Mapeamento Automático de Perfis de Estudantes em Métricas de Software para Análise de Aprendizagem de Programação

Abstract

Abstract. This work presents PCodigo II, a system of an automatic mapping of student profiles in software metrics to analyze programming learning. In addition to profile mapping in 348 software metrics, PCodigo II has mass execution, similar profile grouping, information visualization, and plagiarism analysis capabilities. The first applications of PCodigo II in real programming exercises demonstrate the effectiveness of this system for the diagnostic evaluation of programming learning. The first applications of PCodigo II in real programming exercises show that teachers, taking into account what the metrics say, can recognize the learning difficulties, good programming practices and classes of learning profiles of a whole class in a fast, detailed and holistic way.Resumo. Este trabalho apresenta o PCodigo II, um sistema de de mapeamento automático de perfis de estudantes em métricas de software para análise da aprendizagem de programação. Além do mapeamento de perfis em 348 métricas de software, o PCodigo II possui as funcionalidades de execução em massa, de agrupamento de perfis similares, de visualização da informação e de análise de plágios. As primeiras aplicações do PCodigo II em exercícios reais de programação mostram que professores, atentando para o que as métricas dizem, podem reconhecer as dificuldades de aprendizagem, boas práticas de programação e classes de perfis de aprendizagem de toda uma turma de forma rápida, detalhada e holística.

Automatic Mapping of Student 3D Profiles in Software Metrics for Temporal Analysis of Programming Learning and Scoring Rubrics

“…• Effort measurement and coding complexity [4] • Prediction of performance [7,8] • Programming style analysis [5,9] • Evaluation by software metrics [3,10] • Recognition of signs of plagiarism [3] • Recognition of rubrics [11] • Recommendation of activities [1] • Programming proficiency analysis [12] • Analysis of learning difficulties and good programming practices [3].…”

“…Among the technological solutions of programming learning analysis based on static analysis already proposed, we highlight: the metrics of Halstead and McCabe [4,5,13], the evaluation of programming skills by software metrics [10], the recommendation system of activities according to learning difficulties [1], the analysis of difficulties by software metrics [3], the evaluation of how programming students learn from the analysis of their programming codes [14] and the programming proficiency analysis of SCALE system [12].…”

“…A more recent proposal to analyze programming learning aims to map source codes into software metrics that quantify effort and quality of programming [3]. Through these metrics, for each programming activity, it is possible to compare student's solutions under different variables to identify classes of solutions, common learning difficulties, good practices of programming and even plagiarism.…”

“…Although the proposal of [3] makes it possible to compare student profiles of a class in each programming activity, it is laborious for a teacher through this instrument to verify how these evaluation metrics evolve over time, that is, to each activity of a course, for each student. This type of monitoring allows the programming teacher to identify in which students develop better in their learning processes and where students begin to present learning difficulties.…”

“…In order to meet this need by offering programming teachers an instrument to monitor the learning process of their students, this chapter extends the proposal of [3] generating 3D views of student profiles mapped into selected software metrics. These metrics characterize each student's efficiency, style, and programming effort with each programming solution they develop over a course.…”