Abstract
Apart from being able to support the bulk of student activity in suitable disciplines such as computer programming, Web-based educational systems have the potential to yield valuable insights into student behavior. Through the use of educational analytics, we can dispense with preconceptions of how students consume and reuse course material. In this paper, we examine the speed at which students employ concepts which they are being taught during a semester. To show the wider utility of this data, we present a basic classification system for early detection of poor performers and show how it can be improved by including data on when students use a concept for the first time. Using our improved classifier, we can achieve an accuracy of 85% in predicting poor performers prior to the completion of the course.
Highlights
Computer Science degree programmes have typically suffered from high attrition rates (Beaubouef & Mason, 2005; Biggers, Brauer, & Yilmaz, 2008)
Fine grained data Over a 3 year period at , we have developed a new specialized platform for module delivery, which has been trialed for second year undergraduate Computer Science students on one of their core programming modules (Computer Architecture and Assembly Language Programming)
By the end of the semester the classifier is able to predict fail-pass results with just under an 85% accuracy
Summary
Computer Science degree programmes have typically suffered from high attrition rates (Beaubouef & Mason, 2005; Biggers, Brauer, & Yilmaz, 2008). Concept adoption Apart from compressing successfully compiled programs to estimate the complexity of the code that students are writing, the availability of the code offers other opportunities. It was possible to determine the time in the semester at which a student first successfully compiled a program containing a specific concept.
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