Assessing Learners Behavior by Monitoring Online Tests through Data Visualization
نویسندگان
چکیده
This paper presents an approach and a system to let tutors monitor several important aspects related to online tests, such as learner behavior and test quality. The approach includes the logging of important data related to learner interaction with the system during the execution of online tests and exploits data visualization to highlight information useful to let tutors review and improve the whole assessment process. This paper has focused on the discovery of behavioral patterns of learners and conceptual relationships among test items. For this Characterization and summarization has been used. The Characterization and summarization is implemented efficiently using Attribute Oriented Induction algorithm which discovers patterns for accessing learners behavior. By analyzing the data visualization charts, we have detected several previously unknown test strategies used by the learners. Last, we have detected several correlations among questions, which gave us useful feedbacks on the test quality.
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