Design and implementation of a SMART Learning environment for the Upskilling of Cybersecurity professionals in Mauritius

Teaching and Learning confined to within the four walls of a classroom or even online Learning through Massive Online Courses (MOOCs) and other Learning Content Management Systems (LCMS) are no longer seen as the optimal approach for competency and skills development, especially for working professionals. Each of these busy learners have their own training needs and prior knowledge. Adopting the one-size-fits-all teaching approach is definitely not effective, motivating and encouraging. For some learners, the content might be too easy and for others, it might be too difficult. This is why this research presents the use of SMART Learning Environment that makes use of Intelligent Techniques to personalise the learning materials for each learner. This mismatch in skills is becoming a pressing issue and is having a direct impact on the ICT Sector, which is one of the pillars of the Mauritian Economy. This research, therefore, besides proposing a novel approach to learning, also attempts to address an issue of national importance. Data was collected during three phases, namely an Expert Reference Group Discussion, a pre-test questionnaire and a survey questionnaire. The Expert Reference Group Discussion was carried out to further understand the training needs and expectations of Cybersecurity professionals in Mauritius. A SMART Learning Environment making use of Artificial Neural Networks and Backpropagation Algorithm to personalise learning materials was eventually designed and implemented. The major findings of this research were that personalisation of learning materials through the use of a SMART Learning Environment can be used to address the training needs of Cybersecurity professionals in Mauritius.

     

Eye tracking system for enhanced learning experiences

Nowadays, we are living in a world where information is readily available and being able to provide the learner with the best suited situations and environment for his/her learning experiences is of utmost importance. In most learning environments, information is basically available in the form of written text. According to the eye-tracking technology, eye movements, scanning patterns and pupil diameter are indicators of thought and mental processing involved during visual information extraction. Hence, learners can be supported and guided throughout their learning journey by the real-time information of the precise position of gaze and of pupil diameter. A proper interpretation and consequently an efficient monitoring or supervision of learners’ eye movements by different methods of eye tracking may lead to an enhanced learning process and experience. This research gives an insight of the various existing techniques that contribute to the improvement of the learning mechanism through proposing a real time monitoring system using image processing and eye detection techniques. To portray such a robust mechanism, a multipronged eye tracking approach has been envisioned. The system is deployed with a first identification of the user’s eyes followed by the detection of the iris and pupil movements of the latter. Subsequently, the information about the eyes and pupil movements were analysed and graphs were generated that helps in determining the interest and behaviour of the user. To evaluate the accuracy of the system, some user tests and various scenarios in different application domains have been performed by computing the tracking error rate and as a result it has been noticed that these tests yield to an acceptable efficiency rate and a True Acceptance Rate (TAR) of around 75 %. Moreover, the proposed system is a low cost system and can be compatible with any computer or laptop equipped with an ordinary web camera.