Using FCA to Analyse How Students Learn to Program

In computer science and mathematics education, universities often observe high failure rates among students if they are taught in a traditional, lecturecentric manner. Interactive engagement teaching methods are more successful but in order to develop suitable teaching materials, lecturers must be aware of potential conceptual difficulties of a domain in advance, for example, by analysing the data of student-submitted work from previous sessions. In computer science education, the data collected from computer-based assessment tools provides a possible source for analysing conceptual difficulties students encounter. The data can be analysed with data mining techniques and in particular with FCA as discussed in this paper.