Title: Data Mining to Identify Learning Groups with Difficulties in Programming Education

Year of Publication: Feb - 2016
Page Numbers: 1-10
Authors: Valter S. M. Neto, Rodrigo M. Feitosa, Dejailson N. Pinheiro, Milson L. Lima, Sofiane Labidi
Conference Name: The International Conference on Innovations in Intelligent Systems and Computing Technologies (ICIISCT2016)
- Philippines

Abstract:


The difficulties encountered by students in the teaching and learning of programming has been the focus of much research. Among the proposals discussed to improve this situation called for the use of differentiated instruction, personalized, since it is considered that the classrooms are composed of heterogeneous students with different ways of learning and who have needs and learning preferences private. However, the customization of teaching in classroom mode is difficult to be made by the teacher. But the personalized attention to homogeneous groups of learners is a possibility to be considered. From this perspective, this article aims to describe an experience with the use of techniques of data mining along with a taxonomy of educational objectives, Bloom's Taxonomy, to identify similar groups of learners with learning difficulties in programming teaching with data obtained through assessments. With this, we hope to contribute to the construction of appropriate teaching strategies to student groups with the purpose of improving the learning process on the part of these students.