Adaptive Learning Systems; Personalization; Optimization; Gravitational Search Algorithm

Abstract

One of the main research areas is providing adaptive learning systems with new style for content delivery to users. Content delivery subsystem manages and selects the appropriate contents based on the user model. In order to realize the adaption and personalization, selecting approach should consider the learning capabilities and background knowledge of the learners. Therefore an optimization problem is defined and the system must optimize the difference between the learning capabilities and the learning style of the learner with the difficulty level and the presentation style of the delivered contents. This paper has focused on two aspects. In the problem modeling section, the previous defined model has been extended. The presented model has also considered the adaption between the learning style of the learner and the presentation style of the delivered contents. In problem solving section, this paper has applied two newer optimization algorithms, NBPSO and GSA, thus resulting in producing better answers (more appropriate and more adaptive selection of content). Also, according to the previous approaches, the problem has implemented and solved using PSO. The provided results of three algorithms have been evaluated using two well-known criteria: accuracy (aiming to measure optimality of the delivered answer) and stability (to ensure of getting answer in different performances). The results showed considerable progress (more personalized and adaptive contents).

Keywords

• Adaptive Learning Systems
• Personalization
• Optimization
• Gravitational Search Algorithm