Inference of Knowledge from Automatic Detection of Evidences in the Domain of Computer Programming

• Andres J. Porfirio Federal University of Paraná (UFPR) / Federal Technological University of Paraná (UTFPR)
• Roberto Pereira Federal University of Paraná (UFPR)
• Eleandro Maschio Federal Technological University of Paraná (UTFPR)

DOI: https://doi.org/10.5753/cbie.sbie.2018.1553

Abstract

During the computer programming learning process, the student’s progress is marked by new skills acquisition. The monitoring of this progress, from the teacher’s perspective, can be a complex task when performed from manual perceptions. The situation is even more complex when dealing with a large number of students simultaneously. One way of supporting this activity involves the use of student models, where abilities are mapped in order to facilitate the visualization of the concepts already acquired and the gaps to be completed. The use of this type of tool is beneficial, but it can still need a heavy workload when it requires the model manual feeding, especially because it is necessary to continuously update it. This paper proposes the use of automatic mechanisms, based in source codes analysis, followed by the evidences detection, as inputs for the student model. A set of experiments that demonstrate the viability of the method is presented. The test scenario is composed by a dynamic bayesian network student model and databases of source code in C language.

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