A Mamdani‐type Fuzzy Inference System to Automatically Assess Dijkstra’s Algorithm Simulation

摘要

In education it is very important for both users and teachers to know how much the student haslearned. To accomplish this task, GRAPHs (the eMathTeacher-compliant tool that will be used to simulateDijkstra’s algorithm) generates an interaction log that will be used to assess the student’s learning outcomes. Thisposes an additional problem: the assessment of the interactions between the user and the machine is a timeconsumingand tiresome task, as it involves processing a lot of data. Additionally, one of the most useful featuresfor a learner is the immediacy provided by an automatic assessment. On the other hand, a sound assessment oflearning cannot be confined to merely counting the errors; it should also take into account their type. In thissense, fuzzy reasoning offers a simple and versatile tool for simulating the expert teacher’s knowledge. Thispaper presents the design and implementation of three fuzzy inference systems (FIS) based on Mamdani’smethod for automatically assessing Dijkstra’s algorithm learning by processing the interaction log provided byGRAPHs.