An intelligent adaptive fuzzy-based inference system for computer-assisted language learning

摘要

Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners' needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners' misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students' needs and preferences. (C) 2019 Elsevier Ltd. All rights reserved.