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.
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ABSTRACT: Database files and additional log files of Learning Management Systems (LMSs) contain an enormous volume of data which usually remain unexploited. A new methodology is proposed in order to analyse these data both on the level of both the courses and the learners. Specifically, regression analysis is proposed as a first step in the methodology in order to explore how e-learning contents and characteristics of the course (such as a theory or lab course, a first- or second-year course, etc.) influence performance. Further investigation of each course, according to learners’ usage, is achieved by archetypal analysis, which pinpoints the typical usage. The proposed methodology was successfully applied to LMS data from a Greek University. The results confirmed the validity of the approach and showed a relationship between the educational content which was provided and its usage by the learners.Interactive Learning Environments 12/2014; DOI:10.1080/10494820.2014.881390 · 1.16 Impact Factor