A Study of the Adaptability of Adaptive Learning Systems to Individualized Educational Strategies

Abstract

An adaptive learning system is an effective way to realize personalized learning. The article analyzes the process and implementation of adaptive learning and establishes an accurate diagnosis strategy for students’ knowledge levels using an adaptive learning system. The fuzzy preference relation is used to model students’ learning resource preferences mathematically, and GA solves the fuzzy relation to construct adaptive recommendations for learning resources. The knowledge learning status of students is quantified, and the DKVMN-PLC model for learner knowledge tracking is established by combining relevance weights and potential knowledge features. The effectiveness of the above method in promoting personalized adaptive learning among students has been verified using four different types of real datasets. When the number of external learning behavior influences is 250 and the number of learning resources is less than 60, the optimal time of the adaptive resource recommendation algorithm is only 4.453 s. The DKVMN-PLC model performs best on the ASSISTments dataset, with the AUC value improved by 8.5% and 5.3% compared with the DKT and DKT+LSTM models, respectively. Relying on the adaptive learning system provides students with a knowledge-tracking method, which provides a practical path to promote personalized learning.

Keywords

• adaptive learning system
• intuitionistic fuzzy preference
• ga
• dkvmn-plc model
• personalized education
• 00a35