Abstract
The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which can confuse the choices of learner to select. Although the area of recommender systems has made a significant
Highlights
E-learning [3,4,5, 7, 11, 23, 26, 43, 46, 54] and distance education is growing worldwide
We propose an approach to predict traditional-learning times for e-learning systems in challenging environments
The proposed estimation approach can benefit from the success of these two filtering techniques
Summary
E-learning [3,4,5, 7, 11, 23, 26, 43, 46, 54] and distance education is growing worldwide. Thereby, depending on the performances of both the Internet and the electricity network, we consider three types of learning, namely online learning, offline learning and traditional learning (without a computer) Others factors such as the standard of living and the effects of computer use on eye health and vision motivate the choice of these three types of learning even in developed countries. The area of recommender systems has made a significant progress over the last several years to address this problem, the issue remained fairly unexplored for challenging environments In such systems, the rating process of learning materials is based on learning times which in turn involve online learning times, offline learning times and traditional learning times.
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