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A visualization tool for student assessment and evaluation in online learning

Through learner interaction with content objects, in online learning, much data is exchanged and stored in databases. This data is potentially a valuable source of information both in evaluation of learning progress of the learners and in quality control for instructional content and design. Often however this data remains as large amounts of data elements stored in rows and tables, in form on individual numerical or text values with no visible relationship with one another. This presents a challenge to the users of learning managements to extract any meaningful information from the data. With the help of data mining and graphical visualization, however it is possible to get a good overview of the whole data, and to recognize any underlying processes, patterns of behavior and even detect unusual cases. This paper presents a graphical system based on a novel spiraling coordinate system, besides the Cartesian set; this system offers ease of use, various user options and flexibility and allows visual analysis of large datasets and in particular sequential data which is typical in online learning. The system can be used in order to detect regular and irregular patterns, trends, cyclic behavior and outliers in the data.