On the use of neural networks in intelligent tutoring systems
Journal of Artificial Intelligence in Education
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
Machine Learning - Connectionist approaches to language learning
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
On the effectiveness of a neural network for adaptive external pacing
Journal of Artificial Intelligence in Education
Application of fuzzy logic techniques in the BSS1 tutoring system
Journal of Artificial Intelligence in Education
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Student Models Construction by Using Information Criteria
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Fuzzy neural network in case-based diagnostic system
IEEE Transactions on Fuzzy Systems
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Item difficulty estimation: An auspicious collaboration between data and judgment
Computers & Education
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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.