A combined algorithm for LMS usage assessment

Abstract

LMS are used more and more nowadays. There are some algorithms for LMS usage assessment. The main problem is that these algorithms are rarely combined. This gap can be filled by using S-Algo which combines ranking and suggestion results of two or more suggestion/ranking algorithms into an ultimate and efficient ranking suggestion. Such efficient ranking is based on ranking algorithms used and proposed metrics recorded course attributes. S-algo was applied to Open eClass LMS tracking data of an academic institution. In the context of course assessment from student logged data, two existing algorithms called SUGAL and CCA as ranking/suggestion algorithms were selected. Then, on the results provided from these algorithms, S-algo tested and confirmed the success of its ranking process. © 2011 IEEE.