K. Dahbur (USA)
Distance learning, Classification, Statistical analysis, Association rules.
The popularity of distance learning has increased in recent years to cover the use of World Wide Web as a primary teaching methodology. This in turn resulted in a subsequent decrease in the role of face-to-face interaction between the instructor and the students. The need for an assessment and evaluation system has therefore gained significance and importance. Such a system can be used to monitor a student's progress in the course, and to provide guidelines for the student in the areas that require attention and possible improvement. In this paper, we propose an assessment and evaluation model for distance learning that can be used by both the student and the instructor to provide statistical analysis and classification for the academic and behavioral data that is usually available from a distance learning environment. The classification subsystem incorporates explanations for the results produced by the subsystem, which can be used by the instructor to amend the course's contents and/or difficulty level. The model will also include a predictive subsystem that helps in monitoring, assessing and evaluating a student's status in the course as compared to his/her colleagues based on the course's historical data.
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