Abstract

The usage of web applications can be measured with the use of metrics. In a LMS, a typical web application, there are no appropriate metrics which would facilitate their qualitative and quantitative measurement. The purpose of this paper is to propose the use of existing techniques with a different way, in order to analyze the log file of a typical LMS and deduce useful conclusions. Three metrics for course usage measurement are used. It also describes two algorithms for course classification and suggestion actions. The metrics and the algorithms and were in Open eClass LMS tracking data of an academic institution. The results from 39 courses presented interest insights. Although the case study concerns a LMS it can also be applied to other web applications such as e-government, e-commerce, e-banking, blogs e.t.c.

Highlights

  • Learning Management Systems (LMSs) are extensively used nowadays and they provide a variety of information and communication channels for the users [21]

  • Some of the most well known commercial LMS platforms used for educational purposes worldwide are Blackboard, WebCT and TopClass, while Claroline, Moodle, Ilias and aTutor are freely distributed under appropriate licenses [16]

  • In the first stage of the algorithm, the Enrichment metric is involved in order to identify courses with poor or rich educational content

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Summary

INTRODUCTION

Learning Management Systems (LMSs) are extensively used nowadays and they provide a variety of information and communication channels for the users [21]. Among the features they provide are the development, management, distribution, diffusion and presentation of the educational material as well as tools for the management of users and courses. The objectives of this paper are the analysis of the log file of a typical LMS and deduce useful conclusions.

BACKGROUND
Logging the data
Data pre-processing
Study population and context
PROCESSING THE DATA
Classifier algorithm
Application of classification algorithm
III IV
Suggestion algorithm
Application of suggestion algorithm
DISCUSSION AND CONCLUSION
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