International Journal of Science, Technology and Society 2016; 4(6): 106-109 http://www.sciencepublishinggroup.com/j/ijsts doi: 10.11648/j.ijsts.20160406.14 ISSN: 2330-7412 (Print); ISSN: 2330-7420 (Online) Review Article A Comprehensive Review of Current Applications of Artificial Neural Networks in E-Learning Environment Rana Khudhair Abbas Ahmed Alrafidain University College/Computer Techniques Engineering Department, Baghdad, Iraq Email address: rana_ruc@yahoo.com To cite this article: Rana Khudhair Abbas Ahmed. A Comprehensive Review of Current Applications of Artificial Neural Networks in E-Learning Environment. International Journal of Science, Technology and Society. Vol. 4, No. 6, 2016, pp. 106-109. doi: 10.11648/j.ijsts.20160406.14 Received: December 8, 2016; Accepted: December 17, 2016; Published: January 14, 2017 Abstract: With the rapid increase in the development of online learning technology and the huge amount of learning materials generated on the web. Besides, the learning resources are growing infinitely making it difficult for users to choose appropriate resources for their learning. This paper discusses current applications of artificial neural networks and its great potential to help users in a personal learning environment to identify relevant and interesting items from a large number of items by suggesting actions to learners. Keywords: E-Learning, Neural Networks, Artificial Intelligence, Applications, Developments 1. Introduction An artificial neural network (ANN) does not emulate the thought processes and if then logic of the human brain as done by an expert system. It mimics certain aspects of the information processing and physical structure of the brain with a web of neural connections. Therefore, some writers classified it as a “microscopic”, “white-box” system and an expert system as a “macroscopic”, “black-box” system [1]. Recently, e-Learning has become an active field of research and experimentation, with remarkable investments from all parts of the world. It represents the Web-based delivery of personalized, comprehensive, dynamic learning contents, aiding the development of communities of knowledge, linking learners and practitioners with experts. E-Learning supports the different phases of traditional learning and in some cases it is the only possible method of learning, allowing knowledge acquisition also in particular conditions (e.g. impaired students, absence of teaching structures, etc.). In this context, an important role is played by the definition of educational structure that must be contextualized and tailored on the basis of the requirements of: i) teachers, who have personal teaching approaches, and ii) students, who have personal studying approaches [2]. Today, educational institutions often prefer designing artificial intelligence supported e-learning scenarios and applying them in different courses or educational activities in order to improve teaching and learning experiences. Day by day, more emphasis is given on teachers’ and the students’ role on educational activities and their situations changing dynamically along a typical process [3, 4]. 2. Artificial Neural Networks Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counter parts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers [5, 6, 7, 8, 9].