No abstract

The purpose of this research is to identify the misconceptions held by undergraduate students when taking introductory CS1 courses using Python. The methodology of this work consisted of interviews with instructors of previous sections of an introductory CS1 course in Python at Unicamp, and through the analysis of past exams. As a result of this work, we documented a set of 28 hypothetical misconceptions in Python through the antipattern [1] format, allowing the identification of why, how, and where the mapped misconceptions usually occur. Future work involves the development of a Concept Inventory—a multiple-choice questionnaire in which each misconception is mapped to an incorrect option—in the Python programming language.

No abstract

A partir do estudo de erros frequentes na lógica de programação (Misconceptions) das linguagens C e Python,desenvolveram-se códigos na linguagem Java de forma a detectar novos Misconceptions e a verificar se osencontrados nas linguagens C e Python também ocorrem em Java. Finalmente, elaboraram-se questões a fim de montar um Concept Inventory, um questionário que ajudará a detectar potenciais Misconceptions cometidos por alunos de cursos introdutórios e a reforçar o aprendizado, evitando ao máximo a incidência desses erros.