Implementation of a computerized assessment system by using backpropagation neural networks with R and shiny

The discouragement, that early undergraduate students suffer when they are faced to topics that they struggle to master, could increase owing by the use of inadequate evaluation materials. It is generally found that in the classroom there are students that manage to cope with the material of the courses in a quick manner, while others present difficulties while learning the material. This situation is easily spotted in the examination results, a group of students could get good marks encouraging them to tackle the course optimistically while others would get the wrong perception that the topics are difficulty, and in some cases, forcing them to leave the course or in other cases to change careers. We believe that by the use of machine learning techniques, and in our case the utilization of neural networks, it would be feasible to make an evaluation environment that could adjust to the needs of each student. The latter means that the system could auto tune the difficulty of the given questions to the students, allowing a more dynamic evaluation system which at the end would decrease the feeling of dissatisfaction and drop off the courses.