The effectiveness of a new classification system in higher education as a new e-learning tool

Computers and portable devices are employed in all phases of todays world, and many large collections of materials are available electronically. These developments have created new ways for educators to communicate with learners. Higher education institutions have started to build blended learning approaches, which adopt the use of virtual learning environments (VLEs) into their traditional teaching mechanisms for enhancing learning and teaching, through housing both instructor-generated and learner-generated content. E-learning has been gaining more mainstream adoption in higher education and it is a powerful tool as a complement to the traditional resources on a module, but not a substitute. Findings suggest that students use e-learning materials for reviewing the concepts discussed in lectures they previously attended and they are more effective revision tools than their textbooks and more effcient than learners’ own notes. Technological innovations are providing new opportunities to alter the nature and delivery of teaching without necessarily increasing tutor workloads. Multi-disciplinary fields have been emerged to increase quality of higher education. Apart from VLEs, artificial neural networks are used to understand, differenciate and improve learning strategies of the students and provide detailed information to the educators for improving students’ learning strategies. The prediction of eye direction detection is the one of the popular research topic in human computer interaction area. This paper defines eye gaze detection system for higher education students using VLEs, that predicts students’ motivation through the online sessions. Actually, determining the position of eyes is difficult to estimate the location of gaze which is more challenging. The database of the suggested research is organized as gaze directions of right, left and centre. The database is created with varied ages of images. In this paper, image processing techniques have been applied to the captured images, then back propagation neural networks will be applied in order to categorize the gaze images, providing a detailed information to the educators for understanding the students’ interest on the subject, topics and provided notes and examples.