A fuzzy neural network for item sequencing in personalized cognitive scaffolding with adaptive formative assessment
Adaptive formative assessment is a new approach to implement computerized adaptive learning based on the cognitive scaffolding principle. In adaptive formative assessment, at any stage of a learning session, the system takes into account a student's demonstrated cognitive level to generate the next appropriate formative testing instrument. In this paper, an appropriate strategy to progress students up the cognitive ladder by adaptive item selection is explored using the non-symbolic fuzzy-neural network technology. The proposed model features to learn and memorize good learning paths for different students, and accordingly provide personalized learning sequence for other similar students. The adaptation behavior of the fuzzy neural network to different student categories is investigated by simulated experiments, and its effectiveness is compared with another memory-less binary item selection algorithm. Preliminary results reveal its potential for being an effective adaptive item selection module in adaptive tutoring systems based on cognitive scaffolding with adaptive formative assessment.
cognitive scaffolding item selection adaptive educational systems personalized adaptation neural fuzzy systems