LAUSR

This paper suggests a novel data mining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the course rankings and the suggestion results from any kind of ranking/hierarchical algorithms and evaluates the validity of a course ranking position. The ranking algorithms estimate the quantity and quality of the course content according to users’ actions and interest. S-Algo+ generates an improved final ranking suggestion output, combining the best results of the source ranking algorithms using statistical and mathematic techniques. In this way, the researchers and course instructors can use more accurate results. The efficiency and applicability of the S-Algo+ algorithm was evaluated successfully with a cross-comparison quantitative and qualitative process in a case study at a Greek university. Our new proposed S-Algo+ algorithm may lead to both theoretical and practical advantages. It may also apply not only for course evaluation but for any kind of web application such as e-commerce.