI modeling and feedback approaches. Clearly, based on the FCM-QoI model, pedagogical instructors and decision-makers of HEIs could be assisted to holistically visualize, understand and assess OLEs stakeholders’ needs within the teaching and learning practices.