Adaptive E-Learning for Data Encoding and Computer Networks based on Learner's Styles

In this paper, a new model for adaptive e-learning based on learner's styles is presented. In the previous work, the dimensions of learner's styles given by Felder-Silverman did not consider some important issues of the learner himself. Here, new learner's parameters such as his social environment, health conditions, psychological and economical states are taken into account. Such parameters greatly affect the ability of student to learn and understand. Therefore, in order to perfectly recognize the ability of the student to be interactive in the leaning environment and accept information, new learner's styles are added to the dimensions of Felder-Silverman learning style model and our previous work [24]. The new proposed model is applied for logic gates and functions used in data encoding and computer networks. Such model presents suitable courses for each student in a dynamic and adaptive manner using existing database and workflow technologies. Furthermore, it is powerful, user friendly and easy to interpret. Moreover, it suggests a learning strategy and appropriate electronic media that match the learner’s preference. Keywords—Adaptive learning, Learning styles, Teaching strategies.