Cloud based Dynamic Course Selection Framework using Network Graphs with Term Difficulty Estimation

Abstract

The system developed in this paper uses a cloud based technology to implement and design a software as a service (SAAS) application for adaptive course selection and term difficulty estimation for a networked curriculum. The choice of courses in every term is completely in the hands of the students who enroll for a particular program in Universities. The order of courses taken in every term is ad hoc due to different factors like student interests, uncertainty about the student pass rates, frequent changes in admission policies and curriculum requirements. However, this choice of course plays a vital role in students graduating in time from the university. In this paper, we analyze student success ratios in terms of time to graduation. To illustrate the designed models, data from different colleges of the Public Authority of Applied Education and Training (PAAET), Kuwait, is used. Graph-based complex networks are used for analyzing the courses and how crucial they are. The difficulty levels of courses are estimated based on the institutional data from spring 2013 to fall 2016 and term difficulties are estimated based on the courses chosen. This work presents a robust framework which is adaptable to the courses chosen by the students and the ease of flow of students through the curriculum with the aim of improving the university’s graduation rate.