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Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities

Programs that perform automated assignment grading can generate a great deal of meaningful data not only for the student, but for the instructor as well. Such tools are often used in computer science courses to assess student programming work. In the process of grading, a large amount of intermediate information is gathered. However, in most cases this information is not used beyond assigning scores, so the potential of learning more about the course is lost. At the same time, continuous collection of data over a large number of submissions across many courses presents an interesting but untapped resource for educational data mining. One solution to this problem is to implement a reporting tool for making use of this intermediate data to create meaningful interpretations. This paper describes how an automated grading system, WebCAT, has been extended to provide a reporting mechanism that uses the intermediate data that is gathered during assessment of students’ programs. Our implementation of the reporting tool makes use of the Business Information Reporting Tool (BIRT) developed for the Eclipse IDE.