Determining students’ level of page viewing in intelligent tutorial systems with artificial neural network

Abstract

The concept of level of page viewing (LPV) refers to the extent to which a student actively revises the pages that he or she has to study in tutorial systems. In the present study, an artificial neural network (ANN) model, which is composed of 5 inputs, 20 and 30 neurons, 2 hidden layers, and 1 output, was designed to determine the students’ LPV. After this network was trained, it was integrated into a web-based prototype teaching system, which was developed by ASP.net C# programming language. Additionally, Decision Tree method is tried to determine students’ LPV. However, this method gave wrong results according to expected LPV values. In this system, the student first studies the pages uploaded by the teacher onto the system. After studying all the pages within the scope of a topic, the student can go to the test page for evaluation purposes. LPVs of a student who wants to navigate to the test page are calculated by an ANN module added to the system. On the condition that one or more of the LPV’s are not up to the desired level, the student is not allowed to take the test and is informed of the pages with missing LPV’s so that he can re-study these pages. This prototype system developed based on ANN to determine students’ LPV is essential for intelligent tutorial systems, geared to provide intelligent assistance and guidance. The system can track the pages which the students did not study sufficiently and thus direct them to relevant pages. How much activity the students perform on each page to study is observed before they actually take the test, and the areas which should be further revised are determined much in advance.